An Ecological Resilience Assessment of a Resource-Based City Based on Morphological Spatial Pattern Analysis

Abstract

In the context of ecological civilization construction, resource-based cities (RBCs) are tasked with the dual responsibility of national energy supply and transformation amidst complex social contradictions. These cities face the resource curse dilemma, characterized by resource depletion, conflicts in spatial production, in living, and in ecological spaces, and intertwined ecological–economic–social issues. Enhancing their ecological resilience is a crucial indicator of successful transformation strategies. This study focuses on Jinzhong City in Shanxi Province, employing Morphological Spatial Pattern Analysis (MSPA) to assist in the spatial analysis of urban ecological resilience. Additionally, Conefor Sensinode is utilized to identify key ecological sources that significantly contribute to urban resilience. A novel Risk-Connectivity-Potential (RCP) model is developed to construct a framework of indicators affecting the resilience of RBCs, which is used to assess the ecological resilience of Jinzhong City, particularly in relation to the spatial distribution of mining areas. The results indicate the following: (1) important ecological source areas within the city constitute approximately 39.47% of the total city area, predominantly located in woodland regions; (2) the overall assessment of ecological resilience reveals a heterogeneous pattern, increasing from west to east, with lower resilience observed in low-lying terrains and higher resilience in mountainous plateaus; (3) mines within significant ecological source areas are primarily situated in low-resilience zones near built land and agriculture land, while other mining areas are mainly found between high-resilience zones, posing risks of increased ecological resistance, reduced ecological connectivity, and potential ecological issues. This study explores the application of the ecological resilience framework in RBCs, providing a scientific basis and reference for the rational utilization of resources and urban transformation and development.The methodology and findings can be applied to similar cities globally, offering valuable insights for balancing resource management and ecological protection in the context of sustainable urban development.

1. Introduction

Resource-based cities (RBCs), leveraging their abundant natural resources, have emerged as central regions for basic industries and energy production, providing significant support for China’s economic development [1,2]. From the ecological civilization perspective, these cities currently confront the resource curse dilemma, encompassing resource depletion [3,4], conflicts in the spatial utilization of production space, in living space, and in ecological space [5,6], and intertwined ecological–economic–social issues [7]. The environmental carrying capacity and regional ecosystems are under immense pressure [8,9,10,11], rendering it challenging for RBCs to withstand various risks and disturbances [12,13,14,15]. However, in contrast to general cities, RBCs exhibit unique urban layouts and development patterns [16], primarily limited by the extraction and consumption of mineral resources, a situation unlikely to change in the short term [17]. The ecological restoration period is relatively prolonged, and the effects of restoration may not fully offset the prior environmental damage, leading to lower stability of the restored ecosystems and potential risks of secondary degradation [18,19]. Additionally, resource-based cities confront challenges including an extensive development model and inadequate supervision mechanisms [20,21,22]. Consequently, under the current development model, it is crucial for RBCs to further prevent disturbances and optimize the imbalance between mining activities and ecological protection [8,23].

Initially, research on RBCs focused on economic cycles and strategic innovation [24,25]. In recent years, the focus has gradually shifted towards sustainable resource development under policy strategies [26], urban economic performance evaluation, and transformation capabilities [11,27]. Subsequently, the concept of resilience has been gradually incorporated into the quantitative research of RBCs. For instance, Lian-Gang Li et al. [28] constructed an analytical framework for the economic transformation of the old industrial base in Liaoning to explore regional economic resilience. Hao-Ming Guan et al. [29] employed evolutionary resilience theory to compare the economic transformation processes of Shenyang, Chongqing, and Wuhan. Yuan-Yuan Zhu et al. [30] developed an evaluation index system for the economic resilience of resource-exhausted cities, quantitatively studying the characteristics and mechanisms of economic resilience in Daye City, Hubei Province. However, these studies primarily analyze the transformation process of RBCs from the perspective of economic resilience, with limited evaluations of their potential to resist risks from the ecological resilience perspective. Although research on urban ecological resilience is relatively abundant [31,32,33,34,35], studies measuring the ecological resilience of RBCs are still quite limited, especially lacking in constructing evaluation systems for ecological resilience in these cities and analyzing their characteristics from an ecological dimension. Therefore, enhancing the resilience level of RBCs urgently necessitates addressing the scientific evaluation of urban resilience levels, the regional differentiation characteristics of resilience in RBCs, and the causes of resilience differences. Through in-depth research and exploration, important theoretical support and practical guidance can be provided for the high-quality sustainable development of RBCs.

The study’s specific objectives are as follows: (1) identifying significant ecological source sites within Jinzhong City, (2) establishing a theoretical model and evaluation system to analyze the impact factors on the ecological resilience of resource cities, (3) conducting a multi-level evaluation of the ecological resilience of resource cities using available geographic data, (4) integrating ecological source sites with industrial and mining areas to construct the ecological resilience framework of resource cities, and (5) proposing optimization strategies grounded in the ecological resilience of resource cities.

2. Study Area and Research Methodology

2.1. Overview of the Study Area

Jinzhong City is a prefecture-level city under the jurisdiction of Shanxi Province, situated in the central part of Shanxi Province. It is bordered by the Taihang Mountain to the east and the Fen River to the west, with the provincial capital of Taiyuan to the north. To the south, it connects with the cities of Changzhi and Linfen, while to the southwest, it shares a border with Lvliang City. To the northeast, it is adjacent to Yangquan City. Jinzhong City serves as the core area of Taiyuan City and is an integral component of the central Shanxi urban agglomeration. It holds significant importance as a regional transportation hub and logistics base, not only for Shanxi but also for the broader North China region. The city’s urban zones are in close proximity to Taiyuan, forming an interconnected “three vertical and ten horizontal” road network system. The topography of Jinzhong City comprises plateaus, mountains, and plains. The Taiyuan Basin, Taiyue Mountains, Qinlu Plateau, and Taihang Mountains are arranged in a stepped manner from west to east, exhibiting a fundamental topographic pattern of higher terrain in the east and lower terrain in the west.

2.2. Data Sources

• The Natural Resources Bureau provided land-use data at a 30 m × 30 m resolution.
• From the geospatial data cloud (http://www.gscloud.cn/) (accessed on 13 November 2023), a digital elevation model (DEM) was derived at a resolution of 30 m × 30 m and utilized for slope extraction.
• Remote sensing images, also at a 30 m × 30 m resolution, were sourced from the geospatial data cloud. Using this data, the normalized difference vegetation index (NDVI) was calculated using ENVI 5.3 software.
• Vector data for geological hazards, rivers, transportation routes, and mining areas were acquired from the Bureau of Natural Resources and Planning of Jinzhong.

To facilitate spatial data analysis and processing, all datasets were converted into a unified coordinate and projection system (WGS 1984, UTM Zone 49 N).

2.3. Methods

2.3.1. Ecological Source Area Identification Based on MSPA and Conefor

MSPA (Morphological Spatial Pattern Analysis) serves as a fundamental tool for discerning the spatial topological relationships among structural elements and target image element sets [36]. Drawing upon the current land use dynamics within Jinzhong City (Figure 1), this investigation selected woodland and grassland as foreground data, contrasting with other land uses designated as background data. Utilizing the MSPA analysis tool integrated within the Guidos toolbox, seven distinct landscape types were delineated across the study area, encompassing core, islet, perforation, edge, loop, bridge, and branch, thereby facilitating the identification of ecological source sites.

The Conefor Sensinode 2.6 stands as a pivotal instrument in crafting ecological safety patterns and informing planning decisions [37,38]. To quantify overall landscape connectivity, accessibility between habitat patches was assessed using Conefor Sensinode 2.6.

Building upon the MSPA landscape element classification outcomes, this paper employed core area data as the cornerstone for patch importance calculation. By amalgamating these findings with the importance evaluation results of patches via the Conefor Sensinode 2.6, a comprehensive assessment was conducted to identify significant ecological source by considering the area of the source area and the relative importance index.

The MSPA analysis method is based on graphical principles to identify, classify, and interpret raster layers of land use types to form landscape ecological patches at the image element level [39]. The detailed breakdown of MSPA landscape types and their corresponding interpretations can be found in the GuidosToolbox manual. Typically, based on previous research methodologies, subsets or all of the woodlands, grasslands, waters, and arable lands are designated as foreground elements, while the remaining land is categorized as background [40,41,42]. Subsequently, the entire dataset undergoes raster binarization followed by MSPA analysis utilizing the image refinement analysis processing technique within the Guidos toolbox.

The accessibility measure employed by Conefor Sensinode 2.6 for evaluating habitat patch connectivity relies primarily on two indices: the Index of Connectivity ($IIC$) and the Probability of Connectivity ($PC$). These indices provide a comprehensive assessment of landscape connectivity and the significance of individual patches in sustaining it [43]. The metrics $dIIC$ and $dPC$ signify the extent of change in overall landscape connectivity following the removal of patches, serving as indicators of their importance to overall landscape connectivity. A greater value indicates a higher importance of the patch [38]. The principal calculation formula is outlined below:

$$IIC={\displaystyle \frac{{\sum }_{i=1}^n{\sum }_{i=1}^n\left(\frac{a_i{a}_j}{1+n{l}_{ij}}\right)}{A_L^2}}$$

where n is the total number of landscape patches; $a_i$ and $a_j$ are the areas of patch i and patch j, respectively, and $n{l}_{ij}$ is the connectivity index between patch i and patch j; $A_L$ is the total area of ecological land. When $IIC$ is 0, there is no connectivity between patches; when $IIC$ is 1, all landscape patches are in a connected state (0 ≤ $IIC$ ≤ 1).

$$PC={\displaystyle \frac{{\sum }_{i=1}^n{\sum }_{i=1}^n{a}_i{a}_j{P}_{ij}}{A_L^2}}$$

where n is the total number of landscape patches; $a_i$ and $a_j$ are the areas of patch i and patch j, respectively, and $p_{ij}$ is the maximum likelihood of species dispersal between landscape patches i and j; $A_L$ is the total area of ecological land. The larger the $PC$, the greater the likelihood of connectivity of landscape patches (0 < $PC$ < 1).

$$dIIC={\displaystyle \frac{IIC-II{C}^{\prime }}{IIC}}\ast 100$$

where $dIIC$ is the overall connectivity fluctuation value, and $IIC$′ is the overall connectivity level after excluding the patch.

$$dPC={\displaystyle \frac{PC-P{C}^{\prime }}{PC}}\ast 100$$

where $dPC$ is the possible connectivity fluctuation value, and $PC$′ is the possible connectivity level after excluding the patch.

2.3.2. Establishment of an Ecological Resilience Evaluation Indicator System

Ecological resilience furnishes a theoretical foundation and framework for comprehending ecosystem reactions to regional disruptions. Amid fluctuating environmental circumstances, ecosystems counteract the coercive impacts of risk factors by preserving system equilibrium or transitioning into a novel stable state through self-adjustment and self-organizational capacities [32]. Principal sources of system risk encompass internal human-induced disturbances and external shocks from natural disasters encountered during urban development [44,45,46]. Concurrently, the interconnected landscape fabric of urban areas offers avenues for mitigating urban stresses and risks [31]. The structural and functional connectivity of ecological source patches facilitates the maintenance and enhancement of ecological response processes within the region [47,48]. Ecosystems respond to disturbances commensurate with their capacity to withstand and mitigate risks, aiming to achieve restoration or reinstatement of the system’s equilibrium state [38,49,50].

The PSR (Pressure-State-Response) model [51] and the DPSIR (Driver-Pressure-State-Impact-Response) model [52] have found widespread application in urban risk assessment, disaster prevention planning, and the formulation of ecological security frameworks [53]. Grounded in the logical underpinnings of this theoretical nexus, this study posits that ecological resilience is shaped by ecological stress risk, system connectivity, and ecosystem response potential. Leveraging this theoretical framework in conjunction with the aforementioned risk-response models, the study introduces the RCP (Risk–Connectivity–Potential) model for evaluating ecological resilience (Figure 2).

This study employs the RCP evaluation model to assess the ecological resilience of resource-oriented cities, focusing on three dimensions: ecological risk, ecological connectivity, and ecological potential. In Jinzhong City, 15 determining variables reflecting ecosystem state and potential were identified and organized into an evaluation index system guided by principles of scientific rigor, rationality, and data accessibility. The selection of these indicators is based on an extensive literature review and their proven relevance in assessing ecological resilience.

Ecological risk is classified into anthropogenic and natural interference, with six selected indicators including land use, geologic disaster, distance from mine site, construction sites, roads, and water, assessed using the Euclidean Distance tool in ArcGIS Pro. These indicators have been widely used in previous studies to evaluate ecological risk. For instance, land use changes have been shown to significantly impact ecological resilience, while the proximity to mining sites and urban areas is crucial in resource-based cities [14,54].Connectivity probability ($PC$) and connectivity integral index ($IIC$) were derived from Conefor 2.6 calculations to gauge ecological connectivity. These metrics have been validated in numerous studies as effective measures of landscape connectivity [55]. Landscape indices, such as core area index (CA_MN, CAI_AM), aggregation index (AI), edge density (ED), landscape shape index (LSI), and patch density (PD), were computed using Fragstats to characterize ecological potential. These indices have been extensively used in landscape ecology to assess ecosystem structure and function [56,57]. Additionally, the normalized difference vegetation index (NDVI) was employed to measure vegetation cover, a key component of ecosystem resilience [32].

To assign weights to the 15 evaluation indices, a scoring analysis utilizing the analytic hierarchy process (AHP) model was conducted, ensuring a comprehensive and balanced evaluation approach (

Table 1. Ecological resilience evaluation indicators.

Target Layer	Criteria Layer	Weights	Criteria Sublayer	Weights	Solution Layer	Weights	Correlation
Ecological Resilience in RBCs	A1 Ecological Risk	0.36	B11 Anthropogenic Interference	0.21	C111 Land use	0.08	+
					C112 Distance from mine site	0.06	−
					C113 Distance from construction site	0.04	−
					C114 Distance from road	0.03	−
			B12 Natural Interference	0.15	C121 Geologic disaster	0.10	−
					C122 Distance from water	0.05	+
	A2 Ecological Connectivity	0.20	B21 Patch Connectivity	0.20	C211 Probability of connectivity (PC)	0.12	+
					C212 Integral Index of connectivity (IIC)	0.08	+
	A3 Ecological Potential	0.44	B31 Resistance	0.17	C311 Mean core area index (CAI_MN)	0.08	+
					C312 Edge density (ED)	0.04	+
					C313 Area-weighted core area index (CAI_AM)	0.05	+
			B32 Adapting Capability	0.21	C321 Aggregation index (AI)	0.06	+
					C322 Landscape shape index (LSI)	0.10	+
					C323 Patch density (PD)	0.05	+
			B33 Recovery Capability	0.06	C331 Normalized difference vegetation index (NDVI)	0.06	+

). We constructed pairwise comparison matrices based on the judgments of 10 experts, calculated the eigenvalues and eigenvectors of the matrices, obtained weights, and finally performed consistency checks to ensure the reliability of the weights.

The indicators were weighted to obtain the ecological resilience index (Figure 3), calculated as follows:

$$ER=\sum W_i{R}_i+\sum W_j{C}_j+\sum W_k{P}_k$$

where $ER$ represents the system ecological resilience index; $R_i$ represents the ith ecological risk influencing factor, $C_j$ represents the jth ecological connectivity influencing factor, and $P_k$ represents the kth ecological potential influencing factor; $W_i$, $W_j$, and $W_k$ represent the corresponding weights of the ecological risk, ecological connectivity, and ecological potential influencing factors, respectively. The larger the value of the system ecological resilience index, the higher the background resilience of the landscape in the region, and the stronger the ability to respond to external disturbances.

3. Result

3.1. Ecological Source Identification

The woodland and grassland (9761.23 km²) in Jinzhong City were used as foreground data, other land was used as background data, and MSPA was used for image processing (Figure 4). The core area was mainly distributed in the mountainous and highland areas in the southeastern part of Jinzhong City domain, with an area of 7170.84 km², which accounted for 43.72% of the total area of the study area, whereas the areas of islet, bridge, and branch all accounted for a smaller proportion of the area, which were 2.22%, 3.86%, and 3.01%, respectively. Although the area of ecological source patches was large, the percentage of areas with important connectivity values such as islet, bridge, and branch was limited, indicating that the connectivity between patches was poor, and the overall connectivity of the regional ecosystem was not high (

Table 2. Statistics of the MSPA analysis results.

Landscape Type	Area (km²)	Proportion of Area(%)	Woodland			Grassland
			Area (km²)	Proportion of Woodland (%)	Proportion of the Study Area(%)	Area (km²)	Proportion of Grassland (%)	Proportion of the Study Area (%)
Core	7170.84	72.16%	4694.89	86.00%	65.47%	142.23	3.31%	1.98%
Islet	264.24	2.66%	33.69	0.62%	12.75%	22.56	0.52%	8.54%
Perforation	320.45	3.22%	135.09	2.47%	42.16%	14.50	0.34%	4.52%
Edge	828.73	8.34%	262.77	4.81%	31.71%	26.66	0.62%	3.22%
Loop	421.68	4.24%	119.92	2.20%	28.44%	17.42	0.40%	4.13%
Bridge	632.27	6.36%	123.44	2.26%	19.52%	40.13	0.93%	6.35%
Branch	298.89	3.01%	48.68	0.89%	16.29%	16.10	0.37%	5.39%
Total	9937.10	100.00%	5418.48	99.26%	54.53%	279.60	6.50%	2.81%

).

The core area is the center of gravity of the distribution of ecological source sites and an important component that plays a supporting role for ecological resilience [58,59]. A total of 36 source sites with an area larger than 10km² were selected in the core area patches, and a second screening was conducted by setting a connectivity distance threshold of 2000 m and a connectivity probability of 0.5 through the Conefor Sensinode 2.6 to identify 23 core area patches with an area larger than 10km² and a relative importance index greater than 1 as significant ecological source sites (

Table 3. Ranking of the Core Area Importance Index.

Ecological Source	Area (km²)	Importance Index		Landscape Coincidence Probability
		Integral Index of Connectivity	Probability of Connectivity
1	20.1195	0.0014	0.0011	0.3108
2	46.1152	0.0072	0.0056	0.7124
3	71.2547	0.0171	0.0134	1.1007
4	15.8099	0.0008	0.0007	0.2442
5	14.1929	0.1894	0.4211	0.2192
6	25.2034	0.4845	0.8995	0.3893
7	17.211	0.3295	0.5323	0.2659
8	11.6352	0.2227	0.3773	0.1797
9	13.4891	0.1805	0.3506	0.2084
10	10.7165	0.2049	0.3053	0.1655
11	32.998	0.6338	0.8937	0.5097
12	79.2709	1.5257	2.4287	1.2245
13	24.3301	0.4656	0.6572	0.3758
14	35.719	0.6841	1.0493	0.5518
15	30.6168	0.5887	0.8912	0.473
16	18.7654	0.359	0.5829	0.2899
17	73.9342	1.4267	2.0889	1.1421
18	61.6595	1.2066	1.6351	0.9525
19	4455.6503	96.5801	96.7578	68.8287
20	227.2888	4.5292	6.4675	3.511
21	289.1563	5.738	8.7149	4.4667
22	91.2667	1.2943	2.4341	1.4098
23	807.1325	16.1558	22.7113	12.4682

, Figure 5).

3.2. Integrated Assessment of Ecological Resilience Based on RCP Modeling

The natural breaks (Jenks) was used to categorize the evaluation results of each impact factor into five grades, with Grades 1–5 indicating the toughness level from low to high, to acquire the assessment outcomes for each metric parameter under the three criteria layer of ecological stress risk, ecosystem connectivity, and ecosystem response potential, respectively.

3.2.1. Ecological Risk Assessment

Ecological risk assessment aims to evaluate and analyze potential negative impacts on ecosystems stemming from uncertain events [60]. Risks concerning structural and functional stability are categorized into two main factors: anthropogenic and natural interference. The cumulative anthropogenic disturbance pressure within the city area was assessed, considering four key factors, i.e., land use, distance from mine site, construction sites, and road, to reflect direct or indirect environmental pressures from human activities. Natural disasters have many negative impacts on the ecological environment within the Jinzhong City area. Coupled with accelerated urbanization, the surface water storage and flood retention capacity has declined rapidly, increasing the risk of flooding disasters [61]. Consequently, geologic disaster and distance from water were chosen to assess the risk of natural interference (Figure 6a–f).

3.2.2. Ecological Connectivity Assessment

The stability of urban ecosystems hinges upon the condition of their structural integrity, as a favorable state can mitigate the propagation of ecological risks and facilitate potential ecological recovery. The quality of the ecosystem is contingent upon its state [62]. In this investigation, we determined that the state of ecosystem structure can be delineated by the connectivity of the regional system. Therefore, we employed both the probability of connectivity index and the Integral Index of connectivity to assess the diversity and connectivity of the urban ecosystem at the landscape scale (Figure 6g,h).

3.2.3. Ecological Potential Assessment

Ecological potential is the ability of ecosystems to resist disturbances, which is significant for reducing and hindering ecological risks and stabilizing ecosystems [49]. Consequently, a subset of landscape pattern indices was employed as an indicator of response potential (

Table 4. Calculation results of the landscape pattern index.

Type	Mean Core Area Index	Edge Density	Area-Weighted Mean Core Area Index	Aggregation Index	Landscape Shape Index	Patch Density
forest	5.3183	39.5958	71.0607	90.0111	246.9087	2.7371
grass	4.124	60.6635	53.8221	80.6281	424.322	5.4674
argi	4.7092	38.8293	75.685	90.553	237.5554	2.3189
built	5.9759	9.94	62.8393	84.6272	153.2082	1.3622
water	6.5952	0.1792	67.0175	89.2091	17.6399	0.0189

). The larger the value of this indicator, the greater the ecosystem’s ability to resist disturbances, leading to heightened efficacy in addressing ecological risks and expediting the restoration of the ecological environment system to an equilibrium state (Figure 6i–o).

3.2.4. Comprehensive Assessment of Ecological Resilience

Based on the RCP model, the generated assessment results of the 15 toughness factors were analyzed according to the weighted superposition for the comprehensive assessment of ecological toughness (Figure 7). From the spatial distribution, the overall ecological resilience pattern in Jinzhong City showed the heterogeneous characteristics of increasing from west to east, with high resilience areas mainly distributed in Xiyang, Eastern Heshun, Northern Zuoquan, Qixian, Pingyao, Jiexiu, and the southeastern border of Lingshi, focusing on the forested space in the Taihang Mountains, the Qinlu Plateau, and the Taiyue Mountains, as well as low resilience areas mainly distributed in the southeast of the Taiyuan Basin, i.e., in parts of Yuji, Taiku, Qixian, Pingyao, Jiexiu, and Lingshi. The spatial distribution pattern manifests itself in the low flat terrain at lower toughness, the mountain plateau at higher toughness, and the overall toughness of the east superior west inferior stable state, in which the low toughness area as a whole accounted for a higher proportion of the main distribution of the land for construction and cropland, as well as other land, mostly artificial or semi-artificial semi-natural areas, in the structure of the natural ecosystem and the function of the supply of the more scarce resources, by the strongest interference with human activities. The southeastern part of the mountainous and highland woodland ecosystems have a higher degree of structural integrity and, as natural ecological barrier areas, have a stable and high level of ecological resilience.

3.2.5. Combined with a Significant Ecological Source Resilience Analysis of the Mine Site

Jinzhong City exemplifies a typical resource-oriented municipality, boasting a diverse array of mineral reserves, including coal, bauxite, iron, chromite, and ilmenite [8]. The city hosts a total of 443 mining sites, dispersed across 11 counties and districts. While mineral extraction drives significant socio-economic development, it inevitably exerts adverse effects on local vegetation, particularly through the direct stripping of surface vegetation [15]. Persistent ecological challenges stemming from historical mineral extraction practices remain salient, exacerbating tensions between urban expansion, regional resource exploitation, and ecological sustainability [63]. Leveraging ArcGIS spatial analysis tools to overlay the spatial coordinates of 114 mining sites onto a resilience assessment map of critical ecological zones (Figure 8), it was determined that 44 mining operations within ecological resource accounted for 38.60% of the total mining sites. These mines are mainly concentrated in low ecological resilience zones close to the distribution of landscape types such as agriculture and built, and mines outside the ecological sources are mainly concentrated in the southwestern part of the Taiyuan Basin and between the seams of significant ecological sources in the Qinlu Plateau and Taihang Mountains.

4. Discussion

4.1. Strategies for Optimizing Ecological Resilience

The construction of an evaluation index system constitutes a pivotal aspect of ecological resilience assessment and analysis. This paper utilizes the RCP model to construct the ecological resilience evaluation system of Jinzhong City, with the AHP analysis method for the determination of indicator weights, and clarifies the heterogeneous characteristics of the spatial distribution of ecological resilience in Jinzhong City, which is increasing from west to east, through the superposition of multi-influence factors. The observed distribution pattern can be attributed to several factors: (1) Jinzhong City exhibits a terraced topography, characterized by complex terrain and high ecosystem diversity in its eastern mountainous regions, fostering increased ecological resilience. Conversely, the northwestern lowlands feature flat terrain, relatively homogeneous ecosystems, and lower resilience. (2) The eastern mountainous areas are predominantly forested, with extensive ecological source areas and robust connectivity, thereby exhibiting higher resilience. In contrast, the northwestern lowlands consist mostly of agricultural and construction lands, with limited ecological source areas, poor connectivity, and reduced resilience. (3) The northwestern lowlands experience intensive human activities, high urbanization, and industrialization, exerting considerable pressure on the ecosystem and diminishing ecological resilience. In contrast, the eastern mountainous areas encounter fewer human disturbances, maintaining higher ecological robustness. (4) Mining activities are primarily situated on the peripheries of ecological source areas and within regions of low resilience, contributing to increased ecological resistance, diminished connectivity, and posing threats to overall ecological resilience.

Research on other RBCs has yielded comparable results. An ecological network’s ability to counteract ecosystem service degradation caused by land use alterations was demonstrated by Tang Feng et al. [64]. Esbah Hayriye et al. [65] suggested that greenways could boost urban open spaces’ ecological integrity, which aligns with our connectivity findings. Urban expansion can be effectively limited by growth boundaries, as proposed by Seong-Hoon Cho et al. [66], lending support to our recommendations for mining area regulation. In their examination of Liaoning’s historical industrial regions, Liangang Li et al. [28] uncovered strong links between resilience, terrain, land utilization, and anthropogenic activities. Jian Peng et al. [67] and Sucui Li et al. [68] highlighted ESP identification and optimization as an effective approach to balance urban development, resource exploitation, and nature conservation conflicts. Our study advances the field by quantitatively assessing these factors’ impacts on resilience, with a novel focus on mining distribution effects—an area previously underexplored.

Based on the resilience analysis of significant ecological source areas combined with the spatial distribution of mining zones, it is evident that mining activities are predominantly concentrated on the fringes of ecological sources close to the distribution of landscape types such as built and agriculture. Additionally, mining areas are situated within the interstices of ecological source areas amidst forested regions like the Qinlu Plateau and the Taihang Mountains. This spatial arrangement results in ecological resistance, diminished ecological connectivity, and other potential ecological hazards. To uphold and bolster connectivity and the ecosystem’s capacity for recovery within the source areas, it is imperative to undertake several measures. Firstly, there should be an expedited consolidation of resources through mergers and reorganizations, gradually phasing out dispersed small-scale mines within significant ecological source areas and their interstitial regions. This process aims to enhance the distributional structure of mining operations. Secondly, there is a need to promote environmentally sustainable exploration and exploitation of mineral resources in future mining endeavors, thus minimizing adverse impacts on the geological environment. Additionally, it is essential to contemplate novel mechanisms and approaches for integrated management during the mid to late stages of mining development. It is important to enhance the comprehensive management through innovative mechanisms and models, actively pursuing the integrated development of mining ecological conservation alongside tourism, pensions, and farming. It is essential to promptly establish an integrated solution for both historical and emerging ecological issues associated with mining, thus substantially elevating the urban mining ecological environment’s protection and management standards. Given the variability in regional toughness values, tailored optimization strategies are required. Priority should be accorded to strategic planning for mines located in key areas identified through rigorous toughness assessments, to underpin scientifically based restrictions on unregulated mining activities.

Subsequently, top-level planning for ecological resources and environmental protection should be carried out in conjunction with the results of the analysis, comprehensively integrating ecological and environmental management in Jinzhong City, establishing a mechanism for coordinated management of major environmental problems across districts and counties, and constructing a multi-level ecological resilience building system. Furthermore, leveraging the inherent value of ecosystem services is paramount. This involves harnessing their regulatory and maintenance functions within the ecological environment and deploying nature-based solutions to mitigate regional risks, thereby bolstering the city’s resilience. The approach entails dual strategies: prioritizing the protection and restoration of natural habitats with significant ecological potential while simultaneously enhancing the ecological restoration of artificial or semi-natural areas situated at ecologically critical nodes. This dual strategy aims to construct an ecological resilience network capable of prompt and effective response, fostering sustainable development.

4.2. Limitations and Shortcomings of the Study

The establishment of ecological resilience grading standards is pivotal for assessing ecological resilience, yet varying standards employed by scholars yield divergent research outcomes. In this study, the evaluation system fails to adequately incorporate economic and social factors. Instead, it solely suggests three approaches—natural restoration, assisted regeneration, and ecological reconstruction—to guide the artificial intervention in distinct mining areas. The imperative of devising a more scientifically rigorous ecological vulnerability grading standard for evaluating ecological resilience warrants exploration and deliberation in future research endeavors.

Moreover, the point data of mining areas utilized in this study for Jinzhong City fails to comprehensively capture the diversity of mining types, the land area, and the degree of industrial and mining development. Consequently, it lacks the precision required to effectively guide the enhancement of ecological resilience amidst concurrent industrial and mining activities. Therefore, it is imperative to acquire more comprehensive and specific data regarding mining areas in subsequent research efforts. Such data will facilitate the identification of the extent and intensity of industrial and mining development, enabling the formulation of more precise guiding and restoration strategies to mitigate the impact on Jinzhong City’s ecological resilience.

5. Conclusions

This study aims to explore the spatial pattern of ecological resilience in RBCs and provides an in-depth analysis using Jinzhong City as an example. Jinzhong epitomizes a prototypical resource-driven urban center, wherein the exploitation and utilization of mineral resources disrupt its ecological equilibrium against the backdrop of its natural setting. By integrating considerations of geomorphic morphology, geographical factors influencing ecological security, and landscape attributes, this paper derives the following conclusions from a thorough evaluation and analysis of Jinzhong City’s ecological resilience: (1) This study identifies a total of 23 significant ecological source areas, collectively spanning 6473.54 km², which represents approximately 39.47% of the city’s total area. These areas are predominantly located in the Taihang Mountains, the Qinlu Plateau, the Taiyue Mountains, and their surrounding environments. They exhibit a high level of ecosystem structural integrity and consistently maintain strong ecological resilience, serving as crucial support for the overall ecological framework of the city. (2) The ecological resilience of Jinzhong City exhibits a heterogeneous distribution, with an increasing gradient from west to east. High resilience areas are primarily found in the eastern mountainous plateaus, whereas regions with lower resilience are mainly concentrated in the northwestern lowlands. This spatial pattern offers valuable insights for future urban planning and ecological conservation efforts. (3) Approximately 38.60% of the mining areas are situated within significant ecological source zones, indicating that mineral resource extraction significantly impacts ecological resilience. This impact is especially pronounced in key ecological areas, where mining activities pose a greater threat to ecological connectivity and increase potential ecological risks. Addressing the conflict between mineral resource extraction and ecological protection is imperative to mitigate these risks and ensure sustainable urban development.

In the future, Jinzhong City must enhance the consolidation of resources within ecologically significant source areas and optimize the distribution and scale of mining operations. This entails refining mining development strategies tailored to varying levels of ecological sensitivity. Priority should be given to areas deemed highly ecologically sensitive in order to curtail unregulated mining activities and safeguard ecosystem integrity. Furthermore, fostering cross-disciplinary research and collaboration is essential to consolidate resources and jointly explore the feasibility of integrating ecological civilization with the sustainable development of RBCs.

This study presents an assessment method and perspective on urban ecological resilience in Jinzhong City, which can also make broader contributions to the fields of urban ecology and sustainable development. Through the quantification of ecological resilience and analysis of its spatial distribution characteristics, we offer fresh insights into the complexity and dynamics of urban ecosystems. This research not only enhances urban planning and management practices but also establishes a scientific foundation for crafting more effective ecological conservation policies. Ultimately, it aims to provide scientific references for promoting sustainable development and rational resource utilization in cities reliant on natural resources.