Evaluation and Ranking of Cement Alternatives in South Africa Using Combine Life Cycle Assessment and Multi- criteria Decision-Making Methods

Evaluation and Ranking of Cement Alternatives in South Africa Using Combine Life Cycle Assessment and Multi- criteria Decision-Making Methods | 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 Evaluation and Ranking of Cement Alternatives in South Africa Using Combine Life Cycle Assessment and Multi- criteria Decision-Making Methods Oluwafemi Ezekiel Ige, Daramy Vandi Von Kallon, Dawood Desai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4133462/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Choosing the most suitable alternatives can be challenging in process engineering. Typically, there is a need to evaluate and rank alternatives using various criteria, such as environmental impact, when making decisions. This paper employs a novel integration of Life Cycle Assessment (LCA) and Multi-Criteria Decision-Making (MCDM) methods to evaluate the sustainability of different cement alternatives in South Africa. The LCA assesses the environmental impact, considering 18 midpoint categories, while Complex Proportional Assessment (COPRAS) and Additive Ratio Assessment (ARAS) methods were used as MCDA methods to rank and select the best alternatives. Across 18 impact categories, including global warming, ozone depletion, ecotoxicity, and resource scarcity, CEM I cement exhibited notable global warming emissions, ranking fourth. COPRAS and ARAS methods systematically ranked alternatives based on impact categories, consistently identifying CEM II/B-V cement as the most preferred alternative. This top ranking was attributed to its low environmental impact and high utility score. Notably, CEM III/A cement, despite low global warming emissions, ranked least preferred due to concerns about raw material-related environmental impacts. The paper highlights environmental hotspots for each cement type and underscores the importance of sustainable fuel and raw material selection in production. The results emphasize the necessity of reducing clinker content, exploring alternative fuels and raw materials, and adopting interventions like carbon capture and storage to enhance sustainability in cement production. The paper concludes that the integrated LCA and MCDM approach provides valuable insights for decision-makers in the cement industry, aiding the pursuit of more sustainable practices and calling for further research on the environmental impact of specific raw materials and fuels. Cement production Multi-criteria decision making Complex proportional assessment Additive ratio assessment Life cycle assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The cement industry plays a crucial role in global construction and infrastructure development, as well as in driving the social economy of a nation. Cement remains an essential material in the construction industry and a valuable resource that plays a vital role in our daily lives, impacting national and global economic growth (Verma et al., 2021 , Zhang and Lin, 2019, Putra et al., 2020 ). Cement production is a multi-complex process, starting with extracting and prepping raw materials, heating them to make clinker, grinding them into cement, packaging them, and finally dispatching them (Ahmed et al., 2021 , Juarez and Finnegan, 2021). Every stage contributes significantly to the overall complexity of the process. Cement production is associated with significant carbon emissions and environmental impacts. Better infrastructure development leads to increased productivity and a higher demand for cement. As the demand for cement continues to rise due to urbanization, population growth and widespread use in construction projects, its production will likely increase steadily (Khan et al., 2023 ). Cement production has experienced explosive growth in the last decade, reaching an incredible 3.4 billion tons in 2020 (Mokhtar and Nasooti, 2020). It is expected to increase by 12–23%, causing a 4% increase in carbon dioxide (CO 2 ) emissions by 2050, driven by a 12% increase in global cement production compared to 2018 (Khan et al., 2023 ). The environmental impact of cement production is significant due to its high consumption of raw materials and energy (thermal energy and electricity). Sustainability issues like energy use, CO 2 emissions, waste reduction and resource depletion are major concerns for the cement industry and other construction sectors (Imbabi et al., 2012 ). Therefore, the cement production industry is encountering significant obstacles in satisfying global demand while decreasing CO 2 emissions. Despite its vital role in urbanization and national development, the cement industry emits significant dust emissions, sulfur dioxide and other gases such as CO 2 , nitrous oxide (N ₂ O) and methane (CH 4 ), which are the primary drivers of the greenhouse effect, thereby contributing to global warming and posing a threat to environmental systems and human health, demanding urgent action to mitigate their impact on global warming (Anderson et al., 2016 ). Environmental protection has gained increasing attention in recent years, becoming a crucial consideration for public policy across various social and political settings (Miccoli et al., 2014a, Miccoli et al., 2014b). Cement production is a major emitter of greenhouse gas (GHG) emissions and is responsible for about 5–8% of all CO 2 emissions worldwide (Schneider, 2019 , IEA, 2018 ). This substantial environmental impact is attributable to the widespread use of cement, particularly in infrastructure projects like buildings, bridges and dams (Andrew, 2018 , Turner and Collins, 2013, Li et al., 2023 ). It primarily releases GHGs like CO 2 and NO x, as burning coal is a major contributor to these GHG emissions. For every ton of cement produced, 0.6–0.81 kg CO 2 -eq is released into the atmosphere (Huntzinger and Eatmon, 2009). The extent of the environmental impact of cement production depends on the choices made regarding fuel, raw materials, and production techniques used in the process. Despite progress in developing sustainable cement production, the optimal approach remains uncertain. Identifying the most sustainable option for cement manufacturing, focusing on reducing gas emissions, is vital. The cement types, such as Portland Cement (CEM I), Portland Composite Cement (CEM II, Blast Furnace Cement (CEM III), Pozzolanic Cement (CEM IV) and Composite Cement (CEM V), are determined by the proportions of chemical compounds and added additives during production, as shown in Table 1 . The environmental impact of the cement industry has been investigated by many researchers globally using Life Cycle Assessment (LCA), considering the direct and indirect impacts and identified impacts, such as global warming, acidification, abiotic depletion, and marine ecotoxicity etc. (Palermo et al., 2022 , Stafford et al., 2016 , García-Gusano et al., 2015a , García-Gusano et al., 2015b ). LCA is a widely adopted methodology for evaluating a product or service's environmental and economic impacts, considering all stages of its life cycle, i.e., raw materials extraction to end-of-life disposal (Ristimäki et al., 2013 , Ige et al., 2022 ). It employs a uniform approach to evaluate the environmental impact of products throughout their entire life cycle. The LCA can be utilized to gauge the environmental impacts of cement production from material extraction, production, energy utilization, transportation, maintenance, and final disposal or recycling (Ige et al., 2021 , Ige et al., 2022 , Ige and Olanrewaju, 2023, Soomro et al., 2023 ). Numerous investigations have shed light on the cement production complex and its impacts on humans and the environment, which is now a key focus for policymakers within the public and private sectors (Palermo et al., 2022 , Ma et al., 2016 ). Table 1 Ratios of clinker in different types of cement (Frauenhofer, 2009 , Ige and Olanrewaju, 2023) Cement Type Composition Clinker Ratio Portland Cement It is the most common cement type due to its high clinker content, with a small amount of gypsum added to control the setting time of the cement. CEM I: 95% of clinker by mass Portland Composite Cement It is a blended cement, meaning that it is made from a combination of clinker, limestone and other minor constituents, proportions of supplementary cementitious materials (e.g., blast furnace slag, silica fume, pozzolana, fly ash, calcareous materials), and calcium sulfate as a minor additional constituent. CEM II: 65–94% of clinker by mass CEM II/A-LL: 80–94% of clinker, 6–20% of limestone and 0–5% of gypsum by mass. CEM II/B-LL: 65–79% of clinker, 21–35% of limestone and 0–5% of gypsum by mass Blast Furnace Cement It is a blended cement type that contains clinker with granulated blast furnace slag (GGBS) CEM III/A: 35–64% of clinker, 36–65% of blast-furnace slag by mass. Pozzolanic Cement It is composed of clinker and pozzolanic constituents (i.e., bast furnace slag, silica fume, pozzolana, and fly ash. CEM IV: 45–89% of clinker by mass CEM IV/A: 65–89% of clinker, 11–35% of pozzolanic materials, 0–5% of gypsum by mass. CEM IV/B: 45–64% of clinker, 11–35% of pozzolanic materials, 0–5% of gypsum by mass. Composite Cement It is a cement type that is formed by mixing clinker with two or more types of cementitious, such as fly ash, slag, or natural pozzolana. CEM V: 20–64% of clinker, 0–5% of gypsum by mass. A critical evaluation of the environmental impacts of different cement types is an urgent step toward sustainable construction practices that protect our planet against climate change, environmental and human health, resource scarcity and ecosystem integrity. Various strategies are being researched and implemented to mitigate the environmental impact of cement production. These strategies include quantifying environmental impacts, comparing these impacts to material properties and developing indicators to improve the efficiency of cement and concrete use. One of the most efficient strategies for reducing resource consumption and GHG emissions associated with cement production is to employ industrial waste as a substitute material and alternative fuel source (Ige and Olanrewaju, 2023, Kunche and Mielczarek, 2021). Moreover, the development and assessment of novel cementitious materials have shown potential benefits in reducing the environmental impact of cement production. In addition, focusing on the LCA and waste can significantly reduce the industry's carbon footprint. Developing environmentally efficient cement or other green building materials based on the life cycle assignment can further reduce the environmental impact of construction materials. These practices allow for reducing the volume fraction of cement and the consumption of natural resources, thus addressing the environmental concerns associated with cement production. Implementing an LCA combined with multi-criteria decision-making (MCDM) analysis is crucial for ranking and evaluating different types of Portland cement, enabling stakeholders to make informed decisions about their environmental performance and sustainability, promoting sustainable construction practices, and mitigating the environmental impacts of cement production. Therefore, this paper integrates LCA, MCDM (COPRAS and ARAS), and entropy weight methods to assess and rank cement alternatives in South Africa based on environmental impacts and criteria importance. 2. Literature review In Israel, Pushkar and Verbitsky (2016) utilized the LCA framework to assess the environmental impact of five blended cement composites, incorporating limestone powder, fly ash, and ground blast furnace slag. Variability in results was demonstrated through three allocation methods, indicating that employing supplementary cementitious materials (SCMs) increased environmental loads by 15–55% compared to OPC concrete. The extent of the increase varied based on the specific SCMs employed. Hossain et al. ( 2017 ) examined the environmental implications of various cement types in Hong Kong through the LCA model, particularly emphasizing energy consumption and greenhouse gas emissions. They introduced two eco-friendly approaches to mitigate cement production's energy usage and GHG emissions. They found that transporting raw materials and burning fossil fuels are the main factors contributing to the environmental footprint of Portland cement. Yang et al. ( 2017 ) compared the environmental impacts of six cement strength grades in China through LCA and partial LCC, emphasizing resource consumption and emission variations. Their results showed a direct link between strength grade and environmental impact, with higher-strength grades displaying elevated impact but favorable economic performance. The main contributors to the environmental impacts and economic costs are resource usage, energy consumption, emissions, and long-distance material transport. Hossain et al. ( 2016 ) employed the LCA method to assess and compare the sustainability and environmental impacts of eco-friendly blocks made from pristine materials such as fly ash (FA), setting the functional unit for the analysis at 1 ton of block production. The findings indicated that the eco-blocks required 26–32% less energy and emitted 17–20% fewer greenhouse gases in CO 2 equivalents, reducing GWP. The COPRAS method, widely employed across disciplines for its effectiveness, is a complex decision-making tool (Karaca et al., 2019 ). Utilizing the COPRAS approach, multiple criteria are evaluated, considering both beneficial and non-beneficial aspects. It involves identifying selection criteria, evaluating relevant information (Janhavi Chaidhanya et al., 2022 ) and devising strategies to assess overall performance, including positive and negative characteristics of alternatives (Zapolskytė et al., 2020 ). The ARAS method is a weighted additive approach used to compare and rank alternatives in decision-making by considering multiple criteria, introduced by Zavadskas and Turskis (2010), which calculates a weighted average of the ratio of each alternative's performance on each criterion to the performance of the best alternative on that criterion. MCDM methods integrate criteria and stakeholder preferences into decision-making, using algorithms and mathematical models to assess, rank and compare alternatives based on multiple criteria, enabling a balanced decision-making process by considering trade-offs and highlighting the best options (Taherdoost and Madanchian, 2023). MCDM techniques are employed in engineering to select appropriate materials by considering multiple criteria simultaneously. These techniques, such as the analytic hierarchy process (AHP), complex proportional assessment (COPRAS), additive ratio assessment method (ARAS), multi-objective optimization based on ratio analysis (MOORA), multi-attribute utility analysis (MUA), elimination of choice translating reality (ELECTRA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), analytic network process (ANP), data envelopment analysis (DEA), multi-criteria optimization and compromise solution (VIKOR), etc, offer reliable solutions to multi-criteria decision problems. MCDM has been successfully applied in cement production, addressing material selection, optimization, and sustainability challenges (Alireza Mokhtar et al., 2014, Yoris-Nobile et al., 2023 , Kurda et al., 2019 , Shmlls et al., 2023 , Soni et al., 2022 ). Studies have highlighted the successful application of MCDM methods for cement production (Singh and Modgil, 2020, Silgado et al., 2018 ). Kurda et al. ( 2019 ) employed a novel MCDM approach to evaluate the comprehensive sustainability of cementitious materials. This method comprehensively evaluates non-traditional materials, considering their mechanical properties, environmental impact, economic feasibility and service life. Silgado et al. ( 2018 ) conducted a study in Catalonia, Spain, using the MCDM analysis to determine if recycled materials can offer improved sustainability and economic benefits when used as substitutes for natural materials in concrete production, considering environmental and economic criteria. Furthermore, the study employed the VIKOR method, a variant of MCDM analysis, to identify optimal alternatives for concrete production that balance environmental sustainability and economic viability. Putra et al. ( 2020 ) conducted a sustainability assessment of cement production facilities in Western Indonesia using LCA and MCDM methods to evaluate the social, environmental, and economic impacts and determine the most sustainable plant through integrated analysis with the AHP tool, an MCDM analysis tool. Arukala et al. (2020) used TOPSIS as an MCDM method to compare five concrete formulations comprising Ordinary Portland Cement (OPC), fly ash, Ground Granulated Blast Furnace Slag (GGBS), metakaolin, and composite cement, all at a designated grade to determine the most suitable sustainable cementitious material. According to their results, fly ash-based concrete emerged as the preferred alternative, making it the recommended sustainable choice for decision-making processes in cementitious material selection. By leveraging MCDM techniques, engineers can make informed decisions that align with project objectives and constraints, leading to more efficient and effective outcomes in cement production. The significance of MCDM in addressing the challenges of material selection in engineering is increasingly recognized, paving the way for its wider adoption and application across various domains within the field. 3. Methodology This paper introduces an innovative framework for integrating LCA and MCDM methods. The objective is to evaluate and rank the environmental impact associated with different types of cement, as depicted in Fig. 1 . By merging the environmental insights obtained from LCA with the decision-making framework of MCDM, this integrated approach empowers decision-makers to consider the environmental impact appropriately. Consequently, decision-makers can make well-informed and fair decisions encompassing various alternatives, including social, economic, and technical criteria, promoting sustainability. Nonetheless, the involvement of relevant stakeholders and the maintenance of transparency throughout the decision-making process play a crucial role in achieving significant outcomes. These aspects also enable decision-makers to select the most suitable option for the environment while considering other factors such as cost, feasibility, and social impact. Integrating LCA and MCDM methodologies provides a comprehensive framework for evaluating and ranking different cement types based on their environmental, economic, and social impacts. LCA was employed to comprehensively assess the environmental impacts of various cement types, providing a holistic view spanning from resource extraction to disposal. MCDM methodologies effectively address the complexities inherent in evaluating different cement types by simultaneously enabling structured assessment of multiple criteria. This framework combines quantitative and qualitative data, facilitating meaningful comparisons between options. Subsequently, MCDM was utilized to weigh and compare these impacts alongside other criteria, such as economic and social factors, to determine the optimal choice. 3.1 Life Cycle Assessment Collecting input and output data enables LCA to analyze its environmental, economic, and social impacts across its life cycle (Hauschild, 2018 ). LCA is a standardized method that evaluates the environmental impact of cement production throughout its entire life cycle according to the International Organization for Standardization (ISO) guidelines, providing valuable information for decision-making. The LCA framework establishes the system boundaries, as the ISO guidelines outline (ISO, 2006a ). Employing the ReCiPe 2016 (H) midpoint method allows for a comprehensive evaluation that aligns with ISO 14040 (ISO, 2006a ) and ISO 14044 (ISO, 2006b ) to gauge the environmental impact of cement types. The LCA analysis involves four stages: goal and scope definition, inventory analysis, impact assessment, and interpretation, following ISO guidelines. 3.1.1 Goal and Scope Definition The first stage of the LCA methodology involves defining the goal, scope, boundaries and functional unit. To calculate the environmental impact, this paper focuses on the cradle-to-gate approach of cement production, excluding operational and disposal stages. It aims to analyze eight cement types using 1 kg of cement as the functional unit. This unit allows us to compare and assess the alternatives, considering the system boundaries illustrated in Fig. 2 . 3.1.2 Inventory analysis This stage quantifies all inputs and outputs throughout the life cycle of a product, including material extraction, manufacturing, transportation, use, and end-of-life treatment, using data from the Ecoinvent database (See Table S9) to assess the cradle-to-gate environmental impact of different cement types. 3.1.3 Impact assessment This stage assesses and categorizes the potential environmental impacts of resources and emissions into impact categories such as global warming, climate change, acidification, resource depletion, eutrophication, and ecotoxicity using SimaPro 9.2.0.1 software by PRé Consultants, Netherlands, to evaluate the environmental impact of different cement alternatives. 3.1.4 Interpretation: In the final stage, results from LCI and LCIA are analyzed and interpreted, drawing conclusions and offering improvement recommendations based on identified environmental hotspots and potential sustainable alternatives. Due to trade-offs between impact categories, interpreting environmental impacts across different alternatives through LCA can be complex (Owsianiak et al., 2018 ), leading to two emerging methods: (i) Ranking and merging the impact categories into a single score indicator. (ii) The second approach aims to simplify the result interpretation by using a smaller set of impact categories (Torkayesh et al., 2022 ). 3.2 Multi-criteria decision-making (MCDM) MCDM is a widely used method for ranking alternatives and identifying the best choice in complex scenarios when evaluating various qualitative and quantitative criteria (Keshavarz Ghorabaee et al., 2015 , Juanpera et al., 2020 ). The MCDM problem and weights for the criteria are expressed in terms of Eq. 1. Each criterion's weight and assessment are determined using the Entropy method to ascertain their relative significance in cement production sustainability. These criteria are then integrated with COPRAS and ARAS to derive an overall priority score, designating the most sustainable cement alternative. 3.2.1 Entropy method The entropy-based objective weighting method relies on unbiased data, addressing the limitations of subjective weighting methods (Wang et al., 2017 ). It quantifies the amount of valid information in the data (Wang and Zhan, 2012), using a step-by-step procedure to determine the weight of each criterion based on its information content. The following steps are the fundamental procedure of the Entropy objective weighting method. Step 1: Normalization of the array decision matrix The normalization of the array decision matrix (performance indices) to obtain the project outcomes \({\varvec{P}}_{\varvec{l}\varvec{j}}\) , using Eq. 1. \({\varvec{P}}_{\varvec{l}\varvec{j}}=\frac{{\varvec{x}}_{\varvec{i}\varvec{j}}}{{\sum }_{\varvec{i}=1}^{\varvec{m}}{\varvec{x}}_{\varvec{i}\varvec{j}}}\) …………………………… Eq. 1 Where \({\varvec{P}}_{\varvec{l}\varvec{j}}\) , represent the normalized value of data of the array decision matrix. Step 2: Computation of the normalization of entropy values Computation of the normalization of entropy values of project outcomes using Eq. 2 \({\varvec{E}}_{\varvec{j}}=-\text{k}\sum _{i=1}^{m}{\varvec{P}}_{\varvec{i}\varvec{j}}{1\varvec{n} }_{{\varvec{P}}_{\varvec{i}\varvec{j}}}\) ………….. Eq. 2 Where \(\text{{\rm K}}=1/1n\left(m\right)\) . Step 4. Calculation of the Entropy objective weight Calculating the entropy objective weight \({w}_{j}\) , based on the entropy concept using Eq. 3. \({w}_{j}=\frac{1-{\mathbf{{\rm E}}}_{\varvec{j}}}{{\sum }_{\varvec{j}=1}^{\varvec{n}}\left({1-\text{{\rm E}}}_{j}\right)}\) ……………Equation 3 Where, \({\sum }_{\varvec{j}=1}^{\varvec{n}}{w}_{j}=1\) 3.2.2 Preference ranking This paper employs two MCDM methodologies, COPRAS and ARAS, to develop a unified index for ranking and selecting the most suitable alternatives. 3.2.3 Complex Proportional Assessment (COPRAS) Method The COPRAS method is a pioneering technique in multi-attribute decision-making that evaluates the relative importance of alternatives based on a set of criteria (Karaca et al., 2019 ). It provides a comprehensive picture of the potential of each alternative by analyzing best-case and worst-case scenarios for each criterion. The COPRAS method ranks choices based on diverse and relevant weight criteria, allowing for more informed decision-making (Okunevičiūtė Neverauskienė et al., 2021 , Organ and Yalçın, 2016). By considering diverse weight criteria, COPRAS enables ranking of alternatives, aiding in identifying the most suitable option. This paper utilizes COPRAS to rank cement alternatives in South Africa, considering their whole life cycle, due to its ability to evaluate both quantitative and qualitative criteria (Ighravwe and Oke, 2019). The COPRAS method comprises the following steps: Step 1: Development of the initial Decision Matrix. The decision matrix (Eq. 4) is a table detailing each alternative's evaluation criteria in the decision-making process. The values in the matrix can be either qualitative or quantitative, providing a comprehensive overview of the alternatives' attributes and aiding decision-making. \(\varvec{x}={\left[{\varvec{x}}_{\varvec{i}\varvec{j}}\right]}_{\varvec{m}\varvec{x}\varvec{n}}=\left[\begin{array}{cccc}{\varvec{x}}_{11}& {\varvec{x}}_{12}& \dots & {\varvec{x}}_{1\varvec{n}}\\ {\varvec{x}}_{21}& {\varvec{x}}_{22}& \dots & {\varvec{x}}_{2\varvec{n}}\\ \dots & \dots & \dots & \dots \\ {\varvec{x}}_{\varvec{m}1}& {\varvec{x}}_{\varvec{m}2}& \dots & {\varvec{x}}_{\varvec{m}\varvec{n}}\end{array}\right]\) ………………….. Eq. 4 Where the value \({\varvec{x}}_{\varvec{i}\varvec{j}}\) represents the evaluation of the \(\varvec{i}\) th alternative on the \(\varvec{j}\) th criterion; \(\varvec{i}\) = 1, 2, …, m; \(\varvec{j}\) = 1, 2, …, n. Step 2: Normalization of the decision matrix. Normalizing the decision matrix (Eq. 5) ensures all evaluation criteria are on a common scale. This approach ensures that all criteria are considered and weighed equally during evaluation. It is important to consider that some criteria may be measured in different units, as this can make it difficult to compare and evaluate them. The decision matrix is normalized by using (Eq. 5). \(\varvec{R}={\left[{\varvec{r}}_{\varvec{i}\varvec{j}}\right]}_{\varvec{m}\varvec{x}\varvec{n}}=\frac{{\varvec{x}}_{\varvec{i}\varvec{j}}}{{\sum }_{\varvec{i}=1}^{\varvec{m}}{\varvec{x}}_{\varvec{i}\varvec{j}}}\) ……………………. Eq. 5 Step 3: Calculate the weighted normalized decision matrix . In this step, the normalized decision matrix values (Eq. 6) are calculated by multiplying each element in the normalized decision matrix by the weight of the corresponding evaluation criterion (obtained in the Entropy method). The decision-maker determines the weights and reflects the relative importance of each criterion. \(\varvec{D}={\left[{\varvec{y}}_{\varvec{i}\varvec{j}}\right]}_{\varvec{m}\varvec{x}\varvec{n}}={\varvec{r}}_{\varvec{i}\varvec{j}}\times {\varvec{w}}_{\varvec{j}}\) ………………Equation 6 $$\varvec{i}=1,2,\dots ..,\varvec{m} \varvec{a}\varvec{n}\varvec{d} \varvec{j}=1,2,\dots ..,\varvec{n}$$ Step 4: Weighted sum of the normalized decision matrix This step determined (the beneficial and non-beneficial attributes) of each alternative by summing them across all criteria. Calculating the beneficial and non-beneficial attributes using equations (7) and (8). Beneficial criteria \({\varvec{S}}_{+\varvec{i}}{\sum }_{\varvec{j}=1}^{\varvec{n}}{\varvec{y}}_{+\varvec{i}\varvec{j}}\) …………. Eq. 7 Non-beneficial criteria \({\varvec{S}}_{-\varvec{i}}{\sum }_{\varvec{j}=1}^{\varvec{n}}{\varvec{y}}_{-\varvec{i}\varvec{j}}\) ………….. Eq. 8 Step 5: Determining the relative weight of each alternative. In this step, the relative importance ( \({\varvec{Q}}_{\varvec{i}}\) ) of each alternative beneficial and non-beneficial criteria is calculated using Eq. (9). The higher the \({\varvec{Q}}_{\varvec{i}}\) , the higher the priority (rank) of the alternative is (Okunevičiūtė Neverauskienė et al., 2021 ). Each alternative is ranked in order of importance based on its overall score. \({\varvec{Q}}_{\varvec{i}}={\varvec{S}}_{+\varvec{i}}+\frac{{\varvec{S}}_{-\varvec{m}\varvec{i}\varvec{n}{\sum }_{\varvec{i}=1}^{\varvec{m}}{\varvec{S}}_{-\varvec{i}}}}{{\varvec{S}}_{-\varvec{i}}{\sum }_{\varvec{i}=1}^{\varvec{m}}\left({\varvec{S}}_{-\varvec{m}\varvec{i}\varvec{n}}/{\varvec{S}}_{-\varvec{i}}\right)}\) ………… Eq. 9 $$\left(\varvec{i}=1,2,\dots ..,\varvec{m}\right)$$ Where, \({\varvec{S}}_{-\varvec{m}\varvec{i}\varvec{n}}\) = \({\varvec{S}}_{-\varvec{i}}\) and ( \({\varvec{Q}}_{\varvec{i}}\) )is calculated using Eq. (9) (Viteikiene and Zavadskas, 2007). Step 6: Ranking the alternatives In this step, the alternatives are ranked from largest to smallest in their relative relevance of the alternatives, depending on the degree of utility obtained (Karaca et al., 2019 ). This step determines the utility degree for each alternative using Eq. (10). \({\varvec{U}}_{\varvec{i}}=⌊\frac{{\varvec{Q}}_{\varvec{i}}}{{\varvec{Q}}_{\varvec{m}\varvec{a}\varvec{x}}}⌋\times 100\text{\%}\) ……………Equation 10 In this method, the relative importance of each alternative is calculated by weighing their positive ( \({\varvec{S}}_{+\varvec{i}}\) ) and negative ( \({\varvec{S}}_{-\varvec{i}}\) ) attributes allowing decision-makers to evaluate and rank alternatives in complex problems with multiple criteria. The higher ( \({\varvec{Q}}_{\varvec{i}}\) ) value is considered more effective. It considers both the performance of alternatives and their proximity to the ideal solution, offering a comprehensive assessment for decision-making. 3.2.4 Additive ratio assessment method (ARAS) The ARAS method is a quantitative technique used in decision-making and evaluation processes. It allows decision-makers to compare and rank a finite number of alternatives based on multiple decision criteria. As outlined by Karabašević et al. (Karabašević et al., 2015 ), Zavadskas and Turskis (Zavadskas and Turskis, 2010) and Karabasevic et al. (Karabasevic et al., 2016 ), the ARAS can be employed to address MCDM problems by following these steps: Step 1: The initial decision matrix development. $$\varvec{x}=\left[\begin{array}{ccccc}{\varvec{x}}_{01}& \dots & {\varvec{x}}_{0\varvec{j}}& \dots & {\varvec{x}}_{0\varvec{n}}\\ ⋮& \ddots & ⋮& \ddots & ⋮\\ {\varvec{x}}_{\varvec{i}1}& \dots & {\varvec{x}}_{\varvec{i}\varvec{j}}& \dots & {\varvec{x}}_{\varvec{i}\varvec{n}}\\ ⋮& \ddots & ⋮& \ddots & ⋮\\ {\varvec{x}}_{\varvec{m}1}& \dots & {\varvec{x}}_{\varvec{m}\varvec{j}}& \dots & {\varvec{x}}_{\varvec{m}\varvec{n}}\end{array}\right] ; \varvec{i}=\overline{0,\varvec{m};} \varvec{j}=\overline{1,\varvec{n}}, \dots \dots \dots \dots ..\mathbf{E}\mathbf{q}. 11$$ where \(\varvec{m}\) represents the number of alternatives and \(\varvec{n}\) is the number of criteria, \({\varvec{x}}_{\varvec{i}\varvec{j}}\) is the value representing \(\varvec{i}\) performance value alternative in terms of the \(\varvec{j}\) criterion \({\varvec{x}}_{01}\) represents the best value of \(\varvec{j}\) the criterion Step 2: Calculation of a normalized decision matrix Beneficial criteria \(\overline{{\mathbf{x}}_{\mathbf{i}\mathbf{j}}}=\frac{{\mathbf{x}}_{\mathbf{i}\mathbf{j}}}{{\sum }_{\mathbf{i}=0}^{\mathbf{m}}{\mathbf{x}}_{\mathbf{i}\mathbf{j}}}\) ….…………..Eq. 12 Non-beneficial criteria \({\varvec{x}}_{\varvec{i}\varvec{j}}=\frac{1}{{\varvec{x}}_{\varvec{i}\varvec{j}}^{\mathbf{*}}}\) ; \(\overline{{\varvec{x}}_{\varvec{i}\varvec{j}}}=\frac{{\varvec{x}}_{\varvec{i}\varvec{j}}}{{\sum }_{\varvec{i}=0}^{\varvec{m}}{\varvec{x}}_{\varvec{i}\varvec{j}}}\) ………………..Eq. 13 Step 3: Calculation of weighted normalized decision matrix . \({\widehat{\varvec{x}}}_{\varvec{i}\varvec{j}=\stackrel{-}{{\varvec{X}}_{\varvec{i}\varvec{j}}}}{\varvec{w}}_{\varvec{j}}\) …………………..Eq. 14 where \({\varvec{w}}_{\varvec{j}}\) , represent the weight of the \(\varvec{j}\) criterion and \(\overline{{\varvec{x}}_{\varvec{i}\varvec{j}}}\) , represent the normalized rating of the \(\varvec{j}\) criterion Step 4: Si-Optimality function for \(\varvec{i}\) alternative $${\varvec{S}}_{\varvec{i}}={\sum }_{\varvec{j}=1}^{\varvec{n}}{\widehat{\varvec{x}}}_{\varvec{i}\varvec{j}}; \varvec{i}=\stackrel{-}{0,\varvec{m} ,} \dots \dots \dots \dots \mathbf{E}\mathbf{q}\mathbf{u}\mathbf{a}\mathbf{t}\mathbf{i}\mathbf{o}\mathbf{n} 15$$ where \({\varvec{S}}_{\varvec{i}}\) , represent the value of the optimality function of the \(\varvec{i}\) alternative. There is a need to differentiate between the sum of the beneficial and non-beneficial criteria. Step 5: Calculation of the utility degree for each alternative \({\varvec{K}}_{\varvec{i}}=\frac{{\varvec{S}}_{\varvec{i}}}{{\varvec{S}}_{0}}\) …………..Eq. 16 Where \({\varvec{K}}_{\varvec{i}}\) represents the degree of utility of each alternative and \({\varvec{S}}_{\varvec{i} }\) and \({\varvec{S}}_{0 }\) represents the optimality criterion values. Integrating LCA and MCDM methods creates a comprehensive decision-making tool that combines quantitative accuracy with subjective attributes, prioritizes evidence-based decision-making, promotes openness and replicability, and strengthens the validity and reliability of conclusions. 4. Results and Discussions This section discusses the outcomes of the LCA and MCDM analysis. Detailed results of impact categories for eight cement types produced in South Africa are presented in Table S1 . According to Table 1 , the results showed that the CEM I cement had the highest global warming emission (0.99 kg CO2 eq), followed by CEM II/B-L (0.86 kg CO2 eq), and CEM III/A (0.57 kg CO2 eq) had the lowest. CEM III/A cement exhibited the highest terrestrial ecotoxicity (1.31 kg 1,4-DCB), human non-carcinogenic toxicity (0.63 kg 1,4-DCB), and freshwater ecotoxicity (0.03 kg 1,4-DCB) compared to other types. Considering the impact assessment, CEM III/A cement emerged as the environmentally friendly choice with the lowest environmental impact, while CEM I showed the highest impact. LCIA results were utilized in MCDM (COPRAS and ARAS) methods to rank and select the best alternatives. A detailed MCDM analysis is provided in Tables S2-S8, which comprehensively explain the results. 4.1 Result of MCDM Analysis Table S4 displays the Weighted Normalized Decision Matrix (COPRAS) of the alternatives concerning cement environmental impact, the main criteria. We ranked various cement alternatives based on 18 impact categories from the LCA midpoint method. Impact categories served as criteria, and cement types as alternatives in this paper. Each alternative's weight, based on the impact category, was determined via the entropy weight method. Figure 2 shows the weight of the criteria. With the entropy method, 15 criteria received weights below zero percent, except water consumption, freshwater ecotoxicity, and land use, which received 45.67%, 42.92%, and 8.07% weights, respectively (Fig. 2 ). Regarding their environmental impact, most criteria had equal weight when ranking different cement alternatives. Integrating LCA with the MCDM method, we ranked different cement alternatives based on 18 criteria identified in the LCA, comparing COPRAS with ARAS (Table 2 ). CEM II/B-V cement emerged as the most preferred alternative with the highest utility degree (100), followed by CEM II/A-V (89.93) and CEM II/B-L (84.75) for COPRAS, and CEM II/B-V (0.994), CEM II/A-V (0.894), and CEM II/B-L (0.845) for ARAS. Both methods yielded similar rankings. Conversely, CEM III/A cement was identified as the least preferred (57.56 for COPRAS and 0.588 for ARAS). Following the top three, COPRAS ranked CEM I cement fourth, succeeded by CEM II/A-S, CEM II/B-S, and CEM III/A cement, respectively. Table 2 Results of the MCDM method Cement Types COPRAS ARAS Qi Ui Rank Si Ki Rank CEM I 0,14 76,72 4 0,1203 0,7649 4 CEM II/A-S 0,12 65,37 5 0,1029 0,6540 5 CEM II/A-V 0,17 89,93 2 0,1407 0,8944 2 CEM II/B-S 0,12 62,04 6 0,0973 0,6187 6 CEM II/B-L 0,16 84,75 3 0,1328 0,8445 3 CEM II/B-V 0,19 100,00 1 0,1563 0,9935 1 CEM III/A 0,11 57,56 7 0,0925 0,5883 7 Using COPRAS and ARAS methodologies in this study to analyze and rank different cement alternatives demonstrates the importance of reliable research results. Both methods identified the CEM II/B-V cement as the preferred alternative, demonstrating its potential as a beneficial environmental choice. Despite being the highest emission-reduction cement alternative, CEM III/A cement was ranked as the least preferred alternative. 4.2 LCA Characterization results at midpoint method This section provides a detailed analysis of the midpoint characterization results, highlighting the impact of five production stages on each environment category for various cement types, ranging from the least to the most preferred. Specific process details may vary based on the plant and cement type. The stages are as follows: (i) Raw Material Stage, (ii) Clinker production Stage, (iii) Fuel usage Stage, (iv) Electricity Stage, (v) Transportation Stage. Figure 3 illustrates the midpoint characterization of CEM II/B-V cement. Clinker production (73.42%) is the primary contributor to global warming, followed by electricity usage (21.19%), fuel usage (2.14%), transportation (2%), and raw materials (1.13%), as shown in Fig. 3 . This paper identifies the critical stages of the life cycle of each cement type and their impact contributions. By analyzing each stage's contributions and inefficiencies, the authors can identify the most strategic opportunities for targeted interventions that maximize energy efficiency and reduce environmental impact. Additionally, electricity usage (68.59%) is the main contributor to stratospheric ozone depletion, while raw material, fuel usage, and transportation contribute 24.27%, 5.34%, and 1.61%, respectively. Only the raw material stage contributes to mineral resource scarcity (100%), ionizing radiation (93.17%), land use (94.82%), and water consumption (69.61%). Electricity usage (68.41%) contributes the most to fine particulate matter formation, followed by clinker production (15.35%) and raw material (7.87%), while fuel usage and transportation each contribute (4.13%). Fuel usage is the highest contributor to freshwater eutrophication (97.38%), marine eutrophication (95.64%), freshwater ecotoxicity (53.79%), marine ecotoxicity (55.05%), human carcinogenic toxicity (86.67%), human non-carcinogenic toxicity (77.29%) and fossil resource scarcity (99.38%). Figure 4 provides a detailed analysis of the CEM III/A cement results at the midpoint method. The clinker production stage contributed 62.83% to global warming, followed by the electricity stage with 23.32%. The raw material fuel usage and transportation stages are the lowest contributors, contributing 6.56%, 4.46% and 2.56% of input, respectively. Electricity and raw material stages are the major contributors to stratospheric ozone depletion, contributing 63.47% and 26. 95% and fuel usage and transportation contribute the lowest percentage, with 6.83% and 2.33%, respectively. Fuel usage is the largest contributor to freshwater (89.41%) and marine eutrophication impacts (85.56%), while raw materials have the lowest contributions (10.28% and 13.35%, respectively). Most fine particulate matter formation originates from the electricity stage (60.07%) of the CEM III/A cement. Raw material, clinker production, fuel usage and transportation contribute with individual shares of 15.24%, 10.67%, 8.30% and 4.76%, respectively. Fuel usage contributes 89.41% to freshwater eutrophication, whereas raw material has the most negligible impact, 10.28%. Extraction is the leading cause of several environmental impacts, including ionizing radiation (92.88%), terrestrial ecotoxicity (62.72%), freshwater ecotoxicity (77.90%), marine ecotoxicity (76.24%), human non-carcinogenic toxicity (58.14%), land use (93.96%), mineral resource scarcity (100%) and water consumption (46.13%). Highlighting the significant contribution of raw materials, especially in CEM I cement, to global warming, the need for improved efficiency and potentially implementing carbon capture and storage technologies becomes evident due to the traditional raw materials (limestone) used in the production. The high GWP of CEM I cement is attributed to its energy-intensive production process, which involves grinding raw materials, calcining limestone at high temperatures (1400–1450°C) and grinding clinker. Analyzing the environmental impact of cement production in CEM II/B-V (0.71 kg CO 2 eq) and CEM III/A cement (0.57 kg CO 2 eq) highlighted the importance of selecting sustainable fuels and raw materials. These factors served as crucial parameters for the study's sustainability assessment. The CEM III/A cement ranked the lowest alternative due to the environmental impact of its raw material process. Further research may be needed to understand the environmental impacts of CEM III/A cement. The presence of calciners in cyclone preheaters and kiln exhaust gas recirculation in grinding and drying equipment to produce CEM I cement were key contributors to GHG emissions due to their specific process technology impacts. This is evident in GWP (0.99 kg CO 2 eq). 5 Conclusion This study combines LCA with MCDM to identify the most sustainable cement option in South Africa. Considering various cement alternatives' environmental impact and sustainability aspects, decision-makers can make informed decisions prioritizing affordability and efficiency. LCA assessed environmental impact across five production stages, with 18 categories like global warming and resource scarcity. CEM I cement exhibited the highest global warming emission and was ranked the fourth most preferred alternative. CEM III/A cement demonstrated the highest terrestrial ecotoxicity, human non-carcinogenic toxicity, and freshwater ecotoxicity. The results showed that CEM III/A cement had the lowest overall environmental impact due to its lower clinker content and potential for incorporating alternative materials like fly ash and Ground-Granulated Blast Furnace Slag. For CEM II/B-V cement, the most preferred alternative and clinker production was among the highest contributors to global warming. This paper used COPRAS and ARAS (MCDM) methods to rank and select the best alternatives based on 18 impact categories. The results identified CEM II/B-V cement as the most preferred option by both methods, with the low environmental impact and highest utility score in the MCDM analysis, followed by CEM II/A-V and CEM II/B-L cement. This highlights the potential of using alternative binders and optimizing production processes for sustainability. CEM III/A cement, despite its low global warming emissions, was ranked as the least preferred alternative due to concerns about the environmental impact of its raw materials, especially in terms of ionizing radiation, terrestrial ecotoxicity, and water consumption. Further investigation is required to understand and address this impact. MCDM tools provide a more comprehensive approach to selecting the best cement alternatives by considering multiple factors. CEM III/A cement was found to be the main contributor to global warming and electricity usage, emphasizing the importance of selecting sustainable fuels and raw materials, improving production efficiency, and exploring interventions like carbon capture and storage technologies to reduce the environmental impact of cement production. In conclusion, integrating LCA and MCDM methods provided valuable insights into the environmental impacts of different cement types, informing decision-makers in the industry and promoting more sustainable practices. The paper suggested that further research is needed to understand the environmental impact of alternative raw materials and fuels for CEM III/A cement, which ranked as the least preferred alternative. Declarations Author Contribution OE: writing-original draft preparation, methodology, data curation, investigation, validation, formal analysis. DVVK and DA: conceptualization, resources, supervision, writing-reviewing. DVVK and DA: data curation, supervision, writing-reviewing and editing. References Ahmed M, Bashar I, Alam ST, Wasi AI, Jerin I, Khatun S, andRahman M (2021) An overview of Asian cement industry: Environmental impacts, research methodologies and mitigation measures. 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Desai","email":"","orcid":"","institution":"Tshwane University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Dawood","middleName":"","lastName":"Desai","suffix":""}],"badges":[],"createdAt":"2024-03-20 01:45:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4133462/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4133462/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53280227,"identity":"227645c6-ddbe-4ab2-9e9f-529f229d3e9a","added_by":"auto","created_at":"2024-03-22 19:21:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59520,"visible":true,"origin":"","legend":"\u003cp\u003eLCA and MCDM framework for sustainability assessment of cement production.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4133462/v1/3802e45e06836b3c752a8d5c.jpg"},{"id":53279852,"identity":"1c1b075e-e617-437d-9fce-9dd7e512cbb5","added_by":"auto","created_at":"2024-03-22 19:13:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105372,"visible":true,"origin":"","legend":"\u003cp\u003eWeight of criteria.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4133462/v1/9bb24f345040c6cdf54716f5.jpg"},{"id":53280228,"identity":"3628f787-fe9f-4376-816a-fa21d70e146a","added_by":"auto","created_at":"2024-03-22 19:21:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":308533,"visible":true,"origin":"","legend":"\u003cp\u003eThe midpoint characterization of the CEM II/B-V cement.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4133462/v1/f0100086a4a611fda31d999d.jpg"},{"id":53279855,"identity":"5238fc63-0741-43c9-8459-df606f1dc741","added_by":"auto","created_at":"2024-03-22 19:13:34","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":269190,"visible":true,"origin":"","legend":"\u003cp\u003eThe midpoint characterization of the CEM III/A cement.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4133462/v1/69e7234f725d3dde67f2cf01.jpg"},{"id":65805583,"identity":"cd467b19-6bf0-4325-9065-a1d19c1cc500","added_by":"auto","created_at":"2024-10-03 02:31:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1570682,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4133462/v1/31652fb0-c21b-47ce-b07f-2e895012029f.pdf"},{"id":53279854,"identity":"bfc18152-940d-495d-8e3a-898a435e3e43","added_by":"auto","created_at":"2024-03-22 19:13:34","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":53790,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFileCleanTechnologiesandEnvironmentalPolicy.docx","url":"https://assets-eu.researchsquare.com/files/rs-4133462/v1/2ec03e19a98da7bf45588195.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation and Ranking of Cement Alternatives in South Africa Using Combine Life Cycle Assessment and Multi- criteria Decision-Making Methods","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe cement industry plays a crucial role in global construction and infrastructure development, as well as in driving the social economy of a nation. Cement remains an essential material in the construction industry and a valuable resource that plays a vital role in our daily lives, impacting national and global economic growth (Verma et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Zhang and Lin, 2019, Putra et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Cement production is a multi-complex process, starting with extracting and prepping raw materials, heating them to make clinker, grinding them into cement, packaging them, and finally dispatching them (Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Juarez and Finnegan, 2021). Every stage contributes significantly to the overall complexity of the process. Cement production is associated with significant carbon emissions and environmental impacts. Better infrastructure development leads to increased productivity and a higher demand for cement. As the demand for cement continues to rise due to urbanization, population growth and widespread use in construction projects, its production will likely increase steadily (Khan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cement production has experienced explosive growth in the last decade, reaching an incredible 3.4\u0026nbsp;billion tons in 2020 (Mokhtar and Nasooti, 2020). It is expected to increase by 12\u0026ndash;23%, causing a 4% increase in carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) emissions by 2050, driven by a 12% increase in global cement production compared to 2018 (Khan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The environmental impact of cement production is significant due to its high consumption of raw materials and energy (thermal energy and electricity). Sustainability issues like energy use, CO\u003csub\u003e2\u003c/sub\u003e emissions, waste reduction and resource depletion are major concerns for the cement industry and other construction sectors (Imbabi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, the cement production industry is encountering significant obstacles in satisfying global demand while decreasing CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003cp\u003eDespite its vital role in urbanization and national development, the cement industry emits significant dust emissions, sulfur dioxide and other gases such as CO\u003csub\u003e2\u003c/sub\u003e, nitrous oxide (N\u003csub\u003e₂\u003c/sub\u003eO) and methane (CH\u003csub\u003e4\u003c/sub\u003e), which are the primary drivers of the greenhouse effect, thereby contributing to global warming and posing a threat to environmental systems and human health, demanding urgent action to mitigate their impact on global warming (Anderson et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Environmental protection has gained increasing attention in recent years, becoming a crucial consideration for public policy across various social and political settings (Miccoli et al., 2014a, Miccoli et al., 2014b). Cement production is a major emitter of greenhouse gas (GHG) emissions and is responsible for about 5\u0026ndash;8% of all CO\u003csub\u003e2\u003c/sub\u003e emissions worldwide (Schneider, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, IEA, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This substantial environmental impact is attributable to the widespread use of cement, particularly in infrastructure projects like buildings, bridges and dams (Andrew, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Turner and Collins, 2013, Li et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It primarily releases GHGs like CO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003ex,\u003c/sub\u003e as burning coal is a major contributor to these GHG emissions. For every ton of cement produced, 0.6\u0026ndash;0.81 kg CO\u003csub\u003e2\u003c/sub\u003e-eq is released into the atmosphere (Huntzinger and Eatmon, 2009). The extent of the environmental impact of cement production depends on the choices made regarding fuel, raw materials, and production techniques used in the process. Despite progress in developing sustainable cement production, the optimal approach remains uncertain. Identifying the most sustainable option for cement manufacturing, focusing on reducing gas emissions, is vital.\u003c/p\u003e \u003cp\u003eThe cement types, such as Portland Cement (CEM I), Portland Composite Cement (CEM II, Blast Furnace Cement (CEM III), Pozzolanic Cement (CEM IV) and Composite Cement (CEM V), are determined by the proportions of chemical compounds and added additives during production, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The environmental impact of the cement industry has been investigated by many researchers globally using Life Cycle Assessment (LCA), considering the direct and indirect impacts and identified impacts, such as global warming, acidification, abiotic depletion, and marine ecotoxicity etc. (Palermo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Stafford et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Garc\u0026iacute;a-Gusano et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e, Garc\u0026iacute;a-Gusano et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e). LCA is a widely adopted methodology for evaluating a product or service's environmental and economic impacts, considering all stages of its life cycle, i.e., raw materials extraction to end-of-life disposal (Ristim\u0026auml;ki et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Ige et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It employs a uniform approach to evaluate the environmental impact of products throughout their entire life cycle. The LCA can be utilized to gauge the environmental impacts of cement production from material extraction, production, energy utilization, transportation, maintenance, and final disposal or recycling (Ige et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Ige et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Ige and Olanrewaju, 2023, Soomro et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Numerous investigations have shed light on the cement production complex and its impacts on humans and the environment, which is now a key focus for policymakers within the public and private sectors (Palermo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Ma et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\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\u003eRatios of clinker in different types of cement (Frauenhofer, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Ige and Olanrewaju, 2023)\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 \u003cp\u003eCement Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComposition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClinker Ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortland Cement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIt is the most common cement type due to its high clinker content, with a small amount of gypsum added to control the setting time of the cement.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCEM I: 95% of clinker by mass\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortland Composite Cement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIt is a blended cement, meaning that it is made from a combination of clinker, limestone and other minor constituents, proportions of supplementary cementitious materials (e.g., blast furnace slag, silica fume, pozzolana, fly ash, calcareous materials), and calcium sulfate as a minor additional constituent.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCEM II: 65\u0026ndash;94% of clinker by mass\u003c/p\u003e \u003cp\u003eCEM II/A-LL: 80\u0026ndash;94% of clinker, 6\u0026ndash;20% of limestone and 0\u0026ndash;5% of gypsum by mass.\u003c/p\u003e \u003cp\u003eCEM II/B-LL: 65\u0026ndash;79% of clinker, 21\u0026ndash;35% of limestone and 0\u0026ndash;5% of gypsum by mass\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlast Furnace Cement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIt is a blended cement type that contains clinker with granulated blast furnace slag (GGBS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCEM III/A: 35\u0026ndash;64% of clinker, 36\u0026ndash;65% of blast-furnace slag by mass.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePozzolanic Cement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIt is composed of clinker and pozzolanic constituents (i.e., bast furnace slag, silica fume, pozzolana, and fly ash.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCEM IV: 45\u0026ndash;89% of clinker by mass\u003c/p\u003e \u003cp\u003eCEM IV/A: 65\u0026ndash;89% of clinker, 11\u0026ndash;35% of pozzolanic materials, 0\u0026ndash;5% of gypsum by mass.\u003c/p\u003e \u003cp\u003eCEM IV/B: 45\u0026ndash;64% of clinker, 11\u0026ndash;35% of pozzolanic materials, 0\u0026ndash;5% of gypsum by mass.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComposite Cement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIt is a cement type that is formed by mixing clinker with two or more types of cementitious, such as fly ash, slag, or natural pozzolana.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCEM V: 20\u0026ndash;64% of clinker, 0\u0026ndash;5% of gypsum by mass.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA critical evaluation of the environmental impacts of different cement types is an urgent step toward sustainable construction practices that protect our planet against climate change, environmental and human health, resource scarcity and ecosystem integrity. Various strategies are being researched and implemented to mitigate the environmental impact of cement production. These strategies include quantifying environmental impacts, comparing these impacts to material properties and developing indicators to improve the efficiency of cement and concrete use. One of the most efficient strategies for reducing resource consumption and GHG emissions associated with cement production is to employ industrial waste as a substitute material and alternative fuel source (Ige and Olanrewaju, 2023, Kunche and Mielczarek, 2021). Moreover, the development and assessment of novel cementitious materials have shown potential benefits in reducing the environmental impact of cement production. In addition, focusing on the LCA and waste can significantly reduce the industry's carbon footprint.\u003c/p\u003e \u003cp\u003eDeveloping environmentally efficient cement or other green building materials based on the life cycle assignment can further reduce the environmental impact of construction materials. These practices allow for reducing the volume fraction of cement and the consumption of natural resources, thus addressing the environmental concerns associated with cement production. Implementing an LCA combined with multi-criteria decision-making (MCDM) analysis is crucial for ranking and evaluating different types of Portland cement, enabling stakeholders to make informed decisions about their environmental performance and sustainability, promoting sustainable construction practices, and mitigating the environmental impacts of cement production. Therefore, this paper integrates LCA, MCDM (COPRAS and ARAS), and entropy weight methods to assess and rank cement alternatives in South Africa based on environmental impacts and criteria importance.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eIn Israel, Pushkar and Verbitsky (2016) utilized the LCA framework to assess the environmental impact of five blended cement composites, incorporating limestone powder, fly ash, and ground blast furnace slag. Variability in results was demonstrated through three allocation methods, indicating that employing supplementary cementitious materials (SCMs) increased environmental loads by 15\u0026ndash;55% compared to OPC concrete. The extent of the increase varied based on the specific SCMs employed. Hossain et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) examined the environmental implications of various cement types in Hong Kong through the LCA model, particularly emphasizing energy consumption and greenhouse gas emissions. They introduced two eco-friendly approaches to mitigate cement production's energy usage and GHG emissions. They found that transporting raw materials and burning fossil fuels are the main factors contributing to the environmental footprint of Portland cement.\u003c/p\u003e \u003cp\u003eYang et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) compared the environmental impacts of six cement strength grades in China through LCA and partial LCC, emphasizing resource consumption and emission variations. Their results showed a direct link between strength grade and environmental impact, with higher-strength grades displaying elevated impact but favorable economic performance. The main contributors to the environmental impacts and economic costs are resource usage, energy consumption, emissions, and long-distance material transport. Hossain et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) employed the LCA method to assess and compare the sustainability and environmental impacts of eco-friendly blocks made from pristine materials such as fly ash (FA), setting the functional unit for the analysis at 1 ton of block production. The findings indicated that the eco-blocks required 26\u0026ndash;32% less energy and emitted 17\u0026ndash;20% fewer greenhouse gases in CO\u003csub\u003e2\u003c/sub\u003e equivalents, reducing GWP.\u003c/p\u003e \u003cp\u003eThe COPRAS method, widely employed across disciplines for its effectiveness, is a complex decision-making tool (Karaca et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Utilizing the COPRAS approach, multiple criteria are evaluated, considering both beneficial and non-beneficial aspects. It involves identifying selection criteria, evaluating relevant information (Janhavi Chaidhanya et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and devising strategies to assess overall performance, including positive and negative characteristics of alternatives (Zapolskytė et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The ARAS method is a weighted additive approach used to compare and rank alternatives in decision-making by considering multiple criteria, introduced by Zavadskas and Turskis (2010), which calculates a weighted average of the ratio of each alternative's performance on each criterion to the performance of the best alternative on that criterion.\u003c/p\u003e \u003cp\u003eMCDM methods integrate criteria and stakeholder preferences into decision-making, using algorithms and mathematical models to assess, rank and compare alternatives based on multiple criteria, enabling a balanced decision-making process by considering trade-offs and highlighting the best options (Taherdoost and Madanchian, 2023). MCDM techniques are employed in engineering to select appropriate materials by considering multiple criteria simultaneously. These techniques, such as the analytic hierarchy process (AHP), complex proportional assessment (COPRAS), additive ratio assessment method (ARAS), multi-objective optimization based on ratio analysis (MOORA), multi-attribute utility analysis (MUA), elimination of choice translating reality (ELECTRA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), analytic network process (ANP), data envelopment analysis (DEA), multi-criteria optimization and compromise solution (VIKOR), etc, offer reliable solutions to multi-criteria decision problems. MCDM has been successfully applied in cement production, addressing material selection, optimization, and sustainability challenges (Alireza Mokhtar et al., 2014, Yoris-Nobile et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Kurda et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Shmlls et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Soni et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies have highlighted the successful application of MCDM methods for cement production (Singh and Modgil, 2020, Silgado et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Kurda et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) employed a novel MCDM approach to evaluate the comprehensive sustainability of cementitious materials. This method comprehensively evaluates non-traditional materials, considering their mechanical properties, environmental impact, economic feasibility and service life. Silgado et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) conducted a study in Catalonia, Spain, using the MCDM analysis to determine if recycled materials can offer improved sustainability and economic benefits when used as substitutes for natural materials in concrete production, considering environmental and economic criteria. Furthermore, the study employed the VIKOR method, a variant of MCDM analysis, to identify optimal alternatives for concrete production that balance environmental sustainability and economic viability. Putra et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) conducted a sustainability assessment of cement production facilities in Western Indonesia using LCA and MCDM methods to evaluate the social, environmental, and economic impacts and determine the most sustainable plant through integrated analysis with the AHP tool, an MCDM analysis tool.\u003c/p\u003e \u003cp\u003eArukala et al. (2020) used TOPSIS as an MCDM method to compare five concrete formulations comprising Ordinary Portland Cement (OPC), fly ash, Ground Granulated Blast Furnace Slag (GGBS), metakaolin, and composite cement, all at a designated grade to determine the most suitable sustainable cementitious material. According to their results, fly ash-based concrete emerged as the preferred alternative, making it the recommended sustainable choice for decision-making processes in cementitious material selection.\u003c/p\u003e \u003cp\u003eBy leveraging MCDM techniques, engineers can make informed decisions that align with project objectives and constraints, leading to more efficient and effective outcomes in cement production. The significance of MCDM in addressing the challenges of material selection in engineering is increasingly recognized, paving the way for its wider adoption and application across various domains within the field.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis paper introduces an innovative framework for integrating LCA and MCDM methods. The objective is to evaluate and rank the environmental impact associated with different types of cement, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. By merging the environmental insights obtained from LCA with the decision-making framework of MCDM, this integrated approach empowers decision-makers to consider the environmental impact appropriately. Consequently, decision-makers can make well-informed and fair decisions encompassing various alternatives, including social, economic, and technical criteria, promoting sustainability. Nonetheless, the involvement of relevant stakeholders and the maintenance of transparency throughout the decision-making process play a crucial role in achieving significant outcomes. These aspects also enable decision-makers to select the most suitable option for the environment while considering other factors such as cost, feasibility, and social impact.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIntegrating LCA and MCDM methodologies provides a comprehensive framework for evaluating and ranking different cement types based on their environmental, economic, and social impacts. LCA was employed to comprehensively assess the environmental impacts of various cement types, providing a holistic view spanning from resource extraction to disposal.\u003c/p\u003e \u003cp\u003eMCDM methodologies effectively address the complexities inherent in evaluating different cement types by simultaneously enabling structured assessment of multiple criteria. This framework combines quantitative and qualitative data, facilitating meaningful comparisons between options. Subsequently, MCDM was utilized to weigh and compare these impacts alongside other criteria, such as economic and social factors, to determine the optimal choice.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Life Cycle Assessment\u003c/h2\u003e \u003cp\u003eCollecting input and output data enables LCA to analyze its environmental, economic, and social impacts across its life cycle (Hauschild, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). LCA is a standardized method that evaluates the environmental impact of cement production throughout its entire life cycle according to the International Organization for Standardization (ISO) guidelines, providing valuable information for decision-making. The LCA framework establishes the system boundaries, as the ISO guidelines outline (ISO, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006a\u003c/span\u003e). Employing the ReCiPe 2016 (H) midpoint method allows for a comprehensive evaluation that aligns with ISO 14040 (ISO, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006a\u003c/span\u003e) and ISO 14044 (ISO, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006b\u003c/span\u003e) to gauge the environmental impact of cement types. The LCA analysis involves four stages: goal and scope definition, inventory analysis, impact assessment, and interpretation, following ISO guidelines.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Goal and Scope Definition\u003c/h2\u003e \u003cp\u003eThe first stage of the LCA methodology involves defining the goal, scope, boundaries and functional unit. To calculate the environmental impact, this paper focuses on the cradle-to-gate approach of cement production, excluding operational and disposal stages. It aims to analyze eight cement types using 1 kg of cement as the functional unit. This unit allows us to compare and assess the alternatives, considering the system boundaries illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Inventory analysis\u003c/h2\u003e \u003cp\u003eThis stage quantifies all inputs and outputs throughout the life cycle of a product, including material extraction, manufacturing, transportation, use, and end-of-life treatment, using data from the Ecoinvent database (See Table S9) to assess the cradle-to-gate environmental impact of different cement types.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Impact assessment\u003c/h2\u003e \u003cp\u003eThis stage assesses and categorizes the potential environmental impacts of resources and emissions into impact categories such as global warming, climate change, acidification, resource depletion, eutrophication, and ecotoxicity using SimaPro 9.2.0.1 software by PR\u0026eacute; Consultants, Netherlands, to evaluate the environmental impact of different cement alternatives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4 Interpretation:\u003c/h2\u003e \u003cp\u003eIn the final stage, results from LCI and LCIA are analyzed and interpreted, drawing conclusions and offering improvement recommendations based on identified environmental hotspots and potential sustainable alternatives. Due to trade-offs between impact categories, interpreting environmental impacts across different alternatives through LCA can be complex (Owsianiak et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), leading to two emerging methods: (i) Ranking and merging the impact categories into a single score indicator. (ii) The second approach aims to simplify the result interpretation by using a smaller set of impact categories (Torkayesh et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Multi-criteria decision-making (MCDM)\u003c/h2\u003e \u003cp\u003eMCDM is a widely used method for ranking alternatives and identifying the best choice in complex scenarios when evaluating various qualitative and quantitative criteria (Keshavarz Ghorabaee et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Juanpera et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The MCDM problem and weights for the criteria are expressed in terms of Eq.\u0026nbsp;1. Each criterion's weight and assessment are determined using the Entropy method to ascertain their relative significance in cement production sustainability. These criteria are then integrated with COPRAS and ARAS to derive an overall priority score, designating the most sustainable cement alternative.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Entropy method\u003c/h2\u003e \u003cp\u003eThe entropy-based objective weighting method relies on unbiased data, addressing the limitations of subjective weighting methods (Wang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It quantifies the amount of valid information in the data (Wang and Zhan, 2012), using a step-by-step procedure to determine the weight of each criterion based on its information content. The following steps are the fundamental procedure of the Entropy objective weighting method.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 1: Normalization of the array decision matrix\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe normalization of the array decision matrix (performance indices) to obtain the project outcomes \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{P}}_{\\varvec{l}\\varvec{j}}\\)\u003c/span\u003e\u003c/span\u003e, using Eq.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\varvec{P}}_{\\varvec{l}\\varvec{j}}=\\frac{{\\varvec{x}}_{\\varvec{i}\\varvec{j}}}{{\\sum }_{\\varvec{i}=1}^{\\varvec{m}}{\\varvec{x}}_{\\varvec{i}\\varvec{j}}}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; Eq.\u0026nbsp;1\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{P}}_{\\varvec{l}\\varvec{j}}\\)\u003c/span\u003e\u003c/span\u003e, represent the normalized value of data of the \u003cb\u003earray\u003c/b\u003e decision matrix.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 2: Computation of the normalization of entropy values\u003c/b\u003e \u003c/p\u003e \u003cp\u003eComputation of the \u003cb\u003enormalization\u003c/b\u003e of entropy values of project outcomes using Eq.\u0026nbsp;2\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\varvec{E}}_{\\varvec{j}}=-\\text{k}\\sum _{i=1}^{m}{\\varvec{P}}_{\\varvec{i}\\varvec{j}}{1\\varvec{n} }_{{\\varvec{P}}_{\\varvec{i}\\varvec{j}}}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. Eq.\u0026nbsp;2\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{{\\rm K}}=1/1n\\left(m\\right)\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 4. Calculation of the Entropy objective weight\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCalculating the entropy objective weight \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{j}\\)\u003c/span\u003e\u003c/span\u003e, based on the entropy concept using Eq.\u0026nbsp;3.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({w}_{j}=\\frac{1-{\\mathbf{{\\rm E}}}_{\\varvec{j}}}{{\\sum }_{\\varvec{j}=1}^{\\varvec{n}}\\left({1-\\text{{\\rm E}}}_{j}\\right)}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;Equation 3\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sum }_{\\varvec{j}=1}^{\\varvec{n}}{w}_{j}=1\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Preference ranking\u003c/h2\u003e \u003cp\u003eThis paper employs two MCDM methodologies, COPRAS and ARAS, to develop a unified index for ranking and selecting the most suitable alternatives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Complex Proportional Assessment (COPRAS) Method\u003c/h2\u003e \u003cp\u003eThe COPRAS method is a pioneering technique in multi-attribute decision-making that evaluates the relative importance of alternatives based on a set of criteria (Karaca et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It provides a comprehensive picture of the potential of each alternative by analyzing best-case and worst-case scenarios for each criterion. The COPRAS method ranks choices based on diverse and relevant weight criteria, allowing for more informed decision-making (Okunevičiūtė Neverauskienė et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Organ and Yal\u0026ccedil;ın, 2016). By considering diverse weight criteria, COPRAS enables ranking of alternatives, aiding in identifying the most suitable option. This paper utilizes COPRAS to rank cement alternatives in South Africa, considering their whole life cycle, due to its ability to evaluate both quantitative and qualitative criteria (Ighravwe and Oke, 2019). The COPRAS method comprises the following steps:\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 1: Development of the initial Decision Matrix.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe decision matrix (Eq.\u0026nbsp;4) is a table detailing each alternative's evaluation criteria in the decision-making process. The values in the matrix can be either qualitative or quantitative, providing a comprehensive overview of the alternatives' attributes and aiding decision-making.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\varvec{x}={\\left[{\\varvec{x}}_{\\varvec{i}\\varvec{j}}\\right]}_{\\varvec{m}\\varvec{x}\\varvec{n}}=\\left[\\begin{array}{cccc}{\\varvec{x}}_{11}\u0026amp; {\\varvec{x}}_{12}\u0026amp; \\dots \u0026amp; {\\varvec{x}}_{1\\varvec{n}}\\\\ {\\varvec{x}}_{21}\u0026amp; {\\varvec{x}}_{22}\u0026amp; \\dots \u0026amp; {\\varvec{x}}_{2\\varvec{n}}\\\\ \\dots \u0026amp; \\dots \u0026amp; \\dots \u0026amp; \\dots \\\\ {\\varvec{x}}_{\\varvec{m}1}\u0026amp; {\\varvec{x}}_{\\varvec{m}2}\u0026amp; \\dots \u0026amp; {\\varvec{x}}_{\\varvec{m}\\varvec{n}}\\end{array}\\right]\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. Eq.\u0026nbsp;4\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere the value \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{x}}_{\\varvec{i}\\varvec{j}}\\)\u003c/span\u003e\u003c/span\u003e represents the evaluation of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{i}\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003eth\u003c/sup\u003e alternative on the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{j}\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003eth\u003c/sup\u003e criterion; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{i}\\)\u003c/span\u003e\u003c/span\u003e = 1, 2, \u0026hellip;, m; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{j}\\)\u003c/span\u003e\u003c/span\u003e= 1, 2, \u0026hellip;, n.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 2: Normalization of the decision matrix.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNormalizing the decision matrix (Eq.\u0026nbsp;5) ensures all evaluation criteria are on a common scale. This approach ensures that all criteria are considered and weighed equally during evaluation. It is important to consider that some criteria may be measured in different units, as this can make it difficult to compare and evaluate them. The decision matrix is normalized by using (Eq.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\varvec{R}={\\left[{\\varvec{r}}_{\\varvec{i}\\varvec{j}}\\right]}_{\\varvec{m}\\varvec{x}\\varvec{n}}=\\frac{{\\varvec{x}}_{\\varvec{i}\\varvec{j}}}{{\\sum }_{\\varvec{i}=1}^{\\varvec{m}}{\\varvec{x}}_{\\varvec{i}\\varvec{j}}}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. Eq.\u0026nbsp;5\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 3: Calculate the weighted normalized decision matrix\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eIn this step, the normalized decision matrix values (Eq.\u0026nbsp;6) are calculated by multiplying each element in the normalized decision matrix by the weight of the corresponding evaluation criterion (obtained in the Entropy method). The decision-maker determines the weights and reflects the relative importance of each criterion.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\varvec{D}={\\left[{\\varvec{y}}_{\\varvec{i}\\varvec{j}}\\right]}_{\\varvec{m}\\varvec{x}\\varvec{n}}={\\varvec{r}}_{\\varvec{i}\\varvec{j}}\\times {\\varvec{w}}_{\\varvec{j}}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;Equation 6\u003c/b\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\varvec{i}=1,2,\\dots ..,\\varvec{m} \\varvec{a}\\varvec{n}\\varvec{d} \\varvec{j}=1,2,\\dots ..,\\varvec{n}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 4: Weighted sum of the normalized decision matrix\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis step determined (the beneficial and non-beneficial attributes) of each alternative by summing them across all criteria. Calculating the beneficial and non-beneficial attributes using equations (7) and (8).\u003c/p\u003e \u003cp\u003eBeneficial criteria \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{S}}_{+\\varvec{i}}{\\sum }_{\\varvec{j}=1}^{\\varvec{n}}{\\varvec{y}}_{+\\varvec{i}\\varvec{j}}\\)\u003c/span\u003e\u003c/span\u003e \u003cb\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. Eq.\u0026nbsp;7\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNon-beneficial criteria \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{S}}_{-\\varvec{i}}{\\sum }_{\\varvec{j}=1}^{\\varvec{n}}{\\varvec{y}}_{-\\varvec{i}\\varvec{j}}\\)\u003c/span\u003e\u003c/span\u003e \u003cb\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. Eq.\u0026nbsp;8\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 5: Determining the relative weight of each alternative.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this step, the relative importance (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{Q}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e) of each alternative beneficial and non-beneficial criteria is calculated using Eq.\u0026nbsp;(9). The higher the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{Q}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e, the higher the priority (rank) of the alternative is (Okunevičiūtė Neverauskienė et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Each alternative is ranked in order of importance based on its overall score.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\varvec{Q}}_{\\varvec{i}}={\\varvec{S}}_{+\\varvec{i}}+\\frac{{\\varvec{S}}_{-\\varvec{m}\\varvec{i}\\varvec{n}{\\sum }_{\\varvec{i}=1}^{\\varvec{m}}{\\varvec{S}}_{-\\varvec{i}}}}{{\\varvec{S}}_{-\\varvec{i}}{\\sum }_{\\varvec{i}=1}^{\\varvec{m}}\\left({\\varvec{S}}_{-\\varvec{m}\\varvec{i}\\varvec{n}}/{\\varvec{S}}_{-\\varvec{i}}\\right)}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; Eq.\u0026nbsp;9\u003c/b\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\left(\\varvec{i}=1,2,\\dots ..,\\varvec{m}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{S}}_{-\\varvec{m}\\varvec{i}\\varvec{n}}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{S}}_{-\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e and (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{Q}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e)is calculated using Eq.\u0026nbsp;(9) (Viteikiene and Zavadskas, 2007).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 6: Ranking the alternatives\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this step, the alternatives are ranked from largest to smallest in their relative relevance of the alternatives, depending on the degree of utility obtained (Karaca et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This step determines the utility degree for each alternative using Eq.\u0026nbsp;(10).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\varvec{U}}_{\\varvec{i}}=\u0026lfloor;\\frac{{\\varvec{Q}}_{\\varvec{i}}}{{\\varvec{Q}}_{\\varvec{m}\\varvec{a}\\varvec{x}}}\u0026rfloor;\\times 100\\text{\\%}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;Equation 10\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn this method, the relative importance of each alternative is calculated by weighing their positive (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{S}}_{+\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e) and negative (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{S}}_{-\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e) attributes allowing decision-makers to evaluate and rank alternatives in complex problems with multiple criteria. The higher (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{Q}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e) value is considered more effective. It considers both the performance of alternatives and their proximity to the ideal solution, offering a comprehensive assessment for decision-making.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Additive ratio assessment method (ARAS)\u003c/h2\u003e \u003cp\u003eThe ARAS method is a quantitative technique used in decision-making and evaluation processes. It allows decision-makers to compare and rank a finite number of alternatives based on multiple decision criteria. As outlined by Karabašević et al. (Karabašević et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Zavadskas and Turskis (Zavadskas and Turskis, 2010) and Karabasevic et al. (Karabasevic et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the ARAS can be employed to address MCDM problems by following these steps:\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 1: The initial decision matrix development.\u003c/b\u003e \u003cdiv id=\"Equc\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\varvec{x}=\\left[\\begin{array}{ccccc}{\\varvec{x}}_{01}\u0026amp; \\dots \u0026amp; {\\varvec{x}}_{0\\varvec{j}}\u0026amp; \\dots \u0026amp; {\\varvec{x}}_{0\\varvec{n}}\\\\ ⋮\u0026amp; \\ddots \u0026amp; ⋮\u0026amp; \\ddots \u0026amp; ⋮\\\\ {\\varvec{x}}_{\\varvec{i}1}\u0026amp; \\dots \u0026amp; {\\varvec{x}}_{\\varvec{i}\\varvec{j}}\u0026amp; \\dots \u0026amp; {\\varvec{x}}_{\\varvec{i}\\varvec{n}}\\\\ ⋮\u0026amp; \\ddots \u0026amp; ⋮\u0026amp; \\ddots \u0026amp; ⋮\\\\ {\\varvec{x}}_{\\varvec{m}1}\u0026amp; \\dots \u0026amp; {\\varvec{x}}_{\\varvec{m}\\varvec{j}}\u0026amp; \\dots \u0026amp; {\\varvec{x}}_{\\varvec{m}\\varvec{n}}\\end{array}\\right] ; \\varvec{i}=\\overline{0,\\varvec{m};} \\varvec{j}=\\overline{1,\\varvec{n}}, \\dots \\dots \\dots \\dots ..\\mathbf{E}\\mathbf{q}. 11$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{m}\\)\u003c/span\u003e\u003c/span\u003e represents the number of alternatives and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{n}\\)\u003c/span\u003e\u003c/span\u003e is the number of criteria, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{x}}_{\\varvec{i}\\varvec{j}}\\)\u003c/span\u003e\u003c/span\u003e is the value representing \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{i}\\)\u003c/span\u003e\u003c/span\u003e performance value alternative in terms of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{j}\\)\u003c/span\u003e\u003c/span\u003e criterion\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\varvec{x}}_{01}\\)\u003c/span\u003e \u003c/span\u003e represents the best value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{j}\\)\u003c/span\u003e\u003c/span\u003e the criterion\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 2: Calculation of a normalized decision matrix\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eBeneficial criteria\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\overline{{\\mathbf{x}}_{\\mathbf{i}\\mathbf{j}}}=\\frac{{\\mathbf{x}}_{\\mathbf{i}\\mathbf{j}}}{{\\sum }_{\\mathbf{i}=0}^{\\mathbf{m}}{\\mathbf{x}}_{\\mathbf{i}\\mathbf{j}}}\\)\u003c/span\u003e\u003c/span\u003e \u003cb\u003e\u0026hellip;.\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..Eq.\u0026nbsp;12\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eNon-beneficial criteria\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{x}}_{\\varvec{i}\\varvec{j}}=\\frac{1}{{\\varvec{x}}_{\\varvec{i}\\varvec{j}}^{\\mathbf{*}}}\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\overline{{\\varvec{x}}_{\\varvec{i}\\varvec{j}}}=\\frac{{\\varvec{x}}_{\\varvec{i}\\varvec{j}}}{{\\sum }_{\\varvec{i}=0}^{\\varvec{m}}{\\varvec{x}}_{\\varvec{i}\\varvec{j}}}\\)\u003c/span\u003e\u003c/span\u003e \u003cb\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..Eq.\u0026nbsp;13\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 3: Calculation of weighted normalized decision matrix\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\widehat{\\varvec{x}}}_{\\varvec{i}\\varvec{j}=\\stackrel{-}{{\\varvec{X}}_{\\varvec{i}\\varvec{j}}}}{\\varvec{w}}_{\\varvec{j}}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..Eq.\u0026nbsp;14\u003c/b\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{w}}_{\\varvec{j}}\\)\u003c/span\u003e\u003c/span\u003e, represent the weight of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{j}\\)\u003c/span\u003e\u003c/span\u003e criterion and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\overline{{\\varvec{x}}_{\\varvec{i}\\varvec{j}}}\\)\u003c/span\u003e\u003c/span\u003e, represent the normalized rating of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{j}\\)\u003c/span\u003e\u003c/span\u003e criterion\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 4: Si-Optimality function for\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{i}\\)\u003c/span\u003e\u003c/span\u003e \u003cb\u003ealternative\u003c/b\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$${\\varvec{S}}_{\\varvec{i}}={\\sum }_{\\varvec{j}=1}^{\\varvec{n}}{\\widehat{\\varvec{x}}}_{\\varvec{i}\\varvec{j}}; \\varvec{i}=\\stackrel{-}{0,\\varvec{m} ,} \\dots \\dots \\dots \\dots \\mathbf{E}\\mathbf{q}\\mathbf{u}\\mathbf{a}\\mathbf{t}\\mathbf{i}\\mathbf{o}\\mathbf{n} 15$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{S}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e, represent the value of the optimality function of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{i}\\)\u003c/span\u003e\u003c/span\u003e alternative.\u003c/p\u003e \u003cp\u003eThere is a need to differentiate between the sum of the beneficial and non-beneficial criteria.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 5: Calculation of the utility degree for each alternative\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\varvec{K}}_{\\varvec{i}}=\\frac{{\\varvec{S}}_{\\varvec{i}}}{{\\varvec{S}}_{0}}\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..Eq.\u0026nbsp;16\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{K}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e represents the degree of utility of each alternative and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{S}}_{\\varvec{i} }\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{S}}_{0 }\\)\u003c/span\u003e\u003c/span\u003erepresents the optimality criterion values. Integrating LCA and MCDM methods creates a comprehensive decision-making tool that combines quantitative accuracy with subjective attributes, prioritizes evidence-based decision-making, promotes openness and replicability, and strengthens the validity and reliability of conclusions.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Results and Discussions","content":"\u003cp\u003eThis section discusses the outcomes of the LCA and MCDM analysis. Detailed results of impact categories for eight cement types produced in South Africa are presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. According to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the results showed that the CEM I cement had the highest global warming emission (0.99 kg CO2 eq), followed by CEM II/B-L (0.86 kg CO2 eq), and CEM III/A (0.57 kg CO2 eq) had the lowest. CEM III/A cement exhibited the highest terrestrial ecotoxicity (1.31 kg 1,4-DCB), human non-carcinogenic toxicity (0.63 kg 1,4-DCB), and freshwater ecotoxicity (0.03 kg 1,4-DCB) compared to other types. Considering the impact assessment, CEM III/A cement emerged as the environmentally friendly choice with the lowest environmental impact, while CEM I showed the highest impact. LCIA results were utilized in MCDM (COPRAS and ARAS) methods to rank and select the best alternatives. A detailed MCDM analysis is provided in Tables S2-S8, which comprehensively explain the results.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Result of MCDM Analysis\u003c/h2\u003e \u003cp\u003eTable S4 displays the Weighted Normalized Decision Matrix (COPRAS) of the alternatives concerning cement environmental impact, the main criteria. We ranked various cement alternatives based on 18 impact categories from the LCA midpoint method. Impact categories served as criteria, and cement types as alternatives in this paper. Each alternative's weight, based on the impact category, was determined via the entropy weight method. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the weight of the criteria.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWith the entropy method, 15 criteria received weights below zero percent, except water consumption, freshwater ecotoxicity, and land use, which received 45.67%, 42.92%, and 8.07% weights, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Regarding their environmental impact, most criteria had equal weight when ranking different cement alternatives. Integrating LCA with the MCDM method, we ranked different cement alternatives based on 18 criteria identified in the LCA, comparing COPRAS with ARAS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). CEM II/B-V cement emerged as the most preferred alternative with the highest utility degree (100), followed by CEM II/A-V (89.93) and CEM II/B-L (84.75) for COPRAS, and CEM II/B-V (0.994), CEM II/A-V (0.894), and CEM II/B-L (0.845) for ARAS. Both methods yielded similar rankings. Conversely, CEM III/A cement was identified as the least preferred (57.56 for COPRAS and 0.588 for ARAS). Following the top three, COPRAS ranked CEM I cement fourth, succeeded by CEM II/A-S, CEM II/B-S, and CEM III/A cement, respectively.\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\u003eResults of the MCDM method\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCement Types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCOPRAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eARAS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eSi\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eKi\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eRank\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEM I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76,72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,1203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,7649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEM II/A-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65,37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,1029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,6540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEM II/A-V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,1407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,8944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEM II/B-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,0973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,6187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEM II/B-L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,1328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,8445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEM II/B-V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,1563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,9935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEM III/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57,56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,0925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,5883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing COPRAS and ARAS methodologies in this study to analyze and rank different cement alternatives demonstrates the importance of reliable research results. Both methods identified the CEM II/B-V cement as the preferred alternative, demonstrating its potential as a beneficial environmental choice. Despite being the highest emission-reduction cement alternative, CEM III/A cement was ranked as the least preferred alternative.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 LCA Characterization results at midpoint method\u003c/h2\u003e \u003cp\u003eThis section provides a detailed analysis of the midpoint characterization results, highlighting the impact of five production stages on each environment category for various cement types, ranging from the least to the most preferred. Specific process details may vary based on the plant and cement type. The stages are as follows: (i) Raw Material Stage, (ii) Clinker production Stage, (iii) Fuel usage Stage, (iv) Electricity Stage, (v) Transportation Stage. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the midpoint characterization of CEM II/B-V cement. Clinker production (73.42%) is the primary contributor to global warming, followed by electricity usage (21.19%), fuel usage (2.14%), transportation (2%), and raw materials (1.13%), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis paper identifies the critical stages of the life cycle of each cement type and their impact contributions. By analyzing each stage's contributions and inefficiencies, the authors can identify the most strategic opportunities for targeted interventions that maximize energy efficiency and reduce environmental impact. Additionally, electricity usage (68.59%) is the main contributor to stratospheric ozone depletion, while raw material, fuel usage, and transportation contribute 24.27%, 5.34%, and 1.61%, respectively. Only the raw material stage contributes to mineral resource scarcity (100%), ionizing radiation (93.17%), land use (94.82%), and water consumption (69.61%).\u003c/p\u003e \u003cp\u003eElectricity usage (68.41%) contributes the most to fine particulate matter formation, followed by clinker production (15.35%) and raw material (7.87%), while fuel usage and transportation each contribute (4.13%). Fuel usage is the highest contributor to freshwater eutrophication (97.38%), marine eutrophication (95.64%), freshwater ecotoxicity (53.79%), marine ecotoxicity (55.05%), human carcinogenic toxicity (86.67%), human non-carcinogenic toxicity (77.29%) and fossil resource scarcity (99.38%). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides a detailed analysis of the CEM III/A cement results at the midpoint method. The clinker production stage contributed 62.83% to global warming, followed by the electricity stage with 23.32%. The raw material fuel usage and transportation stages are the lowest contributors, contributing 6.56%, 4.46% and 2.56% of input, respectively. Electricity and raw material stages are the major contributors to stratospheric ozone depletion, contributing 63.47% and 26. 95% and fuel usage and transportation contribute the lowest percentage, with 6.83% and 2.33%, respectively. Fuel usage is the largest contributor to freshwater (89.41%) and marine eutrophication impacts (85.56%), while raw materials have the lowest contributions (10.28% and 13.35%, respectively). Most fine particulate matter formation originates from the electricity stage (60.07%) of the CEM III/A cement. Raw material, clinker production, fuel usage and transportation contribute with individual shares of 15.24%, 10.67%, 8.30% and 4.76%, respectively. Fuel usage contributes 89.41% to freshwater eutrophication, whereas raw material has the most negligible impact, 10.28%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExtraction is the leading cause of several environmental impacts, including ionizing radiation (92.88%), terrestrial ecotoxicity (62.72%), freshwater ecotoxicity (77.90%), marine ecotoxicity (76.24%), human non-carcinogenic toxicity (58.14%), land use (93.96%), mineral resource scarcity (100%) and water consumption (46.13%).\u003c/p\u003e \u003cp\u003eHighlighting the significant contribution of raw materials, especially in CEM I cement, to global warming, the need for improved efficiency and potentially implementing carbon capture and storage technologies becomes evident due to the traditional raw materials (limestone) used in the production. The high GWP of CEM I cement is attributed to its energy-intensive production process, which involves grinding raw materials, calcining limestone at high temperatures (1400\u0026ndash;1450\u0026deg;C) and grinding clinker. Analyzing the environmental impact of cement production in CEM II/B-V (0.71 kg CO\u003csub\u003e2\u003c/sub\u003e eq) and CEM III/A cement (0.57 kg CO\u003csub\u003e2\u003c/sub\u003e eq) highlighted the importance of selecting sustainable fuels and raw materials. These factors served as crucial parameters for the study's sustainability assessment. The CEM III/A cement ranked the lowest alternative due to the environmental impact of its raw material process. Further research may be needed to understand the environmental impacts of CEM III/A cement. The presence of calciners in cyclone preheaters and kiln exhaust gas recirculation in grinding and drying equipment to produce CEM I cement were key contributors to GHG emissions due to their specific process technology impacts. This is evident in GWP (0.99 kg CO\u003csub\u003e2\u003c/sub\u003e eq).\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study combines LCA with MCDM to identify the most sustainable cement option in South Africa. Considering various cement alternatives' environmental impact and sustainability aspects, decision-makers can make informed decisions prioritizing affordability and efficiency.\u003c/p\u003e \u003cp\u003eLCA assessed environmental impact across five production stages, with 18 categories like global warming and resource scarcity. CEM I cement exhibited the highest global warming emission and was ranked the fourth most preferred alternative. CEM III/A cement demonstrated the highest terrestrial ecotoxicity, human non-carcinogenic toxicity, and freshwater ecotoxicity. The results showed that CEM III/A cement had the lowest overall environmental impact due to its lower clinker content and potential for incorporating alternative materials like fly ash and Ground-Granulated Blast Furnace Slag. For CEM II/B-V cement, the most preferred alternative and clinker production was among the highest contributors to global warming.\u003c/p\u003e \u003cp\u003eThis paper used COPRAS and ARAS (MCDM) methods to rank and select the best alternatives based on 18 impact categories. The results identified CEM II/B-V cement as the most preferred option by both methods, with the low environmental impact and highest utility score in the MCDM analysis, followed by CEM II/A-V and CEM II/B-L cement. This highlights the potential of using alternative binders and optimizing production processes for sustainability. CEM III/A cement, despite its low global warming emissions, was ranked as the least preferred alternative due to concerns about the environmental impact of its raw materials, especially in terms of ionizing radiation, terrestrial ecotoxicity, and water consumption. Further investigation is required to understand and address this impact. MCDM tools provide a more comprehensive approach to selecting the best cement alternatives by considering multiple factors. CEM III/A cement was found to be the main contributor to global warming and electricity usage, emphasizing the importance of selecting sustainable fuels and raw materials, improving production efficiency, and exploring interventions like carbon capture and storage technologies to reduce the environmental impact of cement production.\u003c/p\u003e \u003cp\u003eIn conclusion, integrating LCA and MCDM methods provided valuable insights into the environmental impacts of different cement types, informing decision-makers in the industry and promoting more sustainable practices. The paper suggested that further research is needed to understand the environmental impact of alternative raw materials and fuels for CEM III/A cement, which ranked as the least preferred alternative.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eOE: writing-original draft preparation, methodology, data curation, investigation, validation, formal analysis. DVVK and DA: conceptualization, resources, supervision, writing-reviewing. DVVK and DA: data curation, supervision, writing-reviewing and editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed M, Bashar I, Alam ST, Wasi AI, Jerin I, Khatun S, andRahman M (2021) An overview of Asian cement industry: Environmental impacts, research methodologies and mitigation measures. 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Emerg Markets Finance Trade 55(5):967\u0026ndash;979\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cement production, Multi-criteria decision making, Complex proportional assessment, Additive ratio assessment, Life cycle assessment","lastPublishedDoi":"10.21203/rs.3.rs-4133462/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4133462/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChoosing the most suitable alternatives can be challenging in process engineering. Typically, there is a need to evaluate and rank alternatives using various criteria, such as environmental impact, when making decisions. This paper employs a novel integration of Life Cycle Assessment (LCA) and Multi-Criteria Decision-Making (MCDM) methods to evaluate the sustainability of different cement alternatives in South Africa. The LCA assesses the environmental impact, considering 18 midpoint categories, while Complex Proportional Assessment (COPRAS) and Additive Ratio Assessment (ARAS) methods were used as MCDA methods to rank and select the best alternatives. Across 18 impact categories, including global warming, ozone depletion, ecotoxicity, and resource scarcity, CEM I cement exhibited notable global warming emissions, ranking fourth. COPRAS and ARAS methods systematically ranked alternatives based on impact categories, consistently identifying CEM II/B-V cement as the most preferred alternative. This top ranking was attributed to its low environmental impact and high utility score. Notably, CEM III/A cement, despite low global warming emissions, ranked least preferred due to concerns about raw material-related environmental impacts. The paper highlights environmental hotspots for each cement type and underscores the importance of sustainable fuel and raw material selection in production. The results emphasize the necessity of reducing clinker content, exploring alternative fuels and raw materials, and adopting interventions like carbon capture and storage to enhance sustainability in cement production. The paper concludes that the integrated LCA and MCDM approach provides valuable insights for decision-makers in the cement industry, aiding the pursuit of more sustainable practices and calling for further research on the environmental impact of specific raw materials and fuels.\u003c/p\u003e","manuscriptTitle":"Evaluation and Ranking of Cement Alternatives in South Africa Using Combine Life Cycle Assessment and Multi- criteria Decision-Making Methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-22 19:13:29","doi":"10.21203/rs.3.rs-4133462/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"786047e0-ccb7-4289-b3fe-f9ded59d87f6","owner":[],"postedDate":"March 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-25T14:53:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-22 19:13:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4133462","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4133462","identity":"rs-4133462","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}