A Method for the Transformation of Abandoned Coal Mine Clusters and the Coordination Planning of Cultural Tourism Resources

Abstract

To promote the economic revitalization and cultural prosperity of abandoned coal mine clusters and facilitate regional sustainable development, this study involved the construction of a ranking system for coal mines suitable for cultural tourism transformation and a regional cultural tourism resource coordination planning framework. The research findings and innovations are as follows: (1) Through a combination of subjective judgment and quantitative analysis, an AHP–entropy–TOPSIS evaluation model for the transformation of abandoned coal mine clusters was developed. This model significantly enhances the scientific and precise nature of the decision-making process. (2) By integrating cultural tourism land use indicators, a ranking system for the suitability of coal mines for transformation into cultural tourism destinations was established, enabling the identification of the most suitable coal mines for transformation. (3) The most suitable coal mines for transformation were then integrated with regional historical cultural resources. An innovative application of circuit theory was used to optimize the regional road network, while a kernel density analysis was employed to perform the functional zoning of the study area. This resulted in a comprehensive regional cultural tourism resource coordination planning framework. This study offers a valuable reference for transforming abandoned coal mines and integrating cultural tourism, contributing to regional sustainable development.

1. Introduction

China ranks fourth globally in coal reserves, with its production and consumption accounting for approximately 50% of the total global figures [1]. In 2023, China’s raw coal production reached 4.71 billion tons, employing approximately 2.85 million people. Coal consumption accounted for 55.3% of the total energy consumption, maintaining a dominant position in China’s energy consumption structure. However, with the global shift towards a green economy and the gradual depletion of coal resources, an increasing number of coal mines are facing closure or the suspension of operations. Since the 1990s, the number of coal mines in China has significantly decreased from approximately 100,000 to the current figure of around 4300. It is projected that, by 2030, approximately 15,000 coal mines will be closed or abandoned in China [2,3]. Taking the western Beijing area as an example, there were over 1300 coal mines in the late 1990s, primarily underground mines, with an annual coal output reaching 10 million tons. Today, all of these mines have ceased operations and closed. These abandoned coal mines not only waste land resources but also potentially lead to economic losses and hinder the development of the surrounding economy [4,5]. Consequently, the transformation and development of coal mining regions has become an important issue for regional sustainable development [3].

Meanwhile, cultural tourism, as a significant economic sector, demonstrates remarkable growth potential and development prospects. If the integration of abandoned coal mines with cultural tourism is achieved, it will breathe new life into these industrial heritage sites [6]. This fusion not only combines traditional industrial techniques with modern cultural tourism experiences to attract global visitors, but it also holds the potential to become a powerful engine for economic revival and sustainable development [7].

Through the exploration and assessment of industrial heritage tourism resources, scholars have examined how to convert industrial sites into cultural tourism resources. In the case of coal mine transformation, the primary pathways focus on converting these sites into thematic museums [8,9], heritage parks [10], creative industry parks [11], leisure resorts [12,13], film studios, educational bases, and themed hotels [14], among other cultural projects. The transformation of coal mines into cultural tourism hubs has led to the emergence of numerous successful models. For instance, Sawahlunto in Indonesia, once a coal mining town, has successfully reinvented itself as a cultural tourism destination. It has become a renowned heritage tourism city and was inscribed as a UNESCO World Heritage site in 2019 [15]. Similarly, Italy’s Serbariu coal mine, following its transformation into a cultural tourism site, has attracted nearly 220,000 visitors and successfully converted industrial heritage into a unique local resource [6]. The Wendel Mine Complex, located in Europe, stands as one of the largest and most intact coal mining complexes on the continent. Its rich historical legacy and diverse cultural activities have made it a model of successful transformation from coal mining to cultural tourism [16].

Although there has been some progress in research on the transformation of coal mines into cultural tourism destinations, several issues remain that require further investigation: (1) The lack of a scientific selection mechanism: Not all coal mines are suitable for transformation into cultural tourism industries [7]. Currently, there is no systematic research to guide the precise screening of coal mines for transformation. The existing literature tends to focus on successful case studies and lacks unified evaluation standards, selection models, and quantitative indicators. The scientific selection of coal mines is essentially a multi-criteria decision-making process. This study proposes an AHP (analytic hierarchy process)–entropy–TOPSIS (a technique for order preference by similarity to the ideal solution) multi-criteria decision-making method for evaluating the transformation of abandoned coal mine clusters. Compared to decision-making methods such as VIKOR [17], PROMETHEE [18], and the rank sum ratio method [19], this new evaluation approach effectively combines subjective expert judgment with objective data from indicator assessments. It focuses on selecting the option closest to the ideal solution and quantifies the quality of the alternatives through distance measurements. (2) Insufficient pathways for the deep integration of coal mines and cultural tourism resources: The key issues are as follows: First, there has been limited research on the optimization of road networks between coal mines and cultural tourism resources. Currently, road network optimization primarily relies on qualitative research, typically analyzing factors such as the ecological environment, construction technology, cost, and noise in newly developed road areas [20]. However, quantitative research in this field remains relatively scarce. Second, there is a lack of effective integration between the two. The study area contains rich and distinctive cultural resources, but tourists often need several days to fully experience and visit the sites. However, the insufficient availability of dining and accommodation facilities around these cultural resources fails to meet basic needs, potentially causing disruptions to tourist itineraries. Therefore, rationally utilizing coal mine resources as supply stations and distribution centers for cultural tourism would not only alleviate this issue but also promote the deep integration of resources, enhancing the overall tourism experience. Based on the aforementioned issues, this study aimed to (1) develop a ranking system for coal mines suitable for a cultural tourism transformation, enabling the scientific selection of mines for transformation, and (2) promote the deep integration and coordinated development of transformed coal mines with regional cultural resources, achieving dual prosperity in both the economy and culture.

The main contributions of this study are as follows: (1) A comprehensive framework for the transformation of abandoned coal mine clusters and the coordination planning of cultural tourism resources was systematically constructed. This framework consists of two parts: a ranking system for coal mines suitable for a cultural tourism transformation and a regional cultural tourism resource coordination planning framework. (2) An innovative AHP–entropy–TOPSIS evaluation model for the transformation of abandoned coal mine clusters was developed. This model combines subjective judgment, a quantitative analysis, and a ranking ability, forming a comprehensive, objective, and efficient system for selecting coal mines for transformation. (3) Circuit theory was innovatively applied to the coordination planning of cultural tourism resources, optimizing the regional road network. (4) Through a kernel density analysis of historical cultural resources, functional zones within the study area were precisely delineated, enabling the scientific planning of spatial layouts and promoting the deep integration and optimization of cultural resources. These innovations provide a scientific path for exploring the transformation of abandoned coal mines and the integration of cultural tourism industries, laying a solid theoretical foundation and providing practical guidance for research in related fields.

2. Materials and Methods

2.1. Study Area Overview

Beijing’s Western Region is located in the southwestern part of Beijing (115°25′01″–116°14′54″ E, 39°30′22″–40°10′39″ N), encompassing the Mentougou and Fangshan districts. It stretches approximately 71.05 km east–west and 74.58 km north–south, covering a total area of 3437.35 km². The region is characterized by a high northwest and a low southeast terrain, surrounded by mountains on three sides, which has resulted in a unique natural landscape and ecological environment. Due to its complex topography and sparse population, the transportation infrastructure is underdeveloped. Beijing’s Western Region is rich in cultural heritage and tourism resources, featuring both magnificent natural scenery and numerous historical sites and cultural assets. Historically, it has been an important coal mining base for Beijing. However, with economic development and industrial upgrading, many of its coal mines have gradually ceased operations, and the abandoned mine clusters urgently require transformation and sustainable development. This study focused on eight state-owned coal mines, including the Da’anshan Coal Mine and the Qianjuntai Coal Mine, as the research subjects and delved into the pathways for the transformation of abandoned coal mine clusters and the coordination planning of regional cultural tourism resources (Figure 1).

2.2. Study Data

The data used in this study are detailed in

Table 1. Details of the data.

Data Types	Data Time	Resolution	Data Sources
Land use/land cover	2020	30 m	Remote sensing mapping of global land cover (www.globeland30.org (accessed on 2 December 2022))
Road	2023	-	OpenStreetMap (https://openmaptiles.org/ (accessed on 2 March 2023))
Spatial Distribution Data of Geological Hazard Points	2019	-	Resource and Environmental Science Data Platform of China (www.resdc.cn (accessed on 20 March 2024))
Distribution Data of Heavy Metals in Soil	-	30 m	Geographic remote sensing ecological network platform (www.gisrs.cn (accessed on 4 December 2022))
Normalized Difference Vegetation Index (NDVI)	2020	30 m	National Ecosystem Science Data Center, National Science & Technology Infrastructure of China (www.nesdc.org.cn (accessed on 24 May 2023))

and were sourced from authoritative institutions in China and internationally, ensuring their reliability and accuracy.

2.3. Research Methodology

This study developed a ranking system for coal mines suitable for cultural tourism transformations and a regional cultural tourism resource coordination planning framework. The ranking system was used to select the most suitable coal mines for transformation, while circuit theory was applied to optimize the road network. Additionally, a kernel density analysis was used to delineate the functional zoning of the region, thereby achieving the scientific coordination and planning of regional cultural tourism resources (Figure 2).

2.3.1. Evaluation of Cultural Tourism Land Indicators

To ensure the scientific accuracy and precision of the land use indicator evaluation, this study developed a comprehensive evaluation framework consisting of four primary indicators and eleven secondary indicators, based on an extensive review and analysis of the relevant literature (

Table 2. Evaluation of cultural tourism land indicators.

Primary Indicator	Secondary Indicator	Graded Indicators
		Highly Suitable	Moderately Suitable	Marginally Suitable	Unsuitable
Comprehensive Development Advantages	Commercial Agglomeration Degree	[45, +∞)	[30, 45)	[15, 30)	[0, 15)
	Ecological Environment	[0.80, 1]	[0.63, 0.80)	[0.40, 0.63)	[0, 0.40)
	Geographical Advantage in Resource Planning	Excellent	Good	Medium	Poor
	Number of Surrounding Cultural Resources	[17, +∞)	[14, 17)	[11, 14)	[0, 11)
Traffic Accessibility	Public Transport Accessibility	(4, +∞)	[3, 4]	[1, 2]	0
	Road Network Density	[7.99, 34.67]	[4.27, 7.99)	[1.51, 4.27)	[0, 1.51)
	Influence Range of the Menda Railway Line	Mineral Areas along the Route	-	-	Non-Route Mineral Areas
Geological Hazards and Soil Pollution	Geological Hazards	[1000, +∞)	[500, 1000)	[300, 500)	[0, 300)
	Soil Heavy Metal Contamination Level	[0, 59.21)	[59.21, 78.82)	[78.82, 92.58)	[92.58, 129.69]
Current State of the Mining Area	Historical Cultural Value of Buildings	Excellent	Good	Medium	Poor
	Suitability of Office Buildings	Excellent	Good	Medium	Poor

Notes: calculation methods for secondary indicators: (1) The “commercial aggregation degree” [21] was based on the number of commercial POIs within a 1 km radius of the factory area. (2) The “ecological environment” is represented by the “NDVI.” (3) The “geographical advantages in resource planning” were evaluated by experts. (4) The “number of surrounding cultural resources” was determined by the number of historical cultural resources within a 15 km radius of the factory area. (5) The “public transport accessibility” [22,23] was based on the number of bus routes within a 1 km radius of the factory area. (6) The “road network density” [24] was calculated as the ratio of road length to area within a 1 km radius of the factory area, with the unit in km/km². (7) The “influence range of the Menda railway line” was determined by the distance between the factory area and the railway line. (8) “Geological hazards” [25] were calculated based on the buffer zone distance of geological hazard points. (9) The “soil heavy metal contamination level” [26,27] was calculated using GIS data on soil heavy metal contamination and a grading method. (10) The “historical cultural value of buildings” was assessed by experts. (11) The “suitability of office buildings” was assessed by experts.

). For quantification purposes, a grading scale was applied, with suitability scores assigned as follows: 7 points (highly suitable), 5 points (moderately suitable), 3 points (marginally suitable), and 1 point (unsuitable).

2.3.2. AHP–Entropy–TOPSIS Evaluation Model for the Transformation of Abandoned Coal Mine Clusters

The steps of the evaluation model are as follows: (1) The final weights of the secondary indicators are determined using a combination of the AHP and the entropy weight method. (2) The TOPSIS method is employed to rank the evaluation objects based on their superiority and inferiority.

The AHP method, which has been well established for weight determination, is briefly outlined in its specific steps. During the process of determining the weights, we invited five senior experts from relevant research fields to collaboratively discuss and finalize the evaluation matrix. This matrix was subsequently subjected to a consistency test, which validated the professionalism and accuracy of the evaluation results.

The following section focuses on the specific steps of the entropy weight method (Equations (1)–(8)), the combined weighting method (Equations (9)–(10)) [28], and TOPSIS (Equations (11)–(15)) [29,30,31]:

(1)

Entropy Weight Method

An original data evaluation matrix X is established, where there are m evaluation indicators and n evaluation objects:

$$X=\left[\begin{array}{cc}\begin{array}{c}{x}_{11}\\ x_{21}\end{array}& \begin{array}{ccc}\begin{array}{c}{x}_{12}\\ x_{22}\end{array}& \begin{array}{c}\cdots \\ \cdots \end{array}& \begin{array}{c}{x}_{1m}\\ x_{2m}\end{array}\end{array}\\ \begin{array}{c}⋮\\ x_{n1}\end{array}& \begin{array}{ccc}\begin{array}{c}⋮\\ x_{n2}\end{array}& \begin{array}{c}\ddots \\ \cdots \end{array}& \begin{array}{c}⋮\\ x_{nm}\end{array}\end{array}\end{array}\right]$$

(1)

where x_ij (i = 1, 2…, n; j = 1, 2…, m) denotes the value of the i-th evaluation objects in the j-th indicator.

For positive indicators, the normalization formula is as follows:

$$z_{ij}={\displaystyle \frac{x_{ij}-\min x_{ij}}{\max x_{ij}-\min x_{ij}}}$$

(2)

For inverse indicators, the normalization formula is as follows:

$$z_{ij}={\displaystyle \frac{\max x_{ij}-x_{ij}}{\max x_{ij}-\min x_{ij}}}$$

(3)

Now, the normalized matrix can be expressed as Z:

$$Z=\left[\begin{array}{cc}\begin{array}{c}{z}_{11}\\ z_{21}\end{array}& \begin{array}{ccc}\begin{array}{c}{z}_{12}\\ z_{22}\end{array}& \begin{array}{c}\cdots \\ \cdots \end{array}& \begin{array}{c}{z}_{1m}\\ z_{2m}\end{array}\end{array}\\ \begin{array}{c}⋮\\ z_{n1}\end{array}& \begin{array}{ccc}\begin{array}{c}⋮\\ z_{n2}\end{array}& \begin{array}{c}\ddots \\ \cdots \end{array}& \begin{array}{c}⋮\\ z_{nm}\end{array}\end{array}\end{array}\right]$$

(4)

The proportion P_ij of the i-th evaluation object in the j-th indicator z_ij is calculated:

$$P_{ij}=z_{ij}/\sum _{i=1}^n{z}_{ij},(j=\mathrm{1,2},\cdots ,m)$$

(5)

The entropy value E_j of the j-th indicator is calculated:

$$E_j=-{\displaystyle \frac{1}{\ln n}}\sum _{i=1}^n{P}_{ij}\ln P_{ij},(j=\mathrm{1,2},\cdots ,m)$$

(6)

The difference coefficient G_j of the j-th indicator is calculated:

$$G_j=1-E_j$$

(7)

The weight β_j of the j-th indicator is calculated:

$${\beta }_j=G_j/\sum _{j=1}^m{G}_j$$

(8)

(2)

Combined Weighting Method

Let γ_j and δ_j represent the importance coefficients of the subjective weight and objective weight, respectively, and let α_j and β_j denote the subjective weight from the AHP and the objective weight from the entropy weight method, respectively.

$$\left\{\begin{array}{c}{\gamma }_j={\alpha }_j/\left({\alpha }_j+{\beta }_j\right)\\ {\delta }_j={\beta }_j/\left({\alpha }_j+{\beta }_j\right)\end{array}\right.$$

(9)

The combined weight W_j is calculated:

$$W_j={\displaystyle \frac{{\alpha }_j{\gamma }_j+{\beta }_j{\delta }_j}{{\sum }_{j=1}^m\left({\alpha }_j{\gamma }_j+{\beta }_j{\delta }_j\right)}}$$

(10)

(3)

TOPSIS

The positive ideal solution Z⁺, which is constituted by the maximum value of each column of matrix Z, can be obtained as follows:

$$Z^{+}=\left(Z_1^{+}, Z_2^{+},\cdots ,Z_m^{+}\right)=(max\left\{z_{11},z_{21},\cdots ,z_{n1}\right\},max\left\{z_{12},z_{22},\cdots ,z_{n2}\right\},\cdots ,max\left\{z_{1m},z_{2m},\cdots ,z_{nm}\right\})$$

(11)

Similarly, the negative ideal solution Z⁻, which is constituted by the minimum value of each column of matrix Z, can be obtained as follows:

$$Z^{-}=\left(Z_1^{-}, Z_2^{-},\cdots ,Z_m^{-}\right)=(min\left\{z_{11},z_{21},\cdots ,z_{n1}\right\},min\left\{z_{12},z_{22},\cdots ,z_{n2}\right\},\cdots ,min\left\{z_{1m},z_{2m},\cdots ,z_{nm}\right\})$$

(12)

Then, the Euclidean distances can be calculated from the evaluation object to the positive ideal solution and negative ideal solution using the following equations:

$$D_i^{+}=\sqrt{\sum _{j=1}^m{W_j({\mathrm{z}}_{ij}-{\mathrm{Z}}_j^{+})}^2},(j=\mathrm{1,2},\cdots ,m)$$

(13)

The Euclidean distance of the negative ideal solution is as follows:

$$D_i^{-}=\sqrt{\sum _{j=1}^m{W_j({\mathrm{z}}_{ij}-{\mathrm{Z}}_j^{-})}^2},(j=\mathrm{1,2},\cdots ,m)$$

(14)

The composite score index of the evaluation target can be determined as follows:

$$C_i={\displaystyle \frac{D_i^{-}}{D_i^{+}+D_i^{-}}}$$

(15)

2.3.3. Application of Circuit Theory to Optimize the Road Network

Circuit theory, originally developed in physics, was first applied to landscape ecology and genetics by McRae (2006) [32] to model species migration patterns in landscapes [33]. This study is the first to apply circuit theory to road network optimization, aiming to coordinate regional historical cultural resources and promote the efficient integration and allocation of these resources (Figure 3).

(1)

Source Identification

The historical cultural resources, along with coal mines suitable for transformation into cultural tourism, are organized using a 500 m radius as the electric circuit node for their placement.

(2)

Creation of Free-Flow Surface

The “free-flow surface” is used to quantify the smoothness of traffic flow between historical cultural resources and coal mines. Resistance values are assigned based on the ease of passage, with regions of higher traffic barriers having greater resistance. The free-flow surface is derived by equally overlaying habitat quality and road network raster data.

Due to space constraints, the detailed calculation process for the habitat quality model was omitted; refer to the relevant literature for more details [34,35,36], with data available in

Table A1. Threat factor parameter settings.

Threat	Weight	Maximum Distance	Decay Type
Arable land	0.7	6	Linear
Construction land	1	11	Exponential
Railway	0.9	9	Exponential
Highway	1	10	Exponential
Main road	0.9	8	Linear
Secondary road	0.8	5	Linear
Branch road	0.7	3	Exponential

and

Table A2. Sensitivity score of habitat types to threat factors.

Land Use Types	Habitat	Arable Land	Construction Land	Railway	Highway	Main Road	Secondary Road	Branch Road
Arable land	0.5	0.3	0.5	0.1	0.25	0.28	0.22	0.16
Forest land	1	0.4	0.6	0.1	0.1	0.12	0.18	0.24
Grassland	0.55	0.35	0.6	0.25	0.25	0.28	0.29	0.3
Wetland	0.8	0.4	0.7	0.3	0.3	0.3	0.28	0.25
Waterbody	0.9	0.5	0.8	0.4	0.4	0.35	0.3	0.25
Construction land	0	0.5	0.8	0.3	0.3	0.32	0.31	0.3

. The road network raster data were created on the ArcGIS Pro 3.0.0 platform, with the road raster value set to 0 and all other areas assigned a value of 1.

(3)

Circuit Theory Calculation

This study employed the Linkage Mapper 3.1.0 software (https://www.circuitscape.org/ (accessed on 5 June 2023)) for the circuit theory analysis [37,38,39] and generated the least-cost path on the resistance surface. This path represents the optimal solution for the road network, ensuring seamless connectivity between historical cultural resource sites while simultaneously achieving the lowest road construction costs.

2.3.4. Application of Kernel Density Analysis to Determine Functional Zoning

The ArcGIS Kernel Density Analysis tool was used to analyze historical cultural resources, and functional zones were precisely delineated based on the results.

3. Results

3.1. Results of the Weights for Cultural Tourism Land Use Indicators

The evaluation scores for cultural tourism land use can be found in

Table A3. Evaluation matrix for suitability of coal mines for cultural tourism land use.

Primary Indicator	Secondary Indicator	A	B	C	D	E	F	G	H
Comprehensive Development Advantages	Commercial Agglomeration Degree	1	1	1	1	5	7	3	3
	Ecological Environment	5	7	5	5	3	3	3	3
	Geographical Advantage in Resource Planning	7	7	5	5	1	1	3	5
	Number of Surrounding Cultural Resources	5	5	3	5	3	3	7	7
Traffic Accessibility	Public Transport Accessibility	5	3	3	3	7	7	7	3
	Road Network Density	3	1	1	3	3	5	5	3
	Influence Range of the Menda Railway Line	1	7	7	7	1	1	1	1
Geological Hazards and Soil Pollution	Geological Hazards	7	5	5	7	7	7	7	7
	Soil Heavy Metal Contamination Level	3	3	3	3	1	1	3	5
Current State of the Mining Area	Historical Cultural Value of Buildings	7	3	3	5	1	7	7	3
	Suitability of Office Buildings	7	7	7	7	1	1	1	7

Note: A: Da’anshan Coal Mine; B: Qianjuntai Coal Mine; C: Muchengjian Coal Mine; D: Datai Coal Mine; E: Yangtuo Coal Mine; F: Mentougou Coal Mine; G: Fangshan Coal Mine; H: Changgouyu Coal Mine.

, while the AHP, entropy weight method, and final combined weight results for the secondary indicators are presented in

Table 3. Weights of cultural tourism land use indicators.

Secondary Indicator	AHP Weights	Entropy Weights	Final Combined Weights
Commercial Agglomeration Degree	0.0076	0.1381	0.0996
Ecological Environment	0.0152	0.1286	0.0885
Geographical Advantage in Resource Planning	0.0511	0.0588	0.0419
Number of Surrounding Cultural Resources	0.0267	0.0911	0.0581
Public Transport Accessibility	0.0289	0.1252	0.0813
Road Network Density	0.0712	0.0596	0.0500
Influence Range of the Menda Railway Line	0.1755	0.1688	0.1306
Geological Hazards	0.1176	0.0495	0.0739
Soil Heavy Metal Contamination Level	0.0235	0.0571	0.0359
Historical Cultural Value of Buildings	0.4022	0.0423	0.2791
Suitability of Office Buildings	0.0804	0.0809	0.0612

. The results indicate that the “historical cultural value of buildings” held the highest combined weight of 0.2791, highlighting the significant role that the historical cultural value of coal mine buildings and structures play in the process of cultural tourism transformations.

3.2. TOPSIS Evaluation and Coal Mine Selection Results

The TOPSIS ranking results are presented in

Table 4. Ranking of coal mines suitable for cultural tourism land transformation.

Coal Mine Name	Distance to Positive Ideal Solution (D+)	Distance to Negative Ideal Solution (D−)	Comprehensive Score Index	Ranking
Da’anshan Coal Mine	0.55556262	0.73109896	0.56821387	1
Qianjuntai Coal Mine	0.67256313	0.6137112	0.47712311	5
Muchengjian Coal Mine	0.72294851	0.52203339	0.41931002	7
Datai Coal Mine	0.52406975	0.68299845	0.56583253	2
Yangtuo Coal Mine	0.84780021	0.46039805	0.35193293	8
Mentougou Coal Mine	0.64503226	0.76415534	0.54226658	4
Fangshan Coal Mine	0.59338979	0.75305266	0.55929064	3
Changgouyu Coal Mine	0.69704369	0.54972219	0.44091854	6

. The findings indicate that the three coal mines most suitable for transformation into the cultural tourism industry are the Da’anshan Coal Mine, Datai Coal Mine, and Fangshan Coal Mine.

3.3. Road Network System Optimization Results

By overlaying habitat quality and road network raster data with an equal weight, the distribution of the free-flowing surface resistance values was obtained (Figure 4a). The results indicate that high resistance values are primarily concentrated in the high mountain forest areas, located outside the southeastern region, and exhibit a clustered distribution. In contrast, low resistance values are mainly found in areas covered by the existing road network, displaying a grid-like pattern.

Through circuit theory calculations, the least cost-path on the resistance surface was plotted (Figure 4b), representing the optimal solution for road network optimization. By overlaying the existing road network onto this solution, the number and locations of new roads required can be clearly visualized, thereby facilitating the optimization and upgrading of the existing road network (Figure 5).

3.4. Regional Coordination and Functional Zoning Analysis

The transformation of cultural tourism land in the Da’anshan Coal Mine, Datai Coal Mine, and Fangshan Coal Mine should primarily focus on fully exploiting their unique historical, cultural, and industrial values, integrating these with modern cultural and creative elements, and developing culturally distinctive landscapes or creative industry parks (

Table 5. Recommended transformation types for suitable coal mines.

Suitable Coal Mines for Transformation	Recommended Transformation Types for Cultural Tourism
Da’anshan Coal Mine	Mine Museum, Cultural Exhibition Center, Ecotourism in Mining Areas
Datai Coal Mine	Miners’ Cultural Experience Activities, Cultural Arts Festival
Fangshan Coal Mine	Cultural Educational and Research Base, Mining Area Arts Creation and Handicrafts

).

(1)

The Da’anshan Coal Mine can make full use of its historical relics by establishing a mining museum or cultural exhibition center to showcase the history of the coal mine, the lives of miners, and the evolution of mining technologies, thereby promoting coal mining culture to visitors. Additionally, it could transition towards eco-tourism by implementing the ecological restoration of the natural environment surrounding the mining area, creating natural landscapes and hiking trails and allowing visitors to enjoy the scenic beauty while learning about the environmental impact of coal mining and its restoration process.

(2)

The Datai Coal Mine could introduce miner culture experience activities, such as simulating mining work environments, enabling visitors to personally engage in mock mining operations and gain a deeper understanding of the arduous labor of miners and their historical context. Furthermore, by leveraging national intangible cultural heritage projects such as the Qianjuntai Zhuanghu Banner Festival, the mine could regularly host cultural and arts festivals themed around coal mining or folk culture, enriching the cultural experiences of visitors.

(3)

The Fangshan Coal Mine could rely on national-level key cultural heritage sites such as the Wanfo Hall, Kongshui Cave Stone Carvings, and pagodas to develop cultural education and research bases. Additionally, by utilizing the coal mine’s unique cultural backdrop, local artisans could be encouraged to create art or handicrafts related to mining, such as stone carvings and ironworks, with a cultural arts market established for visitors to purchase these items.

At the same time, the transformation of these coal mine sites should focus on constructing a multifunctional integrated service platform, merging dining, accommodation, leisure, and transportation functions into a cohesive whole. This platform would provide comprehensive services for both long-term and short-term visitors to the surrounding areas. Not only would it meet the needs of visitors for dining, lodging, leisure, and daily necessities, but it would also serve as a vital hub, enhancing the overall tourist experience and fostering the sustainable development of regional cultural tourism.

The results of the ArcGIS kernel density analysis revealed that the historical cultural resources of the study area are primarily concentrated in three regions: the central–southern, northeastern, and northwestern areas (Figure 6a). For the sake of regional coordination and planning management, the study area was divided into three functional zones: the Urban Fringe Greens, the Countryside Emerald Hills, and the Distant Peaks Wonderland (Figure 6b). The Urban Fringe Greens, located in the central–southern part of the study area, is adjacent to the urban area and the Fangshan Coal Mine. Visitors exploring this zone can rely on the Fangshan Coal Mine as an integrated service center or enjoy convenient dining and accommodation services in the nearby city. The Countryside Emerald Hills, located in the northeastern part and close to the Datai Coal Mine, offers visitors the Datai Coal Mine as a service center to meet their needs for dining, accommodation, and transport. The Distant Peaks Wonderland, situated in the northwestern region near the Da’anshan Coal Mine, provides a comprehensive service facility that integrates dining, lodging, leisure, and transport functions for visitors.

4. Discussion

4.1. Advantages of the AHP–Entropy–TOPSIS Evaluation Model for the Transformation of Abandoned Coal Mine Clusters

The AHP–entropy–TOPSIS evaluation model for the transformation of abandoned coal mine clusters provides an effective method for scientifically screening abandoned coal mines suitable for cultural tourism transformations, filling a gap in the field.

This evaluation model combines the advantages of three methods, offering both comprehensiveness and flexibility. The AHP allocates weights through expert judgment, while the entropy method assigns weights based on objective data to reduce human bias. After combining both, the TOPSIS method ranks decision-making schemes, making the results more scientific and rational. The evaluation indicators’ positive and negative contributions enhance the comprehensiveness and credibility of the decision-making process. This model is suitable for high-demand decision-making scenarios and is widely applied in areas such as resource allocation and project ranking, improving the decision-making efficiency and scientific rigor.

4.2. Advantages and Challenges of Circuit Theory in Road Network Optimization

This study is the first to apply circuit theory to the optimization analysis of road networks. By assigning resistance values to different land types and calculating parameters such as the current and voltage, the optimal road network was identified, and areas in need of improvement were highlighted. This approach provides a scientific foundation for infrastructure optimization. The application of circuit theory opens a novel pathway for road network optimization, demonstrating both its pioneering and forward-looking nature.

Although circuit theory offers innovative ideas for road network optimization, its practical application still faces numerous challenges. The identified road network may require dynamic adjustments due to factors such as conflicts of interest, technical construction difficulties, and geological disaster risks, in order to ensure the smooth progress of the project. Therefore, it is recommended to make flexible adjustments to the optimization results during the construction phase to address the constantly changing construction environment.

4.3. Analysis of the Universality of the Ranking System

This study focused on the ranking system for abandoned coal mine clusters suitable for cultural tourism transformations, which demonstrates broad universality, as reflected in the following two aspects:

(1)

Universality of the Research Object: the ranking and selection strategy for coal mine clusters shares similar challenges and opportunities with other types of industrial heritage sites, indicating the framework’s potential for cross-disciplinary application.

(2)

Broad Adaptability to Transformation Land Types: while this study used “cultural tourism land” as an example of transformation, the system is equally applicable to various land types, such as residential, commercial, and office spaces, showcasing its strong flexibility and universality.

In conclusion, the ranking system is clear, rigorous, and efficient, with an exceptional adaptability and flexibility, providing a solid theoretical foundation and practical support for the transformation selection of industrial heritage sites.

4.4. Limitations and Future Work

In the evaluation of cultural tourism land use, water resources are a critical factor. However, due to the inability to obtain relevant data from government sources, statistical reports, and databases, this study did not address this aspect. It is hoped that such data will be available in the future to enable more in-depth research.

Although the AHP–entropy–TOPSIS model provides an effective theoretical framework for this study, the complexity of real-world situations presents several limitations, especially in terms of the comprehensiveness of the indicator system, the determination of weights, and the model’s adaptability. Firstly, the indicator system is not perfect and needs to be gradually optimized through multiple empirical studies and comparisons to ensure its applicability in different contexts. Secondly, determining the weights remains a challenge. Although this study combined both subjective and objective methods, further research is needed to scientifically evaluate the importance of each indicator and assign precise weights. Finally, adaptability issues must also be considered, especially in rapidly changing environments, where adjustments to the model’s weights and decision-making updates often lag, affecting the timeliness and effectiveness of the decisions.

Future research could consider the following improvements: (1) Select more relevant indicators to enhance the comprehensiveness of the indicator system and ensure its applicability across different fields and contexts. (2) Introduce a dynamic weight adjustment mechanism to flexibly adjust weights based on real-time data and environmental changes, improving adaptability. (3) Integrate artificial intelligence technologies to enable the model to self-learn and autonomously adjust decision-making strategies, enhancing decision support capabilities. These improvements will make the AHP–entropy–TOPSIS model more efficient and accurate in addressing complex decision-making problems.

Additionally, due to space constraints, this study primarily focused on road network optimization and the functional zoning of cultural resources, without fully exploring the coordinated development of cultural tourism resources. Future research could expand to include the multidimensional coordination of cultural resources, covering areas such as cultural heritage preservation, industrial collaboration, and market development, to promote the rational allocation and efficient use of cultural resources and foster the sustainable development of regional culture and cultural industries.

5. Conclusions

The deep integration of abandoned coal mine clusters with the cultural tourism industry not only injects new economic vitality into declining coal mines but also incorporates mining history into modern tourism experiences, becoming a new growth point for regional economies. This study takes Beijing’s Western Region as an example and proposes a comprehensive research framework aimed at achieving the transformation of coal mine clusters and the coordinated planning of cultural tourism resources. The specific conclusions are as follows:

(1)

Theoretical Framework for Coal Mine Transformation and Regional Resource Integration: A ranking system for coal mines suitable for cultural tourism transformations, as well as a research framework for the coordination of regional cultural tourism resources, was established. This framework provides systematic theoretical support for the planning of coal mine transformations and regional resource integration, offering data-driven evidence for policy formulation.

(2)

Innovation in Coal Mine Transformation Evaluation Model: By integrating cultural tourism land use indicators, an innovative AHP–entropy–TOPSIS evaluation model for abandoned coal mine clusters was proposed. This model identified the most suitable coal mines for transformation—namely, the Da’anshan, Datai, and Fangshan Coal Mines. The evaluation model offers a scientific and quantitative basis for decision making in coal mine transformation, demonstrating strong practical applicability.

(3)

Forward-Looking Application of Road Network Optimization: By incorporating circuit theory, the road network in the study area was innovatively optimized. This new theoretical perspective and methodology provide a forward-looking and innovative approach to road network optimization, offering valuable insights for future transportation planning.

(4)

Scientific Planning of Cultural Resource Spatial Layout: Based on a kernel density analysis of historical cultural resources, the functional zones within the study area were precisely delineated, promoting the deep integration and optimization of cultural resources. This ensures the efficient use and rational distribution of cultural tourism resources.

This study not only provides a theoretical foundation for the transformation of abandoned coal mines and the deep integration of cultural tourism resources but also offers practical recommendations for policymakers and practitioners. In the future, it is recommended that policymakers strengthen the coordinated planning of regional resources during coal mine transformation, emphasize innovation-driven development in the cultural tourism industry, and promote the deep integration of coal mines and cultural tourism to foster sustainable regional economic growth. Additionally, attention should be given to the social impact and environmental protection in the practical implementation of coal mine transformations, ensuring the sustainability and societal benefits of the transformation process.