Research on Comprehensive Evaluation Indicators and Methods of World-Class Open-Pit Coal Mines

Abstract

Since the 20th century, with the rapid development of the global economy, human demand for fossil energy such as coal has been increasing. In the face of fierce competition, the state and various ministries and commissions have put forward the goals and requirements of building a world-class enterprise, and how to become a world-class open-pit coal mine and establish the corresponding evaluation standards has become an urgent scientific problem to be solved. According to the characteristics of open-pit coal mine production and operation, from the perspective of benchmarking world class, the key factors affecting the construction of world-class open-pit coal mines are extracted by using the fuzzy DEMATEL method, and the comprehensive evaluation index system, evaluation standards, and methods of open-pit coal mines are established, which provides benchmarks and guidelines for the construction of world-class open-pit coal mines and improves the construction level of world-class open-pit coal mines.

1. Introduction

Since the 20th century, the global open-pit coal industry has seen rapid development. Open-pit coal mines have shown outstanding performance in technological advancements and the intelligent integration of equipment. The production of open-pit coal mines has also shown a trend of stable growth year by year, with the proportion of coal mining in the entire production continuously increasing. Countries around the world have made open-pit coal mines a focus of development, actively promoting and increasing the volume of open-pit coal mining, leading to a general increase in the proportion of open-pit coal extraction. According to statistics, the proportion of open-pit coal production in some major coal-producing countries accounts for more than 50%, and in some countries, the proportion of open-pit coal production even exceeds 90%. This indicates that open-pit mining occupies a dominant position in the coal production of these countries. In addition, more than 70% of the global open-pit recoverable coal reserves are concentrated in countries such as the United States, Russia, and Germany, indicating that the open-pit mining markets in these countries have tremendous potential. Overseas open-pit coal mines have experienced three main stages of rapid development to steady growth. From 1913 to 1980, the average annual growth rate of open-pit coal production in developed countries was 4.19%, and the proportion of open-pit coal production exceeded that of underground mining, marking the rapid development phase for open-pit coal mines. From 1981 to 2000, open-pit coal production in some developing countries grew rapidly, and open-pit coal mining entered a stage of stock development. From 2001 to 2021, the average annual growth rate of open-pit coal production was 2.58%, and open-pit coal mines entered a stage of steady improvement. In 2021, the production of open-pit coal mines in foreign coal-producing countries was about 3.2 billion tons, accounting for about 81% of the total coal production. The proportion of open-pit coal production in 11 major coal-producing countries is more than 50%, among which India, Indonesia, Germany, and Canada have an open-pit coal production proportion of more than 90%. Australia and Russia have an open-pit coal production proportion between 70% and 90%. The United States, South Africa, and Kazakhstan have an open-pit coal production proportion between 50% and 70%. China’s open-pit coal mines have gone through a century-long process from low fluctuations to accelerated development. Before the year 2000, the development of China’s open-pit coal mines was slow, with coal production long remaining at a low level, accounting for 3% to 4%. Since the 21st century, especially after 2003, the industry entered a phase of rapid development, with a large number of modern open-pit coal mines with an annual production capacity of tens of millions of tons being concentrated on construction and production. From 2003 to 2021, the coal production of China’s open-pit coal mines increased by about 6.3 times. The average output of open-pit coal mines is 3.2 times that of underground coal mines. In 2021, the coal production of China’s open-pit coal mines was 950 million tons, accounting for 23% of the total coal production in China, which has exceeded the total coal production of any other country in the world. In line with the requirements of important national documents such as “Made in China 2025, Internet Plus, and the National Informatization Development Strategy Outline”, the transformation from traditional mines and digital mines to smart mines aims to build safe, efficient, green, and intelligent smart mines. By 2025, it is anticipated that the proportion of open-pit coal production will reach around 30%. The focus of open-pit coal production is expected to shift westward at an accelerated pace. In Inner Mongolia, the proportion of open-pit coal production within the region is expected to increase from the current 40% to 50–60% by 2025. In Xinjiang, the proportion of open-pit coal production within the region is expected to increase from the current 50% to 60–70% by 2025.

In recent years, a small number of enterprises that are at the forefront of the industry on a global scale have been referred to as world-class companies. Their leading positions are sustainable and have a certain credibility; for example, world-class companies such as Amazon, JPMorgan Chase, Berkshire Hathaway, Microsoft, Ping An of China, and Toyota. Scholars at home and abroad have conducted a large number of studies on world-class companies. Fortune magazine in the United States points out that the characteristics of world-class companies mainly include product and service quality, innovation capability, and corporate management level. The Boston Consulting Group points out that the characteristics of world-class companies include orderly planning, competitive advantage, high management flexibility, etc. Domestic scholars support the idea that world-class companies mainly include continuous attention to market dynamics, outstanding leadership, excellent business performance, scale benefits, diversified development of related businesses, etc. [1,2,3,4,5]. Coal enterprises, as the carriers of coal resources [6], engage in economic activities that can generate both economic and environmental benefits during the development and utilization of coal resources. The key intrinsic elements of these activities include the rational use of coal resources, the maintenance of the ecological environment, the preservation of ecological balance, and the achievement of green and low-carbon utilization of coal resources, as well as the sustainable development of the economy and ecology [7]. As the main force in China’s energy market, coal enterprises have long been in a leading position in the energy market. At the same time, after nearly a decade of development, Chinese coal enterprises have experienced changes in both “form” and “momentum”. In terms of “form”, the overall scale of China’s leading coal enterprises has reached a global level, laying the foundation for building world-class companies, including domestic-listed coal enterprises such as China Shenhua, China Coal Energy, Yankuang Energy, and Shaanxi Coal Industry. In terms of “momentum”, China has embarked on a new journey of comprehensively building a modern socialist country [8,9,10]. However, at present, there is relatively little research on world-class coal enterprises at home and abroad, which mainly focuses on the performance of coal enterprises.

S. Das, S. C. Patnaik, and H. K. (2013) and Galvin J. M. (1995) treated environmental concerns and resource development and utilization as a unified system for comprehensive exploration, incorporating social factors and the concept of sustainable development in their mineral environmental assessments [11,12]. John (1998) proposed the concept of “triple surplus” performance, emphasizing the need to comprehensively consider economic, environmental, and social performance when evaluating corporate performance [13]. Ma Li (2016) developed an evaluation index system for open-pit coal mines that includes five aspects: production, safety, environmental protection, economy, and management. A comprehensive evaluation of the HDG open-pit coal mine was also conducted [14]. Modak, Mousumi, Pathak, et al. (2017) constructed a BSC–FAHP performance evaluation system and used the fuzzy analytic hierarchy process to comprehensively evaluate the performance levels of Indian coal mines [15], whereas Wu Leng (2020) selected seven sub-goals, including basic conditions of mines and green mining areas, to construct an evaluation index system for green mines. The TOPSIS method to establish an evaluation model and Smith model was also used [16]. Furthermore, Perera (2013) incorporated social responsibility into the evaluation system and confirmed through empirical research that there is a positive correlation between business performance and social responsibility [17]. Yang Yang analyzed the causes and impact factors of coal mine accidents over the past 7 years, established a safety audit evaluation index system for coal enterprises, and conducted case analyses [18]. Ning Fang studied the correlation between open-pit mining and material state pollution and, at the same time, established and improved plans related to mining and mine layout [19]. C. Adam (2015) emphasized balancing economic profit, environmental protection, and social responsibility to adapt to development trends [20]. Yang Xiaojie (2019) created an evaluation system for green mining of large open-pit coal mines as the foundation for green mining assessment [21]. Liu Peng (2020) constructed an evaluation index system from the three aspects of safety, efficiency, and environmental protection [22]. Haodang Li (2019) predicted that open-pit coal mining technology would achieve new breakthroughs in model innovation, intelligent mining, efficiency and safety guarantees, green mining construction, and professional talent cultivation [23]. Ziling Song (2020) proposed green mining criteria and introduced the concept of “greenness” to construct an evaluation index system for open-pit coal mines [24]. Jiandong Sun (2020) advocated for using intelligent open-pit coal mine mapping to construct and establish a smart evaluation system and suggested adopting advanced decision-making methods to solve problems in intelligent planning for open-pit coal mines [25]. Li Le (2024) has constructed a comprehensive evaluation index system for the carbon performance of coal enterprises, which includes the utilization of energy resources, economic development, carbon emissions, environmental protection investment, and the institutional system [26].

In the evaluation of world-class coal companies, Ding Rijia and others (2015) constructed a construction system for world-class coal companies based on six dimensions: safety, efficiency, intelligence, greenness, innovation, and harmony [27]. Zhou Jianbo (2019) used technical analysis tools and case study methods to clarify the connotations and characteristics of world-class enterprises [28]. Han Haobo and others (2020) elaborated in depth on the connotations and characteristics of world-class enterprises and constructed an evaluation index system for the construction of a world-class corporate culture demonstration [29]. Liang Yueqiang and others (2023) clarified the connotation of world-class coal companies. Further, they proposed ideas for the construction of world-class coal companies [30].

It can be seen that scholars are still preliminarily exploring the construction of world-class coal enterprises. In that way, what constitutes the comprehensive evaluation standard for world-class open-pit coal mines? What evaluation index system and method should be employed? How are standard values determined for more scientific and reasonably evaluated world-class construction standards? These are critical theoretical and technical issues that urgently need to be addressed. This article delves into the aforementioned issues, developing a comprehensive evaluation index system for world-class open-pit coal mines within the ZN Group and establishing the corresponding scoring method. It offers benchmarks and criteria for constructing world-class open-pit coal mines, thereby enhancing the level of world-class open-pit coal mine construction.

2. Materials and Methods

2.1. Construction of Influencing Factor Analysis Model

2.1.1. Triangular Fuzzy Numbers

Triangular fuzzy numbers can effectively represent information that is difficult to be described with accurate numerical values and can also be flexibly converted with other fuzzy numbers to solve related problems in many fields, so it has been extensively studied by many experts and scholars [31,32]. In the domain A, the triangular fuzzy number is expressed in the form $S=(x^L\le x^M\le x^U)$, where $x^L\le x^M\le x^U$, $x^L$, $x^M$, and $x^U$ represent the corresponding upper, median, and lower bounds, respectively, and are degraded to real values when $x^L=x^M=x^U$. Assuming that $S_1=x_1^L,x_1^M,x_1^U$ and $S_2=x_2^L,x_2^M,x_2^U$ are two triangular fuzzy numbers, they must satisfy the following operation rules.

$$S_1+S_2=x_1^L+x_2^L,x_1^M+x_2^M,x_1^U+x_2^U$$

(1)

$$S_1-S_2=x_1^L-x_2^L,x_1^M-x_2^M,x_1^U-x_2^U$$

(2)

$$S_1·S_2=x_1^L·x_2^L,x_1^M·x_2^M,x_1^U·x_2^U$$

(3)

$$S_1/S_2=x_1^L/x_2^L,x_1^M/x_2^M,x_1^U/x_2^U$$

(4)

$${\lambda S}_1={(\lambda x}_1^L,\lambda x_1^M,\lambda x_1^U)$$

(5)

$${{(S}_1)}^y={{\left[\right(x}_1^L)}^y,{\left(x_1^M\right)}^y,{\left(x_1^U\right)}^y]$$

(6)

The membership function [33] of the triangular fuzzy number $S=\left(x^L,x^M,x^U\right)$is expressed as follows.

$${\mu }_s\left(x\right)=\left\{\begin{array}{c}0,x\le x^L\\ {\displaystyle \frac{x-x^L}{x^L-x^M}},x^L\le x\le x^M\\ {\displaystyle \frac{x-x^U}{x^U-x^M}},x^L\le x\le x^M\\ 0,x>x^L\end{array}\right.$$

(7)

where $x^M-x^L$ and $x^U-x^M$ are the previous and the next of ${\mu }_s\left(x\right)$, respectively.

2.1.2. DEMATEL

The decision test and Evaluation experiment method was proposed by A. Gabus and E. Fontela of Battelle Laboratory in the United States at A conference in Geneva in 1971. DEMATEL is a system-analysis method proposed by using graph theory and matrix tools to solve complex and difficult system problems in the real world [34,35,36,37]. The advantage of the DEMATEL method is that it simplifies the logical relationship between various elements in a complex system, considering the direct influence relationship while also considering the influence of indirect factors. At the same time, it can use subjective data to judge the relationship between the factors responsible for the system, which is helpful to deeply understand the structure and operation mode of the system and provide guidance for system design and improvement. This method has strong intuitiveness and a wide application range. It is very effective in factor analysis and system influence analysis. Compared with AHP and ANP, these methods focus on the determination of weights and the ordering of priorities [38,39,40], while DEMATEL pays more attention to the analysis of causality and influence among factors. While the ISM interpretive structure model (ISM) focuses on building a hierarchical structure model, DEMATEL provides a more detailed analysis of the relationship between factors. DEMATEL is divided into seven steps, as shown below.

Step 1: Label all objects and elements in the system that need to be studied S1, S… Sn.

Step 2: Quantify the interaction between various factors in the system, using the “~0–4 scale method”. The degree of direct influence between each factor was scored by means of expert interviews and questionnaires. No. 0 indicates no impact. No. 1 represents a weaker effect. No. 2 represents the general impact. No. 3 represents a strong influence. No. 4 represents strong influence. Hence, the direct influence matrix $O={\left[O_{ij}\right]}_{m\times n}$.

$$O=\left[\begin{array}{cccc}0& O_{12}& \cdots & O_{1n}\\ O_{21}& 0& \cdots & O_{2n}\\ ⋮& ⋮& \cdots & ⋮\\ O_{n1}& O_{n2}& \cdots & 0\end{array}\right]$$

(8)

Step 3: Analyze the indirect influence relationships of the factors in the system and normalize the direct influence matrix O to obtain the normalized direct influence matrix $Z={\left[Z_{ij}\right]}_{mXn}$.

$$Z={\displaystyle \frac{O}{max\sum _{j=1}^n{O}_{ij}}}$$

(9)

In the formula, $O\le z_{ij}\le 1,$ and $max\sum _{j=1}^n{z}_{ij}$

Step 4: Calculate the integrated impact matrix $T(T={\left[t_{ij}\right]}_{nxn})$ according to the formula. In the formula, the factor $t_{ij}$ in the matrix $T$ represents the combined influence level of factor i on factor j, including the direct and indirect influence levels. The calculation formula is shown below.

$$T={\displaystyle \frac{Z}{1-Z}}(I \text{is the unit matrix})$$

(10)

Step 5: Calculate the degree of influence $a_i$, the degree of being influenced $b_i$, the degree of centre $M_i$, the degree of cause $N_i$ for each element in the system. The specific calculation formula is as follows.

$$a_i={\sum }_{j=1}^n{t}_{ij}i=1,2,\cdots ,n$$

(11)

$$b_i={\sum }_{j=1}^n{t}_{ij}i=1,2,\cdots ,n$$

(12)

$$M_i={\sum }_{j=1}^n{t}_{ij}+{\sum }_{j=1}^n{t}_{ji}i=1,2,\cdots ,n$$

(13)

$$N_i={\sum }_{j=1}^n{t}_{ij}+\sum _{j=1}^n{t}_{ji}i=1,2,\cdots ,n$$

(14)

According to the calculation results, the centre degree $M_i$ is ranked, and the key risk factors can be determined. Risk factor attributes are determined by the positivity and negativity of the cause degree $N_i$. Finally, the result factors and cause factors are derived.

2.1.3. Fuzzy DEMATEL Method

In the 1970s, the DEMATEL method was introduced initially to tackle complex issues that defied consensus conclusions, such as those concerning energy utilization and environmental factors. This method leverages mathematical models and system theories to analyze and discern the mechanisms of interaction among various elements, establishing their connections and interdependencies. Open-pit coal mining enterprises face numerous complex factors that influence their performance evaluation. Thus, the adoption of the DEMATEL method is essential for streamlining, analyzing, and organizing the extensive information. Initially, the DEMATEL method employed a specific numerical value to assess the relationships among factors. However, due to the intricate interplay among elements, relying on a single numerical value proved insufficient to capture the full complexity and objectivity, marking one of its limitations. This limitation arises because the interrelation among elements transcends mere linear or nonlinear relationships, encompassing an inherent ambiguity within pairs or among multiple elements. Expert evaluations sometimes suffer from inaccuracies, introducing semantic and fuzzy elements into the information [41,42]. To address this, this paper integrates triangular fuzzy numbers with the DEMATEL method, introducing the fuzzy DEMATEL approach, aimed at enhancing the reliability of results and the precision of evaluations. The process of selecting influencing factors of open pit enterprises by the fuzzy DEMATEL model is shown in Figure 1.

Step 1: Construct an influencing factor system based on the research object and problem, set as A1, A2, A3… An.

Step 2: Use the expert scoring method with levels set as “no impact”, “slight impact”, “moderate impact”, “strong impact”, and “very strong impact” to analyze the impact factors of each factor before converting them into trigonometric function values (

Table 1. Semantic translation table.

Language Operator	No Impact	Weak Impact	Weak Influence	Micro Strong Impact	Strength Impact
Value of trigonometric function	(0, 0, 0.2)	(0, 0.2, 0.4)	(0.2, 0.4, 0.6)	(0.4, 0.6, 0.8)	(0.6, 0.8, 1)

).

Step 3: Standardize the triangular fuzzy number (assuming the triangular function value to be (r, m, l)).

Standardization of triangular fuzzy numbers:

$${\mathrm{x}\mathrm{l}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}={\displaystyle \frac{{\mathrm{l}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}-\underset{1\le \mathrm{k}\le \mathrm{K}}{\min }{\mathrm{l}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}}{{\mathsf{\Delta }}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{m}\mathrm{a}\mathrm{x}}}}$$

(15)

$${\mathrm{x}\mathrm{m}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}={\displaystyle \frac{{\mathrm{m}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}-\underset{1\le \mathrm{k}\le \mathrm{K}}{\min }{\mathrm{l}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}}{{\mathsf{\Delta }}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{m}\mathrm{a}\mathrm{x}}}}$$

(16)

$${\mathrm{x}\mathrm{r}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}={\displaystyle \frac{{\mathrm{r}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}-\underset{1\le \mathrm{k}\le \mathrm{K}}{\min }{\mathrm{l}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}}{{\mathsf{\Delta }}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{m}\mathrm{a}\mathrm{x}}}}$$

(17)

$${\mathsf{\Delta }}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{m}\mathrm{a}\mathrm{x}}=\underset{1\le \mathrm{k}\le \mathrm{K}}{\mathrm{m}\mathrm{a}\mathrm{x}}{\mathrm{r}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}-\underset{1\le \mathrm{k}\le \mathrm{K}}{\mathrm{m}\mathrm{i}\mathrm{n}}{\mathrm{l}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}$$

(18)

Normalize the values on the left and right sides:

$${\mathrm{x}\mathrm{r}\mathrm{s}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}={\displaystyle \frac{{\mathrm{x}\mathrm{r}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}}{1+{\mathrm{x}\mathrm{r}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}-{\mathrm{x}\mathrm{m}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}}}$$

(19)

$$\mathrm{xl}{\mathrm{s}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}={\displaystyle \frac{\mathrm{x}{\mathrm{m}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}}{1+\mathrm{x}{\mathrm{m}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}-\mathrm{x}{\mathrm{l}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}}}$$

(20)

Calculate the clear value after deblurring

$${\mathrm{x}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}=\left[{\mathrm{x}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}\left(1-{\mathrm{x}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}\right)+{\mathrm{x}\mathrm{r}\mathrm{s}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}\times {\mathrm{x}\mathrm{r}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}\right]/[1-{\mathrm{x}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}+{\mathrm{x}\mathrm{r}\mathrm{s}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}$$

(21)

$${\mathrm{z}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}=\mathrm{m}\mathrm{i}\mathrm{n}{\mathrm{l}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}+{\mathrm{x}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}{△}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{m}\mathrm{a}\mathrm{x}}$$

(22)

Calculate the average clarity value:

$${\mathrm{z}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{1}{\mathrm{n}}}({\mathrm{z}}_{\mathrm{i}\mathrm{j}}^1+{\mathrm{z}}_{\mathrm{i}\mathrm{j}}^2+\dots {\mathrm{z}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}})$$

(23)

Step 4: Process to obtain the standardized direct impact matrix M.

$$\underset{1\le \mathrm{i}\le \mathrm{n}}{\mathrm{P}=\mathrm{m}\mathrm{a}\mathrm{x}}{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{z}}_{\mathrm{i}\mathrm{j}}$$

(24)

M = Z/P

(25)

Step 5: Calculate the comprehensive impact matrix N, factor impact degree C value, affected degree D value, centrality D + C value, and cause degree D − C value.

$$\mathrm{N}={\displaystyle \frac{\mathrm{M}}{1-\mathrm{M}}}$$

(26)

$${\mathrm{C}}_{\mathrm{i}}={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{t}}_{\mathrm{i}\mathrm{j}},\mathrm{i}=1,2,\cdots ,\mathrm{n}$$

(27)

$${\mathrm{D}}_{\mathrm{i}}={\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{t}}_{\mathrm{i}\mathrm{j}},\mathrm{j}=1,2,\cdots ,\mathrm{n}$$

(28)

$${\mathrm{D}}_{\mathrm{i}}+{\mathrm{C}}_{\mathrm{i}}={\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{t}}_{\mathrm{i}\mathrm{j}},=1,2,\cdots ,\mathrm{n}+{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{t}}_{\mathrm{i}\mathrm{j}}\mathrm{i}=1,2,\cdots ,\mathrm{n}$$

(29)

$${\mathrm{D}}_{\mathrm{i}}-{\mathrm{C}}_{\mathrm{i}}={\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{t}}_{\mathrm{i}\mathrm{j}},\mathrm{j}=1,2,\cdots ,\mathrm{n}-{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{t}}_{\mathrm{i}\mathrm{j}},\mathrm{i}=1,2,\cdots ,\mathrm{n}$$

(30)

Step 6: Draw the centrality-cause-degree graph, the impact-influence-degree graph, and the comprehensive impact relationship graph to analyze the importance of each factor and the impact relationships between them.

Based on the calculation results of centrality and cause degree, the corresponding positions of influencing factors are found in the coordinate system, and the centrality-cause degree of key influencing factors is obtained. The horizontal axis is the centrality (D + C), and the vertical axis is the cause degree (D − C). The two lines are the average value of centrality and No. 0, respectively. Among them, the centrality D + C is the importance of the factor, and the cause degree D−C describes the relationship between factors.

If D−C > 0, it indicates that the element is not easily affected by other elements but is easily affected by other elements and is a cause factor; specifically, it can be subdivided into: ① the first quadrant: both centrality and cause degree are high; that is, the importance of the element is high and it is a cause factor; ② the second quadrant: both centrality and cause degree are low; that is, the importance of the element is low and it is a cause factor.

If D−C < 0, it indicates that the element is susceptible to the influence of other elements but not easily affected by other elements and is a result factor. Specifically, it can be subdivided into: ③ Quadrant III: both centrality and cause degree are low, indicating that the element is of low importance and is a result factor; ④ Quadrant IV: both centrality and cause degree are high, indicating that the element is of high importance and is a result factor.

Step 7: Based on the data results of impact, affectedness, centrality, and causality, indicators with small and weak impact, and select key influencing factors, should be removed.

2.2. Data Acquisition Based on Fuzzy Methods Handling

2.2.1. Preliminary Screening of Impact Factors for Comprehensive Evaluation of Open-Pit Coal Mines

Based on existing research results, in-depth interviews with experts are conducted. Considering the characteristics of the research subjects and ensuring comprehensive and complete influencing factors, an initial screening of influencing factors is performed. The influencing factors for the construction of world-class open-pit coal mines are analyzed from both internal and external perspectives (excluding uncontrollable factors such as market conditions). A total of 20 key factors are summarized and collated, identifying the main influencing factors and their meanings (

Table 2. Main influencing factors and meanings of world-class open-pit coal mine construction.

Number	Factor	Meaning
1	Technological level A1	The degree of intelligent, informational, and digital technology applied in open-pit coal mines
2	Safe production A2	The ability to prevent and control safety accidents in the production process of open-pit coal mines
3	Human Capital A3	The professional skills, knowledge, and ability of the employees at open-pit coal mines are determined by their contribution to the coal mine
4	Economic benefit A4	The level of economic income, investment efficiency, and income distribution of open-pit coal mines
5	Corporate growth ability A5	The capability of an open-pit coal mine in terms of resource allocation, technological innovation, market expansion, and management optimization
6	Social benefit A6	The value created by open-pit coal mines for society and the contribution made to social development and residents’ lives within a certain period
7	Environmental benefit A7	It refers to the degree of environmental impact caused by the exploitation of resources in open-pit coal mines and the effectiveness of the environmental protection measures taken by coal mines
8	Resource utilization A8	The utilization efficiency and degree of resource elements in the process of resource extraction in open-pit coal mines
9	Intelligent A9	It refers to the level of improving production efficiency and reducing costs by applying intelligent technology and equipment to open-pit coal mines
10	Innovation level A10	The level of innovation in management, operation, technology application, and scientific and technological level of open-pit coal mines
11	Management A11	The management level of open-pit coal mines in safety management, quality management, cost control, etc.
12	Corporate Culture A12	The vision, cultural concepts, values, spirit, and moral standards formed within the open-pit coal mine
13	Input–output A13	The corresponding output results of resource input in open-pit coal mines
14	Technological development A14	The ability to innovate, transform, and apply technology in open-pit coal mines
15	Comprehensive quality of employees A15	Refers to the professional skills, work attitude, and learning and innovation ability of employees in open-pit coal mines
16	Energy consumption A16	Material resources and energy resources consumed during the mining process of open-pit coal mines
17	Production cost A17	The total cost of the production process of the open-pit coal mine, including fixed cost and variable cost
18	Customer satisfaction A18	The degree of satisfaction of customers with the products or services provided by open-pit coal mines
19	Brand influence A19	The ability of an open-pit coal mine to use its own brand to develop and occupy the market and obtain profits
20	Business model A20	The way in which open-pit coal mines achieve operational results through their implementation of business activities

).

2.2.2. Semantic Transformation and Triangular Fuzzy Processing

Scholar Robbin proposed that the number of participants in decision-making should be controlled within a certain range, while scholar Marlin proposed that the most effective decisions are made when five-to-seven experts participate. Therefore, seven experts in the field of open-pit coal mining are invited to score the factors. The scoring criteria are set as follows: no impact = 1, weak impact = 2, relatively weak impact = 3, slightly strong impact = 4, relatively strong impact = 5. The expert scoring data are then subjected to fuzzy processing.

2.3. Analysis of Influencing Factors Based on the DEMATEL Method

According to (8), the standardized direct impact matrix M of the influencing factors of the index evaluation of open-pit coal mine enterprises is obtained as shown in

Table 3. Direct influence matrix M for standardization of influencing factors on performance evaluation of open-pit coal mine enterprises.

	A1	A2	A3	A4	A5	A6	A7	A8	A9	A10	A11	A12	A13	A14	A15	A16	A17	A18	A19	A20
A1	0	0.079	0.053	0.053	0.026	0.026	0.079	0.026	0.079	0.026	0	0	0.079	0.105	0	0.056	0.079	0	0.026	0
A2	0.053	0	0.053	0.079	0.079	0.079	0.079	0.079	0.079	0.026	0	0	0.079	0	0	0	0.079	0.079	0.053	0.026
A3	0.053	0.026	0	0	0.079	0	0	0.053	0	0.053	0.053	0.026	0.053	0	0	0	0	0	0.079	0.079
A4	0	0	0	0	0	0.026	0	0	0.053	0	0	0	0.053	0	0.053	0	0.026	0.079	0	0
A5	0	0	0	0.079	0	0	0	0.105	0.079	0	0	0	0.026	0	0	0	0.053	0.026	0	0
A6	0.026	0.079	0.026	0	0.053	0	0.053	0.079	0	0.053	0.026	0.079	0.026	0	0	0.026	0.079	0.079	0.026	0.053
A7	0	0.079	0.053	0.079	0.079	0.079	0	0.053	0	0.026	0	0.053	0.079	0.079	0	0.079	0.079	0.079	0	0.026
A8	0	0	0	0.079	0.079	0	0.026	0	0.079	0	0	0	0	0.026	0	0	0.026	0.079	0	0
A9	0.079	0.053	0.026	0.053	0.026	0	0	0.026	0	0.079	0.026	0.026	0.053	0.026	0	0	0.026	0	0.026	0
A10	0.079	0.079	0.026	0.105	0.026	0	0.026	0.053	0.079	0	0.053	0.053	0.079	0.079	0	0	0.079	0.079	0.026	0.079
A11	0.026	0.053	0.026	0.053	0.079	0.026	0	0.053	0.026	0	0	0	0.053	0	0	0	0	0	0	0
A12	0.079	0.079	0	0.053	0	0.079	0.079	0	0	0.026	0.026	0	0.053	0.026	0	0.026	0.053	0.026	0	0
A13	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.026	0	0	0.026	0	0
A14	0.079	0.079	0	0.026	0	0	0	0.026	0.026	0.053	0	0	0.079	0	0	0	0	0	0	0
A15	0.053	0	0	0.053	0	0	0	0.026	0	0	0	0	0	0.026	0	0	0	0	0	0
A16	0	0	0	0	0	0.079	0.079	0	0	0	0	0	0.026	0	0	0	0.026	0	0	0
A17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.026	0	0
A18	0	0	0	0.026	0	0	0	0	0	0	0	0	0.026	0	0	0	0	0	0	0
A19	0	0.079	0.026	0	0	0	0	0	0	0.026	0	0	0.079	0.079	0	0	0	0	0	0.053
A20	0.079	0.026	0	0	0	0	0	0	0	0.079	0.026	0.026	0.053	0.079	0	0	0	0	0.079	0

, and the comprehensive impact matrix n of the influencing factors of the index evaluation of open-pit coal mine enterprises is obtained as shown in

Table 4. Comprehensive influence matrix N of influencing factors of performance evaluation of open-pit coal mine enterprises.

	A1	A2	A3	A4	A5	A6	A7	A8	A9	A10	A11	A12	A13	A14	A15	A16	A17	A18	A19	A20
A1	0.044	0.123	0.077	0.103	0.064	0.058	0.109	0.069	0.116	0.057	0.013	0.022	0.143	0.132	0.009	0.093	0.124	0.05	0.02	0.021
A2	0.117	0.054	0.105	0.138	0.122	0.103	0.109	0.125	0.126	0.061	0.017	0.027	0.15	0.044	0.011	0.021	0.133	0.137	0.077	0.054
A3	0.084	0.064	0.019	0.044	0.105	0.016	0.021	0.084	0.038	0.075	0.063	0.038	0.102	0.036	0.006	0.011	0.033	0.032	0.095	0.095
A4	0.011	0.008	0.004	0.012	0.005	0.029	0.005	0.006	0.056	0.007	0.003	0.005	0.063	0.006	0.055	0.002	0.034	0.087	0.003	0.003
A5	0.011	0.009	0.005	0.099	0.014	0.005	0.006	0.112	0.097	0.009	0.004	0.004	0.043	0.009	0.006	0.002	0.065	0.049	0.004	0.002
A6	0.066	0.123	0.052	0.054	0.063	0.034	0.112	0.065	0.036	0.076	0.039	0.099	0.088	0.037	0.005	0.044	0.125	0.125	0.047	0.073
A7	0.042	0.12	0.072	0.13	0.114	0.111	0.04	0.118	0.043	0.029	0.013	0.097	0.14	0.101	0.011	0.091	0.127	0.135	0.02	0.045
A8	0.014	0.014	0.007	0.102	0.088	0.008	0.031	0.018	0.097	0.012	0.004	0.006	0.025	0.035	0.006	0.004	0.043	0.098	0.005	0.003
A9	0.11	0.09	0.048	0.096	0.054	0.02	0.025	0.057	0.039	0.097	0.037	0.038	0.103	0.056	0.008	0.012	0.063	0.037	0.04	0.017
A10	0.131	0.133	0.055	0.165	0.067	0.032	0.06	0.095	0.129	0.039	0.066	0.069	0.158	0.12	0.013	0.018	0.128	0.131	0.05	0.096
A11	0.043	0.068	0.038	0.082	0.099	0.037	0.015	0.077	0.055	0.013	0.006	0.007	0.08	0.012	0.006	0.006	0.025	0.029	0.01	0.009
A12	0.108	0.121	0.028	0.096	0.034	0.109	0.113	0.036	0.035	0.048	0.035	0.022	0.108	0.056	0.008	0.047	0.101	0.075	0.015	0.019
A13	0.002	0	0	0.002	0	0	0	0.001	0	0	0	0	0.001	0.001	0.026	0	0	0.027	0	0
A14	0.103	0.104	0.02	0.06	0.023	0.017	0.023	0.05	0.058	0.068	0.008	0.01	0.117	0.024	0.007	0.011	0.032	0.03	0.011	0.012
A15	0.059	0.01	0.005	0.063	0.007	0.006	0.007	0.033	0.014	0.051	0.002	0.002	0.016	0.036	0.005	0.005	0.01	0.011	0.002	0.002
A16	0.01	0.02	0.011	0.016	0.015	0.091	0.092	0.015	0.007	0.008	0.004	0.015	0.044	0.011	0.002	0.011	0.046	0.022	0.005	0.009
A17	0	0	0	0.001	0	0	0	0	0	0	0	0	0.001	0	0	0	0	0.026	0	0
A18	0	0	0	0.027	0	0.001	0	0	0.001	0	0	0	0.028	0	0.002	0	0.001	0.003	0	0
A19	0.029	0.1	0.039	0.023	0.017	0.011	0.014	0.02	0.02	0.045	0.007	0.008	0.112	0.094	0.004	0.004	0.02	0.022	0.016	0.065
A20	0.111	0.07	0.021	0.037	0.021	0.017	0.023	0.025	0.031	0.098	0.035	0.036	0.103	0.111	0.005	0.012	0.031	0.026	0.09	0.017

according to (9). Table 3 and Table 4 can be found below.

2.3.1. Data Calculation

Using Formulas (13) to (16), the impact degree (C), the degree of influence (D), the centrality (D + C), and the degree of causation (D − C) for each influencing factor are calculated, as shown in

Table 5. Calculation results of influence degree C, affected degree D, center degree D + C, and cause degree D−C of each element.

Factor	Influence Degree C Value	Influence D Value	Centrality D + C Value	Reason Degree D − C Value
Technical level A1	1.447	1.09	2.537	0.357
Safety production A2	1.728	1.227	2.956	0.501
Human capital A3	1.041	0.599	1.64	0.441
Economic benefit A4	0.405	1.346	1.75	−0.941
Enterprise growth ability A5	0.553	0.907	1.46	−0.355
Social benefit A6	1.363	0.699	2.062	0.664
Environmental benefit A7	1.599	0.802	2.401	0.797
Resource utilization A8	0.621	1	1.621	−0.379
Intelligent A9	1.042	0.997	2.039	0.046
Innovation level A10	1.757	0.746	2.503	1.01
Management A11	0.715	0.353	1.069	0.362
Corporate Culture A12	1.215	0.503	1.718	0.711
Input–output A13	0.062	1.624	1.686	−1.562
Technological development A14	0.771	0.917	1.688	−0.146
Comprehensive quality of employee A15	0.292	0.192	0.484	0.099
Energy consumption A16	0.447	0.392	0.838	0.055
Production cost A17	0.028	1.138	1.166	−1.11
Customer satisfaction A18	0.065	1.148	1.213	−1.083
Brand influence A19	0.666	0.508	1.174	0.158
Business model A20	0.913	0.539	1.452	0.374

.

Based on the values of centrality (D + C) and causality (D−C) in the table above, a centrality–causality distribution diagram for key impact factors in the construction of open-pit coal mines is created (Figure 2). The horizontal axis represents centrality (D + C), and the vertical axis represents causality (D − C). The two lines in the diagram are the average value of centrality and No. 0. Figure 3 illustrates the distribution of the impact degree–influence degree for the main impact factors in constructing world-class open-pit coal mines, while Figure 4 provides a comprehensive impact relationship diagram for key factors in the construction of world-class open-pit coal mines, reflecting the mutual relationship between impact factors. The numbers on the arrows represent the magnitude of impact, with larger numbers indicating greater impact.

2.3.2. Analysis of Influencing Factors

Based on the analysis results of fuzzy DEMATEL [19] and the obtained cause–effect degree and influence–influence degree diagrams, the influencing factors are classified into cause–effect elements and effect–result elements:

(1)

Analysis of cause-related elements:

From Table 3, it can be seen that the nine cause elements of innovation capability (A10), environmental benefits (A7), corporate culture (A12), social benefits (A6), safe production (A2), human capital (A3), business model (A20), management (A11), and technological level (A1) have a significant and high impact. The remaining four elements also affect other elements to some extent.

(2)

Analysis of result-oriented elements:

From Table 3, it can be seen that the input–output (A13), production cost (A17), customer satisfaction (A18), and economic benefits (A4) are highly influenced by other factors, while the remaining three elements are less affected.

In summary, cause-based factors are easier to control and change than outcome-based factors. Therefore, when constructing a model for influencing factors in world-class open-pit coal mines, more emphasis should be placed on cause-based factors. By adjusting these factors, we can improve and perfect the outcome-based factors.

2.3.3. Key Elements Confirmation

When determining key elements, it is necessary to consider both the degree of causation (D − C) and the degree of centrality (D + C). The former reflects the impact of the element on other elements, with the value being proportional to its influence, while the latter reflects the importance of the element, with the value being proportional to its significance. Based on these criteria, eight key influencing elements are selected: safety production (A2), technological level (A1), innovation level (A10), environmental benefits (A7), social benefits (A6), intelligence (A9), economic benefits (A4), and corporate culture (A12). To ensure the accessibility and effectiveness of each key element, according to the impact degree–influence degree and the degree of centrality–degree of causation, the Delphi method is adopted to determine the factors affecting the construction of a world-class open-pit coal mine, which are categorized into five aspects: safety, benefits, green, technology, and management. Each influencing element is allocated to one of these aspects. Furthermore, there are interactions and influences among factors within and across these aspects. The influencing elements can be represented as:

$$\mathrm{POCM}=\mathrm{f}\left({\mathrm{u}}_1,{\mathrm{u}}_2,{\mathrm{u}}_3,{\mathrm{u}}_4,{\mathrm{u}}_5\right)$$

(31)

Among them, POCM represents the construction level of world-class open-pit coal mining enterprises, where ${\mathrm{u}}_1$ represents the safety level, ${\mathrm{u}}_2$ represents the benefit level, ${\mathrm{u}}_3$ represents the green level, ${\mathrm{u}}_4$ represents the technical level, and ${\mathrm{u}}_5$ represents the management level.

3. Results

3.1. Construction of Evaluation Indicator System

3.1.1. Construction Principles

The evaluation index of open-pit coal mine enterprises aims to investigate the performance of their key work behaviors in a specific time, space, and responsibility. When constructing indicators, it is necessary to consider the connection and correlation between things so as to promote the development of enterprises for the better. The principles followed are as follows:

(1)

Scientific principle: select the most representative, practical, and scientific indicators, standardize the evaluation content, conform to the current environment and social development level, and adapt to the current business status of the enterprise.

(2)

Principle of combining qualitative and quantitative indicators: comprehensively consider qualitative and quantitative indicators, clarify the inspection content and interpretation of qualitative indicators, and ensure that the concept of quantitative indicators is clear and that the calculation formula is accurate and rigorous.

(3)

Principle of universality and comparability: the indicators should be universal and comparable, as well as suitable for horizontal comparison among enterprises and vertical change investigation of enterprises themselves.

(4)

The principle of comprehensiveness: the evaluation index system should fully consider the internal and external environment, combine the advanced enterprise data, reflect the whole process of enterprise construction, establish the target layer, and subdivide it into specific indicators.

(5)

Principle of importance: select indicators according to their impact on open-pit coal mine construction, distinguish primary and secondary, and highlight indicators that directly reflect enterprise construction.

(6)

Availability principle: considering the actual operability of the index, select the available data to replace or eliminate the index when it cannot be obtained.

3.1.2. Selection Criteria for Evaluation Indicators

Based on the fuzzy DEMATEL analysis results, as well as the cause-degree–centrality and influence-degree–being-influenced-degree diagrams obtained, the identified influencing factors are divided into cause elements and result elements. The cause elements are ranked by their influence degree from high to low as follows: Innovation Level A10, Environmental Benefits A7, Corporate Culture A12, Social Benefits A6, Safety Production A2, Human Capital A3, Business Model A20, Management A11, Technological Level A1, Brand Influence A19, Employee Comprehensive Quality A15, Energy Consumption A16, and Intelligentization A9. The result elements are arranged in descending order of their influence degree as follows: Input–Output A13, Cost Management A17, Customer Loyalty A18, Benefit Level A4, Resource Utilization A8, Corporate Growth Capability A5, and Technological Development Capability A14. Additionally, based on the centrality analysis, the importance of each element is determined, and the ranking is as follows. The centrality analysis reveals the importance of elements in the following order: Safety Production A2, Technological Level A1, Innovation Level A10, Environmental Benefits A7, Social Benefits A6, Intelligentization A9, Economic Benefits A4, Corporate Culture A12, Technological Development A14, Input–Output A13, Human Capital A3, Resource Utilization A8, Corporate Growth Capability A5, Business Model A20, Customer Satisfaction A18, Brand Influence A19, Production Cost A17, Management A11, Energy Consumption A16, and Employee Comprehensive Quality A15. By observing the distribution of corresponding indicators, it can be seen that the importance of the following eight elements is higher than the others: Safety Production A2, Technological Level A1, Innovation Level A10, Environmental Benefits A7, Social Benefits A6, Intelligentization A9, Economic Benefits A4, and Corporate Culture A12. Therefore, they are considered key elements affecting the performance evaluation of open-pit coal companies.

3.1.3. Selection of Evaluation Index

Based on the existing research results, combined with the relevant literature at home and abroad, the key elements are extracted, and the centrality, cause degree, influence degree, and affected degree of the elements are analyzed by fuzzy DEMATEL and other methods. And the obtained influencing factors are divided into cause indicators and result indicators. Finally, six influencing factors of open-pit coal mine enterprise performance evaluation are determined.

Pocm = f (U1, U2, U3, U4, U5)

Pocm represents the performance of open-pit coal mine enterprises, U1 is the safety level, U2 is the benefit level, U3 is the green level, U4 is the technical level, and U5 is the management level.

3.2. Description of Evaluation Indicators

The world-class open-pit coal mine evaluation index system encompasses 5 target layers, which are safety, benefits, green, technology, and management; 15 criteria layers, including safety management, accident loss, occupational health, profitability, value maintenance and appreciation, and per capita income; and 41 indicator layers, including safety production standardization level, death rate per million tons, injury rate per thousand people, proportion of direct economic loss from accidents to total assets, incidence rate of occupational diseases, and unit total cost (

Table 6. World-class open-pit coal mine construction indicator system.

Target Layer	Criterion Layer	Indicator Layer	Unit	Meaning
Security	Security management	Safety Production Standardization Level	-	Overall safety production conditions of open-pit coal mine
	Accident losses	Million-ton death rate	%	Death toll due to accidents per million tons of raw coal produced by open-pit coal mines
		Injury rate of 1000 people	‰	The number of injured personnel due to accidents among every thousand employees in the open-pit coal mine
		The proportion of direct economic loss of accident to total assets	‰	The value of personal injury, property loss caused by the accident of the open-pit coal mine, and the proportion of the expenses generated in the aftermath in the total assets of the enterprise
	Occupational health	Occupational disease incidence rate	%	The proportion of new cases of occupational diseases among the on-the-job employees in the open-pit coal mine during the reporting period
Benefit	Profitability	Unit total cost	Yuan/ton	The cost of direct materials, direct labor, and manufacturing expenses per unit of production for open-pit coal mining enterprises
		Return on net assets	%	Efficiency of open-pit coal mine enterprises in gaining net profit using their own capital
		Cost–expense profit ratio	%	The profit value that the open-pit coal mine enterprise can obtain by paying the unit cost
		Operating income margin	%	The ability of open-pit coal mine enterprises to make profits in operating activities
	Value preservation and appreciation	Total assets growth rate	%	Growth of asset size of open-pit coal mining enterprises in this period
		The rate of the maintenance and appreciation of state-owned capital	%	The ratio of the owner’s equity of the open-pit coal mining enterprise after deducting objective factors at the end of the period to the owner’s equity at the beginning of the period
	Per-capita income	Per-capita salary	times	Per-capita income level of open-pit coal mining enterprises
		Per-capita profit	10,000 yuan/person	The ratio of the total profit of the open-pit coal mining enterprise to the total number of employees in a certain period
Green	resource utilization	Industrial sewage treatment and recycling rate	%	The proportion of industrial sewage treatment capacity of open-pit coal mines to total sewage discharge; the proportion of recycled sewage to total discharged sewage
		Comprehensive resource recovery rate of coal seam	%	Percentage of actual coal extraction to design coal extraction in open-pit coal mine
		Waste utilization rate	%	The utilization degree of gangue in open-pit mining technology of open-pit coal mine
		Raw coal washing rate	%	Percentage of annual output of coal preparation plant to annual production of original coal mine
	Energy conservation and emission reduction	Reclamation rate	%	The ratio of the amount of land being utilized to the amount of land being occupied and destroyed by mines
		Comprehensive energy consumption per ton of raw coal production	kg of standard coal per ton	The comprehensive energy consumption of various resources consumed by the production unit in the process of stripping and mining, converted into standard coal
		Total water resource consumption per unit of extraction and extraction	m3/t	The water consumption required for coal extraction ton
		Land resource occupation of unit stripping and extraction Total amount of	h2/Mt	Land resources occupied for coal extraction of millions of tons
		carbon emission intensity	Tons/10,000 yuan	CO2 emissions increase with the growth of the total output value of unit coal mine
		utilization rate of dewatering water	%	The proportion of the volume of dewatering water utilization to the total volume of mine drainage in the same period
		Compliance with standards for the discharge of three wastes	branch	Whether the emissions of waste gas, wastewater, and waste slag generated during the production process of coal products meet the national or local emission standards
technology	Intelligence	Level of intelligence level	branch	Intelligent construction situation of the production process in the open-pit coal mine
	production efficiency	Raw coal efficiency	T/workday	The ratio of raw coal output to working hours completed by raw coal production workers in a certain period
	Technical progressiveness	Proportion of advanced production capacity	%	The proportion of advanced open-pit coal mines in the total production capacity
		Level of technology and equipment	branch	the advanced level of technology and equipment used in open-pit coal mining
		Number of international leading technologies	term	The number of international leading technologies used by open-pit coal mining enterprises
Administration	Innovate	Intensity of R&D investment	%	Percentage of operating revenue invested in research and development by open-pit coal mining enterprises
		Total number of patents and standards	Item/thousand people/year	The total number of patents and standards obtained by open-pit coal mining enterprises in a certain period
		Number of science and technology awards at or above the group company level	Item/thousand people/year	Output level of scientific and technological achievements of open-pit coal mining enterprises
	Quality	Qualification rate of coal quality	%	The ratio of the quantity of coal produced that meets the specified standards or contract requirements to the total quantity of coal produced
		Customer satisfaction	-	The matching degree between the customer’s expectations for the services provided by the enterprise and the customer’s experience of the relevant behaviors given by the enterprise.
	Brand	Production ranking	-	Ranking of production capacity of open-pit coal mines
		Brand recognition	-	Brand influence of open-pit coal mine
	Personnel	Proportion of excellent talents	%	the rate of excellent talents in open-pit coal mining enterprises
		Proportion of bachelor’s degree or above	%	Cultural degree of employees in open-pit coal mine enterprises
		Proportion of personnel with intermediate and above professional titles	%	Quality Level of Open-pit Coal Mine Employees
		Technician proportion	%	Professional level of employees in open-pit coal mines
		Training hours per person	Class hour/person/year	The importance attached to talent cultivation in open-pit coal mines

).

4. Conclusions

The study of performance evaluation in open-pit coal companies is a complex research area involving a wide range of factors and posing significant challenges. Based on stakeholder theory, fuzzy theory, and other foundations, this paper organizes and summarizes the current research status and results, identifies issues in current research, and analyzes the meaning of performance evaluation factors in open-pit coal companies and the relationships between these factors. The main research conclusions are as follows.

(1)

This paper combines triangular fuzzy numbers with the DEMATEL method to propose an integrated fuzzy DEMATEL approach. Using this method, we analyzed the centrality, cause degree, influence degree, and being-influenced degree of performance evaluation factors in open-pit coal companies. The influencing factors obtained were then categorized into causal indicators and result indicators, ultimately identifying six key elements affecting the performance evaluation of open-pit coal companies: safety, efficiency, green, management, technology, and party building.

(2)

Based on the fuzzy DEMATEL analysis results, as well as the cause-degree–centrality and influence-degree–being-influenced-degree diagrams obtained, the identified influencing factors are divided into cause elements and result elements. The cause elements are ranked by their influence degree from high to low as follows: Innovation Level A10, Environmental Benefits A7, Corporate Culture A12, Social Benefits A6, Safety Production A2, Human Capital A3, Business Model A20, Management A11, Technological Level A1, Brand Influence A19, Employee Comprehensive Quality A15, Energy Consumption A16, and Intelligentization A9. The result elements are arranged in descending order of their influence degree as follows: Input–Output A13, Cost Management A17, Customer Loyalty A18, Benefit Level A4, Resource Utilization A8, Corporate Growth Capability A5, and Technological Development Capability A14.

(3)

The centrality analysis reveals the importance of elements in the following order: Safety Production A2, Technological Level A1, Innovation Level A10, Environmental Benefits A7, Social Benefits A6, Intelligentization A9, Economic Benefits A4, Corporate Culture A12, Technological Development A14, Input–Output A13, Human Capital A3, Resource Utilization A8, Corporate Growth Capability A5, Business Model A20, Customer Satisfaction A18, Brand Influence A19, Production Cost A17, Management A11, Energy Consumption A16, and Employee Comprehensive Quality A15. By observing the distribution of corresponding indicators, it can be seen that the importance of the following eight elements is higher than the others: Safety Production A2, Technological Level A1, Innovation Level A10, Environmental Benefits A7, Social Benefits A6, Intelligentization A9, Economic Benefits A4, and Corporate Culture A12. Therefore, they are considered key elements affecting the performance evaluation of open-pit coal companies.

In summary, the main contributions of this paper include the following three points. First, this paper combines triangular fuzzy numbers with the DEMATEL method to comprehensively propose the fuzzy DEMATEL method, which has identified the factors affecting the performance of coal companies. Second, based on the analysis results of fuzzy DEMATEL, as well as the cause-degree–centrality and influence-degree–being-influenced-degree diagrams obtained, the identified influencing factors are categorized into cause elements and result elements. Third, by classifying the influencing factors, six key elements affecting the performance evaluation of open-pit coal companies have been ultimately determined: safety, efficiency, green, management, technology, and party building. However, the performance evaluation of open-pit coal companies involves a multitude of influencing factors, and the concepts and technologies in its construction process are continuously being perfected with the development of society. Therefore, the performance evaluation of open-pit coal companies is a constantly changing process that needs to evolve with changes in objective conditions, requiring timely updates and supplementation of indicators. Moreover, the coal industry is gradually achieving the intelligent monitoring of mine safety, which effectively improves the efficiency and safety of mining, providing new opportunities for the transformation and upgrading of coal companies. By introducing advanced information technology and automated equipment, coal companies can reduce their reliance on human resources, decrease labor intensity and risks, and enhance the controllability and stability of the production process. The construction of intelligent mines not only helps to improve the overall competitiveness of the coal industry but also promotes its sustainable development, bringing long-term economic and social benefits to companies. In addition, intelligent transformation can also help companies better meet environmental requirements and social responsibilities, achieving efficient resource utilization and minimizing environmental impact through precise monitoring and data analysis.