Multicriteria Decision-Making for Sustainable Mining: Evaluating the Transition to Net-Zero-Carbon Energy Systems

Abstract

Transitioning to sustainability is particularly challenging in the mining domain since operations must also be economically viable and meet operational efficiency requirements. Several competing criteria, including stakeholder interests and technological uncertainties, complicate the selection of appropriate sustainable technologies. This study evaluates sustainable mining technologies by using a novel multicriteria decision-making framework. Six alternatives were assessed against ten criteria through expert consultation with eight academic professionals. The research employs three fuzzy methods (TOPSIS, COPRAS, and VIKOR) integrated through a proposed Geometric Inverse Distance Aggregation (GIDA) approach. The results demonstrate that waste heat recovery systems are the optimal solution with the highest GIDA score (0.0319) and agreement (99.99%), followed by solar-powered mining (0.0232, 82.12% agreement). The findings suggest a practical implementation pathway, prioritizing proven technologies while preparing for emerging solutions. This research contributes to the sustainable mining literature by providing a comprehensive evaluation framework and practical implementation guidance for mining companies transitioning to sustainable operations.

1. Introduction

The mining sector is critical to most world economies [1]. It has significantly improved many countries’ gross domestic product (GDP) [2]. Though mining activities boost economies, they negatively affect ecosystems [3]. The chain of activities involved in the different mining processes has a peculiar hazardous impact on the environment, miners’ health and safety, indigenes, and residents of communities of mining sites [4]. The summation of the adverse effects of mining activities results from improper stakeholder engagement, unlawful mining activities, dangerous mining methods, unqualified mining personnel, and the lack of routine environmental cleaning and rehabilitation [5]. Consequently, many perceived mining activities degrade sites and communities [6]. As a result, communities, residents, and stakeholders, most of the time, have significant reservations and detest mining activities in their environments [7]. The mining business is, therefore, affected by these chains of impacts and perceptions of it being a hazardous and polluting venture [8]. The combination of negative perceptions and the importance of emphasizing the need for sustainable mining activities are elaborated in this study. The results obtained from this study will be of great importance in providing a sustainable solution to the challenges encountered by mining activities and their industrial implementation. This study also highlights the transition of the energy systems of mining activities to a net-zero-carbon energy system.

Sustainable mining and a robust decision-making process are necessary to solve the sustainability problem in the mining sector and can change the dynamic of the mining industry’s operational activities. This is due to the importance of a friendly environment, operational safety, economic stability, and the optimal usage of mineral resources [9]. Mining is the extraction of mineral resources from the earth’s surface [10]. Sustainable mining comprises a reduction in the hazardous effects of mining activities on miners, the environment, and the economy [11]. According to Laurence [12], a mine is sustainable if it is safe, economically strong, environmentally friendly, and controlled by a community. Therefore, sustainable mining is the application of the different operational stages in the mining process to achieve a safer mine and reduce environmental degradation, resulting in improving social development while safeguarding the interests of all stakeholders and the mining communities [13]. In recent decades, the mining sector has witnessed considerable evolution, especially in terms of regulation and economic development [14]. Even though mining activities have recorded tremendous improvement in economic development, they have initiated several concerns in the areas of miners’ safety, environmental protection, and the effects on the society and residents of mining communities [15]. For a mining activity to be sustainable, it must be free, to a large extent, from risks; economically viable; and environmentally safe [16]. Sustainable mining examines several risks involved in a mining activity to ascertain its viability [17]. This involves a critical multicriteria decision-making process to achieve the objective. The transition of mining activities to a net-zero-carbon energy system requires an assessment of the criteria. It is, therefore, pertinent to examine the multicriteria decision-making process in sustainable mining that contains an assessment of transitioning to a net-zero-carbon energy system.

An evaluation of the related literature has necessitated the importance of further study in the net-zero-carbon energy systems for sustainable mining. Firstly, most available studies concentrated on policies with less emphasis on implementation. Secondly, the available studies have researched the effect of mining on the environment; the safety of mines; and the human, economic, legal, political, and social aspects, with no emphasis on the multicriteria decision-making processes for achieving net-zero carbon emissions for sustainable mining through adequate energy system implementation. Furthermore, an assessment of the existing literature does not give credence to their application in energy systems for sustainable and net-zero-carbon energy systems. To fill these technical research gaps, this study explores the recent literature on the subject area to evaluate sustainable mining and its transition to a net-zero-carbon energy system. The aim of this study is to perform a multicriteria assessment that will assist in the decision-making and sustainability of the mining sector. A total of five criteria and six alternatives, relevant to the study area, were considered. The data for this study were obtained through expert consultation. The review of the existing body of knowledge serves as a guide for the mode of data collection. The fuzzy TOPSIS, fuzzy COPRAS, and fuzzy VIKOR procedures were used to perform the analysis in this study. In addition, a sensitivity analysis was carried out to test and demonstrate the strength of the results.

This study will provide a critical multicriteria decision-making process for sustainable mining activities. Additionally, it will assist in a seamless transition to adopting a net-zero-carbon energy system for sustainable mining.

2. Literature Review

2.1. Overview of Sustainable Mining and Net-Zero-Carbon Goals

Several studies have been conducted on sustainable mining and its advancement into net-zero-carbon mining operations. Mining activities require a considerable amount of energy. This often leads to the massive deployment of heavy diesel machines for these activities. These heavy machines emit gases that are harmful to humans and the environment [18,19]. To overcome the exposure of these harmful gases to both humans and the environment, mining operators are transitioning to electrically powered, eco-friendly systems [20,21]. Adopting alternative energy sources can reduce dependence on diesel-powered engines for mining activities and increase reliance on a more sustainable and reliable electrical-energy-powered system [22]. The electrical-energy-powered system dramatically reduces noise and gaseous pollution, propagating the net-zero-carbon agenda and sustainable mining [23,24]. The transition of mining activities to a net-zero-carbon energy system places the involved economy in a wide range of opportunities for advancement [25]. Jiang et al. [26] studied modern trends in smart mining activities. The authors considered automated electric locomotives in China. The results of their investigation revealed that only six mines are globally operated using automated electric locomotives. Recent developments in the mining sector support mining activities that are environmentally friendly and safe for humans [27]. A study on copper mining by using solar energy was conducted in Chile [28]. This study explored several solar mining methods in addition to elaborating on their associated challenges. The findings from this study proposed an optimal mining process for solar-energy-powered mining in India. It also revealed several routes of integrating solar energy into the mining of copper. An assessment of the pros and cons of controlling the use of solar energy in the mining sector has been performed and diligently reported [29]. The results showed that solar energy is viable for providing adequate energy for sustainable mining.

Strategies for the achievement of neutral carbon emissions for mines in China have been reported [30]. The authors focused on ways to accomplish maximum and neutral carbon emissions. This study revealed that renowned mining companies have been able to reduce their carbon emissions to the barest minimum. Additionally, this study showed that improving mining techniques enhances the efficiency of mining activities and reduces the emission of harmful gases. An assessment of the likelihood of the risks that are linked with mining projects was conducted by using the fuzzy logic approach [31]. The findings from this study revealed that the technical and economic risks were ranked higher than the social, political, safety, and environmental concerns. This study advocated the importance of formulating policies that reduce the risk of affecting the supply of minerals for emission-free production. The Chinese mining sector was assessed by using a modern technique, known as Green and Climate-Smart Mining (GCSM) [32]. This study applied the fuzzy Delphi method, fuzzy multicriteria decision-making analysis, and sensitivity analysis to test the viability of the mining sector. The findings from this study revealed that the technical and operational criteria are the criteria with the highest ability to hinder the realization of GCSM. The efficiency of iron mining activities has also been investigated [33]. The investigation was conducted over 26 years, across seventeen different countries. This study showed that the mining sector will be sustainable and efficient using green power generation energy sources.

Globally, there is a target of significant carbon emission reduction in all sectors of human activities, even though the achievement of this goal has been a great challenge [34,35]. Most human activities are highly dependent on fossil fuels and different gaseous emissions; therefore, the transition to a net-zero-carbon energy system for sustainable mining will be multi-dimensional [36,37,38]. There is a correlation between the emission of greenhouse gases and global climate change [39]. Globally, climate change is currently of great concern [40]. This has attracted the attention of global institutions and authorities in regulating the activities of production and consumption by all stakeholders [37]. A net-zero-carbon energy system is essential in reducing the effects of greenhouse gases and climate change [41]. Rennê [42] reported that transitioning to a carbon-free energy system is technically and economically viable. This transition is significant for developing countries since it will assist in breaking the barrier of underdevelopment and contribute to launching weak economies into prosperity, while at the same time achieving a carbon-free society [43,44]. Additionally, it will assist in reducing the impact of carbon emissions on the health of residents of developing countries [45,46].

2.2. Challenges in Sustainable Mining

Assessing the challenges involved in sustainable mining is pertinent to achieving a net-zero-carbon energy system. In addition, mining activities can lead to wealth if desirable and proper technologies are applied [47]. An evaluation of the complex impact encompassing the economic, environmental, and social effects demands a multi-dimensional understanding of their actions on one another and the assemblage of their different constituent elements [48]. The procedures depict a thinking system that must be coordinated through adequate planning and modeling. The system considers the problem as a whole and as part of the system rather than viewing it as an individual part. The coordinated quality of the system brings together several distinct parts of the system, spanning the conceptual, organizational, and social borders [49]. The intricacy of energy systems, the importance of merging theories from different fields, and the knowledge required in planning, developing, and sizing the net-zero-carbon energy systems for sustainable mining constitute serious challenges.

Considerable technoeconomic barriers on top of environmental, social, and operational complexities limit the mining industry’s transition to net-zero-carbon systems. This includes the substantial upfront capital expenditures in building renewable infrastructure, including solar arrays, hydrogen systems, and battery storage, which are not economically viable for every mining operation, especially in developing regions or remote areas. A significant contribution to the understanding of energy transitions in developing contexts is provided by Fasesin et al. (2024) [50], who comprehensively analyzes the challenges and funding gaps hindering renewable energy deployment in developing countries. Their study emphasizes innovative financing mechanisms—such as green bonds, crowdfunding, public–private partnerships, and microfinance—in accelerating renewable energy adoption. The findings from their case studies in Bangladesh, Zambia, and Malaysia reveal how tailored financing solutions, combined with regulatory support and international collaboration, can successfully deploy sustainable technologies despite systemic funding limitations. This work is critical to the present study, as it underscores the systemic and financial constraints that directly influence the feasibility, scalability, and prioritization of alternatives like solar-powered mining or hydrogen-based equipment in resource-constrained environments. Both studies advocate for context-aware and structurally supportive approaches—one through financial innovation and the other through expert-driven, multicriteria prioritization—highlighting the need for complementary strategies to bridge technological potential with implementation realities.

Moreover, technical limitations like intermittency, scalability issues, inefficient storage, and a dearth of integration with legacy diesel-based systems add further friction to deployment. From an economic standpoint, mining companies often grapple with market volatility, uncertain regulatory environments, and challenges in obtaining long-term financing, making large-scale transitions risky. Further development of digital innovations like AI, IoT, and predictive maintenance will also demand heavy investment in infrastructure, cybersecurity, and workforce upskilling. These challenges require coordinated approaches encompassing policy incentives, public–private partnerships, and phased implementation models. Sovacool et al. [51] further argue that addressing technoeconomic limitations is very important for the practical success of sustainable mining projects and meeting net-zero-carbon targets.

Decision-making for sustainable mining to achieve a net-zero-carbon energy system is a complex process that involves several environmental, technical, social, political, economic, and other criteria that can be integrated. They become more complex as the requirements increase. The rivalry of importance of these criteria over one another makes them complex [52]. There is always uncertainty in every attempt to forecast future occurrences [53]. This makes decision-makers adopt frameworks that provide adequate pathways to determine alternatives, important criteria, and their respective weights to obtain the appropriate solution [54]. The multicriteria decision-making (MCDM) technique is used for assessing the impact of uncertainties of every phase in making decisions and to evaluate the inputs by using sensitivity analysis [55]. The MCDM technique assists the decision-makers by helping them to assess the incompatible criteria, share their distinct options, rank and select the best alternative, and make the best decision.

2.3. Introduction to Multicriteria Decision-Making (MCDM)

The multicriteria decision-making route is a framework used to ascertain the most favorable alternative when subjected to multiple criteria [56]. The MCDM concept is applied in several fields involving decision-making [57]. The concept aims to obtain the best alternative for providing optimal solutions from a critical decision-making process [58]. Different types of MCDM approaches have been successfully adopted for various kinds of problems for decision-making [59,60]. The MCDM is involved in the organization, decision-making, and formulating procedures to obtain the best solution in line with the decision-maker’s choices [61,62]. One of the foremost studies on MCDM was conducted by Benjamin Franklin, who named it moral algebra. It was used when making decisions involving multiple criteria [63]. Several other studies have used the MCDM approach in the decision-making process. MCDM has its merits and demerits. The merits of MCDM include how it takes into cognizance the decisions that are out of proportion and have a controversial influence. The demerits of MCDM include how during the generation of solutions by using the different MCDM approaches, some of the genuine results that were set out to be achieved are let go without an optimum target due to the type of problem [64]. Fuzzy MCDM has been reported to be capable of selecting the optimal alternative [65]. The fuzzy MCDM decision-making method enhances the standard of the decisions arrived at by improving the efficiency, capability of reasoning, and details [66]. The advantages of fuzzy MCDM have made it a widely adopted option by several researchers [67].

2.4. Applications of Fuzzy MCDM in Sustainability and Energy Systems

Various energy sources exist, including solar, wind, hydro, biomass, and geothermal. Mining activities demand the use of high energy because their operation involves the following procedures: exploration, extraction, processing, and refining [68]. Most developing countries across the world expend a considerable amount of energy on mining activities [69,70]. The significant energy consumption in mining activities, powered mainly by fossil fuel, pollutes the environment, which in turn increases the greenhouse gases that are associated with climate change [71,72]. The adoption of renewable energy for mining purposes to reduce the effect of greenhouse gases has been receiving wide acceptance by various stakeholders in the mining sector [68]. There have been enormous advancements in the adoption of renewable energy for mining purposes in several studies [73]. Furnaro [74] performed an analysis of the connection between investing in renewable energy for mining activities and the pattern of energy consumption in the South American country of Chile. This study revealed that adopting the renewable energy scenario will improve the Chilean ecosystem and enhance the sustainability of mining activities.

The ability to generate energy for mining activities in South Africa, through renewable energy, has also been investigated [75]. This study suggested that the hybridization of renewable energy systems for mining activities will be economically and environmentally viable. Zharan et al. [76] investigated the challenges and obstacles in implementing renewable energy for mining activities. This study’s outcome revealed the elements that support or are against decision-making regarding using renewable energy for mining activities. In addition, globally, companies in the business of mining are beginning to embrace renewable energy as their source of energy for their activities [77]. The transition of mining companies to renewable sources of energy (by moving away from fossil fuel) results in a reduction in the emission of gases, and it is economically viable [20]. This renewable energy attribute, for the mining industry, will no doubt drastically improve sustainable mining activities [78].

2.5. Research Gaps and Objectives

The reviewed literature (current study) has evaluated the research conducted in sustainable mining regarding net-zero carbon, the challenges of sustainable mining, MCDM approaches, and applications of fuzzy MCDM. The existing studies being assessed focused on a broad overview of sustainable mining, creating a research gap in the transition to a net-zero-carbon energy system using the fuzzy MCDM approach for sustainable mining activities. To bridge the technical research gap and contribute to the body of knowledge in this topical field of study, this current research adopts three MCDM approaches, i.e., the fuzzy TOPSIS, the fuzzy COPRAS, and fuzzy VIKOR. The reason for adopting the fuzzy TOPSIS and fuzzy VIKOR for the analysis is that they are highly applicable in solving engineering and environmental challenges. Additionally, although different MCDM approaches are used in making good decisions, the chances of making a better decision increase when more than one MCDM approach is used [79].

3. Materials and Methods

The MCDM process is a highly relevant decision-making method to overcome a selection problem with multiple contending criteria. This study employed MCDM to assess the possibility of transitioning to a net-zero-carbon energy system for sustainable mining. This study’s decision depended on different criteria, which were technical, economic, environmental, social, and political, for the best alternative. This is because a single criterion cannot capture sufficiently the best alternative. The methodology used for this study is presented in this section. The fundamental principle underlying the adopted fuzzy sets is presented. The fuzzy technique for the order of preference, by similarity to the ideal solution (TOPSIS) and the fuzzy ViseKritejumska Optimizacija I Kompromisno Resenje (VIKOR) methods, was implemented (Figure 1).

3.1. Fuzzy Set Theory

Fuzzy sets are mathematics procedures that permit semi-set membership. They are usually used to depict terms that are not clearly stated, are indefinite, and lack precision. The idea of the fuzzy set, which was earlier presented in 1965 by Zadeh for the theoretical expansion of the principles of classical set theory, was restricted to binary function membership [80,81].

3.2. Triangular Fuzzy Quantity (TFQ)

This quantity, which is represented as $\overline{L}$, is ascertained on Y, which is a real number set. The membership function is represented as ${\mu }_{\overline{L}}\left(\varkappa \right):\mathrm{Y}\to \left[\mathrm{0,1}\right]$. Equation (1) represents the expression for the membership function [82].

$${\mu }_{\overline{L}}\left(\varkappa \right)=\left\{\begin{array}{c}{\displaystyle \frac{x-\mathsf{\alpha }}{\mathsf{\beta }-\mathsf{\alpha }}} \mathsf{\alpha }\le x\le \mathsf{\beta }\\ {\displaystyle \frac{\mathsf{\gamma }-x}{\mathsf{\gamma }-\mathsf{\beta }}} \mathsf{\beta }\le x\le \mathsf{\gamma }\\ 0, otherwise\end{array}\right.$$

(1)

where

$\mathsf{\alpha }$ is the fuzzy quantity’s lowest level bound;

$\mathsf{\beta }$ is the fuzzy quantity’s mid-level bound;

$\mathsf{\gamma }$ is the fuzzy quantity’s highest level bound.

These quantities form the three points in a triangle (Figure 2). The TFQ is represented mathematically as follows:

$$\overline{L}=(\mathsf{\alpha },\mathsf{\beta },\mathsf{\gamma })$$

(2)

3.3. TFQs and Arithmetic Operations

The TFQs allow the merging and evaluation of the fuzzy numbers in various patterns, while performing operations on them. Given that two different TFQs are represented mathematically, as ${\overline{L}}_1=({\mathsf{\alpha }}_1,{\mathsf{\beta }}_1,{\mathsf{\gamma }}_1)$ and ${\overline{L}}_2=({\mathsf{\alpha }}_2,{\mathsf{\beta }}_2,{\mathsf{\gamma }}_2)$, this means that additions, subtractions, multiplications, divisions, and reciprocals can be executed on them. These are represented as follows:

Addition

$${\overline{L}}_1\oplus {\overline{L}}_2=({\mathsf{\alpha }}_1+{\mathsf{\alpha }}_2,{\mathsf{\beta }}_1+{\mathsf{\beta }}_2, {\mathsf{\gamma }}_1+{\mathsf{\gamma }}_2)$$

(3)

Subtraction

$${\overline{L}}_1\ominus {\overline{L}}_2=({\mathsf{\alpha }}_1-{\mathsf{\gamma }}_2,{\mathsf{\beta }}_1-{\mathsf{\beta }}_2, {\mathsf{\gamma }}_1+{\mathsf{\alpha }}_2)$$

(4)

Multiplication (this is conducted with all the boundaries that have a positive value)

$${\overline{L}}_1\otimes {\overline{L}}_2=({\mathsf{\alpha }}_1\times {\mathsf{\alpha }}_2,{\mathsf{\beta }}_1\times {\mathsf{\beta }}_2, {\mathsf{\gamma }}_1\times {\mathsf{\gamma }}_2)$$

(5)

Division

$${\overline{L}}_1\oslash {\overline{L}}_2=({\displaystyle \frac{{\mathsf{\alpha }}_1}{{\mathsf{\gamma }}_2}},{\displaystyle \frac{{\mathsf{\beta }}_1}{{\mathsf{\beta }}_2}},{\displaystyle \frac{{\mathsf{\gamma }}_1}{{\mathsf{\alpha }}_2}})$$

(6)

Reciprocal

$$L_1^{-1}=({\displaystyle \frac{1}{{\mathsf{\gamma }}_1}},{\displaystyle \frac{1}{{\mathsf{\beta }}_1}},{\displaystyle \frac{1}{{\mathsf{\alpha }}_1}})$$

(7)

This study uses a triangular 5-point linguistic scale (Figure 3). Figure 3 shows that the five linguistic terms with their corresponding triangular fuzzy numbers (TFNs) are represented as (a,b,c) triplets. NI/NT represents not important (NI)/not high (NT) with TFNs (1,1,2), where the membership function has a vertical left edge at x = 1, indicating absolute certainty at the minimum value. SI/SH is slightly important (SI)/slightly high (SH) with TFNs (1,2,3), showing a symmetrical triangular distribution across the lower portion of the scale. MI/MH represents moderately important (MI)/moderately high (MH) with TFNs (2,3,4), illustrating the central linguistic value in the assessment range. I/H is important (I)/high (H) with TFNs (3,4,5), demonstrating an increased significance as the system approaches the upper end of the scale. Finally, VI/VH represents very important (VI)/very high (VH) with TFNs (4,5,5). The overlapping regions between the adjacent fuzzy sets illustrate the gradual transition between linguistic terms, simultaneously allowing for partial membership in multiple categories. This fuzzy representation captures human judgment’s inherent uncertainty and imprecision when evaluating the criteria’s importance or the alternative performance’s importance. The triangular shapes provide a simple yet effective route to mathematically model the linguistic assessments for further computational processing in a multicriteria decision analysis.

3.4. Fuzzy Analytical Hierarchy Process (Fuzzy AHP)

The fuzzy AHP is a well-known analytical decision-making method that places relative importance on a set of decisions, based on different criteria. Nevertheless, in real-life scenarios, expert opinion is pre-disposed to some level of ambiguity during the process of assessment of the weights of the attributes, through a pair-wise comparison [83]. A fuzzy AHP is adopted to fuse fuzzy logic with the well-known traditional AHP to resolve this challenge. This allows the decision-making process to be determined using fuzzy numbers and non-precise values. The steps taken when the fuzzy AHP was used are highlighted in the scheme below.

Step 1: Make a construction of a pair-wise comparison matrix.

A pair-wise comparison matrix, $\overline{C}$, is constructed, and the criteria are compared with the pair-wise form to ascertain their comparative significance. The fuzzy quantities are then represented by using ${\overline{C}}_{ij}$ in Equation (8), through their comparative worths.

$$\overline{C}=\left[\begin{array}{cccc}1& {\overline{C}}_{12}& \cdots & {\overline{C}}_{1n}\\ 1/{\overline{C}}_{12}& 1& \dots & {\overline{C}}_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ 1/{\overline{C}}_{1n}& 1/{\overline{C}}_{2n}& \cdots & 1\end{array}\right]$$

(8)

where

${\overline{C}}_{ij}$ depicts the relationship between the fuzzy criterion, $i$, and the fuzzy criterion, $j$.

Step 2: calculate the geometric means and weights.

The second step is to calculate the geometric means of each criterion. This is calculated by using the equation for the geometric mean, which is represented as ${\overline{g}}_i$ in Equation (9) for the $i$ criterion.

$${\overline{g}}_i={({\overline{C}}_{i1}\otimes \times {\overline{C}}_{i2}\otimes \dots \otimes {\overline{C}}_{in})}^{{\displaystyle \frac{1}{n}}}$$

(9)

Step 3: The pair-wise comparison is performed by using Equation (10) to obtain the fuzzy number for each of the criteria.

$${\overline{w}}_i=\left({\displaystyle \frac{{\overline{g}}_i}{{\overline{g}}_1\otimes {\overline{g}}_2\otimes \dots \otimes {\overline{g}}_n}}\right)$$

(10)

Step 4: The relative weight of each of the criteria is exemplified by using Equation (11).

$${\overline{w}}_i=\left\{{\mathsf{\alpha }}_{wi},{\mathsf{\beta }}_{wi},{\mathsf{\gamma }}_{wi}\right\}$$

(11)

where

${\mathsf{\alpha }}_{wi}$ is the weight’s lowest level limit;

${\mathsf{\beta }}_{wi}$ is the weight’s mid-level limit;

${\mathsf{\gamma }}_{wi}$ is the weight’s highest level limit.

Overview of fuzzy Technique for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS).

The TOPSIS is a well-known MCDM approach. The method was discovered by Hwang and Yoon [84,85] in 1981. The fuzzy TOPSIS is an advancement of the classical TOPSIS method, and technically it has been rewarding when put into practice in many application areas [86,87]. The fuzzy TOPSIS has been adopted by various studies to solve different decision-making problems. It has been adopted in the areas of business [88], the environment [89], the stock exchange [90], information technology [91], manufacturing [92], energy [93], healthcare [94], supply chain management [95], and construction [96]. The fuzzy TOPSIS method has the advantage of being simple to use and efficient, and it has a broad and clear computational concept. The ordered steps for the fuzzy TOPSIS implementation are as presented below [60]. The procedure is as follows:

Step 1: compute the weighted normalized matrix.

${\overline{W}}_{ij}$ represents the weighted normalized matrix, and it is computed by multiplying each element of the normalized matrix with the corresponding weight for each criterion by using Equation (12):

$${\overline{W}}_{ij}={\overline{N}}_{ij}\otimes v_j$$

(12)

where

$v_j$ is the fuzzy weight of the $B_j$ criterion.

Step 2: identify the positive and negative ideal solutions.

The positive ideal solution (PIS) and the negative ideal solution (NIS) are obtained by using Equations (13) and (14):

$$\mathrm{P}\mathrm{I}\mathrm{S}={\overline{P}}^{+}=\left({\overline{W}}_1^{+},{\overline{W}}_2^{+},\dots {\overline{W}}_n^{+}\right)$$

(13)

where

$${\overline{W}}_j^{+}={max}_i{\overline{W}}_{ij}$$

$$\mathrm{N}\mathrm{I}\mathrm{S}={\overline{P}}^{-}=\left({\overline{W}}_1^{-},{\overline{W}}_2^{-},\dots {\overline{W}}_n^{-}\right)$$

(14)

$${\overline{W}}_j^{-}={min}_i{\overline{W}}_{ij}$$

Step 3: compute the distances to the PIS and NIS.

The distance between each alternative and the positive “ideal” solution or negative “ideal” solution is computed by using Equations (15) and (16), known as the Euclidean distance measurement.

$$d_i^{+}={\sum }_{j=1}^n{\left({\overline{W}}_{ij}-{\overline{W}}_j^{+}\right)}^2$$

(15)

$$d_i^{-}={\sum }_{j=1}^n{\left({\overline{W}}_{ij}-{\overline{W}}_j^{-}\right)}^2$$

(16)

Step 4: compute the closeness coefficient.

The closeness coefficient ${CC}_i$ for the alternative of the individual criterion is computed by using Equation (17):

$${CC}_i={\displaystyle \frac{d_i^{-}}{d_i^{-}-d_i^{+}}}$$

(17)

The closeness coefficient measures the closeness of each alternative to the ideal solution. The higher the closeness coefficient, the better the value of the alternative.

Step 5: rank the alternatives.

The value of the closeness coefficient determines the ranking and selection of the alternatives. The alternative with the highest value of the closeness coefficient ${CC}_i$ is the optimal system.

3.5. Overview of Fuzzy VIseKritejumska Optimizacija I Kompromisno Resenje (Fuzzy VIKOR)

The VIKOR technique is an MCDM method with a wide application area. The fuzzy VIKOR is an advancement of the VIKOR approach [97]. The VIKOR was discovered in 1979, and it was first applied by Opricovic in 1980 to solve a problem. It is used to perform assessments and make comparative analyses that assist in critical decision-making processes [98]. It solves MCDM problems, and it is used to represent the alternative’s distance to the ideal solution. The fuzzy VIKOR decision-making approach was invented by the fuzzy MCDM method to provide solutions to fuzzy MCDM problems that cannot be measured and have opposing criteria through the adoption of a common standard [99]. The fuzzy VIKOR approach has characteristics similar to those of the fuzzy TOPSIS approach. They are both dependent on distance measurement. Each of the alternatives is represented as $A_1,A_2,\dots ,A_n$. For alternative $A_j$, the comparative rating [97] of the $i$^th criterion is represented as follows: $f_{ij}\left(i=\mathrm{1,2},\dots ,m; j=\mathrm{1,2},\dots ,n\right)$. The feasible solution that is the closest to the “ideal” $F^{\ast }$ is the compromise solution $F^c$. The compromise solution comprises an understanding between the entities, is required to follow a specific mutual concession, and is represented in Equations (18) and (19):

$${∆f}_1=f_1^{\ast }-f_1^c$$

(18)

$${∆f}_2=f_2^{\ast }-f_2^c$$

(19)

The fuzzy VIKOR procedure is as follows:

Step 1: Obtain the most favorable $f_1^{\ast }$ and the least favorable $f_1^{-}$ values of the whole criterion functions, $i=\mathrm{1,2},\dots ,m.$ The $i$^th function can depict a benefit or cost. If

$f_i^{\ast }=\underset{j}{\max }{f}_{ij}, f_i^{-}=\underset{j}{\min }{f}_{ij}$, then the $i$^th function depicts a benefit. If

$f_i^{\ast }=\underset{j}{\min }{f}_{ij}, f_i^{-}=\underset{j}{\max }{f}_{ij}$, then the $i$^th function depicts a cost.

Step 2: calculate the values of $S_j$ and $R_j$ by using Equations (20) and (21):

$$S_j=\sum _{i=1}^n{w}_i{\displaystyle \frac{\left(f_i^{\ast }-f_{ij}\right)}{\left(f_i^{\ast }-f_i^{-}\right)}}$$

(20)

$$R_j=max\left[w_i{\displaystyle \frac{\left(f_i^{\ast }-f_{ij}\right)}{\left(f_i^{\ast }-f_i^{-}\right)}}\right]$$

(21)

Step 3: calculate the values of $Q_j$ where $j=\mathrm{1,2},\dots ,n,$ in line with $S_j$ and $R_j$ by using Equation (22):

$$Q_j=v{\displaystyle \frac{\left(S_j-S^{\ast }\right)}{\left(S^{-}-S^{\ast }\right)}}+\left(1-v\right){\displaystyle \frac{\left(R_j-R^{\ast }\right)}{\left(R^{\ast }-R^{\ast }\right)}}$$

(22)

where

$$S^{\ast }=\underset{j}{\min }{S}_j,S^{-}=\underset{j}{\max }{S}_j,$$

$$R^{\ast }=\underset{j}{\min }{R}_j,R^{-}=\underset{j}{\max }{R}_j,$$

where $v$ represents the weight of the decision-making strategy of the major criteria.

Step 4: Rank the alternatives. $S$, $R$, and $Q$ values are ranked in a decreasing order.

Step 5: suggest a compromise solution to the most favorable alternative $A_1$, by the measure of the minimum $Q$, if these two requirements are met [97,100].

3.6. Overview of Fuzzy Complex Proportional Assessment (Fuzzy COPRAS)

The COPRAS method is an MCDM method that Zavadskas first adopted [101]. The fuzzy COPRAS was used to solve major decision-making problems [102]. This MCDM approach combines the rare qualities and cost attributes in a problem with more than one criterion to ascertain the most favorable alternative to solve a decision-making problem [103]. The relevant criteria, together with the ranking of the alternatives, are combined during this process [104]. The fuzzy COPRAS method performs the rating ranking by applying an alternative fitness degree. This is conducted by calculating the values of the cost and alternative orientations [105]. Equation (23) is used for the combination of the ratings of the benefit criteria of the alternatives [106,107].

$$B_i^{+}=\sum _{j=1}^n{r}_{ij}^{+}$$

(23)

Equation (24) is used for the combination of the cost criteria of the alternative [106,107].

$$B_i^{-}=\sum _{j=n+1}^{\overline{n}}{r}_{ij}^1$$

(24)

The values obtained from Equations (23) and (24) are used to calculate the relative significance level of the alternatives using Equation (25):

$$S_i=B_i^{+}+{\displaystyle \frac{B_{min}^{-}\sum _{i=1}^n{B}_i^{-}}{\sum _{i=1}^m\frac{{B^{-}}_{min}}{B_i^{-}}}}$$

(25)

$$B_{min}^{-}=\underset{\forall i}{\mathrm{Min}}\left\{B_i^{-}\right\}$$

(26)

Equation (27) is used to calculate the alternatives’ fitness degrees

$$U_i={\displaystyle \frac{S_i}{S_{max}}}\times 100\%$$

(27)

The ranking of the alternatives is performed with the highest and least fitness degrees, considered to be the most and least preferred alternatives, respectively [108].

$$S_{max}=\underset{\forall i}{\mathrm{Max}}\left\{S_i\right\}$$

(28)

3.7. Case Study Description

The case study evaluates and ranks sustainable mining technologies to enhance environmental performance and operational efficiency. Through expert consultation and a comprehensive literature review, six alternative technologies were identified. They are the following: solar-powered mining operations (A1), hydrogen-powered mining equipment (A2), electrification of the mining fleet (A3), waste heat recovery systems (A4), deployment of AI for operational efficiency (A5), and the transition to circular economy models (A6) (see

Table 1. Alternative technologies and critical criteria for evaluating alternatives.

Alternative
Alternative	Code	Description
Solar-powered mining operations	A1	Using solar energy to power mining equipment and processes reduces reliance on fossil fuels [50]. This alternative is exceptionally viable in regions with high solar irradiance and limited grid access [19].
Hydrogen-powered mining equipment	A2	Deploying hydrogen fuel cell technology to operate mining machinery with zero emissions [109]. It offers a long-term clean energy solution but requires significant infrastructure investment.
Electrification of the mining fleet	A3	Replacing diesel-powered vehicles and equipment with electric-powered alternatives. This transition can reduce fuel costs and emissions but may be constrained by battery performance in heavy-duty applications [110].
Waste heat recovery systems	A4	Capturing and reusing waste heat from mining operations to improve energy efficiency. It is a technically mature solution that can be integrated into existing thermal processes with modest investment [68].
Deployment of AI for operational efficiency	A5	Leveraging AI tools to optimize mining processes, minimize waste, and improve decision-making. AI enhances predictive maintenance and productivity but requires skilled personnel and digital infrastructure [111].
Transition to circular economy models	A6	Adopting recycling, reuse, and waste minimization strategies to create sustainable mining ecosystems [50]. This approach supports long-term sustainability but may need policy alignment and stakeholder engagement.
Criteria
Criterion	Code	Description
Community Impact	C1	Mining operations’ positive or negative effects on local communities, including job creation and social benefits. This criterion captures the social sustainability of mining practices and their acceptance by host communities [112].
Workforce Safety	C2	Measures taken to protect workers from accidents, injuries, and occupational health risks. Ensuring safety improves worker morale and reduces costs related to health and insurance [113].
Cost Efficiency	C3	The balance between operational costs and benefits gained from the mining practice [19]. It reflects the economic feasibility of implementing the alternative.
Carbon Emissions	C4	The extent to which the alternative reduces greenhouse gas emissions. This is a key metric in meeting global climate goals and regulatory targets [114].
Land Restoration	C5	Efforts to rehabilitate and restore mining sites after operations. Effective restoration enhances environmental recovery and community trust [115].
Regulatory Compliance	C6	Adherence to environmental regulations and industry standards. Failure to comply can result in fines, project delays, or license revocation [116].
Energy Efficiency	C7	The effectiveness of the alternative in minimizing energy consumption while maintaining productivity. Higher efficiency translates to operational cost savings and reduced environmental impact [46].
Technological Feasibility	C8	The practicality of implementing the alternative within current technological constraints. This determines how readily the alternative can be adopted with existing tools and systems [46].
Revenue Potential	C9	The sustainable mining alternative generates long-term financial benefits. High revenue potential increases investor interest and project scalability [117].
Policy Incentives	C10	Support or incentives provided by governments or institutions to promote the alternative. This can significantly reduce implementation costs and accelerate adoption [118].

). For evaluation, this study identified ten critical criteria. They are the following: community impact (C1), workforce safety (C2), cost efficiency (C3), carbon emissions (C4), land restoration (C5), regulatory compliance (C6), energy efficiency (C7), technological feasibility (C8), revenue potential (C9), and policy incentives (C10). Expert evaluations were collected using linguistic terms, mapped to triangular fuzzy numbers, to capture the assessment uncertainties.

Three fuzzy MCDM methods were used to evaluate the sustainable mining alternatives, including the fuzzy TOPSIS, fuzzy COPRAS, and fuzzy VIKOR. These methods were integrated using the novel Geometric Inverse Distance Aggregation (GIDA) approach to enhance the reliability of the result. The fuzzy TOPSIS emphasized the closeness to ideal solutions, the fuzzy COPRAS assessed the utility degrees, while the fuzzy VIKOR focused on compromise solutions. The evaluation process incorporated responses from eight academic experts with extensive experience in sustainable mining practices. Their assessments were systematically captured using a five-point linguistic scale, which was later converted to triangular fuzzy numbers for analysis. This comprehensive approach ensured a robust evaluation of the alternatives while accounting for the uncertainties in the expert judgments.

3.8. Data Collection

A comprehensive questionnaire (see Appendix A:

Table A1. Kindly rate the sustainable mining alternatives (on their suitability for adoption in mining operations) against the following criteria. (Please select one of the following items in all blank cells: Very high/High/Moderately high/Slightly high/Not high).

Alternative	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
A1: Solar-powered mining operations
A2: Hydrogen-powered mining equipment
A3: Electrification of the mining fleet
A4: Waste heat recovery systems
A5: Deployment of AI for operational efficiency
A6: Transition to circular economy models

and

Table A2. Kindly click on the drop-down box to rate the importance of the following criteria in the selection of sustainable mining alternatives. (Please select one of the following items in all blank cells: not important/slightly important/Moderately Important/important/very important).

Criteria	Importance Rating
Community Impact (C1)
Workforce Safety (C2)
Cost Efficiency (C3)
Carbon Emissions (C4)
Land Restoration (C5)
Regulatory Compliance (C6)
Energy Efficiency (C7)
Technological Feasibility (C8)
Revenue Potential (C9)
Policy Incentives (C10)

) was developed to explore expert perspectives on the evaluation criteria and the alternatives for sustainable mining technologies. The survey development was underpinned by an exhaustive literature review, specifically tailored to the fuzzy multicriteria decision-making methodologies in the context of sustainable mining practices and industrial energy transitions. Expert assessors were selected based on strict criteria, thereby ensuring only the highest quality assessments were included. The panel consisted of eight scholars with graduate degrees: six with PhD degrees and two with M.Sc. degrees. They also had varied levels of experience relevant to the role, from 6 to 21 years, which helped create a solid foundation for the evaluation. All experts were based at their academic or research institutions, providing theoretical expertise and a research-oriented lens to assess the data. The strong educational background of the panel, combined with their substantial field experience, ensured the comprehensive evaluation of sustainable mining alternatives while maintaining the theoretical rigor in the assessment process.

Quantitative data were collected through the questionnaire, and the qualitative insights were integrated using linguistic terms systematically mapped to the triangular fuzzy numbers. The experts unanimously agreed on three fundamental aspects: the importance of validating sustainability strategies, the necessity of multicriteria evaluation approaches, and the prioritization of renewable energy solutions in mining operations.

This study followed ethical guidelines; all participants were informed that their participation was voluntary and that their responses would be used solely for research purposes while preserving anonymity. The participants indicated their consent by returning their completed questionnaires. Since this study involved neither physical nor psychological interventions, formal ethical approval from the University or the Ministry of Education was not required. This study was conducted by the principles set out in the Declaration of Helsinki and in compliance with the relevant laws on data protection, thus covering confidentiality maintenance and the proper storage and destruction of all data. The evaluation of the transition to net-zero-carbon energy systems for sustainable mining activities is depicted in Figure 4.

4. Results

4.1. Expert Opinions

The expert panel consultation reveals strong consensus on key aspects of sustainable mining practices. The panel comprises eight academics, with advanced qualifications (six Ph.D. and two M.Sc. holders) and significant industry experience ranging from 6 to 21+ years, providing credible expertise for the evaluation. All the experts unanimously agreed on three fundamental aspects: the importance of validating sustainability strategies in mining operations, using multiple criteria for evaluating sustainable alternatives, and prioritizing renewable energy solutions over traditional fossil fuel systems. This consensus strongly supports the multicriteria approach that is adopted in this study. The expert demographic profile is notably academic-focused, with all the participants affiliated with research or academic institutions. While this might suggest potential bias towards theoretical frameworks, their substantial field experience (the majority having between 6 and 15 years) adds practical credibility to their assessments. Including experts with 21+ years of experience provides a valuable long-term industry perspective. The uniform agreement on prioritizing renewable energy solutions (solar, hydrogen) over the traditional systems indicates a clear shift in the academic thinking towards sustainable mining practices. This alignment supports this study’s focus on evaluating the various sustainable technologies while considering the multiple criteria for a comprehensive assessment. The expert panel’s composition and unanimous responses validate this study’s methodological approach, and the responses reinforce the importance of systematically evaluating sustainable mining technologies. Their consensus, mainly, strengthens the credibility of the multicriteria decision-making framework employed in assessing the various sustainable mining alternatives.

4.2. Weight Determination and Decision Matrix Aggregation

The linguistic responses from the experts on the importance of the criteria on the selection of sustainable mining alternatives are shown in

Table 2. Fuzzy weight assigned by the experts.

Criteria	Expert 1	Expert 2	Expert 3	Expert 4	Expert 5	Expert 6	Expert 7	Expert 8
Community Impact (C1)	VI	VI	I	VI	VI	VI	VI	VI
Workforce Safety (C2)	VI	VI	VI	SI	VI	SI	SI	VI
Cost Efficiency (C3)	VI	VI	I	VI	MI	VI	VI	NI
Carbon Emissions (C4)	VI	VI	VI	VI	I	VI	VI	VI
Land Restoration (C5)	VI	I	MI	MI	VI	MI	I	VI
Regulatory Compliance (C6)	I	I	MI	MI	MI	MI	I	I
Energy Efficiency (C7)	VI	VI	I	I	I	I	I	SI
Technological Feasibility (C8)	I	MI	VI	VI	MI	VI	I	SI
Revenue Potential (C9)	VI	MI	I	I	SI	I	VI	MI
Policy Incentives (C10)	I	MI	VI	VI	SI	VI	VI	VI

NI = Not Important/Not High; SI = Slightly Important/Slightly High; MI= Moderately Important/Moderately High; I = Important/High; VI = Very Important/Very High.

. The responses are aggregated to estimate the weight of each of the criteria, based on the respective triangular fuzzy numbers using Equation (29). The fuzzy AHP weights are calculated and the de-fuzzified values are shown in

Table 3. Fuzzy AHP for weights determination.

Criterion	(l, m, u)	De-Fuzzified Value	Normalized Weight	Rank
C1	(3.875, 4.875, 5.000)	4.583	0.1159	1
C2	(2.875, 3.875, 4.250)	4.583	0.1159	1
C3	(3.250, 4.125, 4.500)	4.042	0.1022	3
C4	(3.875, 4.875, 5.000)	3.958	0.1001	4
C5	(3.000, 4.000, 4.625)	3.917	0.0991	5
C6	(2.500, 3.500, 4.500)	3.875	0.098	6
C7	(3.000, 4.000, 4.750)	3.75	0.0948	7
C8	(2.875, 3.875, 4.500)	3.667	0.0927	8
C9	(2.750, 3.750, 4.500)	3.667	0.0927	8
C10	(3.250, 4.250, 4.625)	3.5	0.0885	10

, while the weights are shown in Figure 5.

$${\tilde{A}}_{ij}=\left(l_{ij}, m_{ij}, u_{ij}\right)={\displaystyle \frac{1}{n}}\left[\sum _{k=1}^n{l}_{ij}^k, \sum _{k=1}^n{m}_{ij}^k, \sum _{k=1}^n{u}_{ij}^k\right]$$

(29)

where ${\tilde{A}}_{ij}$ is the aggregated fuzzy rating for criterion $i$ compared with criterion $j$; $l_{ij}, m_{ij}, u_{ij}$ are the lower, middle, and upper values of the aggregated triangular fuzzy number; $l_{ij}^k, m_{ij}^k, u_{ij}^k$ are the corresponding values from expert $k$’s judgment; and $n$ is the number of experts (in this study, $n=8$).

The community impact (C1), which had the highest normalized weight (0.1159) (Figure 5) and a high fuzzy number (3.875, 4.875, 5.000), was unanimously deemed to be the most important by the experts during the analysis, indicative of the consensus among the professionals and experts regarding the implications of social issues on sustainable development in the decision-making with regard to mining activities (Table 3). This ranking aligns with an increasing focus on the social dimension in the mining industry, as reported by Yu et al. [119]. The weight allocated to the carbon emissions (C4) (0.1159), which also aligned with the community impact with fuzzy values of (3.875, 4.875, 5.000), indicated that the experts prioritized the environmental impacts equally. The community impact and the carbon emission rankings may be related to both issues’ impact on the sustainability of the host community and the mining industry. The parity between the social and environmental factors suggests a balanced approach to the sustainability criteria. Policy incentives (C10) was the third-ranked criterion derived from the fuzzy AHP analysis, with a weight of 0.1022 and fuzzy values of (3.250, 4.250, 4.625), emphasizing the acknowledged significance of governmental backing and regulatory frameworks in the mining industry. This criterion has an excellent performance, which reflects the increasing importance of the alignment of policy regarding sustainable development. Cost efficiency (C3) was the fourth (weight of 0.1001, fuzzy numbers of 3.250, 4.125, 4.500) as a critical component, reinforcing the view that the financial aspects are still important. However, they must not override environmental and social concerns. This stance marks a departure from conventional profit-first decision-making.

Energy efficiency (C7) ranked fifth (weight of 0.0991, with the fuzzy values of 3.000, 4.000, 4.750), indicating a moderate to high importance within the overall criteria hierarchy. The number-spread fuzzy indicates a diverse opinion among experts regarding the relative priority of energy efficiency in the mining industry. Land restoration (C5) recorded a weight of 0.0980 (3.000, 4.000, 4.625), placing it in the middle range of importance. This position reflects a balanced consideration of environmental rehabilitation, alongside other sustainability factors. Based on expert opinion, technological feasibility (C8) was given a weight of 0.0948 (2.875, 3.875, 4.500); this value reflects a medium importance with some degrees of variation among the experts. Although the technical aspects are relevant, they are perceived as not being the major drivers behind sustainability decisions in the mining industry. Workforce safety (C2) and revenue potential (C9) shared the eighth position with weights of 0.0927, showing a moderate importance, but with a significant expert variation in their assessment. Their relatively lower ranking, mainly for safety, warrants further investigation, given their traditional prioritization in industrial projects. Regulatory compliance (C6) ranked the lowest with 0.0885 weight and the widest fuzzy spread (2.500, 3.500, 4.500), suggesting a significant expert disagreement on its relative importance. This unexpected positioning reflects assumptions about compliance being a prerequisite rather than a weighted criterion.

The consistency analysis of the fuzzy AHP weights reveals a strong agreement among expert judgments, as indicated by the negative CR value (−0.4508). This value substantially outperforms the standard threshold of 0.1, demonstrating exceptional consistency in the prioritization of sustainability criteria in mining operations. The low CI (−0.6718) further supports this finding, suggesting a minimal random variation in the expert assessments. The Eigenvalue (λmax = 3.9542), compared to the number of criteria (n = 10), indicates that the experts maintained consistent logical relationships across their evaluations of different sustainability aspects, particularly in their high prioritization of environmental impacts and community engagement.

This consistency is a significant indicator as it reinforces the reliability of the subsequent MCDM analysis and validates the weight assignment in the multicriteria decision concept. The fuzzy AHP analysis reveals a clear shift towards the environmental and social priorities in sustainable development decision-making, with the traditional economic and operational factors assuming supporting roles. These priorities signal the potential evolution of the industry towards the placement of the community impact and environmental stewardship above profit margins. Although experts differ on the importance of certain criteria, the results indicate that the agenda for sustainable development in the mining sector is an ongoing transformation.

4.3. Matrix Formation

Based on the aggregated decision matrix (Equation (30)), solar-powered mining (A1) scores high across environmental criteria with a spotlight on carbon emissions (C4: 3.6,4.6,4.9) and community impact (C1: 3.5,4.5,4.8), indicating that experts view solar power as a critical sustainable solution. The strong ratings given to this option demonstrate its potential in minimizing environmental impact while ensuring operational efficiency. A2 (hydrogen-powered equipment) scores well in regulatory compliance (C6: 3.4,4.4,4.6) and workforce safety (C2: 3.0,4.0,4.5), illustrating hydrogen’s potential as a safe and compliant clean energy solution. C3 (sustainable waste heat recovery) performs notably well in terms of energy efficiency (C7: 3.2,4.2,4.6) and cost efficiency (C3: 3.3,4.3,4.6), hence indicating its dual benefits in technical viability and economic feasibility. A5 (AI operational efficiency) and the A6 (circular economy) appear to have moderate but consistent ratings across most of the criteria, thereby reflecting their contributory rather than transformative roles as sustainability solutions. The scores are low in terms of policy incentives (C10: all the alternatives have between 2.3 and 2.5 lower bounds), and hence they show potential regulatory and support gaps that need to be addressed for successful implementation.

4.4. MCDM Methods

This section discusses the outcome of the three MCDM methods implemented in this study.

Table 4. Results of the fuzzy TOPSIS, fuzzy COPRAS, and fuzzy COPRAS methods.

Alternative	F-TOPSIS (Closeness Coefficient)	Rank	F-VIKOR (Q Value)	Rank	F-COPRAS (Utility Degree)	Rank
A1 (solar-powered mining)	0.08498	1	0.2452	3	98.94	2
A2 (hydrogen-powered equipment)	0.08331	5	0.4772	5	97.84	3
A3 (mining fleet electrification)	0.08396	4	0.6018	4	98.44	5
A4 (waste heat recovery)	0.08496	2	0.0000	1	100	1
A5 (AI operational efficiency)	0.08447	3	0.5707	2	99.48	4
A6 (circular economy models)	0.08179	6	0.6379	6	95.9	6

presents the ranking results of the fuzzy TOPSIS, fuzzy COPRAS, and fuzzy VIKOR methods.

$$\begin{gathered} \begin{array}{lllllllllll} & \mathrm{C}1(\mathrm{l},\mathrm{m},\mathrm{u}) & \mathrm{C}2(\mathrm{l},\mathrm{m},\mathrm{u})& \mathrm{C}3(\mathrm{l},\mathrm{m},\mathrm{u})& \mathrm{C}4\left(\mathrm{l},\mathrm{m},\mathrm{u}\right)& \mathrm{C}5(\mathrm{l},\mathrm{m},\mathrm{u})& \mathrm{C}6(\mathrm{l},\mathrm{m},\mathrm{u})& \mathrm{C}7(\mathrm{l},\mathrm{m},\mathrm{u})& \mathrm{C}8(\mathrm{l},\mathrm{m},\mathrm{u})& \mathrm{C}9(\mathrm{l},\mathrm{m},\mathrm{u})& \mathrm{C}10(\mathrm{l},\mathrm{m},\mathrm{u})\end{array} \\ \check{D_{ag}}=\begin{array}{c}A1\\ A2\\ A3\\ A4\\ A5\\ A6\end{array}\left[\begin{array}{cccccccccc}\left(\mathrm{3.5,4.5,4.8}\right)& \left(\mathrm{2.9,3.6,4.3}\right)& \left(\mathrm{3.4,4.4,4.6}\right)& \left(\mathrm{3.6,4.6,4.9}\right)& \left(\mathrm{2.1,3.1,4.0}\right)& \left(\mathrm{3.0,4.0,4.8}\right)& \left(\mathrm{3.4,4.4,4.6}\right)& \left(\mathrm{3.5,4.5,4.8}\right)& \left(\mathrm{2.8,3.8,4.5}\right)& \left(\mathrm{2.4,3.4,4.3}\right)\\ \left(\mathrm{3.4,4.4,4.6}\right)& \left(\mathrm{3.0,4.0,4.5}\right)& \left(\mathrm{2.5,3.5,4.3}\right)& \left(\mathrm{3.3,4.3,4.5}\right)& \left(\mathrm{3.0,4.0,4.5}\right)& \left(\mathrm{3.4,4.4,4.6}\right)& \left(\mathrm{3.0,4.0,4.5}\right)& \left(\mathrm{3.1,4.1,4.5}\right)& \left(\mathrm{3.0,4.0,4.5}\right)& \left(\mathrm{2.3,3.3,4.0}\right)\\ \left(\mathrm{3.6,4.6,4.8}\right)& \left(\mathrm{3.1,4.1,4.5}\right)& \left(\mathrm{3.0,4.0,4.5}\right)& \left(\mathrm{3.1,4.1,4.5}\right)& \left(\mathrm{2.8,3.8,4.3}\right)& \left(\mathrm{3.1,4.1,4.5}\right)& \left(\mathrm{3.1,4.1,4.5}\right)& \left(\mathrm{3.0,4.0,4.5}\right)& \left(\mathrm{2.9,3.9,4.4}\right)& \left(\mathrm{2.5,3.5,4.3}\right)\\ \left(\mathrm{3.5,4.5,4.8}\right)& \left(\mathrm{2.8,3.8,4.3}\right)& \left(\mathrm{3.3,4.3,4.6}\right)& \left(\mathrm{3.4,4.4,4.6}\right)& \left(\mathrm{3.1,4.1,4.5}\right)& \left(\mathrm{3.3,4.3,4.6}\right)& \left(\mathrm{3.2,4.2,4.6}\right)& \left(\mathrm{2.9,3.9,4.4}\right)& \left(\mathrm{2.8,3.8,4.3}\right)& \left(\mathrm{2.5,3.5,4.3}\right)\\ \left(\mathrm{3.3,4.3,4.6}\right)& \left(\mathrm{2.9,3.9,4.4}\right)& \left(\mathrm{3.1,4.1,4.5}\right)& \left(\mathrm{3.2,4.2,4.5}\right)& \left(\mathrm{3.0,4.0,4.5}\right)& \left(\mathrm{3.4,4.4,4.6}\right)& \left(\mathrm{3.3,4.3,4.6}\right)& \left(\mathrm{3.0,4.0,4.5}\right)& \left(\mathrm{3.1,4.1,4.5}\right)& \left(\mathrm{2.4,3.4,4.1}\right)\\ \left(\mathrm{3.4,4.4,4.6}\right)& \left(\mathrm{2.6,3.6,4.3}\right)& \left(\mathrm{3.0,4.0,4.5}\right)& \left(\mathrm{3.4,4.4,4.6}\right)& \left(\mathrm{2.5,3.5,4.1}\right)& \left(\mathrm{3.2,4.2,4.6}\right)& \left(\mathrm{2.9,3.9,4.4}\right)& \left(\mathrm{2.8,3.8,4.3}\right)& \left(\mathrm{2.9,3.9,4.4}\right)& \left(\mathrm{2.4,3.4,4.1}\right)\end{array}\right] \end{gathered}$$

(30)

4.5. Fuzzy TOPSIS

The fuzzy TOPSIS analysis reveals the distinct patterns in the prioritization of sustainable mining technologies. The preferred alternative is solar-powered mining operations (A1), with a closeness coefficient of 0.08498; this is closely followed by waste heat recovery systems (A4) with a coefficient of 0.08496. AI operational efficiency (A5) is ranked in the third position of the most preferred alternative with a closeness coefficient of 0.08447. These results indicate that these technologies offer the most balanced performance across the chosen evaluation criteria, particularly in environmental impact reduction and operational feasibility domains. The results illustrate the relative advantage of integrating renewable energy solutions with advanced operational efficiency measures. Solar power’s dominance aligns with its proven track record in mining operations, especially in remote locations where grid connectivity presents challenges. The fourth and fifth positions are the alternatives to electrifying the mining fleet and deploying hydrogen-powered equipment with a weight of 0.08396 and 0.08331, respectively. While promising for long-term sustainability goals, these technologies require significant infrastructure developments and technology maturation before widespread adoption becomes feasible. A6 (circular economy models, 0.08179) ranks the lowest but maintains a relatively high coefficient, indicating its potential as a complementary approach rather than a standalone solution. Furthermore, the narrow spread of coefficients across all the alternatives suggests that a multi-faceted approach to sustainability might be the most beneficial route, as it incorporates different elements from multiple technologies based on site-specific requirements and their associated constraints.

4.6. Fuzzy COPRAS

The fuzzy COPRAS method returned waste heat recovery (A4) as the preferred alternative with the highest utility degree of 100.00%, followed by AI operational efficiency (A5), with a utility degree of 99.48%, and A1 (the solar-powered mining) is the third, with a utility degree of 98.94%. These results are a pointer that highlights the technologies that optimize resource utilization while minimizing the environmental impact. The close utility degrees among the top alternatives (within a 1.06% range) suggest that these technologies offer comparable benefits through different approaches. The emergence of waste heat recovery as the number one innovation reflects its dual benefit in energy efficiency and cost reduction. At the same time, A1’s second place demonstrates the growing importance of digital optimization in sustainable mining. A3 (mining fleet electrification, 98.44%) and A2 (hydrogen-powered equipment, 97.84%) show strong potential, but they are slightly lower in their utility degrees, due to their implementation complexity and infrastructure requirements. Their high scores indicate their viability as part of a comprehensive sustainability strategy. A6 (circular economy models, 95.90%) ranks the lowest but maintains a high degree of utility, suggesting its value as an overarching framework, rather than a standalone solution.

4.7. Fuzzy VIKOR

According to the fuzzy VIKOR analysis, A4 (waste heat recovery) has the least value of Q, making it the best solution, followed by the A1 (solar-powered mining, 0.2452) and the A2 (hydrogen-powered equipment, 0.4772). The solutions focus on the trade-offs between the varying criteria and minimizing regret. The top position of waste heat recovery reflects its minimal compromise across the set criteria, particularly in balancing the environmental benefits with the implementation feasibility. The significant gap between A4 and the other alternatives (Q-value difference > 0.24) indicates its superior performance in meeting the diverse sustainability requirements with minimal trade-offs. The ranking for A5 (AI operational efficiency, 0.5707) and A3 (mining fleet electrification, 0.6018) shows moderately compromised levels, suggesting that these technologies require more balanced trade-offs between benefits and implementation challenges. Their Q-values indicate acceptable compromise solutions, highlighting the areas needing attention during implementation. A6 (circular economy models, 0.6379) shows the highest compromise level, although still within an acceptable range. This positioning reflects the complexity of implementing the system-wide circular economy principles in mining operations.

4.8. Geometric Inverse Distance Aggregation

To combine the strengths of the three MCDM approaches discussed above (F-TOPSIS, F-COPRAS, and F-VIKOR), a Geometric Inverse Distance Aggregation (GIDA) approach is proposed. The GIDA method offers several key advantages. It balances the weighting effectively by using inverse distances, which helps to reduce potential biases in the evaluation process. The method’s incorporation of geometric mean integration serves to minimize the impact of extreme values on the results. Additionally, GIDA accounts for the consistency of rankings by utilizing the standard deviation of ranks, thereby providing a measure of reliability across different evaluation methods. An important feature of this approach is its ability to preserve the original characteristics of scores across various methods, hence maintaining the integrity of the initial evaluations. These capabilities can be implemented by using the required scores and inputs for $T_i$ (TOPSIS scores), $C_i$ (COPRAS scores), $V_i$ (VIKOR scores), and ${\sigma }_i$ (standard deviation). The steps for the GIDA approach are given as follows:

• Inverse Distance Normalization.

For each alternative, $i$ is as follows:

$${IDS}_i={\left[{\displaystyle \frac{1}{T_i}}+{\displaystyle \frac{1}{C_i}}+{\displaystyle \frac{1}{V_i}}\right]}^{-1}$$

where $T_i$ is the TOPSIS scores, $C_i$ is the COPRAS scores, and $V_i$ is the VIKOR scores (adjusted as 1-$V_i$)

2.

Geometric mean integration.

For each alternative, $i$ is as follows:

$${GIDA}_i=(T_i\times C_i\times {(1-V}_i){)}^{{\displaystyle \frac{1}{3}}}\times {IDS}_i$$

3.

Final score calculation:

$${{FS}_i=GIDA}_i\times (1-{\sigma }_i)$$

where ${\sigma }_i$ is the standard deviation of the normalized ranks across methods for the alternative, $i$

As shown in Figure 6, the Geometric Inverse Distance Aggregation (GIDA) analysis identifies waste heat recovery (A4) as the optimal solution, hence achieving the highest GIDA score (0.0319) with near-perfect method agreement (99.99%). This exceptional consensus across methodologies validates its position as the primary sustainable technology intervention. Solar-powered mining (A1) emerges as the second-highest ranked alternative, demonstrating a strong methodological agreement (82.12%), while maintaining a substantial performance gap from the leading option (GIDA score: 0.0232).

Hydrogen-powered equipment (A2) occupies the third position with moderate method agreement (65.71%), reflecting increased uncertainty in the performance predictions. The lower ranked alternatives—A1 operational efficiency (A5), mining fleet electrification (A3), and the circular economy models (A6)—show progressively decreasing method agreement levels (58.09%, 56.18%, and 54.71%, respectively), indicating an increasing uncertainty in their evaluations across methodologies.

5. Discussion

The GIDA analysis provides practical guidance for mining companies transitioning towards sustainable operations. The waste heat recovery systems emerge as the most immediate viable investment, offering a clear starting point for sustainability initiatives. Mining operations can leverage this technology’s proven reliability to capture energy efficiency gains while building organizational confidence in sustainable technologies. The solar power implementation, while highly promising, requires site-specific considerations, including geographical location, available land space, and local climate conditions. Mining companies should consider starting with pilot installations at suitable sites, gradually expanding based on performance data and operational experience. This approach allows organizations to develop their internal expertise while managing their associated investment risks.

The findings regarding hydrogen-powered equipment offer valuable insights into long-term planning. While showing exceptional potential, this technology requires substantial infrastructure development. Mining companies should consider the establishment of viable partnerships with equipment manufacturers and hydrogen suppliers to prepare for future implementation and at the same time monitor technology maturation and cost reduction trends. Mining operations should adopt an exploratory approach for technologies that show low consensus, such as A1 systems, fleet electrification, and circular economy models. This approach could involve small-scale trials of A1 applications in specific operational areas, testing electric vehicles in controlled environments, and implementing circular economy principles in targeted processes. Such targeted experimentation allows organizations to build practical experience while limiting exposure to implementation risks.

The results from these experiments will ultimately support a pragmatic pathway to sustainable mining. Organizations can begin with proven heat recovery systems and systematically expand into solar power terrain, where appropriate, while preparing for future technologies through strategic partnerships and controlled trials. This measured approach will allow mining companies to balance immediate sustainability gains with long-term technological transformation, ensuring operational stability throughout the transition process.

Sensitivity Analysis

The sensitivity analysis focused explicitly on C1 (community impact) and C4 (carbon emissions) as these criteria received the highest weights (0.1159 each) in the original fuzzy AHP analysis. These two criteria represent the primary environmental and social dimensions of sustainable mining technologies, making them particularly important for testing the robustness of the results. By examining how variations in these highest weighted criteria affect the overall rankings, it becomes possible to effectively assess whether the conclusions depend on specific weighting schemes or represent genuinely superior alternatives. The C1 sensitivity chart demonstrates remarkable stability in rankings across all weight variations (Figure 7). When the community impact criterion weight is varied from −30% (0.0811) to +30% (0.1507), waste heat recovery systems (A4) consistently maintain their top position. The GIDA score for A4 shows only minor variations (ranging from 0.0313 to 0.0325), indicating that its performance is not heavily dependent on this specific criterion. Similarly, solar-powered mining (A1) maintains its second position throughout all weight variations, with a consistent gap separating it from A4.

The C4 sensitivity analysis reveals similar stability when varying the carbon emission criterion weight (Figure 8). The ranking order remains unchanged across all variations, with A4 consistently ranked first. Interestingly, the gap between A4 and other alternatives slightly widens as the carbon emission weight increases, suggesting that waste heat recovery performs exceptionally well in greenhouse gas reduction, reinforcing its position as the optimal solution for environmentally conscious mining operations.

The equal weight scenario provides additional confirmation of the findings’ robustness. Even when all criteria are weighted equally (0.1 each), eliminating any potential bias from the weighting process, A4 maintains a substantial lead over all other alternatives (Figure 9). This demonstrates that waste heat recovery’s superior ranking is driven by consistently strong performance across multiple criteria rather than the specific prioritization of certain factors. This comprehensive sensitivity analysis provides strong evidence that the recommendation to prioritize waste heat recovery systems represents a genuinely robust conclusion. The stability of rankings across various weighting scenarios indicates that the results are not artifacts of particular weight assignments but reflect the inherent strengths of the alternatives evaluated.

6. Challenges and Limitations

This study has some limitations, which include the presence of an academically oriented expert panel. While these experts were carefully selected, based on their advanced qualifications (six Ph.D. and two MSc holders) and substantial experience (of 6–21+ years), the lack of industry practitioners may restrict the breadth of perspectives and potentially introduce theoretical bias. Nonetheless, the fuzzy MCDM methods allow for the representation of expert opinions through linguistic variables, mapped on the triangular fuzzy numbers, contributing to mitigating some of these limitations. Future studies could include a more diverse expert group, covering the industrial and operational domains, thereby enhancing the representativeness of findings. The selected criteria are extensive but may not cover all relevant sustainable mining technology drivers and obstacles in the various geographical and operational contexts. Site-specific scenarios, regional regulations, or access to natural resources can heavily influence these interventions’ success, especially in mining activities, where solutions may face additional restrictions.

Although this study employed three fuzzy MCDM methods integrated through the GIDA framework, the findings can be diverse if other decision-making approaches are applied, thereby influencing the robustness of results. Additionally, the current methodology did not explicitly capture how the selected criteria may relate to one another, either by deriving from or influencing another, such as energy efficiency impacting cost effectiveness metrics or technological feasibility informing implementation timelines. In addition, the framework for evaluation did not incorporate temporal aspects of the evolution of the technology and/or market maturity. Generally, as sustainable technologies advance and mature, their relative benefits and implementation challenges may evolve. This information allows for direct plug-and-play capability in mining operations, thereby empowering the operators to make informed decisions about whether such services should be employed, ensuring efficiency and productivity, whilst accounting for the associated costs of implementation and the expected return on investment.

These findings offer opportunities for future research that could address the limitations of this study, including the incorporation of industry practitioners, the establishment of broader criteria representative of other mining contexts, the consideration of temporal aspects, and conducting deeper economic analyses. Leveraging advanced analytical tools, including machine learning algorithms, could also improve the robustness of the assessment framework.

7. Conclusions

By integrating various fuzzy MCDM methods, this study has developed and applied a systematic framework to assess the sustainability of mining technologies. This study’s primary aim was attained by first establishing a robust criteria weighting system through the fuzzy AHP, which revealed the predominance of community impact and carbon emissions (both weighted at 0.1159) in a sustainable technology selection. This weighting indicated a pronounced realignment of the priorities of mining sustainability, away from economic considerations alone and towards a balanced consideration of environmental and social concerns.

The comparative analysis of the alternatives, by using the fuzzy TOPSIS, fuzzy COPRAS, and fuzzy VIKOR methods, provided valuable insights into technology preferences. Results from the different methods provided a unique perspective: the fuzzy TOPSIS demonstrated the fact that solar-powered mining is the best alternative, considering the ideal solution distance; the fuzzy COPRAS underlined the evaluation of the utility degrees by identifying the waste heat recovery as the top option; and the fuzzy VIKOR results supported the finding on the minimum compromise solution regarding waste heat recovery.

The novel GIDA method successfully synthesized these varied perspectives, thereby providing a unified evaluation framework that accounts for methodological differences while maintaining result reliability. The GIDA results conclusively identified the waste heat recovery systems as the optimal solution with the highest score (0.0319) and near-perfect method agreement (99.99%), followed by solar-powered mining (0.0232, 82.12% agreement). This robust consensus validates the framework’s effectiveness in providing clear, actionable guidance for sustainable technology adoption.

This study has notable implications for sustainable mining from theoretical and practical standpoints. Its theory proposes a new methodology for integrating different MCDM approaches while retaining their distinctive analytical advantages. From a practical perspective, it presents a clear roadmap for mining companies towards adopting these technologies. It suggests a phased-out approach that begins with established solutions, such as waste heat recovery, and evolves with the need to incorporate new solutions stepwise. These findings provide the direction and path required for implementing changes that support a responsible transition in the mining industry toward sustainability. This study provides an initial framework for subsequent studies needed to assess sustainable mining technologies across regional contexts, economic voting structures, and the operational integration of emerging technologies.

Based on the evaluated technologies’ relative rankings and technoeconomic readiness, a phased implementation pathway is proposed to guide the adoption of sustainable solutions in the mining industry. Phase 1 (Immediate Implementation) should prioritize mature and cost-effective technologies such as waste heat recovery systems (A4), which demonstrate strong technical viability and minimal disruption to existing operations. Phase 2 (Early Adoption) should focus on technologies like solar-powered mining (A1) and AI for operational efficiency (A5), which offer strong sustainability potential but require contextual adaptation and infrastructure support. Phase 3 (Strategic Investment and Infrastructure Development) should be reserved for long-term transformative solutions such as hydrogen-powered equipment (A2) and circular economy models (A6), which necessitate extensive policy support, ecosystem development, and stakeholder engagement. This phased strategy aligns with innovation diffusion theory and offers a practical roadmap for the mining sector’s transition to net-zero-carbon operations.

While the rankings presented in this study reflect the consensus of an expert academic panel and the current sustainability priorities in mining, the conclusions should be interpreted considering this study’s scope and limitations. The findings are context-specific and shaped by the selected criteria, regional assumptions, and composition of the expert group. However, the proposed framework is adaptable and can be customized to suit diverse geographic and industrial contexts by modifying criteria, adjusting expert inputs, or integrating local policy considerations. Future studies may validate the generalizability of the findings by involving a broader range of stakeholders and case-specific data.