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Article

An Indicator-Based Framework for Sustainable Mining Using Fuzzy AHP

by
Saleem Raza Chalgri
1,*,
Muhammad Saad Memon
2,
Fahad Irfan Siddiqui
1 and
Shakeel Ahmed Shaikh
2
1
Department of Mining Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, Pakistan
2
Department of Industrial Engineering and Management, Mehran University of Engineering and Technology, Jamshoro 76062, Pakistan
*
Author to whom correspondence should be addressed.
Earth 2025, 6(2), 23; https://doi.org/10.3390/earth6020023
Submission received: 25 February 2025 / Revised: 19 March 2025 / Accepted: 27 March 2025 / Published: 2 April 2025
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Abstract

:
The mineral extraction industry is vital for nations’ economic growth, as it provides raw materials for various industries. Implementing sustainable mining practices in this sector can contribute to its long-term growth and stability. However, Pakistan lacks a well-defined sustainability assessment framework for mining, leaving a critical gap in research and practice. Existing internationally developed frameworks are not directly applicable, as they were designed for contexts where the mining industry predominantly uses mechanized operations. In contrast, Pakistan’s extraction process relies heavily on manual methods, making it necessary to develop a context-specific framework. A fuzzy analytical hierarchy process (AHP) was employed to prioritize these indicators and sub-indicators for the sustainability assessment of Pakistan’s mineral industry. The findings of this study highlight that the environmental dimension ranks as the highest priority, followed by social and economic dimensions. Among the environmental indicators, pollution and smart technologies each received a weight of 0.40, which was also the case for the social indicator of discrimination and nepotism, as well as the economic indicators of GDP growth and wealth creation. Furthermore, the results suggest that the extensive use of smart technologies for pollution control is a key factor in fostering environmental sustainability.

1. Introduction

The mineral extraction industry plays a crucial role in economic development by supplying the raw materials essential for manufacturing various goods. This demand has significantly increased in recent years due to the rapid growth of the industrial sector [1,2]. However, mining activities, while essential, have substantial environmental, social, and economic impacts, which are becoming a growing concern for both developed and developing countries. In this context, the mining industry’s sustainability has become a pressing issue [3,4]. Many countries focus on sustainable pathways in the mineral industries to ensure the smooth supply of raw materials to the industries [5]. The global attention in various sectors is due to the commitments of nations. The concept of sustainability has already gained global attention in multiple sectors due to nations’ commitment to meeting the Sustainable Development Goals (SDGs). The adoption of sustainability in the mineral sector has always remained a challenge. Many developed countries have adopted sustainable industry frameworks [6,7,8,9]. However, in developing countries, the challenge is much more complex to overcome, as meeting the global demand for minerals requires adopting sustainable practices [10,11].
Similar to other developing countries, Pakistan also has enormous reserves of indigenous minerals, coal, copper, and gold [12,13]. Despite having such mineral potential, the mineral sector of Pakistan contributes to only 3% of the country’s GDP, and mineral exports are 0.1% of global mineral trade [14]. In addition, huge reserves and industrial challenges such as strategic planning, governance, and environmental issues hinder Pakistan’s sustainable exploitation of minerals [15,16]. However, the poor quality of Environment Impact Assessment (EIA) reports, financial constraints, and human resource unavailability complicate Pakistan’s reporting system The constraints mentioned above need to be explored for sustainable mineral sector development. The developed framework would, therefore, serve as a tool for measuring and assessing the impacts of mining operations, addressing all three aspects of sustainability: environmental, social, and economic. Furthermore, evaluating the sustainability of various indicators and sub-indicators is essential for effectively managing and utilizing mining reserves sustainably [17,18]. These sustainability indicators vary in importance depending on the context, region, and stakeholders involved.
Additionally, several barriers, such as technological limitations, financial constraints, and weak regulatory frameworks, hinder the adoption of sustainable practices in the mineral industry [19,20,21]. Despite these challenges, there is a growing interest in implementing sustainable practices. Unfortunately, few studies have focused on systematically prioritizing sustainability indicators in developing countries. Still, no frameworks have been proposed to deal with the interdependencies between indicators and the complexities of their implementation [22,23,24]. Available international sustainability frameworks deliver standard benchmarks for mining environmental, social, and economic performance; however, variations exist from region to region. Countries with developed economies operate under strict regulatory bodies with advanced technologies aligned with well-established frameworks. The Pakistani mining industry primarily runs on manual labor and weak environmental regulations, which call for region-specific sustainability measurement processes. Global sustainability models should be adjusted precisely for Pakistan’s mineral industry, as its regulatory system differs from that of developed economies regarding technological development and resource management. As a result, there is a pressing need for a comprehensive sustainability assessment framework to support the growth of Pakistan’s mineral sector, particularly regarding manual mining practices.
The fuzzy analytical hierarchy process (AHP) is one of the most effective techniques in multi-criteria decision analysis (MCDA) [25,26]. It has been widely used for prioritizing and evaluating complex decision-making processes and sustainability assessments [27,28,29]. It has also been applied to sustainability frameworks for assessing sustainability in the mineral sector [30,31]. This technique helps researchers prioritize and evaluate the most critical indicators and sub-indicators related to environmental, social, and economic sustainability. In addition, the weight and ranks of the indicators are assigned based on experts’ judgment. A pairwise correlation matrix to compare various indicators is performed, and weights are determined [32,33]. Therefore, experts’ input is essential for this decision tool. However, the barriers to adopting sustainable practices in Pakistan differ from those in developed countries [34]. The key difference is the current practices adopted for decades i.e., the manual extraction method for minerals. Therefore, this study may open new avenues for engaging various stakeholders in initiating the implementation of sustainable practices. Policymakers and sustainability experts’ involvement in this study was essential for providing insights and contributing to developing strategies to improve the sustainability performance of mining operations [35]. Moreover, this research adds value to the mineral extraction industry by providing a basis for policymakers, stakeholders, and the government to initiate sustainability assessments within the mining business.
This research identifies the most critical indicators and sub-indicators for sustainability in the mineral extraction industry. Previous studies have primarily highlighted the risks hindering the sustainability of mining [36,37] and employed strategic planning tools, such as SWOT analysis, to address sustainability in the sector [34]. However, no research has developed a sustainability framework for Pakistan’s mineral industry. In Pakistan, the mineral industry relies heavily on manual extraction methods, making existing frameworks developed for mechanized extraction challenging to implement. A sustainability framework tailored for manual mining practices has been designed to address this, with a comprehensive assessment of all relevant sustainability indicators. Furthermore, two basic research questions were also discussed in the present study: (1) What are the most critical sustainability criteria/sub-criteria for the mineral extraction industry of Pakistan? and (2) How can the fuzzy Analytical Hierarchy Process prioritize these essential criteria of sustainability/sub-criteria?
To pursue advancements in sustainable practices, it is essential to evaluate existing research gaps. An integrated view of the available literature suggests that there is no framework for the sustainable mineral industry of Pakistan [32,34,36]. This gained the attention of researchers for conducting novel, geographically based research. The existing literature suggests a sufficient gap to carry out this study. This research contributes to filling those gaps by developing a fuzzy-AHP-based framework that proposes a systematic and well-structured approach for evaluating the sustainable practices in the mineral industry of Pakistan by integrating various sustainability indicators and sub-indicators, as well as unique contextual features of Pakistan’s mineral sector. This model might support stakeholder’s commitment to ensuring sustainability in the industry.

2. Materials and Methods

MCDA has wide applications in various fields. This helps decision-makers to choose or select appropriate decisions at different intervals of any project [38,39]. For the sustainability assessment, MCDA was widely used by researchers and is considered a popular approach [40]. One of the techniques for MCD analysis is the Analytical Hierarchy Process (AHP) method, which has broader applications in assessing and evaluating sustainability in any industry, as reported by Saaty [41]. This technique is handy for solving complex industry decision-making by setting indicators and sub-indicators to prioritize and weigh the most important indicators. In this research, the Fuzzy Analytical Hierarchy Process was utilized to prioritize the sustainability indicators and sub-indicators for the mineral industry of Pakistan in terms of manual mining practices. Decision-makers mostly apply AHP when numerical values assignments are confident, but Fuzzy AHP works best under conditions of uncertainty and subjective evaluations [42,43]. The FAHP methodology provides exceptional value for assessment problems and strategic decision-making processes such as sustainability evaluations and mining and environmental risk systems. Therefore, Fuzzy AHP was preferred in this study because it reflects the fuzziness of human thinking [44]. The prioritization of indicators and sub-indicators was achieved based on expert opinions, and they were finally ranked according to their degree of importance in achieving sustainability [45].

2.1. Identification of Indicators

In the literature, several indicators were introduced for assessing sustainability, making identifying relevant indicators a chaotic process. Considering this fact, this study sought to identify the most influential sustainability indicators that may promote sustainability in the mineral sector of Pakistan [46]. A comprehensive literature survey was performed to screen the indicators and sub-indicators based on input from local experts, in order to formulate the assessment framework for manual mining practices. The critical part was to extract the relevant literature to identify the indicators and sub-indicators and to consult the identified parameters with local experts in the mining field. This was the most important and difficult research task, and it was performed in various stages to finalize the most relevant indicators and sub-indicators. Figure 1 depicts the final round of this study resulted in the identification of five leading indicators and 33 sub-indicators related to environmental sustainability. For social sustainability, 23 indicators were finalized, comprising four leading indicators and 19 sub-indicators. Three main indicators and 18 sub-indicators defined the economic sustainability dimension. The selection of these indicators was based on the relevancy and practical implementation as per the requirement of the current mineral industry of Pakistan [32,36].

2.2. Selection of the Expert Panel

Experts were selected based on their knowledge, skill levels, and key industry roles. The minimum experience for selecting experts was more than 5 years, as reported in Table 1. All the experts were selected from the country’s esteemed academic institutes and reputable mining companies. These experts had key roles in their fields and were aware of the mining operations and policies of their companies/institutes. To avoid conflicts of interest among experts, specialized professionals representing academia, combined with industry and regulatory bodies, formed the panel. This preserves balance and stops domination from any particular stakeholder collective. During blind reviews, the experts received instructions to maintain anonymous responses during comparative assessment procedures. A questionnaire designed in pairwise format, containing indicators and sub-indicators with a relative importance scale, was assigned to judge the criteria on a priority basis.
Hierarchical frameworks for sustainable development are presented in three areas, including environmental, social, and economic dimensions (see Figure 1). Critical aspects of environmental sustainability, such as resource efficiency and pollution control, were captured through main indicators and their respective measurable sub-indicators for detailed evaluation. Further, in social sustainability, we included various dimensions, such as labor rights, organizational accountability, workplace safety, and equity, and each is coded for quick reference with sub-indicators such as job security and health facilities. Economic sustainability is evaluated by focusing on the financial side, such as productivity, cost management, and economic impact, which are further divided into specific metrics that include labor costs, mine automation, and the cost of reclamation. These leading indicators and their respective sub-indicators provide well-structured frameworks for assessing and improving sustainability practices.
The fuzzy analytical hierarchy process (FAHP) is preferred over classical AHP in removing fuzziness and maintaining accuracy in decision-making [25]. FAHP is often applied and considered a very effective decision-making tool, as it incorporates the AHP with fuzzy set theory. AHP is the systematic approach to arranging complicated decision-making problems. At the same time, the fuzzy set theory assists in managing the imprecise and ambiguous nature of human decision-making criteria or preferences. Incorporating the results of these two approaches into FAHP helps decision-makers incorporate linguistic variables and measure the fuzziness of their preferences [27].

2.3. Fuzzy Scale

A series of fuzzy sets portray the levels of linguistic terms that may link with numerical and verbal expressions. In the present study, the 9-level fuzzy scale, depicted in Figure 2, was used to show the relative importance of leading indicators and sub-indicators. Furthermore, to achieve the objective, three levels were set for assessing sustainability in the mineral sector of Pakistan. In level 1, the goal was to set and measure sustainability in mining; level 2 presents the key criteria, i.e., environmental, social and economic; and level 3 shows the sub-criteria, as illustrated in Figure 1. The added supplementary questions reduced expert opinion variability, thus leading to more accurate answers. The researcher developed specific questions while keeping Pakistan’s current mining situation in mind to enhance their applicability for this study.
The initial stage of finalizing the sustainability index starts by formulating the criteria to be used for the assessment. These criteria act as a variable for the calculation of the level of sustainability. Normalization, weighting, and aggregation are the key steps to assess these criteria in the AHP process. Normalization is carried out because the selected criteria have different units and scales. Once the normalization is performed, the weighting of criteria is analyzed.

2.4. Weighing Process for Criteria

In this study, a well-structured questionnaire was provided to the experts. Data gathered from the experts through the questionnaire were then used as input for data processing using the AHP. The main reason for selecting the AHP technique is the pairwise comparison, the main feature in which decision-makers may compare two criteria in pairs as per their relative importance. Thus, relative weights and rankings for each criterion and their alternatives were calculated. The experts use Saaty’s 9-point scale to determine the importance of two criteria. This is performed using 1 to indicate equal preference, and 3, 5, 7, and 9 to indicate increased preference. Pairwise comparison starts by placing the criteria into a square shape, and a value is assigned to represent the importance of once criterion over another; for the reverse relationship, from criterion B to criterion A, the reciprocal value is assigned. The following steps describe the results of the processed data.
All criteria and sub-criteria were weighed based on environmental, social, and economic factors, and the weights assigned to each criterion within every indicator were taken into account. Pairwise comparisons from the questionnaire survey were added to the comparison matrix, as presented in Equation (1).
A = 1 ⋯ a 1 j ⋮ ⋱ ⋮ a 31 ⋯ 1
Once the matrix was completed, the data were normalized. Normalization was acquired by dividing each element of matrix A by the total number of elements in each column. Using these data, the average value was calculated using the matrix rows to obtain the priority score, which is known as the Eigenvector. The consistency index (CI) was then determined using the eigenvectors. The CI value can be calculated using Equation (2), as suggested by Saaty [41].
C I = λ − n n − 1
where λ Is the maximum eigenvalue, and n is the number of criteria. Consistency ratio (CR) was calculated using Equation (3), as reported in the literature [44]. CR values are important in analyzing different indicators and their respective sub-indicators. If the CR value is <0.1, then the relationship between the criteria is consistent; if the CR value is >0.1, then the relationship between criteria is inconsistent. The relationship between the main indicators and the criteria was consistent in the present study.
C R = C I R I

3. Results

The data obtained were analyzed using the fuzzy AHP technique to calculate the CR values. The CR values were fetched from the weighting of the criteria and sub-criteria for each indicator and sub-indicator, which are reported in Table 2. The CR values for the criterion remain in the range of 0.02 to 0.1, as presented in Table 2, which indicates that the relationship between the criteria is consistent.
Table 2 and Figure 3 show that pollution and smart technology (EN1) have the highest score, whereas the government involvement, investment, and organization involvement (EN5) have the lowest weights of 40.1 and 4.0, respectively. This finding aligns with the results of previous studies, which indicate that there is a gap in technological development in the mineral sector of Pakistan [13,15]. It is evident from the findings that, as per the expert opinion, in order to implement sustainability in the mineral sector, there is a dire need for the usage of pollution reduction and smart technology [2]. The experts emphasized and assigned more significant value to the indicator. This criterion is followed by EN2.1, i.e., post-mining and land use. Post-mining and land use assessments were conducted in the mineral sector, as mineral excavation damages soil fertility and disturbs the topsoil [37,45]. However, the results in Table 2 also highlight that the role of government and organizations, i.e., EN5, has a lower priority weight of 4.0.
Table 3 and Figure 4 show the priority weights of the indicators, the CR between the criteria, and the ranking of the social criteria. Respect and Discipline (SC3.4) has the highest priority weight of 40.0, followed by safety and trend culture (SC4), and ergonomics and its prevention (SC4.5), as shown in Table 3, with values of 37.3 and 31.4, respectively. Furthermore, Table 4 and Figure 5 show priority weight and CR ranking social criteria between indicators and sub-indicators.
Table 4 and Figure 5 highlights that GDP and wealth creation (EC1) for the economic sustainability assessment have the highest priority criteria weight of 40.2, while productivity and efficiency (EC2) have the second highest priority weight of 32.7. The relationship between the economic indicators is consistent, as the CR value is less than 0.1 across all criteria.

4. Discussion

This study presents a comprehensive assessment framework for prioritizing sustainability indicators in Pakistan’s mineral industry. This paper utilized the fuzzy AHP approach to determine the weight of the mineral sector’s relevant sustainability indicators and sub-indicators. This study’s findings guide the performance evaluation of countries comparable in scale to developing countries. Prioritizing indicators and sub-indicators for sustainability assessment in the mineral sector is considered a critical process [17,35,43]. A pairwise questionnaire, as suggested by the literature, was developed with the environmental, social, and economic dimensions in mind for the sustainability assessment of Pakistan’s mineral industry [33,35]. The mineral industry of Pakistan relies heavily on manual extraction methods; therefore, the sustainability frameworks available around the world are irrelevant in the assessment process because these studies developed a framework for mechanized mining practices [29,30].
To assess the ecological impact of the mining sector, the environment sustainability framework was assessed via Fuzzy AHP by identifying the critical environment indicators. Pollution and Smart Technologies (EN1), with 40.1%, ranked number 1 as the most dominant indicator, highlighting the urgent need for adopting technological innovation to mitigate emissions, reuse waste, and improve efficiency. Various authors have emphasized using smart technology to reduce carbon footprints and control pollution in mineral resource industries [47]. The high weightage indicator (EN1) highlights that environmental concerns must be a priority, reducing energy consumption and controlling pollution to strengthen sustainable development. This correlation highlights the need for a better regulatory framework, cleaner technologies, and better resource utilization to reduce environmental impacts.
Furthermore, the second sub-indicator, with a significant value of 32.1%, was post-mining land use (2.1), which underscores the value of mining site rehabilitation for ecological balance in long-term environmental restoration [18]. Rehabilitating mining sites is essential for implementing sustainable mineral extraction industries. Another critical indicator identified was global warming and deforestation (EN2), which had a decent significant weight, i.e., 25.3%, showing concerns about deforestation due to mining activities, which ultimately negatively affects the climate. As reported in Table 2, the weight of the indicator renewable energy use (EN3.3) is 22.8%, emphasizing that there is a quick need for adopting efficient, less pollutant equipment to reduce the environmental footprints in the mineral industry of Pakistan [43,45]. Moreover, environmental protection and regulation (EN4) and government involvement, investment, and organization role (EN5) have significance weights of 9.3% and 4%, indicating that work in the regulation and protection of the environment for the mineral sector of Pakistan is not up to date in comparison with developed countries [48,49,50]. Soil erosion, biodiversity, and migration of birds and animals are due to mineral extraction; therefore, the proper resolution of these issues must be given equal consideration for sustainable growth [51]. Developed countries have emphasized the induction of corporate social responsibilities (CSRs) within mineral industries to overcome the challenges [52,53].
The results of the Fuzzy AHP suggest a foundational basis for developing a robust sustainability framework for the social dimensions within the mineral extraction industry of Pakistan. For the assessment, a total of 23 social sustainability indicators and sub-indicators were finalized, and their significance weights and ranking were determined using Fuzzy AHP. Among all the criteria analyzed, discrimination and nepotism (SC3.4) achieved a substantial weight, i.e., 40%, and were ranked higher. This indicator is dominant among all the indicators and sub-indicators in the metrics, which emphasizes the importance of workplace inequalities for ensuring fair behavior among all workers. Thus, this might adversely affect the miners’ trust in the industry [54,55,56].
Furthermore, safety trend and culture (SC4) were ranked second, which was followed by the availability of PPEs (SC4.5) based on their significance weight, i.e., 37.30% and 31.4%, respectively, as shown in Table 3. The underscoring indicators of safety and culture are identified as critical factors among all the social indicators for minimizing environmental risks and hazards. The safety issues in mining are always of great importance in developing countries such as Pakistan, where manual mining practices are followed, and unavailability and proper training on the usage of PPEs is common [57,58]. The lack of the adoption of appropriate safety practices, fewer technological advancements, inadequate accident reporting systems, and inefficient, outdated mining laws may increase concerns over safety issues [15]. Safety issues are another emerging factor influencing job security and life insurance (SC2.2), and they also highlight the value of employees’ financial and welfare needs in difficult, high-risk businesses. Equal opportunities and fair treatment of employees are the emerging parameters for assessing the social sustainability in the industry, which is why promotion and fair opportunities (SC2.1) have a significance weight of 28%, while labor relationships (SC3.3) have a significance weight of 26%, highlighting their great impact. These sub-indicators emphasize the coordination and collaboration between management and employees to improve the industry’s overall productivity. Sub-indicators such as the safety of the working environment (SC4.1) and the conduct of training and workshops (SC1.1) show that these factors must be considered for the long-term social sustainability assessment framework. All the sustainability category metrics had low consistency ratios, i.e., less than 0.1, which validates the reliability of the structured elements of social sustainability in the industry [59,60,61]. This social sustainability assessment framework may ensure a balanced consideration in various areas, such as workplace fairness, safety of employees, and the overall well-being of employees to foster holistic social sustainability in the mineral extraction industry [62].
Economic sustainability is of great interest to the stakeholders and the government, as the mineral industry is a significant part of economic growth for many countries [18,62]. Therefore, selecting indicators and sub-indicators is a complex process in assessing economic sustainability. The findings from Fuzzy AHP help us prioritize the economic sustainability indicators and their sub-indicators based on their significance and weight presented in Table 4. The pairwise comparison matrix results also showed consistency, as the ratio was 0.0546 for indicators EC1, EC2, and EC3, thus strengthening the validity of the derived indicators and adding value to the proposed sustainability framework. A total of 22 indicators were finalized. Among them, GDP and wealth creation (EC1) have the highest significance weight, i.e., 40.1%. Growth in industry and wealth generation are essential in the decision-making approach to the sustainability framework [63]. The mining industry is considered the leading sector that boosts the economic development of any country. These findings agree with previous findings, showing that, in global trends, GDP is the dominant indicator for the evaluation of economic sustainability [64]. Economic sustainability is essential to promote environmental and social sustainability. Wealth creation is always dependent on productivity and efficiency (EC2); therefore, this sub-indicator achieved a weight of 32.7%, highlighting the optimization of operations. However, health insurance, safety, and reclamation (EC3) show a 15.8% significance weight, as all these indicators are also critical for economic sustainability. In recent years, the concept of circular economy has also been highlighted. The present study shows that the sub-indicator cost of circular economy initiation (EC1.6) received a weight of 32.7% and was ranked second, highlighting its role as a key driver for the GDP indicator [4,51]. Moreover, other sub-indicators, such as material costs and health and well-being costs achieved weights of 20.8% and 25.2%, respectively, being also highlighted as the dominant sub-indicators in their category. The consistency ratios in all metrics show reliability, with values less than 0.1, as suggested by Saaty [41]. This study’s findings emphasize fostering sustainable development within Pakistan’s mineral extraction industry.
The collective results of Fuzzy AHP for environmental sustainability demonstrate that fast adoption of technological development and a multidimensional comprehensive assessment framework may be incorporated within the mineral extraction industry of Pakistan. Experts in this study highlighted a great concern over the manual mineral extraction method. They highlighted that shifting the manual mining technique to mechanization is challenging, but strategies should be formulated. This study showed the multidimensional approach that may be applied to foster the implementation of sustainability. This research framework can be used in any other developing countries that rely on manual or semi-mechanized mining. The proposed model can be improved by utilizing region-based data and adjusting the weighing of indicators. Considering additional elements, such as informal mineral extraction might make it more accurate. These changes will facilitate the application of the model across various mining sectors.

5. Conclusions

The mineral extraction industry in Pakistan is growing, but achieving sustainability in this sector remains a significant challenge. This study proposes a set of indicators and sub-indicators to assess sustainable practices within Pakistan’s mineral industry. The findings suggest that the extensive use of smart technologies to control pollution is a critical concern, and managing these technologies could significantly contribute to fostering environmental sustainability. Additionally, using renewable energy and restoring land post-mining are vital indicators of social sustainability within the mineral industry. Economic sustainability is primarily assessed through GDP growth and efficient production. This comprehensive sustainability framework encompasses all significant parameters for formulating sustainable practices in the mineral sector. Moreover, developing practical strategies that address existing barriers could be instrumental in creating a sustainable mineral exploitation policy for Pakistan.

Limitations and Future Recommendations

Despite achieving its aim, this study has certain limitations. The developed framework might help stakeholders assess sustainable mineral industry operations. However, this model has limitations, as it only focuses on underground and open-pit mining methods. The input parameters for the sustainability assessment framework for the metal, cement, and mineral processing industries might help devise a comprehensive sustainable model. The operations and challenges in these industries are different. Therefore, this gap should be addressed in the future.

Author Contributions

Conceptualization and methodology, S.R.C.; writing, editing, supervision, M.S.M.; data collection, F.I.S.; original draft preparation, S.A.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

This research work is a partial completion of the ongoing PhD of the corresponding author. The data in this study may be available (or provided on request) after the completion of PhD dissertation. This is due to privacy and ethical restrictions.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Assessment model for sustainability in mining.
Figure 1. Assessment model for sustainability in mining.
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Figure 2. Fuzzy scale suggested by Saaty [45].
Figure 2. Fuzzy scale suggested by Saaty [45].
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Figure 3. Main Indicator and sub-indicator for environment weight chart.
Figure 3. Main Indicator and sub-indicator for environment weight chart.
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Figure 4. Main indicator and sub-indicator for social weight chart.
Figure 4. Main indicator and sub-indicator for social weight chart.
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Figure 5. Main indicator and sub-indicator for economic weight chart.
Figure 5. Main indicator and sub-indicator for economic weight chart.
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Table 1. Demographic features of experts.
Table 1. Demographic features of experts.
DesignationAgeExperienceIndustry
Deputy Chief Executive Officer2042Coal Industry
Senior Mining Engineer2042Coal Industry
Manager Production—Coal Supply1540Coal Industry
Associate Professor1237Coal Industry
Assistant Professor1739Academia
Senior Researcher1237Academia
Senior Mining Engineer634Academia
Table 2. Environment indicators and sub-indicator weight, CR value, and ranking of criteria.
Table 2. Environment indicators and sub-indicator weight, CR value, and ranking of criteria.
IDWeightSignificance (%)Consistency Ratio (CR)RelationRanking
EN10.4040.100.07Consistent1.00
EN20.2525.303.00
EN30.2121.308.00
EN40.099.3030.00
EN50.044.0033.00
EN1.10.2323.100.10Consistent5.00
EN1.20.1414.0018.00
EN1.30.1919.3012.00
EN1.40.2423.704.00
EN1.50.2020.0010.00
EN2.10.3232.100.04Consistent2.00
EN2.20.2020.209.00
EN2.30.1515.0015.00
EN2.40.1110.5026.00
EN2.50.1211.8022.00
EN2.60.1010.4027.00
EN3.10.2221.700.08Consistent7
EN3.20.1818.2013.00
EN3.30.2322.806.00
EN3.40.2221.707.00
EN3.50.1615.6014.00
EN4.10.1515.000.02Consistent15.00
EN4.20.2019.8011.00
EN4.30.1312.6020.00
EN4.40.1514.7016.00
EN4.50.1010.4027.00
EN4.60.1414.2017.00
EN4.70.1313.4019.00
EN5.10.099.400.06Consistent29.00
EN5.20.1110.5025.00
EN5.30.1212.1021.00
EN5.40.1111.1023.00
EN5.50.088.0032.00
EN5.60.099.3030.00
EN5.70.099.1031.00
EN5.80.1010.4027.00
EN5.90.109.5028.00
EN5.100.1110.8024.00
Table 3. Social indicators and sub-indicator weights, CR values, and the ranking of criteria.
Table 3. Social indicators and sub-indicator weights, CR values, and the ranking of criteria.
IDWeightSignificance (%)Consistency Ratio (CR)RelationRanking
SC10.2423.500.07Consistent8
SC20.1918.6012
SC30.2120.5010
SC40.3737.302
SC1.10.1716.500.05Consistent17
SC1.20.1717.1015
SC1.30.1716.9016
SC1.40.2020.2012
SC1.50.1615.5019
SC1.60.1413.8020
SC2.10.2827.800.01Consistent5
SC2.20.3029.604
SC2.30.2020.3011
SC2.40.2222.209
SC3.10.1818.40 14
SC3.20.1615.6018
SC3.30.2626.006
SC3.40.4040.001
SC4.10.1312.700.0363Consistent22
SC4.20.2424.407
SC4.30.1313.0021
SC4.40.1918.5013
SC4.50.3131.403
Table 4. Social indicators and sub-indicator weights, CR values, and the ranking of criteria.
Table 4. Social indicators and sub-indicator weights, CR values, and the ranking of criteria.
IDWeightSignificance (%)Consistency Ratio (CR)RelationRanking
EC10.40240.20.0545Consistent1
EC20.32732.72
EC30.15815.810
EC1.10.13613.60.0706Consistent14
EC1.20.14914.912
EC1.30.0969.618
EC1.40.141413
EC1.50.16416.49
EC1.60.27127.13
EC1.70.15715.711
EC2.10.0969.60.0635Consistent
EC2.20.0919.119
EC2.30.12512.517
EC2.40.21721.75
EC2.50.20820.86
EC2.60.13113.116
EC2.70.13213.215
EC3.10.25225.20.0296Consistent4
EC3.20.25225.24
EC3.30.19419.47
EC3.40.12512.517
EC3.50.17717.78
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Chalgri, S.R.; Memon, M.S.; Siddiqui, F.I.; Shaikh, S.A. An Indicator-Based Framework for Sustainable Mining Using Fuzzy AHP. Earth 2025, 6, 23. https://doi.org/10.3390/earth6020023

AMA Style

Chalgri SR, Memon MS, Siddiqui FI, Shaikh SA. An Indicator-Based Framework for Sustainable Mining Using Fuzzy AHP. Earth. 2025; 6(2):23. https://doi.org/10.3390/earth6020023

Chicago/Turabian Style

Chalgri, Saleem Raza, Muhammad Saad Memon, Fahad Irfan Siddiqui, and Shakeel Ahmed Shaikh. 2025. "An Indicator-Based Framework for Sustainable Mining Using Fuzzy AHP" Earth 6, no. 2: 23. https://doi.org/10.3390/earth6020023

APA Style

Chalgri, S. R., Memon, M. S., Siddiqui, F. I., & Shaikh, S. A. (2025). An Indicator-Based Framework for Sustainable Mining Using Fuzzy AHP. Earth, 6(2), 23. https://doi.org/10.3390/earth6020023

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