Evaluating Ecological Shifts in Mining Areas Using the DPSIR Model: A Case Study from the Xiaoxing’an Mountains Metallogenic Belt, China

Abstract

Mineral resource exploitation poses substantial pressure on regional ecological environments. The Xiaoxing’anling mineral belt—a critical ecological functional area and a major mineral-rich zone in China—exemplifies such environmental vulnerability. Conducting a scientific assessment of ecological changes in mining-affected regions is essential for balancing resource development and environmental protection. Based on the DPSIR (Driver-Pressure-State-Impact-Response) model, this study developed a comprehensive indicator system tailored for evaluating ecological changes in mining areas. Using the Xiaoxing’anling mineral belt in Heilongjiang Province as a case study, we integrated remote sensing, geographic information, statistical yearbooks, and field survey data, and applied an objective weighting method to quantitatively assess ecological changes from 2010 to 2020. The results indicate the following: (1) Ecological evolution exhibits significant spatiotemporal heterogeneity, with persistently high ecological pressure in the eastern region leading to continued environmental degradation. (2) Socioeconomic transformation driven by new energy development has weakened the overall development driver, though Yichun City remains a core driver due to its super-large mineral deposits. (3) Ecological impacts demonstrate a spatial spillover effect, extending to urban residential areas, while ecological response measures lag severely and are misaligned with pressure distribution—nature reserves have become high-value response zones rather than the actual mining sites. (4) The comprehensive ecological restoration index is on a downward trend. The measures currently adopted by society to improve the ecology of mining areas, such as using greener mining methods and increasing vegetation coverage, are unable to counteract the adverse effects of previous mining activities. This study identifies passive and lagging responses as the key bottlenecks impeding ecological recovery. We emphasize that future management strategies must shift from passive remediation to proactive intervention, and propose clear spatial and institutional directions for sustainable governance in mining areas.

1. Introduction

The evolving global emphasis on sustainable development has introduced new perspectives and practical approaches to environmental management and ecological conservation in mining regions. There is growing recognition of the need to reconcile mineral extraction efficiency with the ecological consequences of mining activities, including their long-term impacts on surrounding ecosystems and the challenges of post-mining restoration. This shift reflects a broader transition in mining area development—from a narrow focus on short-term economic gains toward a more integrated paradigm that seeks to balance economic benefits with ecological protection and sustainability.

Central to this evolving paradigm is the coordination between contemporary developmental needs and long-term ecological well-being. There is an increasing emphasis on maintaining and restoring natural ecosystems during resource exploitation to preserve natural capital and ensure a healthier ecological legacy for future generations.

The Xiaoxing’anling metallogenic belt represents a critical region for examining these dynamics. Rich in mineral resources and economically significant, it also exhibits high ecological sensitivity and typical mining-induced disturbance patterns. These characteristics make it an ideal area for studying ecological evolution and restoration mechanisms in mining-affected landscapes, underscoring its value for both environmental research and sustainable resource management.

Mining ecosystems can be understood as complex ecological complexes centered around extraction activities, encompassing both the mining area and its surrounding environment. Mining development exerts broad and profound influences on the natural world, including alterations to surface and subsurface structures, soil and water contamination, the degradation of vegetation cover, and habitat loss and fragmentation. As such, the ecological condition of mining areas reflects not only the baseline state of natural systems but also the sustained imprint of human engineering on ecological patterns and processes.

Globally, ecological management of mining areas has become a significant topic in environmental protection and sustainable development. Many countries have gradually established relatively comprehensive regulatory systems and research frameworks, providing a theoretical basis and technical support for the ecological restoration of mining areas. For instance, the United States introduced the Surface Mining Control and Reclamation Act (SMCRA) in 1977, which strictly regulates mining activities and promotes land reclamation in mining areas through legislation [1]. As early as 1994, scholars conducted systematic investigations and analyses on the natural vegetation restoration status of 15 abandoned mines [2]. Since 2002, Australia has been continuously carrying out vegetation restoration and ecological monitoring work in waste rock sites. Vickers et al. explored the optimal path for land use in post-industrial landscapes through long-term comparative studies between restoration areas and natural reference areas [3]. In 2015, Macdonald et al. further proposed the “Forestry Reclamation Method”, aiming to restore mining disturbance areas into fully functional forest ecosystems [4].

In terms of technical methods, Sweigard et al. [5] proposed that intelligent reclamation with low compaction and grading has good economic significance in the process of forest reclamation; many scholars have focused on the subsequent changes in mining areas from the perspective of plant restoration [6]; Thompson et al. also studied the methods of forest regeneration in mining areas, emphasizing the importance of vegetation restoration to ecosystem function [7]. In addition to research from the plant perspective, the changes in water bodies around mining areas are also a key focus of scholars’ observations and studies. Whether considering the water source issues in the mining industry from an economic cost perspective [8]; examining the well water environment [9]; or focusing on the current status of mine water treatment technology and systematically comparing and analyzing common and emerging treatment technologies for mine water containing suspended solids, high salinity, and fluoride [10], all have reference significance for the current overall ecological situation of mining areas. In-depth research has been conducted through long-term monitoring and evaluation, providing a scientific basis for the continuous improvement of ecological restoration effects [11]; based on remote sensing data, the Arid Mining Landscape Status (AMLS) index was developed to assess the ecological impact of arid mining areas [12]; over a period of 36 years, the Remote Sensing Ecological Index (RSEI) was used to monitor and evaluate the ecological environment changes in the Shendong mining area [13]; based on the theory of system fluctuation and stability, three indicators were proposed to characterize the stability and processes of vegetation landscapes, and applied to a typical semi-arid grassland coal mining area [14]; the Huolinhe coalfield, one of the largest mines in China, was monitored. The LandTrendr algorithm was used to analyze the time series data of MLDI and MLRI on Google Earth Engine into 3675 Landsat images from 1990 to 2020 [15]; there was also discussion on the accuracy and applicability of different downscaling models for rare earth mining areas. Based on data fusion and mixed pixel decomposition methods, the Temperature Decrease through Image Fusion and Spectral Unmixing (TDIFSU) model was used for temperature reduction, and the Temperature Decrease through Data Fusion and Spectral Unmixing (TDDFSU) model was constructed. It was also compared with the commonly used Radiation LST Decrease Decomposition (DisTrad) model [16]; the coupled effects of multiple driving factors on vegetation changes in mining areas were addressed, and the characteristics of various driving factors, especially mining activities, were discovered [17].

In 2020, a comprehensive review paper [18] concluded that reclamation is a feasible approach to reduce the negative impacts of abandoned mining areas and ensure the productive and effective utilization of mine wastelands. Compaction, low or high pH, low water-holding capacity, gullies, bulk density, and deficiencies in micronutrients and macronutrients are the main factors limiting the productivity of mine wastelands. In 2024, Zine et al. [19] proposed a “green” reclamation method based on vegetation restoration technology to address key issues. Although there are challenges such as species selection and harsh environments, vegetation restoration and reclamation technology have many benefits, including enriching soil, restoring habitats, and promoting the recovery of local biodiversity. In addition, emerging technologies such as nanomaterials have been proven to effectively improve soil fertility. Meanwhile, some scholars have established an evaluation index system using the Pressure-State-Response (PSR) model, taking Chongqing, China, as an example [20]. Furthermore, a projection pursuit (PP) model enhanced by the Sparrow Search Algorithm (SSA) is introduced to measure the suitability of land reuse; a framework based on a three-dimensional cube is utilized to identify the most suitable future land use, and a hotspot analysis method is adopted to identify spatial clustering.

In China, as early as 2003, some scholars have studied the pollution and ecology of heavy metals in the soil of mining areas [21]. Later, some scholars took the migration of heavy metal elements to study the ecological environment of mining areas as a doctoral topic [22]. The exploration of ecological civilization evaluation index [23] and ecological civilization law [24] also promoted the development of ecological restoration in mining areas. With the continuous improvement of technology, it is common to expand fields and innovate methods to conduct research in the way of delimiting typical research areas. Field surveys are conducted to explore the effect of soil remediation [25]. Research on the risk of mine water inrush and sand bursting in fragile mining areas [26]. Using the “pattern process effect” in landscape ecology for reference, and based on the relevant theories of landscape ecology, ecology and ecological risk assessment, this paper studies and evaluates the spatio-temporal evolution of landscape pattern and its ecological effects in mining areas [27]. Monitoring the ecological environment of the mining area by means of quantitative remote sensing [28]. The analytic hierarchy process and fuzzy mathematics comprehensive evaluation method were introduced into the ecological environment evaluation of the mining area, and six indicators including water density, vegetation coverage, residential density, topography, mining land occupation and ecological diversity were selected to establish the ecological evaluation index system [29]. Under the concept of sustainable development and carbon neutrality, we not only pay attention to the ecological development of the mining area, but also establish the framework of the ecological carbon sink system in the coal mining area from the three main categories of soil carbon sink, vegetation carbon sink and wetland carbon sink, including ecological carbon sink planning, carbon sink monitoring and investigation, carbon sink function improvement and carbon sink loss prevention and control, to study the ecological carbon sink system in the coal mining area [30]. Based on the concept of “using waste and fixing carbon, from where to where, it also explores new disposal paths of solid waste in coal mining areas, such as harmlessness, reduction, recycling and low-carbon, and innovates and develops solid waste disposal in mining areas and makes full use of underground space in mined out areas [31].

This year, domestic research on mining area ecology has primarily focused on landscape ecological assessment [32]; soil carbon density within mining areas [33]; establishing ecological environment knowledge maps [34]; trace element migration in groundwater within mining areas [35]; constructing ecological networks for mining cities [36]; characteristics and ecological reuse of mine water [37]; ecological vulnerability of open-pit mines [38]; considering mining area ecology in the context of carbon neutrality [39]; and the functional value of mining area ecosystems [40].

A comparison of domestic and international research reveals that, despite differences in specific practices of ecological management in mining areas across countries, the common goal is to achieve sustainable development in mining areas. Although China’s development in this field has been remarkable, especially in policy formulation, existing research still focuses on the later recovery situation and policy changes. There is still a lack of studies on mining ecological changes from the perspective of long-term time series and high spatial resolution visualization. Compared with the international advanced level, further strengthening interdisciplinary cooperation is still needed to enhance the effectiveness and efficiency of ecological restoration in mining areas. This paper focuses on the long-term ecological changes in key mining areas in Heilongjiang Province, aiming to explore new trends in ecological management in mining areas, assess the various impacts of mining area development on the environment, and propose scientific and reasonable ecological restoration strategies. Through comprehensive analysis of the dynamic changes in the mining area ecosystem, we hope to provide theoretical basis and a technical support for achieving sustainable development in mining areas and promoting harmonious coexistence between humans and nature.

This study establishes a comprehensive ecological assessment framework for mining areas based on the DPSIR (Driving Forces, Pressures, States, Impacts, and Responses) model. Applying this framework to the Xiaoxing’an Mountains metallogenic belt, we evaluate ecological changes from 2010 to 2020 to enhance the accuracy of long-term habitat quality assessment. The primary goal is to develop a robust and practical evaluation model that supports ecological restoration and informed decision-making for mining regions in Northeast China. Our work aims to provide a scientific basis and technical support for the effective ecological restoration of local mining areas, which have experienced rapid development but currently lack tailored, applicable solutions.

2. Materials and Methods

2.1. Overview of the Study Area

Heilongjiang Province, located in northeastern China, is characterized by a diverse topography of mountains, plains, and river systems, and experiences a temperate continental monsoon climate. In Figure 1, the region represents a critical area for studying the interplay between mineral resource development and ecological sustainability.

The Xiaoxing’anling ore concentration area lies within a complex tectonic setting at the junction of the Siberian, North China, and Pacific plates. This geodynamic history has resulted in multiple phases of magmatic activity and the development of fault systems that facilitate mineralization.

The stratigraphy of the area consists mainly of Paleozoic to Mesozoic sedimentary and volcanic rocks. Carbonate-rich units, such as the Cambrian Xilin Group and Ordovician Xiaojingou Formation, provide favorable host rocks for skarn-type mineralization. Magnatism occurred in two main stages: Early Mesozoic granitoids related to crust–mantle interactions, and Late Mesozoic intrusive-volcanic assemblages formed in an extensional setting, associated with varied deposit types.

Key mineral deposits in the region include:

Porphyry Mo (Cu, Au) deposits (e.g., Huojihe, Luming);

Skarn Fe-Mo polymetallic deposits (e.g., Cuihongshan);

Epithermal Au deposits (e.g., Gaosongshan);

Hydrothermal vein-type Pb-Zn deposits (e.g., Xiaoxilin).

The geographical conditions and distribution of mineral deposits are summarized in the figure below.

2.2. Introduction to Data

The data utilized in this study were derived from multiple public and authoritative sources. Population data were sourced from the Sixth National Population Census of China and the Seventh National Population Census of China. Land use data and administrative boundaries were obtained from the Resource and Environment Science and Data Center of the Chinese Academy of Sciences. Remote sensing images were acquired from the Geospatial Data Cloud platform. Information on nature reserves was compiled from official administrative planning reports. Socioeconomic and demographic profiles for the period 2010–2021 were extracted from the Heilongjiang Provincial Statistical Yearbook (see

Table 1. Data source table.

Input Data	Period	Description	Source
Land use/land cover	2010 2015 2020	Environmental Science Data Center of the Chinese Academy of Sciences and land use data produced by the Ministry of Resources of China	https://www.resdc.cn/ (accessed on 15 November 2025)
Population data	2010 2015 2020	Population data include age distribution, population distribution density, etc.	Census of China Bureau of Statistics
Digital Elevation Model data	2010 2015 2020	The data download comes from the Geospatial Data Cloud (China) with a resolution of 30 m × 30 m.	http://www.gscloud.cn/sources/ (accessed on 15 November 2025)
Yearbook data (Meteorological data, social data, economic data, infrastructure information data)	2010–2020	The annual statistical summary of the economic, cultural, natural and other numerical data of the year.	https://www.cnki.net/ (accessed on 15 November 2025)

).

2.3. Policies and Regulations

Against the backdrop of accelerating industrialization and urbanization, mining activities in China have posed increasing challenges to ecological systems. To balance economic development with environmental protection, the Chinese government has introduced a suite of laws and regulations governing mineral resource exploitation (see

Table 2. Compliance with Laws and Regulations in the Study Area.

Name	Time	Regarding the Development of Mining Areas
Environmental Protection Law of the People’s Republic of China	26 December 1989	The law requires that measures must be taken to protect the ecological environment when exploiting natural resources; governments at all levels should protect the agricultural environment and prevent the occurrence and development of phenomena such as land desertification and impoverishment, which also includes land reclamation and ecological restoration work in mining areas; no unit or individual may produce, sell, transfer, or use processes, equipment, and products that seriously pollute the environment; this also applies to the emission of pollutants generated during mining in mining areas.
Law of the People’s Republic of China on Mineral Resources	9 March 1986	Promote the rational development and utilization of mineral resources. Strengthen the protection of mineral resources and the ecological environment. Safeguard both the rights and interests of the state as owner and the legitimate rights and interests of mining rights holders. Clearly stipulate ecological restoration requirements and measures for mining areas. Emphasize the need to strengthen ecological environment protection and implement ecological restoration work in mining areas during mineral resource development.
Law of the People’s Republic of China on Land Administration	25 June 1986	Any construction activities, including land use required for mineral resource development, must be applied for and approved in accordance with the law. The construction and mining of mining areas need to comply with strict land use control and construction land approval procedures; the law stipulates the responsibility for reclamation of land damage caused by mining of mineral resources.
Law of the People’s Republic of China on the Prevention and Control of Soil Pollution	31 August 2018	The competent departments of ecology and environment and natural resources of people’s governments at all levels shall strengthen the supervision and management of soil pollution prevention and control in mineral resource development areas in accordance with the law; during the development of mineral resources, strict control shall be imposed on the emission of key pollutants that may cause soil pollution in accordance with relevant standards and total quantity control requirements; the operators and managers of tailings ponds shall strengthen the safety management of tailings ponds in accordance with regulations and take measures to prevent soil pollution; for dangerous, hazardous, diseased, and other tailings ponds that require key supervision, the operators and managers shall also conduct monitoring and regular assessment of soil pollution status; if soil pollution is caused by mineral resource development activities, the relevant responsible persons shall bear the corresponding obligations of soil pollution risk control and restoration.
Law of the People’s Republic of China on the Prevention and Control of Water Pollution	11 May 1984	Wastewater generated during the development of mineral resources must be effectively treated and meet national or local emission standards before being discharged; mining enterprises should take measures to prevent groundwater pollution during mining and mineral processing; tailings pond operators and management units shall set up tailings water pollution prevention and control facilities in accordance with regulations to prevent direct discharge of tailings water into the environment and cause pollution; after the end of mineral resources development, land reclamation and ecological environment restoration work should be carried out, including restoring damaged surface vegetation and water bodies to reduce the impact on the local water environment.

). These instruments address multiple dimensions—including land management, soil pollution, and water pollution control—and implement rigorous oversight measures to hold mining enterprises accountable for environmental impacts. By mandating corporate responsibility alongside economic gains, this legal framework underpins the transition toward sustainable resource management in mining regions [41,42,43].

3. Methods

3.1. DPSIR Model

The DPSIR (Driving forces–Pressures–State–Impacts–Responses) framework is a widely recognized conceptual model proposed by the European Environment Agency (EEA) to systematically analyze the interactions between human activities and environmental systems [44]. It establishes a causal chain that links socio-economic development to environmental change and management responses. In this framework, driving forces refer to the underlying social, economic, and demographic factors that initiate environmental change [45,46], such as population growth or land use expansion. Pressures represent the direct stresses exerted by human or natural activities on the environment [47,48,49,50], including pollutant emissions, resource extraction, and habitat disturbance. State describes the current condition of the environmental system [51,52], typically quantified through indicators such as water quality, vegetation cover, or glacial stability. Impacts denote the resulting effects of state changes on ecosystems, human health, and socio-economic systems [53,54], such as biodiversity loss or increased disaster risk. Finally, Responses encompass policy, management, and behavioral actions implemented to mitigate or adapt to these impacts [55,56].

By providing a structured cause–and–effect chain, the DPSIR model enables a comprehensive assessment of environmental problems and facilitates the identification of effective management strategies. It has been extensively applied in ecological vulnerability assessment, water resource management, and climate change studies. In this study, the DPSIR framework serves as an analytical foundation to integrate multi-dimensional factors—ranging from socio-economic drivers to ecological feedback—thereby enhancing the interpretability and policy relevance of the assessment results.

Utilizing multi-temporal (2010–2020) satellite imagery and land use data obtained from the Chinese Academy of Sciences Resource and Environmental Science Data Center and the Geospatial Data Cloud, this study conducts a longitudinal analysis of habitat quality and ecological restoration in key mining areas of Heilongjiang Province. We identify and quantitatively analyze, via GIS-based methods, the drivers behind the observed changes in restoration pace and effectiveness. Furthermore, an ecological restoration model for Northeastern China’s mining areas is developed based on the DPSIR (Driving Force, Pressure, State, Impact, Response) framework. A case study application demonstrates the model’s utility in providing a scientific foundation for spatial planning and environmental management by relevant authorities (see Figure 2).

3.2. Constructing an Ecological Research Model for Mining Areas

This study aims to provide a quantifiable tool for assessing ecological restoration in mining areas by employing a DPSIR framework. We first classify the key influencing factors into four categories: social, natural, economic, and engineering. A coupled model is then developed to integrate these factors and calculate a composite Ecological Restoration Index. The output of this model quantifies and spatially visualizes the degree of ecological restoration, serving as a critical evidence base for differentiated zoning and management strategies (see

Table 3. Ecological Factors of Mining Areas Based on the DPSIR Model.

Category	Influencing Factors	Description	Method
Driving force	Urbanization rate	The continuous advancement of urbanization has gradually increased the demand for mineral resources, driving mining area development activities and affecting the speed and quality of ecological restoration.	Urban area/total area * 100%
	Demand for mineral development	The demand factor for mineral extraction quantifies the driving intensity of society towards the extraction of specific minerals by integrating indicators such as economic growth, resource prices, technical conditions, and policy orientation. It reflects the dynamic balance between resource development and market demand.	Development demand = (resource potential * 0.5) + (reverse value of mining difficulty * 0.3) + (economic benefit * 0.2)
	Per capita GDP	Per capita GDP is an effective tool for people to understand and grasp the macroeconomic operation status of a country or region. It is commonly used as an indicator to measure economic development in development economics and is one of the most important macroeconomic indicators.	Data of natural resources center of Chinese Academy of Sciences
	General public budget expenditure	General public budget expenditure refers to the allocation and use of funds raised by the national treasury to meet the needs of economic construction and various undertakings.	Assign values to vector files through yearbook data.
Pressure	Development intensity of mining area	The development intensity of a mining area refers to the scale and density of mining activities for mineral resources within a specific area within a certain period of time.	Carry out point density analysis on the ore point data and superimpose the mining difficulty.
	Air quality index	The air index, also known as the air quality index or air pollution index, is based on the environmental air quality standards and the impact of various pollutants on human health, ecology, and the environment.	AQI itemized calculation shall be carried out according to national standards.
	Human disturbance index	The Human Interference Index is a comprehensive indicator that quantitatively evaluates the spatial distribution and intensity of the impact of human activities on the natural environment.	(Standardized Lighting * 0.7) + (Standardized Road Density * 0.3)
	Water consumption	The pressure of water resource consumption on ecology refers to the degree of risk of ecological degradation caused by unsustainable human surface and groundwater extraction that exceeds the renewable carrying capacity of ecosystems.	Visualize yearbook data.
	Land subsidence rate	The land subsidence rate refers to the percentage of the surface deformation area caused by mining, geological activities, etc., in the total area of the study area per unit time, which is used to quantify the degree of damage to the surface stability caused by human activities or natural factors.	Surface deposition area/unit area * 100%
	Human interference index	The human disturbance index is a comprehensive indicator used to quantify the degree of impact on a certain region by human activities.	Data of natural resources center of Chinese Academy of Sciences
Status	Forest coverage	Forest coverage refers to the proportion of forest area in a certain region to the total area of that region, usually expressed in percentage. It is one of the important indicators to measure the abundance of forest resources and the condition of ecological environment in a region.	Forest area/unit area * 100%
	Soil organic matter content	Soil organic matter content refers to the total amount of plant and animal residues, as well as microbial decomposition products (such as humus and organic carbon) in soil. It reflects soil fertility, structure, and water and nutrient retention capacity, and is a key indicator for assessing soil health and agricultural sustainability.	Sandy soil * sandy soil organic matter content weight + silt * silt organic matter content weight + clay * clay organic matter content weight
	Water quality compliance rate	The water quality compliance rate refers to the proportion of water bodies at specific monitoring points that meet the national water quality standards. It is a key indicator for assessing the quality of water resources, pollution control, and the effectiveness of treatment. A higher value indicates that the water quality is closer to or meets the safe usage standards.	Visualize yearbook data.
	Level of habitat degradation	The level of habitat degradation refers to the extent to which the quality of habitats in a certain area has declined, reflecting the damage to the structure and function of natural ecosystems.	The invest model is used to process and visualize the data.
Impact	Ecological service function	Such as the restoration of ecological functions like water conservation capacity and carbon sequestration capacity.	The invest model is used to process and visualize the data.
	Residents’ health indicators	Resident health indicators are standards for measuring the health status of a specific population, including life expectancy, infant mortality rate, disease incidence rate, etc., which are used to evaluate the effectiveness of public health services and the improvement of population health level.	Visualize yearbook data.
	Biodiversity	Biological richness refers to the number of species of organisms in a specific area or ecosystem.	(0.25 * “land use score”)+ (0.20 * “NDVI”)+ (0.15 * “distance from road _; standardization”)+ (0.15 * “distance from water body _; standardization”)+ (0.10 * “elevation diversity”)+ (0.15 * “distance from the reserve _0; standardized”)
	Rate of decrease in forest stock volume	The rate of forest stock volume reduction refers to the percentage decrease in the forest stock volume (i.e., the total volume of trees) within a certain area over a certain period of time.	(forest volume of the previous year—forest volume of this year)/forest volume of the previous year * 100%
Response	“three wastes” pollution treatment rate	The “three wastes” pollution treatment rate refers to the proportion of wastewater, waste gas, and waste residue generated in industrial production and daily life that have been effectively treated to meet environmental standards. It reflects the level of environmental pollution control and the efficiency of resource recycling.	Visualize yearbook data.
	High interference land coverage	High-disturbance land coverage refers to the proportion of land area affected by high-intensity human activities in a certain region, as a percentage of the total area of that region.	High interference land area/total area * 100%
	Coverage rate of natural protected areas	The coverage rate of natural protected areas refers to the proportion of land area officially designated as natural protected areas within a certain region, as compared to the total area of that region.	Area of nature reserve/unit area * 100%
	Vegetation coverage	Vegetation coverage refers to the proportion of land area covered by plants in a certain region to the total area of the region. It is one of the important indicators for assessing the ecological environment status, land use, and management effectiveness of a region.	Data of natural resources center of Chinese Academy of Sciences

).

3.3. Weight Calculation

Indicator weights were determined using the Entropy Weight Method (EWM), which assesses the dispersion degree of each indicator via its entropy value. A lower entropy value indicates greater dispersion and, therefore, a higher weight, signifying the indicator’s stronger impact on the ecological resilience of urban mining areas. This allows for the calculation of each factor’s influence range and degree (see

Table 4. Weight of ecological factors in mining area based on DPSIR model.

Category	Weight	Influencing Factors	2010	2015	2020
Driving force	0.2	Urbanization rate	0.54	0.55	0.58
		Demand for mineral development	0.1	0.1	0.07
		Per capita GDP	0.2	0.19	0.18
		General public budget expenditure	0.16	0.16	0.17
Pressure	0.2	Development intensity of mining area	0.12	0.17	0.15
		Air quality index	0.18	0.1	0.11
		Human disturbance index	0.03	0.05	0.06
		Human interference index	0.43	0.45	0.44
		Land subsidence rate	0.03	0.03	0.04
		Water consumption	0.21	0.2	0.2
Status	0.2	Forest coverage	0.21	0.2	0.24
		Soil organic matter content	0.08	0.1	0.04
		Water quality compliance rate	0.14	0.15	0.07
		Level of habitat degradation	0.57	0.55	0.65
Impact	0.2	Ecological service function	0.11	0.1	0.1
		Residents’ health indicators	0.7	0.69	0.7
		Biodiversity	0.12	0.15	0.13
		Rate of decrease in forest stock volume	0.07	0.05	0.07
Response	0.2	“Three wastes” pollution treatment rate	0.17	0.15	0.19
		High interference land coverage	0.47	0.49	0.35
		Coverage rate of natural protected areas	0.24	0.29	0.36
		Vegetation coverage	0.1	0.05	0.1

). The calculation procedure is as follows:

(1)

Firstly, based on m ecological factors of mining areas and their corresponding n regions, construct an evaluation index matrix:

$$\mathrm{R}=\left\{\begin{array}{ccc}{X}_{11}& \dots & X_{1n}\\ ⋮& \ddots & ⋮\\ X_{m1}& \dots & X_{mn}\end{array}\right\}$$

(2)

Normalization processing, which converts indicators with different dimensions into the same dimension.

Positive indicator:

$$X_{ij}={\displaystyle \frac{X_{ij}-\min \left(X_j\right)}{\max \left(X_j\right)-\min \left(X_j\right)}}$$

Negative indicators:

$$X_{ij}={\displaystyle \frac{\max \left(X_j\right)-X_{ij}}{\max \left(X_j\right)-\min \left(X_j\right)}}$$

In the formula: xij represents the value of the ecological factor in the j-th mining area of the i-th region, max(xj) denotes the maximum value among the ecological factors in the j-th mining area, min(xj) signifies the minimum value among the ecological factors in the j-th mining area, and Xij stands for the normalized standard value of the ecological factor in the j-th mining area of the i-th region.

(3)

Calculate the index proportion of the i-th region under the ecological factor of the j-th mining area.

$$f_{ij}={\displaystyle \frac{X_{ij}}{\sum _{i=1}^m{X}_{ij}}}$$

(4)

Calculate the entropy value of the ecological factor in the j-th mining area.

$$H_{ij}=-{\displaystyle \frac{1}{\ln m}}\sum _{i=1}^m{Y}_{ij}\ln Y_{ij}$$

(5)

After determining the index entropy, calculate the weights.

$$W_j={\displaystyle \frac{1-H_j}{\sum _{j=1}^n\left(1-H_j\right)}}$$

In the formula, Wj represents the weight of the j-th indicator, and 0 ≤ Wj ≤ 1.

4. Results and Analysis

4.1. Driving Force

The driving force sub-model developed in this study spatially couples the underlying factors influencing mining area development and visualizes the results in Figure 3. Among them, the comprehensive economic and social factors of mining area development intensity are included in the scope of driving force thinking. The results reveal a general weakening trend in development drivers, largely attributable to the growing adoption of new energy sources, which has reduced reliance on fossil fuels and consequently decreased the intensity of mining activities. Spatially, the driving force shows a clear heterogeneity: while its influence is gradually diminishing in the southwestern part of the study area, regions with high driving force remain concentrated in Yichun City, Heilongjiang Province. Yichun hosts China’s largest molybdenum deposit, along with the Cuihongshan iron-polymetallic mine—a complex yet sizable deposit with total reserves estimated at 150 million tons, both of which sustain local mining impetus. In contrast, the southwestern area falls within Heihe City, where ongoing extraction at sites such as the Duobao Mountain copper mine has entered a stable phase. Moreover, the continued discovery of concealed mineral deposits across the region has gradually dispersed developmental focus, thereby slowly reducing the relative influence of resource exploitation within the Xiaoxing’an Mountains mining region.

4.2. Pressure

The pressure sub-model developed in this study captures the adverse anthropogenic activities directly induced by socioeconomic drivers and exerted on the local environment, serving as the immediate cause of environmental degradation. This model integrates multiple indicators, including mining intensity, land subsidence rate, and air pollution index. As illustrated in Figure 4, the eastern region has experienced consistently high pressure since 2010. Initially, this was largely attributable to population density and high water resource consumption, which contributed significantly to the overall pressure in that year, alongside notable water pollution and scarcity issues. By 2015 and 2020, although the eastern area remained the most pressured, the contributing factors shifted to be dominated by mining development intensity and land subsidence rate, leading to the spatial-temporal pattern depicted in the figure.

4.3. Status

The state sub-model developed in this study characterizes measurable changes in environmental systems—including physical, chemical, and biological attributes—resulting from anthropogenic pressures. It reflects the qualitative and quantitative conditions of the environment in a given region and time period. In this paper, the state is evaluated through a set of indicators comprising forest coverage, habitat degradation level, and water quality compliance rate. As illustrated in Figure 5, notable spatial–temporal shifts in environmental state are observed. Regions in favorable condition initially located in the east gradually transitioned toward the central and western parts of the study area, with only the eastern region maintaining a relatively high state level by the end of the study period. The results also reveal clear regional disparities, with certain areas experiencing pronounced degradation. Moreover, a correspondence is identified between pressure and state dynamics: areas subjected to higher pressure consistently exhibited more significant deterioration in environmental state.

4.4. Impact

The impact sub-model developed in this study assesses the alterations in environmental conditions that subsequently influence both ecosystem functionality and socioeconomic well-being. This model comprises three key indicators: habitat quality, the rate of forest stock volume reduction, and resident health status, collectively capturing the multifaceted impacts induced by regional mining development. Beyond direct mining activities, the model also accounts for influences from forest dynamics and other environmental factors. As shown in Figure 6, ecologically impacted areas extend beyond the immediate vicinity of mining sites to include urban zones with high resident density. Spatially, the impact exhibits limited overall variation, with moderate to high changes predominantly concentrated in the eastern and southwestern portions of the study area.

4.5. Response

The response sub-model developed in this study captures the mitigation measures and policy interventions implemented to alleviate negative ecological impacts, improve environmental quality, or address the root drivers and pressures of ecosystem degradation. It reflects the extent of practical action and investment dedicated to ecological restoration. The model integrates multiple indicators, including the treatment rate of “three wastes” (wastewater, exhaust gas, and solid waste), the coverage of natural protected areas, and vegetation coverage. As revealed in the accompanying figure, the spatial pattern of response is predominantly shaped by the distribution of natural protected areas, with all high-response regions concentrated in zones of high protection coverage. Notably, these high-response areas do not coincide spatially with active mining sites, which are dispersed across the Xiaoxing’an Mountains region. Many of these mining locations remain in the preliminary exploration phase and have not yet entered active extraction. It was not until 2021 that systematic restoration of mining areas—including post-mining ecological recovery—gained substantial policy and institutional attention in the region (see Figure 7).

4.6. Result

The ecological restoration index, developed within the DPSIR framework for mining areas in Northeast China, quantitatively assesses the degree to which societal response measures mitigate or counteract the adverse ecological effects of mineral resource development. As depicted in Figure 8, the overall capacity for ecological mitigation and restoration shows a declining trend over the study period. Between 2010 and 2015, a general reduction in restoration effectiveness was observed, particularly in the southeastern part of the study area, which experienced pronounced impacts from mining activities. By 2020, the level of ecological mitigation was predominantly classified as low to lower-middle. During this period, regional socioeconomic factors were significantly influenced by the COVID-19 pandemic, leading to a shift in policy focus toward epidemic control at the expense of environmental remediation efforts. In recent years, however, ecological restoration in mining areas has regained attention as a regional sustainability priority.

5. Discussion

Based on the DPSIR framework, this study reveals the driving mechanisms and spatial logic of ecological evolution in the mining areas of the Xiaoxing’anling metallogenic belt from 2010 to 2020. Results show a strong spatial coupling between ecological pressure and state, exhibiting a distinct “east-high, west-low” pattern. Concurrently, overall driving forces have weakened, reflecting complex human–environment interactions shaped by regional energy restructuring and localized resource endowment.

From a geographical perspective, the sustained high ecological pressure in the eastern region has directly led to the deterioration of the local environmental conditions, establishing a clear causal relationship between pressure and state. This spatial heterogeneity highlights the role of human activities as a direct driving force for changes in natural systems. It is worth noting that although the macro level socio-economic transformation has reduced the overall development momentum, there are key resource endowments such as large molybdenum deposits in the administrative area of Yichun City, which have maintained a high driving force in specific regions. This indicates that the economic geography of high-value mineral resources continues to strongly influence the local development trajectory.

From an ecological process perspective, impacts have expanded from mining sites to surrounding residential areas, suggesting that ecological disturbances are evolving into regional socio-ecological risks. However, the spatial distribution of response measures largely overlaps with existing nature reserves, showing a significant mismatch with the most severely affected mining areas. This misalignment reveals the passive and lagging nature of current restoration efforts, which rely heavily on the ecological baseline of pre-established protected areas while lacking targeted interventions in the core zones of mining-induced degradation. As a result, the comprehensive DPSIR-based ecological restoration index has continued to decline, indicating that societal responses have so far failed to effectively offset ecological losses.

While the DPSIR model applied in this study has successfully identified key mechanisms, there remains room for refinement. Future research could enhance the model’s analytical power by integrating more detailed data on pollution extent and restoration investments, as well as validating results using multi-source remote sensing platforms such as UAVs. Such improvements would also offer more precise spatial guidance for achieving Heilongjiang Province’s goal of rehabilitating all historical mining sites by 2035. Accordingly, policy measures should prioritize restoration in eastern high-pressure–low-state areas and implement full-lifecycle management in high-value mining zones, thereby shifting ecological restoration from a reactive practice toward a spatially targeted, systematic, and proactive governance pathway.

6. Conclusions

This study is based on the DPSIR model framework and systematically evaluates the evolution process and internal logic of the ecosystem in the Xiaoxing’anling metallogenic belt mining area from 2010 to 2020. Through comprehensive analysis of the five subsystems of driving force, pressure, state, influence, and response, as well as their final comprehensive results, we have drawn the following core conclusions:

The ecological effects of mining area development exhibit significant spatiotemporal heterogeneity, and the “pressure state” chain relationship is clear. During the research period, areas with high ecological pressure were consistently concentrated in the eastern part of the study area, which is closely related to the region’s historical high-intensity development, population density, and water resource consumption. This sustained pressure directly leads to the deterioration of the ecological environment, manifested as the migration of high-value areas from the east to the central and western regions, and the eastern region becoming a relatively fragile ecological area. This clear “pressure state” response relationship confirms that human activities are the key direct driving forces behind regional ecological environment changes.

Socioeconomic transformation and energy structure adjustment are fundamental forces driving the ecological evolution pattern of mining areas. The overall trend of weakening driving force deeply reflects the macro background of global energy transformation and China’s new energy strategy. The rapid development of new energy has reduced dependence on traditional mineral energy, weakening the intrinsic driving force for mining area development. However, due to the extremely high economic value of its large molybdenum and other polymetallic resources, Yichun City has become a continuously high-value area for mining area development factors, indicating that the economic location of resource endowment is still the core factor determining development willingness.

The negative “impacts” of ecosystems have spatial spillover effects, while human “response” measures exhibit passivity and lag. Research has found that ecological impacts are not limited to mining sites themselves, but have spread to urban residential areas, reflecting the social nature of environmental issues. However, the high-value areas of response measures mainly coincide with the distribution of nature reserves, rather than overlapping with the mining areas with the most severe ecological pressure/impact. This reveals that the current investment in ecological restoration tends to “avoid the heavy and focus on the light”, relying more on the ecological functions of existing protected areas rather than actively targeting the most severely damaged mining areas for targeted treatment. In 2021, systematic mine restoration work was put on the agenda, and the serious lag in response measures was the main reason for the poor ecological restoration effect.

The comprehensive results of DPSIR indicate that social response measures have not effectively offset the ecological negative effects of mineral resource development. The “Ecological Restoration Index” constructed in this study essentially measures the game between social response and ecological loss. The results show that from 2010 to 2020, the index showed an overall downward trend, indicating that the restoration effect is gradually weakening and the ecological deficit is expanding. Especially in 2020, the low-level recovery level dominated, which was not only related to the lack of long-term response, but was also potentially affected by the short-term impact of resource tilt caused by public emergencies (such as the COVID-19 epidemic).

Based on the research findings, this article proposes a comprehensive set of policy recommendations to promote the sustainable development of the Xiaoxing’anling mining area: zoning management should be implemented in space, and ecological restoration should be prioritized in the high-pressure, low-state areas in the east. In terms of management, it is necessary to promote the transformation of green mining and enforce a full lifecycle model of “mining while repairing” in high driving force areas such as Yichun, in order to internalize ecological costs. At the same time, enhance the pertinence and initiative of response measures, tilt investment towards ecologically damaged areas of historical legacy and active mines, and establish a responsibility and compensation mechanism for “whoever develops, who protects, whoever destroys, who governs”. In addition, a long-term monitoring and early warning mechanism should be established, and the DPSIR model should be continuously used for dynamic evaluation and scientific decision support. In summary, this study reveals the complex game between socio-economic drivers and environmental protection responses in this region, and clearly points out that the shortcomings of the current response system are the key bottleneck of ecological restoration. Future management strategies must shift from passive lag to active intervention in order to ultimately achieve a long-term balance between mineral resource development and ecological environment protection.

7. Future Research Directions

For model enhancement, future studies could integrate detailed data on the actual pollution footprint of individual mines and incorporate specific public funding allocations for green mining initiatives. Such refinements would enable a more site-specific and financially contextualized analysis.

Regarding model validation, UAV-acquired big data offers a promising approach for systematic regional assessment. Cross-referencing model outputs with field-monitored data would strengthen the reliability and accuracy of the results.

At the policy level, in accordance with the directives of Heilongjiang’s Department of Natural Resources, locally tailored restoration plans should be formulated with clear phased objectives. These measures would support the provincial goal of completing the ecological remediation of historical legacy mines by 2035.