Satisfaction evaluation of ecological restoration in abandoned mines based on the residents’ perception: a case study in Ganzhou, Jiangxi

Abstract

The ecological restoration of historical abandoned mines has emerged as a prominent issue that has been of great concern to the Chinese government and academia in recent years. In order to enhance the systematic and comprehensive evaluation of ecological restoration efforts in abandoned mines, this study explores and researches a new perspective, i.e. the subjective perception perspective based on residents’ satisfaction, and applies it to the ecological restoration performance evaluation of abandoned mines in Ganzhou, which is the most representative city of mineral resources in the southern hilly and mountainous area. An ecological restoration performance evaluation index system has been developed, encompassing three criteria layers: ecology, economy, and society, as well as twenty indicator layers. In this paper, a combination of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and importance-performance analysis (IPA) method is proposed to empirically analyze the ecological restoration performance of abandoned mines in the study area. The study revealed that the three counties predominantly prioritized ecological benefits, whereas the satisfaction of residents with social and economic benefits was lacking. Based on the findings, corresponding countermeasures were proposed, offering a fresh perspective and theoretical framework for assessing ecological restoration in abandoned mines.

Introduction

Mining plays a crucial role in China’s economic development, serving as a fundamental industry¹. As shown in Fig. 1, in the first decade of the 21st century, the share of mining GDP in GDP rose rapidly, with an average growth rate of more than 20%, far exceeding GDP growth. Mining, despite its economic benefits, has also resulted in a range of ecological and environmental issues. These include geological disasters, degradation of soil surface and soil quality, pollution of air and water, ecological degradation, and loss of biodiversity². Even after mining activities have ceased, the enduring impact on the ecosystem remains. For example, Brosse et al. found that in six rivers affected by different types of gold mining in the Guyana Shield, the resilience of fish communities after mining ceased was strongly affected³. Liu et al. reviewed four major negative environmental impacts of mine closures in Inner Mongolia⁴. Boldy et al. argue that mining activities may have irreversible impacts on ecosystems⁵. In recent years, there has been a significant increase in the implementation of ecological restoration projects for abandoned mines by local governments in China⁶. In particular, Ganzhou City, an important non-ferrous metal production base in China, enjoys the reputation of “world tungsten capital” and “rare earth Kingdom”, and has carried out a large number of ecological restoration projects of abandoned mines in recent years. Consequently, the ecological restoration of abandoned mines has garnered considerable attention from both the government and academia⁷.

Ecological restoration pertains to the mitigation of ecological function degradation by means of human intervention in ecosystem activities^8,9,10. Based on the triple bottom line theory, this paper holds that the standard of ecological restoration of abandoned mines should be comprehensively considered from three dimensions: ecology, economy and society. In addition, the state after restoration should also be improved in three dimensions, i.e., whether the surrounding ecological environment of the abandoned mine is more suitable, and whether the social and economic benefits are improved (such as the development of corresponding mine tourism projects). The above standards and state are closely related to the perception of residents, so we believe that it is very suitable to evaluate the effect of ecological restoration of abandoned mines from the subjective perception of residents. Currently, there is a significant emphasis on mine ecological restoration in various countries, with a primary focus on mitigating environmental impacts and reinstating ecological functions^11,12. Scholars have conducted extensive research on the techniques and methods of ecological restoration, including vegetation restoration, soil reconstruction, landscape reproduction, and other restoration techniques^13,14,15, various restoration methods, such as physical, chemical, and biological approaches^16,17,18.

Fig. 1

(Source: National Bureau of Statistics)

The proportion and growth rate of mining GDP from 2000 to 2021.

Undoubtedly, the careful selection of ecological restoration technology and restoration methods plays a pivotal role in the process of mine ecological restoration. Regardless of the chosen method or technology, the objective is to achieve the utmost comprehensive advantages, encompassing ecological, social, and economic benefits¹⁹. Therefore, how to evaluate the comprehensive benefits (such as ecological benefits, social benefits and economic benefits) after ecological restoration is equally important. A large number of scholars also try to evaluate the effect of ecological restoration from different aspects and perspectives.

The researches of various scholars on ecological restoration evaluation can be roughly divided into three categories. The first category pertains to the assessment of ecological restoration quality, which is evaluated from various perspectives. These perspectives include ecological security quality, ecosystem health, ecosystem stability, and ecosystem service functions^20,21,22,23. The second category is the evaluation of ecological restoration benefits. Various scholars adopt different evaluation methods and systems based on their theoretical perspectives. Currently, there is no universally accepted authoritative system for reference. However, most evaluations of ecological restoration benefits encompass three key aspects: ecological benefits, social benefits, and economic benefits^24,25,26. Of course, there are scholars who contend that this evaluation of benefits is outcome-oriented, neglecting the implementation process and solely concentrating on the government level, while disregarding the perspectives of other stakeholders, such as farmer¹⁹. The third category encompasses the evaluation of ecological restoration measurement methods, primarily centered on the rehabilitation of watersheds, wetlands, and other ecosystems. The majority of research in this area is dedicated to river restoration, particularly from a national standpoint. For instance, Palmer et al. observed that approximately 50% of the river restoration projects in California have implemented evaluative frameworks that allow for the measurement of restoration outcomes using quantitative data²⁷. However, Tischew, Baasch, Conrad and Kirmer, in their research on salt marsh restoration in northwestern Europe and compensation for road construction in Germany that assessing ecological restoration processes can be challenging²⁸.

The fundamental objective of ecological restoration evaluation is to provide guidance for the rehabilitation of a degraded system, aiming to restore it to a state that closely resembles its previous structure or composition^29,30. Therefore, certain scholars have opted to utilize satellite remote sensing images, aerial photographs, maps, and other relevant sources, in order to assess ecological restoration based on the perspective of changes in historical information³¹. To facilitate a more comprehensive evaluation of ecological restoration in mining contexts, Davis et al. have devised a six-step criterion for achieving mine closure and restoration³². Of course, from a process perspective, it is essential to monitor the ecological restoration projects’ risks and to ensure that all actors involved in the project fulfill their responsibilities. In this regard, Unger et al. proposed a maturity model that was successfully applied in an Australian jurisdiction³³. This model also demonstrates its potential applicability to abandoned mines worldwide.

In summary, the existing literature primarily focuses on evaluating the ecological restoration of abandoned mines, with a particular emphasis on tangible material output performance. In particular, the majority of these studies adopt the perspective of the government or project initiators. However, there is a dearth of research that examines the intangible subjective perception performance and evaluates the ecological restoration of mines from the standpoint of resident satisfaction in the surrounding areas. Drawing upon the public value theory, the level of satisfaction among residents emerges as a crucial determinant in assessing the efficacy of an ecological restoration project. The ecological environment after the restoration of abandoned mines belongs to public goods. The public value theory advocates respect for the individual value and personality of residents, and the public value is actually the integration of countless individual values and value differences. Therefore, to measure the restoration effect of abandoned mines, it is not only from the perspective of the government, but also necessary to consider the participation of surrounding residents, that is, the subjective perception of residents. In order to enhance the effectiveness of ecological restoration and management of abandoned mines,, this paper explores a new perspective and applies it to conduct research on the performance evaluation of ecological restoration in abandoned mines. Based on this new perspective, this study developed a comprehensive performance evaluation index system for assessing the ecological restoration of abandoned mines. The evaluation system takes into account the subjective perception of residents’ satisfaction in the vicinity of the mines. We conducted field visits and investigations on numerous residents living near rehabilitated abandoned mines for verifying that the constructed index conforms to the actual situation of residents.

Based on the collected research data, empirical analyses were conducted to assess the overall performance of ecological restoration in these abandoned mines using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. Considering that TOPSIS can only carry out relative evaluation of ecological restoration effect of abandoned mines in each county, it neglects absolute evaluation of restoration effect. In addition, TOPSIS method does not take into account the perceived importance of each evaluation indicator to residents. Importance-performance analysis (IPA) method can not only carry out absolute evaluation but also directly reflect the residents’ perceived importance of the restoration effect. Therefore, this paper applied the IPA method to further analyze the factors that influence the effect of ecological restoration in these abandoned mines. The aim of this study is to promote regional ecological and economic sustainable development and provide valuable insights for the formulation of policies related to the ecological restoration of abandoned mines worldwide.

The contributions of this paper are:

• (1) We introduce a new perspective to evaluate the performance of ecological restoration of abandoned mines, provide a reference for the successful implementation of global ecological restoration projects of abandoned mines, and enrich the evaluation system in the field of ecological restoration of abandoned mines.

• (2) The combination of TOPSIS method and IPA method is proposed to provide an evaluation method considering residents’ subjective feelings for the ecological restoration evaluation of abandoned mines. At the same time, considering the operability, it provides a new method for local governments to conveniently evaluate the ecological restoration benefits of abandoned mines from different perspectives.

Materials and methods

Study area: Ganzhou City, Jiangxi Province

Ganzhou City is recognized as a significant hub for non-ferrous metal production in China, earning the titles of the “Tungsten Capital of the World” and the “Kingdom of Rare Earths”. Ganzhou City possesses abundant mineral resources, encompassing a diverse range of minerals. Nevertheless, in light of the swift progress in social and economic advancement, the city of Ganzhou has witnessed a significant rise in the exploitation and utilization of mineral resources. Consequently, the ecological environment is facing mounting pressure with each passing day.

On July 13, 2022, the Ministry of Finance allocated a fund of 180 million yuan for the ecological rehabilitation demonstration project of historical abandoned mines in Jiangxi Province. Ganzhou City is situated in the southern hilly and mountainous area, which falls under the category of “three zones and four belts.” The region is characterized by a significant number of abandoned mines, resulting in substantial ecological damage. Consequently, mine ecological restoration has become a primary focus in addressing the main ecological problems in the area. As of July 2023, the initial phase of ecological restoration demonstration projects for historical abandoned mines in select areas of Ganzhou City, namely Huichang County, Yudu County, Dayu County, Quannan County, Xinfeng County, and Xingguo County, has been successfully completed. However, most of the restoration sites in Jeonnam, Xinfong and Xingguk counties are more than 10 km away from residents and are not suitable as research areas. These areas are deemed unsuitable for the study area from the subjective perception of the residents. Restoration sites in three other counties are close to residents. Therefore, the selection of Huichang County, Yudu County and Dayu County has a strong representation. The study area of this paper is shown in Fig. 2. The effect of ecological restoration on one of the abandoned mines is illustrated in Fig. 3 below. The research plan of this paper is to select as many abandoned mine restoration sites as possible under limited conditions. In addition, in order to more accurately capture the subjective perception of residents, we chose as close to the residents as possible and restored earlier abandoned mines. The type, number and distribution of abandoned mines selected in this paper are shown in Supplementary Table S1online.

Fig. 2

The abandoned mines investigated in this paper are distributed in Huichang county, Yudu county, Dayu county, Dayu county of Ganzhou City, Jiangxi Province, China.

The map was produced using ArcGIS10.8. (https://desktop.arcgis.com/de/arcmap/latest/get-started/setup/arcgis-desktop-system-requirements.htm). From the Ministry of Natural Resources Standard map Service website GS (2023)2767 standard map(http://bzdt.ch.mnr.gov.cn/), the base map boundary has not been modified.

Fig. 3

Comparison before and after ecological restoration of abandoned mine in Backeng Village, Huichang County. (left is before restoration, right is after restoration).

Research design

Performance evaluation index system of ecological restoration of abandoned mines from the perspective of residents’ satisfaction

The integration of economic, environmental, and social aspects is essential in forming a comprehensive system^34,35. Based on the triple bottom line theory, the evaluation index system in this study is determined to consist of ecological benefits, economic benefits, and social benefits, three criteria layers.

In order to develop a set of scientific, reasonable, and targeted indicators, the research team conducted extensive theoretical studies, on-site investigations, and demonstrations in the early stages. Firstly, we conducted preliminary research in the restored mines around the Zhanggong District of Ganzhou. In addition, we also consulted the local government and Longnan Huaxing Mining Co., Ltd., referring to their public participation survey on mine environmental restoration and land reclamation. Furthermore, this paper builds upon the experience of Zhang et al. in evaluating the environmental management performance of coal mine areas in Hebei, Henan, Shandong, and Anhui provinces in the central and eastern plains of China⁶. The study establishes an indicator system for evaluating the performance of ecological restoration of abandoned mines, with a focus on residents’ satisfaction. The evaluation encompasses three main aspects: ecological benefits, economic benefits, and social benefits. (as shown in Table 1).

Among them, Soil and water erosion(C₁), Soil improvement(C₃), Drainage facilities(C₆), Geological disaster prevention(C₇), Hydrological condition(C₉), Air quality(C₁₀), Economic income(C₁₂) and Landscape(C₂₀) refer to the research of Zhang et al.⁶; Landform remodeling(C₂), Slope protection(C₄), Vegetation restoration(C₅), Biodiversity (C8) refer to relevant literature^{36,37,38,39,40}. Land utilization rate(C₁₁), Mine resource protection(C₁₃), Travel improvement(C₁₄), Satisfaction with the government(C₁₅), Project implementation(C₁₆), Resident participation(C₁₇), Custodial condition(C₁₈), Sign and warning board(C₁₉) refer to local governments and Longnan Huaxing Mining Co., Ltd. Survey results of public participation in mine environmental governance. In fact, in the process of ecological restoration of abandoned mines, the government will interfere with the daily life of surrounding residents. The government should place corresponding signs and warning signs in prominent positions around the construction site to enhance the safety of residents. Moreover, the government should solicit the opinions of residents and call on them to participate in the restoration project, so as to improve their satisfaction with the government. Therefore, it is necessary to choose indicators such as Satisfaction with the government, Project implementation and Sign and warning board.

Table 1 Evaluation index system of ecological restoration of abandoned mines from the perspective of residents’ satisfaction.

Questionnaire design, data collection and analysis

In this study, the subjective perceived data of the residents living near the abandoned mines were collected through a questionnaire, which was divided into two parts. The first part includes the social attributes of the respondents, such as gender, age, education, length of residence, location of residence, occupation, and annual household income of local residents. These attributes represent the demographic characteristics of the respondents. The second part involves the evaluation of the ecological restoration of abandoned mines from the perspective of the residents living around them. This evaluation includes performance (satisfaction) and importance ratings of the indicators. The questionnaire was based on a Likert 5-point scale. There were five options for each indicator of performance (satisfaction): “very dissatisfied”, “dissatisfied”, “average”, “satisfied”, and “very satisfied”. Similarly, there were five options for each indicator of importance: “very unimportant”, “unimportant”, “average”, “important”, and “very important”. Each option was assigned a score from 1 to 5. Although Likert scale can directly reflect the subjective perception of residents, it will still cause some measurement errors due to the low educational level of residents. Therefore, we considered this problem, increased the interview time for each resident, and gave more detailed descriptions and explanations to some residents with low education level.

In order to ensure the validity and reliability of the questionnaire, the research team first visited several mining enterprises in Ganzhou City in June 2023. We consulted 20 experts in the field of mining, enterprise managers, and managers of the government’s natural resources departments (10, 5, and 5, respectively) by letter. Additionally, we initially distributed 100 copies of the questionnaire to the residents living around the repaired and abandoned mines in Zhanggong and Nankang Districts of Ganzhou City. 100 questionnaires were distributed and filled out in two ways. The first method involved the interviewees filling out the questionnaires themselves. The second method involved the research team members communicating with the residents using the questionnaire questions as a topic of conversation. In this method, the team members filled out the questionnaires on behalf of the residents during the conversation or at the end of the exchange. In total, 72 questionnaires were effectively recovered. Through preliminary visits, correspondence, and communication with residents around the mine, we adjusted and modified some questions and indicators of the questionnaire to better reflect the actual situation of the residents. In early July 2023, our research team began the official research in Huichang, Yudu, and Dayu counties of Ganzhou City, which lasted for approximately 100 days. By the end of mid- to late-October 2023, a total of 1,016 questionnaires had been distributed. Due to the research area being predominantly comprised of townships and remote villages, and the fact that most young and middle-aged people had left their hometowns to work and live elsewhere during the research period, the townships and villages were mainly inhabited by elderly people and children. Additionally, there were limitations in terms of literacy levels and dialect. As a result, the number of valid questionnaires recovered after screening was 613 (206 in Huichang County, 212 in Yudu County, and 195 in Dayu County, with an effective recovery rate of 60.33%.

TOPSIS model

The TOPSIS method, also known as the Technique for Order of Preference by Similarity to Ideal Solution, was first proposed by Hwang and Yoon⁴¹. This method calculates the proximity of the evaluation vectors of each evaluation object to the optimal objective based on the established indicator system. It is a commonly used evaluation method that considers multiple objectives. In this study, three counties were selected as research areas, so it is necessary to compare the ecological restoration effects of abandoned mines in different counties. However, Analytic Hierarchy Process (AHP) and other evaluation methods are suitable for absolute evaluation, and the weight assignment is subjective. In addition, although DEA method is mainly used to evaluate the relative efficiency of input-output. But Data Envelopment Analysis (DEA) relies too much on the assumptions, if the assumptions are not true, it may affect the accuracy of the results. And the calculation process of DEA is relatively complicated. Therefore, TOPSIS method was used to evaluate the ecological restoration effect of abandoned mines in different counties. In recent years, TOPSIS models have also been widely used in the field of ecological environment^{42,43,44,45,46}. The main concept is to identify the optimal and worst solutions by using a matrix created from the original data. Then, the distance between each object and the optimal and worst solutions is calculated. Finally, the degree of proximity is calculated, which serves as a criterion for evaluating the strengths and weaknesses of the objects. The specific steps are as follows:

Step 1

Establishment of the initial evaluation matrix. With m evaluation objects and n evaluation indicators, denoting the raw data of the ith evaluation object under the jth evaluation indicator, the evaluation matrix of ecological restoration performance of abandoned mines from the perspective of residents’ satisfaction is as follows:

$$\widetilde {Y} = {\left[ {{{\widetilde {y}}_{ij}}} \right]_{m \cdot n}} = \left( {\begin{array}{*{20}{c}} {{{\widetilde {y}}_{11}}}; \ldots ;{{{\widetilde {y}}_{1n}}} \\ \vdots; \ddots ; \vdots \\ {{{\widetilde {y}}_{m1}}}; \cdots ;{{{\widetilde {y}}_{mn}}} \end{array}} \right), {\text{i}} = {\text{1}},{\text{2}},{\text{3, }},{\text{m}};{\text{ j}} = {\text{1}},{\text{2}},{\text{3, }},{\text{n}}$$

(1)

Step 2

Dimensionless processing for obtaining a normalized evaluation matrix. The process of processing the data to obtain the normalization matrix is as follows.

①Let

$$\widetilde {Z}={\left[ {{{\widetilde {z}}_{ij}}} \right]_{m \cdot n}}$$

(2)

where benefit-oriented indicator${\widetilde {z}_{ij}}$=$\frac{{{{\widetilde {y}}_{ij}} - \hbox{min} {{\widetilde {y}}_j}}}{{\hbox{max} {{\widetilde {y}}_j} - \hbox{min} {{\widetilde {y}}_j}}}$

where cost-oriented indicators${\widetilde {z}_{ij}}$=$\frac{{\hbox{max} {{\widetilde {y}}_j} - {{\widetilde {y}}_j}}}{{\hbox{max} {{\widetilde {y}}_j} - \hbox{min} {{\widetilde {y}}_j}}}$

②Then normalized evaluation matrix

$$\widetilde {V}=\left[ {{{\widetilde {v}}_{ij}}} \right]=\left( {\begin{array}{*{20}{c}} {{{\widetilde {v}}_{11}}}; \ldots;{{{\widetilde {v}}_{1n}}} \\ \vdots; \ddots; \vdots \\ {{{\widetilde {v}}_{m1}}}; \cdots ;{{{\widetilde {v}}_{mn}}} \end{array}} \right)$$

(3)

where${\widetilde {v}_{ij}}$=$\frac{{{{\widetilde {z}}_{ij}}}}{{\sqrt {\sum\limits_{{i=1}}^{m} {\widetilde {z}_{{ij}}^{2}} } }}$, i = 1,2,3,∙∙∙,m; j = 1,2,3,∙∙∙,n

Step 3

The normalization matrix is weighted in order to derive a weighted normalized evaluation matrix $\widetilde {X}$, the calculation process is as follows:

$$\widetilde {X} = {\left[ {{{\widetilde {x}}_{ij}}} \right]_{m \cdot n}} = {\left[ {{{\widetilde {v}}_{ij}} \cdot {{\widetilde {w}}_j}} \right]_{m \cdot n}}, {\text{i}} = {\text{1}},{\text{2}},{\text{3, }},{\text{m}};{\text{ j}} = {\text{1}},{\text{2}},{\text{3, }},{\text{n}}$$

(4)

${\widetilde {w}_j}$is the weight of each indicator calculated using the entropy weighting method.

Step 4

Determine the positive and negative ideal solutions from the weighted normalized evaluation matrix $\widetilde {X}$, the calculation process is as follows:

Positive ideal solutions:

$$x_{j}^{+}=\left\{ {x_{1}^{+},x_{2}^{+}, \cdot \cdot \cdot ,{\text{x}}_{n}^{+}} \right\}=\left\{ {{{\hbox{max} }_j}\left( {{{\widetilde {x}}_{ij}}} \right)} \right\}, {\text{i}} = {\text{1}},{\text{2}},{\text{3, }},{\text{m}}$$

(5)

Negative ideal solutions:

$$x_{j}^{ - }=\left\{ {x_{1}^{ - },x_{2}^{ - }, \cdot \cdot \cdot ,{\text{x}}_{n}^{ - }} \right\}=\left\{ {{{\hbox{min} }_j}\left( {{{\widetilde {x}}_{ij}}} \right)} \right\}, {\text{i}} = {\text{1}},{\text{2}},{\text{3, }},{\text{m}}$$

(6)

Step 5

Calculate the distances$D_{i}^{+}$and$D_{i}^{ - }$ from the evaluation object to the positive and negative ideal solutions, calculated as follows:

$$D_{i}^{+}=\sqrt {\sum\limits_{{j=1}}^{n} {{{({{\widetilde {x}}_{ij}} - x_{j}^{+})}^2}} }, {\text{i}} = {\text{1}},{\text{2}},{\text{3, }},{\text{m}}$$

(7)

$$D_{i}^{ - }=\sqrt {\sum\limits_{{j=1}}^{n} {{{({{\widetilde {x}}_{ij}} - x_{j}^{ - })}^2}} }, {\text{i}} = {\text{1}},{\text{2}},{\text{3, }},{\text{m}}$$

(8)

Step 6

Calculate the closeness, denoted as S_i.and rank all evaluation objects based on the magnitude of the closeness S_i. A higher value of closeness indicates a higher rating for the evaluation object. The formula for calculating closeness is as follows:

$${S_i}=\frac{{D_{i}^{ - }}}{{D_{i}^{+}+D_{i}^{ - }}}, {\text{i}} = {\text{1}},{\text{2}},{\text{3, }},{\text{m}}$$

(9)

IPA model

The Importance-Performance Analysis (IPA) approach, initially introduced by Martilla and James in 1977, has become a widely adopted marketing model within the service industry^47,48,49. The fundamental concept involves assessing customer satisfaction through the comparison of customers’ expectations regarding various attributes of a product (importance) with the actual performance of the product (satisfaction). KANO model and (American Customer Satisfaction Index) ACSI model are also common satisfaction evaluation models. However, the KANO model is suitable for measuring customer loyalty and willingness to recommend, and focuses on tapping customers’ interest in products or services. The ACSI model is based on the theory of customer behavior and needs to clarify the causal relationship between variables, which is mainly used to evaluate the macro-economic operation. Therefore, this paper adopts a simpler and more targeted IPA method. In recent years, there has been a growing trend among scholars to utilize IPA modeling in the domains of engineering, energy, and ecological environment, resulting in notable advancements and improved outcomes^50,51,52,53.

Based on the two dimensions of importance and satisfaction, the attributes of the evaluated objects were classified into four categories. The origin coordinates of the image is determined by mean satisfaction and mean importance. The horizontal axis represents satisfaction, while the vertical axis represents importance. This categorization is illustrated in Fig. 4 below. In the first quadrant, the evaluator’s high importance and satisfaction performance is considered a strength that should be preserved throughout their subsequent productive life. In the second quadrant, the prioritization of areas for improvement lies in the evaluation object that holds high importance but exhibits low satisfaction. Consequently, it is imperative to implement suitable policies and measures to enhance this aspect in the future. In the third quadrant, the evaluation object exhibits a low level of importance and satisfaction. This indicates a secondary area for improvement, where the attributes of the object can be upgraded with adequate resources. In the fourth quadrant, characterized by low importance of the subject of evaluation and high satisfaction, it is recommended to maintain the current policy in order to preserve the existing high satisfaction status quo. Based on the segmentation results obtained from the IPA model, the relevant government departments can develop rational and focused policies for various indicators in order to enhance the efficiency of public resource utilization.

Fig. 4

IPA quadrants.

Results and analyses

Sample description

According to the survey results, there is a slightly higher number of male respondents who conducted research on the residents living near the abandoned mines in Huichang County, compared to the number of female respondents. Additionally, the number of male and female respondents who conducted research on the mines in Yudu and Dayu Counties is approximately equal. The demographic composition of the residents primarily consists of individuals in the middle-aged and elderly categories. The study conducted interviews with individuals in this population, revealing that they generally fall within the age range of 36–60 years old. Furthermore, the educational background of these residents is primarily concentrated in middle and high school education. The majority of residents surveyed in each region have been living in the area for over 10 years, constituting 49.51%, 45.75%, and 57.44% of the total respondents in each respective region. To ensure a comprehensive assessment of the subjective perceived value of ecological restoration of abandoned mines, the research group adopted a prioritization strategy. Firstly, residents who resided within a distance of less than 1 km from the abandoned mines were given priority. Subsequently, individuals living beyond 1 km were included in the study. However, it is worth noting that the majority of interviewees were located within a radius of 5 km from the abandoned mines. In relation to the occupational distribution, it is observed that the majority of residents residing near abandoned mines in all three study areas are primarily involved in agricultural, forestry, or animal husbandry activities (59.22%, 63.21%, and 50.77% respectively). Additionally, a portion of the population is engaged in mineral-related industries, with annual household incomes below 60,000 yuan.

Reliability and validity analysis

The analysis of the reliability and validity of the data collected from the questionnaire is essential in order to enhance the authenticity and validity of the data. In the present study, the reliability and validity of the questionnaire were analyzed using SPSS 26.0 software. The findings indicated that the Cronbach’s α coefficients for the satisfaction scale section were 0.824, 0.789, and 0.823 for the recovered questionnaires in Huichang, Yudu, and Dayu counties, respectively. These coefficients exceeded the threshold of 0.7, indicating good internal consistency. The Kaiser-Meyer-Olkin (KMO) values for the three variables were 0.716, 0.709, and 0.743, respectively. All values of the variables were found to be greater than 0.7, indicating high reliability. Additionally, the Bartlett’s test of sphericity yielded a significant result (p < 0.001), suggesting that the assumption of sphericity was violated. In the section on the importance scale, the Cronbach’s alpha coefficients were 0.956, 0.963, and 0.961, all of which exceeded the threshold of 0.7. The Kaiser-Meyer-Olkin (KMO) values, namely 0.933, 0.912, and 0.934, all exceeded the threshold of 0.7, indicating adequate sampling adequacy. Additionally, the Bartlett’s test of sphericity yielded a significant result (p < 0.001). Therefore, the questionnaire exhibits strong reliability and validity, thereby providing robust support for subsequent data .

Determination of weights for evaluation indicators

Compared to the subjective assignment method, the objective assignment method offers a certain degree of avoidance to the limitations imposed by experts’ subjective cognitive bias. This results in a relatively more objective and credible assignment of indicators. The entropy weight method is a type of objective assignment method that is commonly employed in target decision-making and comprehensive evaluation processes^54,55. The principle involves assigning weights to indicators based on the variations in indicator values among the samples under evaluation. By adjusting the differences in indicator values, the utility value of the statistical data can be more comprehensively reflected. In this study, the entropy weight method will be employed to ascertain the weight of each indicator. This method analyzes the relevance of the indicators by considering the original data information and combines the amount of information provided by each indicator. The aim is to determine the weight in a more objective manner. To be specific, according to the survey data, firstly, the index is standardized, then the probability matrix of each index is calculated, and finally the weight of each index in each county is calculated using the entropy weight formula. The following are the steps involved in the calculation process⁵⁶.

Step 1

A data matrix has been established to evaluate the performance of ecological restoration from the perspective of residents’ satisfaction around abandoned mines, based on the selected indicators. As below.

$$X=\left( {\begin{array}{*{20}{c}} {{x_{11}}}& \ldots &{{x_{1n}}} \\ \vdots & \ddots & \vdots \\ {{x_{m1}}}& \cdots &{{x_{mn}}} \end{array}} \right)$$

(10)

where m denotes the subject of the evaluation and n denotes the evaluation indicator,$i \in m=\left\{ {1,2, \cdot \cdot \cdot ,m} \right\}$, $j \in n=\left\{ {1,2, \cdot \cdot \cdot ,n} \right\}$

Step 2

In the process of data normalization, the original data matrix X undergoes a conversion to yield the normalized matrix$Y={\left[ {{Y_{ij}}} \right]_{m \cdot n}}$, The calculation method employed is identical to the Eqs. (2)-(4) mentioned above.

$$Y=\left( {\begin{array}{*{20}{c}} {{y_{11}}}& \ldots &{{y_{1n}}} \\ \vdots & \ddots & \vdots \\ {{y_{m1}}}& \cdots &{{y_{mn}}} \end{array}} \right)$$

(11)

Step 3

Calculate the probability matrix$P={\left[ {{p_{ij}}} \right]_{m \cdot n}}$,${p_{ij}}$ which is the share of the ith evaluation object in the jth indicator.

$$P=\left( {\begin{array}{*{20}{c}} {{p_{11}}}& \ldots &{{p_{1n}}} \\ \vdots & \ddots & \vdots \\ {{p_{m1}}}& \cdots &{{p_{mn}}} \end{array}} \right)$$

(12)

where${p_{ij}}=\frac{{{y_{ij}}}}{{\sum\limits_{{i=1}}^{m} {{y_{ij}}} }}$, i = 1,2,3,∙∙∙, m; j = 1,2,3,∙∙∙,n

Step 4

Calculate the entropy of the jth indicator.

$${e_j}= - \frac{1}{{\ln m}}\sum\limits_{{i=1}}^{m} {{p_{ij}}} \cdot \ln {p_{ij}}$$

(13)

Step 5

Calculate the entropy weight of the jth indicator${\widetilde {w}_j}$

$${\widetilde {w}_j}=\frac{{1 - {e_j}}}{{\sum\limits_{{j=1}}^{n} {(1 - {e_j})} }}$$

(14)

According to the entropy weighting method formulas (11)-(14) mentioned above, the weights of each indicator in each study area were calculated using Matlab software. The calculation process is illustrated in Fig. 5. Subsequently, the arithmetic mean of each indicator in the three areas was computed. Finally, the final weights of each indicator were obtained and are presented in Table 2.

Fig. 5

The weight of each indicator in different regions.

Table 2 Weight of each indicator (the final weight of each index is the arithmetic average of the weight of each region).

Results and analysis of TOPSIS evaluation from the perspective of residents’ satisfaction

From Fig. 6, it is evident that the average scores of the performance of ecological restoration of abandoned mines in Huichang, Yudu, and Dayu counties are closely aligned, Consequently, it can be inferred that, overall, the residents residing in these three study areas are generally content with the performance of the ecological restoration efforts conducted on the abandoned mines. However, when considering the performance of ecological, economic, and social benefits, there are notable differences among the three regions, particularly in terms of economic benefits.

Fig. 6

The average satisfaction of each index in the three regions is evenly divided Chordal graph.

The positive and negative ideal solutions are presented in Fig. 7. This analysis indicates that Yudu County outperforms in terms of Economic income (C₁₂), Resident participation (C₁₇), and Custodial condition (C₁₈). On the other hand, Dayu County excels in Vegetation restoration (C₅), while Drainage facilities (C₆) show the highest level of performance. Therefore, it is recommended that the relevant departments of local governments in each of the three regions engage in increased exchanges and cooperation, and learn from one another, in order to collectively enhance the local capacity for ecological restoration of abandoned mines. This is because each region possesses unique strengths in this field.

Fig. 7

Positive and negative ideal solutions of each index.

The results of the evaluation using the TOPSIS model are shown in Table 3. From Table 3, it can be seen that the ecological restoration performance of abandoned mines in the three regions is ranked as follows: Yudu County > Dayu County > Huichang County. Among the counties examined, Yudu County exhibited the highest degree of closeness, measuring at 0.7533, while Huichang County demonstrated the lowest degree of closeness, with a value of 0.2624. The calculation results exhibit a discrepancy with the distribution of positive and negative ideal solutions, potentially attributed to significant variations in the performance of each evaluation index in Huichang County. From the above analysis, it is evident that Huichang County exhibits a notable disparity in performance evaluation scores across different indicators. The Landscape (C₂₀) indicator attains the highest average score, indicating a strong performance in this aspect. Conversely, the Economic income (C₁₂) indicator receives the lowest performance evaluation score, suggesting a relatively weaker performance in this area. Yudu County exhibits the highest performance evaluation score for Resident participation (C₁₇), while it demonstrates the lowest performance evaluation score for Sign and warning board (C₁₉). Furthermore, an examination of Fig. 7 reveals that the indicator values of the positive ideal solution in Huichang County exhibit a high degree of similarity to those of the other two regions. Conversely, the indicator values of the negative ideal solution in Huichang County display significant divergence from those of the other two regions, resulting in a reduced closeness in the final calculation.

Zhang et al. (2021) conducted a resident survey of coal mine governance in Hebei, Henan, Shandong, and Anhui, and the results also show that there are ecological, social, and economic differences between different regions⁶. Zeng et al. (2024) also found that surface coal mining in Xilingol Steppe, Inner Mongolia has different ecological, social and economic impacts in different regions⁵⁷. This is consistent with the results of the ecological restoration effect evaluation of abandoned mines from the perspective of residents’ subjective perception, which further indicates the rationality of the results of this paper.

Table 3 The calculation results of TOPSIS method.

Sensitivity analysis

The purpose of sensitivity analysis is to further analyze how the performance ranking of different regions will change under different evaluation criteria. Table 4 and Fig. 8 reflect the performance ranking of Huichang County, Dayu County and Yudu County under four different conditions. From the overall evaluation criteria of ecology, economy and society, the ecological restoration performance of abandoned mines is ranked as follows: Yudu County, Dayu County, Huichang County. In terms of economic or social evaluation criteria alone, the ranking was not affected. However, from the perspective of ecological evaluation criteria, the ranking of performance becomes: Dayu County, Huichang County, Yudu County. According to the results of sensitivity analysis, although the residents of Yudu County have high satisfaction with economic and social performance, they have low satisfaction with ecological performance. Affected by economic and social indicators, the overall performance satisfaction is increased. This is consistent with the previous speculative analysis, indicating that TOPSIS method is suitable for this study.

Table 4 Result of sensitivity analysis of TOPSIS method for ecological restoration performance ranking.

Fig. 8

Sensitivity analysis result.

Analysis of importance and satisfaction levels

By conducting a comparative analysis of the disparity between the anticipated importance and the subjective perceived value of the residents residing in the vicinity of the mine, it is possible to assess the effectiveness of the local government’s ecological restoration efforts on abandoned mines from a relatively objective standpoint, as perceived by the residents. When the I-P value is greater than 0, it signifies that the ecological restoration efforts of the indicator have not fully met the expectations of the residents and require further improvement. Conversely, a value less than or equal to 0 indicates that the restoration efforts are able to meet the requirements of the residents.

In this study, the degree of significance and difference of the factors was determined using a paired sample t-test (95% confidence) and a 2-tailed p-value test. When the p-value is greater than 0.05, it suggests that there is no statistically significant difference between the factors. Conversely, when the p-value falls between 0.01 and 0.05, it indicates that the factors are significantly different. Finally, when the p-value is less than 0.01, it signifies that the differences between the factors are highly significant.

Table 5 presents the results of the satisfaction indicators analysis, indicating that out of the 20 indicators, Huichang County exhibits significant differences in 16 factors such as Soil and water erosion (C₁), Landform remodeling (C₂), Soil improvement (C₃), and Landscape (C₂₀). The p-values associated with these 16 factors are less than 0.05, suggesting that there exists a notable disparity among these variables, making them suitable for further analysis using the IPA method. This observation suggests that the residents’ expectations regarding the impact of certain indicators after rehabilitation have not been met to a significant extent. In relation to the mean difference between importance and satisfaction (I-P), it is observed that Biodiversity (C8), Custodial condition (C₁₈), and Sign and warning board (C₁₉) exhibit I-P values below 0. This indicates that residents are satisfied with these factors. The importance-performance (I-P) values of the remaining factors are all above zero. Among the factors analyzed, Drainage facilities (C₆), Economic income (C₁₂), Mine resource protection (C₁₃), Satisfaction with the government (C₁₅), and Resident Participation (C₁₇) exhibit an I-P value exceeding 0.6. This suggests that the residents residing near the abandoned mines in Huichang County express dissatisfaction with these four factors. Consequently, it is imperative for the local government to implement suitable measures to enhance these aspects in the future.

Based on the data presented in Table 6, it is evident that the p-values associated with the 20 factors in Yudu County are less than 0.05. This suggests that there are statistically significant differences among all the factors. From the analysis of the mean difference between importance and satisfaction (I-P), it is observed that only Biodiversity (C₈) and Custodial condition (C₁₈) have an I-P value less than 0, suggesting that the residents are satisfied with these aspects. On the other hand, Drainage facilities (C₆), Economic Drainage facilities (C₆), and Economic income (C₁₂) have an I-P value greater than 0.6, indicating that the government should focus on enhancing the effectiveness of ecological restoration of abandoned mines in relation to these factors. In contrast to Huichang County, the I-P values for Resident participation (C₁₇) and Satisfaction with the government (C₁₅) in the other county were 0.283 and 0.165, respectively. These values were found to be significantly lower than those observed in Huichang County. This observation suggests that the Yudu County government places significance on the subjective sentiments of its residents. The government actively seeks the input of neighboring residents and encourages their involvement in the ecological restoration of abandoned mines, both prior to and during the restoration process.

According to the data presented in Table 7, the p-values for 19 out of the 20 factors in Dayu County indicate a significant probability (p < 0.05), with the exception of Custodial condition (C₁₈). From the analysis of the mean difference between importance and satisfaction (I-P), it was observed that two indicators, namely Biodiversity (C₈) and Custodial condition (C₁₈), had I-P values less than 0. This suggests that residents were dissatisfied with these aspects. On the other hand, indicators such as Economic income (C₁₂) and Resident participation (C₁₇) had I-P values greater than 0.6, indicating that Danyu County neglected the subjective feelings and participation of neighboring residents during the ecological restoration of abandoned mines.

Table 5 The mean and standard deviation of satisfaction and importance, the difference of importance-satisfaction and significance test in Huichang County (N = 206).

Table 6 The mean and standard deviation of satisfaction and importance, the difference of importance-satisfaction and significance test in Yudu County (N = 212).

Table 7 The mean and standard deviation of satisfaction and importance, the difference of importance-satisfaction and significance test in Dayu County (N = 195).

Four-quadrant graph of importance-performance analysis

The IPA models for Huichang, Yudu, and Dayu counties are depicted in Figs. 9, 10, 11. The horizontal axis represents the level of residents’ satisfaction (P), while the vertical axis represents the level of residents’ expectation (I). The entire axis is divided into four distinct regions based on the average scores of satisfaction and importance.

The first quadrant is characterized by the dominant region, which includes Huichang County with eight indicators in the region, namely Soil and water erosion (C₁) and Vegetation restoration (C₅). Yudu County also falls within this quadrant and has three indicators, including Soil and water erosion (C₁), Vegetation restoration (C₅), and Resident participation (C₁₇). Additionally, Dayu County, located in the first quadrant, has eight indicators in the region, such as Soil and water erosion (C₁), Soil improvement (C₃), and Vegetation restoration (C₅). The results demonstrate that the ecological restoration of abandoned mines in the three study areas has effectively addressed soil and water erosion (C₁) and facilitated vegetation restoration (C₅). However, it is crucial to acknowledge the significance of this area, as even a minor decline in satisfaction levels can have a substantial adverse effect on the residents. Therefore, it is imperative for the local government to continue prioritizing these indicators in future restoration efforts.

The second quadrant is designated as the improvement area, with three indicators identified in Huichang County: Drainage facilities (C₆), Economic income (C₁₂), and Resident participation (C₁₇). In Yudu County, there are seven indicators in the improvement area: Drainage facilities (C₆), Geological disaster prevention (C₇), Hydrological condition (C₉), Air quality (C₁₀), Economic income (C₁₂), and Mine resource protection (C₁₃). Four indicators have been incorporated to assess the conditions in Daiyu County. These indicators include hydrological condition (C₉), land utilization rate (C₁₁), economic income (C₁₂), and resident participation (C₁₇). In this quadrant, the significance of the factors is notably high, while the level of satisfaction is comparatively low. This suggests that the ecological restoration efforts have not adequately met the expectations of the residents. Therefore, it is imperative to prioritize the enhancement of indicators within this domain. In the context of future ecological restoration efforts for abandoned mines, it is imperative for local governments to prioritize the identification and consideration of these key indicators. These indicators should serve as the basis for formulating appropriate policies and implementing effective measures aimed at enhancing and improving the restoration effect.

The third quadrant is classified as the secondary improvement area, also known as the opportunity area. Based on the analysis of the collected data, it was found that Huichang County does not have any indicators falling within this area. Yudu County contains three indicators, Biodiversity (C₈), Project implementation (C₁₆), Sign and warning board (C₁₉); Dayu County contains two indicators, Drainage facilities (C₆), Sign and warning board (C₁₉). Both the perceived importance and satisfaction levels of the population in this region are relatively low, and there has been a relatively low prioritization of improvements. Numerically, the perceived importance of these factors remains higher than satisfaction. Therefore, it may be advisable to explore further enhancements in their effectiveness, provided that adequate funding and resources are available.

The fourth quadrant is the maintenance area and Huichang County contains nine indicators: Landform remodeling (C₂), Soil improvement (C₃), Slope protection (C₄), Biodiversity (C₈), Air quality (C₁₀), Travel improvement (C₁₄), Project implementation (C₁₆), Custodial condition (C₁₈), Sign and warning board (C₁₉). Yudu County contains seven indicators: Landform remodeling(C₂), Soil improvement(C₃), Slope protection(C₄), Land utilization rate(C₁₁), Travel improvement(C₁₄), Satisfaction with the government(C₁₅), Custodial condition(C₁₈). Dare County includes five indicators: Landform remodeling (C₂), Slope protection (C₄), Biological species change (C₈), Travel improvement (C₁₄), Custodial condition (C₁₈). The findings indicate that the residents in the area perceive the indicators to be of low importance but are highly satisfied with them. This suggests that the indicators meet the expectations of the residents and should be maintained in the future.

Fig. 9

Importance-performance analysis of ecological restoration performance of abandoned mines in Huichang County, the origin coordinates is (4.293,3.919).

Fig. 10

Importance-performance analysis of ecological restoration performance of abandoned mines in Yudu County, the origin coordinates is (4.214,3.908).

Fig. 11

Importance-performance analysis of ecological restoration performance of abandoned mines in Dayu County, the origin coordinates is (4.199,3.909).

Discussion

Evaluation indicators and methods from the perspective of residents’ subjective perceived satisfaction

In contrast to the conventional approach of evaluating the ecological restoration performance of abandoned mines solely from the standpoint of the government or project initiators, this study aims to examine the government’s ecological restoration program of abandoned mines from a novel perspective. Specifically, it investigates the subjective perceptions of the residents residing in the vicinity of these mines. Based on the aforementioned information, a comprehensive performance evaluation index system for the ecological restoration of abandoned mines has been developed. This system builds upon previous research on the evaluation of ecological restoration performance in abandoned mines, and aims to provide a theoretical framework for conducting scientific, objective, and realistic evaluations of ecological restoration efforts in such sites.

On the one hand, this paper employs the TOPSIS model to objectively assess the overall performance of the study area based on residents’ subjective perception. Additionally, the paper utilizes the IPA model to analyze the factors that influence the effectiveness of ecological restoration in abandoned mines. The integration of the TOPSIS-IPA approach in this comprehensive evaluation demonstrates its rationality and applicability. Moreover, this study provides a methodological reference for other scholars interested in investigating residents’ satisfaction with ecological restoration projects in abandoned mines.

On the other hand, the evaluation index system for ecological restoration of abandoned mines, as developed in this study, incorporates input from experts in relevant fields, government officials, and enterprise technicians. Additionally, preliminary surveys are conducted to gather the perspectives of local residents living near the mines. This comprehensive approach ensures that the final evaluation index system is both objective and reasonable. Furthermore, this study aims to empirically examine the perceived importance and satisfaction of residents residing in three regions of Ganzhou City regarding the ecological restoration performance of abandoned mines. This investigation utilizes an established evaluation index system to assess the aforementioned factors. The empirical analysis findings demonstrate that the perspective chosen and the evaluation index system developed in this study are both scientifically and logically sound. This not only enhances and expands the existing research on the evaluation of ecological restoration performance of abandoned mines, but also offers a fresh perspective on the subject.

Policy implication

In general, based on the perspective of residents, the outcomes of ecological restoration efforts in various regions for abandoned mines exhibit both merits and drawbacks. It is mainly reflected in the difference of economic and social benefits after ecological restoration of abandoned mines in different counties. Among them, Huichang County introduced the overall contracting model, that is, the project implementation unit as the social capital side, is responsible for the fund raising and implementation of the ecological restoration project of abandoned mines in the county. However, the implementation process may not be “one mine, one policy” and classified repair. There are no proper recreational roads in the surrounding areas where residents live, which may affect the transportation of residents. In addition, from the perspective of improving the living environment, the project implementation unit is negligent in the supervision of the project management and care period, resulting in the social and economic benefits after restoration are not guaranteed for a long time. On the contrary, the government of Yudu County established the relevant assessment and scheduling mechanism in the abandoned mine restoration project, considered the living environment of the surrounding residents in the early stage, and set up leisure roads to provide residents with a place for leisure walking. In addition, Yudu County attaches great importance to the development of mining tourism, and has established related theme parks in many abandoned mine sites, which has increased the economic income of the surrounding residents. In Dayu County, the concept of green mine has been formulated and the subsidies for enterprises to build green mines have been increased. The government of Dayu County has strengthened the follow-up management and protection of the repaired area and strengthened the law enforcement of mine resources. A restoration fund account system has been established, and the implementation of ecological restoration plans by enterprises has been strictly reviewed. In Dayu County, the concept of green mine has been formulated and the subsidies for enterprises to build green mines have been increased. The government of Dayu County has strengthened the follow-up management and protection of the repaired area and strengthened the law enforcement of mine resources. A restoration fund account system has been established, and the implementation of ecological restoration plans by enterprises has been strictly reviewed. In addition, according to the different ecological environment of mining areas, Dayu County restored different mining areas to forest land, cultivated land and tourism land. And Dayu County is preparing to build the Xihuashan National Mine Park to increase the income of local residents. Therefore, according to local social and economic conditions, different measures taken by different county governments have certain differences in the economic and social benefits of ecological restoration in abandoned mines.

Relevant local government departments should enhance inter-regional exchanges and cooperation, aiming to regularly assess restoration outcomes and leverage each other’s expertise. This approach enables the timely identification and rectification of emerging issues in the restoration process. Specifically, this paper puts forward the following suggestions for ecological restoration of abandoned mines in different regions:

Policy recommendation 1: According to TOPSIS analysis results, Yudu County attaches great importance to the economic and social benefits of restoration projects. Huichang County and Dayu County governments should learn from Yudu County government and pay attention to residents’ subjective emotions. Before and during the restoration process, local governments should actively solicit the opinions of surrounding residents and encourage them to participate in the ecological restoration of abandoned mines. Only when residents actively respond to the call of the government and actively participate in the restoration work, can the comprehensive benefits of the restoration project be improved eventually. The government of Yudu County made use of the local abandoned mine resources, developed the related mine theme park, developed the tourism industry, not only promoted the employment of the residents around the mine, but also promoted the local economic development. As a result, residents have a high degree of satisfaction with economic and social indicators. The governments of Huichang County and Dayu County can also make use of local resource advantages to develop related tourism industry chains. At present, 10 green mines have been built in Dayu County, of which 5 mining areas have been selected in the National Green Mine list. The government of Dayu County can use this as a model to build a demonstration project for ecological restoration of abandoned mines. The government should strengthen the follow-up management and protection of repaired areas, establish and improve the ecological restoration fund account system, strictly examine the implementation of enterprises’ ecological restoration plans, and urge enterprises to fulfill their obligations of mine environmental restoration and land reclamation. The Huichang County government should actively explore market-oriented ways to promote mine ecological restoration, carry out cooperation with third parties, adopt PPP model, and fully invest in mine ecological restoration funds by social capital. For example, the mine ecological restoration adopts the “1 + N” model, that is, the batch of mines included in a program, and the establishment of a general ecological restoration project, including land consolidation, linking the increase and decrease of urban and rural construction land, reclamation and utilization of abandoned industrial and mining land, mining environmental governance and other sub-projects. Specifically, Huichang, Yudu and other counties as the pilot counties of the “1 + N” model, and gradually expanded to other cities across the country. According to the local economic and geographical conditions and social and political background, other cities follow Ganzhou’s example, focusing on ecological restoration and integrating other industries. For example, “ecological restoration + planting”, “ecological restoration + cultural and tourism integration”, “ecological restoration + new energy” and so on. In addition, local governments should make full use of green credit and actively introduce non-governmental organizations such as social capital. Using the construction land index generated by ecological restoration to trade with other cities in the country can not only solve the problem of return on social capital investment, but also promote the marketization of ecological restoration of abandoned mines. In this way, the local government can find the path to realize the value of ecological products with more economical restoration and more accurate reuse mode.

Policy recommendation 2: It is evident to see from the four-quadrant of the IPA model that the three counties emcompass different indicators in the second quadrant (improvement area). The Huichang County government should enhance the drainage infrastructure following the ecological restoration of abandoned mines to mitigate the risk of water accumulation during rainy periods, thereby minimizing potential safety hazards and maximizing ecological benefits; in order to achieve economic benefits, it is necessary to enhance economic revenues and enhance the relevant legislation and regulatory mechanisms, which will ensure that land reclamation, land use balancing, and replenishment, as well as the participation of social capital, are conducted in a fair and equitable manner; in relation to social benefits, it is necessary to emphasis the involvement of local residents, who will be engaged in future ecological restoration efforts pertaining to abandoned mines. In order to enhance ecological benefits, the government of Yudu County enhance its drainage facilities, prioritize the prevention and control of geological disasters, and further enhance the quality of drinking water and air; in relation to the economic benefits, augment economic revenues and implement stringent preservation of mining resources; in terms of social benefits, improve the landscape effect after ecological restoration of abandoned mines. In order to maximize ecological benefits, it is imperative for the government of Dayu County to enhance the quality of drinking water; additionally, in order to optimize economic benefits, the government should focus on improving land utilization and increasing economic income; lastly, to ensure social benefits, it is crucial for the government to prioritize the active participation of local residents.

The limitations of this paper and the prospect of the future

Due to the constraints imposed by limited research time and conditions, this paper is subject to certain limitations. Firstly, the survey sample was exclusively drawn from abandoned mines in Ganzhou City. And the sample size is small, which may cause potential subjective perception bias. To enhance the generalizability of the findings, future research should consider including residents living near rehabilitated abandoned mines in other cities across China. In addition, the sample size should be increased in the future to reduce the subjective bias caused by questionnaires. In order to weaken the subjective bias, it is also necessary to select the pilot area of ecological restoration with strong representation. Because these areas are close to the surrounding residents, it can more accurately and effectively capture the subjective perception of residents. Also, the pilot area has developed a more stringent regulatory system and attaches importance to the improvement of residents’ living environment, which can more truly reflect the effect of policy implementation. Additionally, the research team primarily visited rural areas and townships characterized by a low per capita literacy level. Consequently, the team encountered challenges in comprehending the local residents’ dialects, resulting in a certain level of communication resistance. Furthermore, a portion of the residents exhibit reluctance to participate in the survey or complete the questionnaire in a haphazard manner. This ultimately leads to a distortion in the final retrieval of the questionnaire and the accuracy of the data. In the future, the time and method of investigation should be considered, and certain material compensation should be given to residents who are willing to actively cooperate with the investigation, which can improve the willingness of residents to cooperate. Secondly, despite the considerable time and effort invested in developing an indicator system to assess the ecological restoration performance of abandoned mines from the subjective perception of local residents, it is evident that the current system is not sufficiently comprehensive. Therefore, future research should focus on further enriching and expanding the dimensions and indicators used, as well as exploring additional perspectives, in order to align the system more accurately with the actual conditions. Thirdly, this study neglects to investigate the conduction path and driving mechanism of ecological restoration performance of abandoned mines from the residents’ perspective. This aspect can be further examined in future research by employing methodologies such as structural equation modeling and game theory, and by integrating disciplines such as psychology and neuroscience.

Conclusions

Starting from the subjective perception of residents’ satisfaction with the mines, this study develops a comprehensive set of 20 indicators to assess the ecological restoration performance of abandoned mines. The evaluation criteria include ecological, economic, and social benefits. Empirical investigations are conducted in Huichang, Yudu, and Dayu counties in Ganzhou City using the TOPSIS and IPA methods. Based on the findings, the study draws the following conclusions:

Firstly, the average scores of residents’ satisfaction with the ecological restoration of abandoned mines in the three regions are relatively similar and close to 4 (indicating satisfaction). This suggests that the ecological restoration efforts in the study area have generally yielded positive outcomes. There are variations in the effectiveness of ecological restoration among different regions, as indicated by various indicators. Based on the evaluation results obtained through the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the ecological restoration performance of the three regions can be ranked as follows: Yudu County > Dayu County > Huichang County.

Secondly, the ecological restoration of abandoned mines in the three regions exhibits notable strengths and weaknesses, with the outcomes revealing distinct variations and shared characteristics. In the prominent region of the IPA quadrant map, Huichang County encompasses eight indicators, namely Soil and Water Erosion (C₁), Vegetation Restoration (C₅), Geological Disaster Prevention (C₇), Hydrological Condition (C₉), Land Utilization Rate (C₁₁), Mine Resource Protection (C₁₃), Satisfaction with the Government (C₁₅), and Landscape (C₂₀). Yudu County encompasses three key indicators, namely soil and water erosion (C₁), vegetation restoration (C₅), and resident participation (C₁₇). Dayu County encompasses a range of indicators, including soil improvement (C₃), vegetation restoration (C₅), geological disaster prevention (C₇), air quality (C₁₀), mine resource protection (C₁₃), satisfaction with the government (C₁₅), and landscape (C₂₀). This finding suggests that the ecological benefits of restoring abandoned mines, such as addressing soil and water erosion (C₁) and restoring vegetation (C₅), are commonly highlighted in the study area. This aligns with the field research conducted by the research team. However, the social and economic benefits did not meet the residents’ expectations. In the context of the IPA quadrant map, it can be observed that both Huichang County and Dayu County have not been successful in meeting the expectations of their residents in terms of Economic income (C₁₂) and Resident participation (C₁₇). On the other hand, Yudu County has made some progress in these areas, but there still exists a gap between the county’s performance and the expectations of its residents.