Spatio-Temporal Analysis about Resource and Environmental Carrying Capacity (RECC) of Mining Cities in Coal-Concentrated Areas: A Case Study of Huaihai Economic Zone in China

Abstract

The over-exploitation and utilization of natural resources in mining cities has caused sharp contradictions between urban development and ecological protection. In addition, dynamic changes in resources and environmental carrying capacity (RECC) will be changed by the different ways and degrees of the specific utilization of natural resources. In order to better study the dynamic trends and reasons of the RECC in mining cities, so as to provide methods and suggestions for the mining cities to save resources, improve the ecology, and adjust the industrial structure, this article will construct an evaluation index for the RECC of mining cities. Taking Huaihai Economic Zone as the research object, we used the entropy method to determine the index weight. Then, the TOPSIS model was used to analyze the spatial and temporal characteristics of the development of the RECC of mining cities in coal-concentrated areas during 2012–2018. After the analysis, the study found five results. (1) Except Zaozhuang, the RECC of the six mining cities in the Huaihai Economic Zone showed a steady upward trend during 2012–2018; (2) among the three subsystems, natural resources have the greatest impact on the RECC; (3) in terms of space, the carrying capacity in this region gradually has a polarization phenomenon centered on Xuzhou and Jining, and will continue to increase in the future; (4) the types of mining cities will have an impact on the change characteristics of the RECC; and (5) most cities improve the level of ecological carrying capacity (ECC) and social economic carrying capacity (SECC) at the cost of the decline of resources carrying capacity (RCC). Based on the results, the research can provide optimized reference strategies for the transformation and development of mining cities to ecological cities in Huaihai Economic Zone.

1. Introduction

After the 18th National Congress, China has vigorously promoted the construction of ecological civilization, which has become an important concept leading the harmonious coexistence of human and nature in the new era [1]. At present, China is in an important stage of transition from industrial civilization to ecological civilization. Urban civilization represented by industrial and mining cities emerged a lot in the period of industrial civilization. Such cities have many characteristics that will affect the transition period to ecological civilization, especially in terms of resources and environmental carrying capacity (RECC). There are 244 mining cities in China, including 125 prefecture level cities, accounting for 42.6% of the total prefecture level cities in China, whose percentage is very high [2]. The evaluation of RECC is very important for land space planning, which is an important basis and means for the implementation of ecological civilization, and plays a significant role in improving scientific and operational planning. Therefore, it is particularly needed to deal with the resource, environment, social, and economic problems of mining cities and to improve the comprehensive RECC.

A mining city is a special type of city that grows up relying on resource development and takes the mining and processing of mineral resources as the leading industry [3]. As a typical resource-based city, mining cities are needed to provide large amount of energy and resources for themselves and other cities in the development process, so it is difficult to avoid damaging the ecological environment in the production process [4], which reduces the RECC. Ecological environment problems in mining cities commonly include soil pollution [5], water pollution [6,7], air pollution [8], underground goaf [9], mining wasteland, and surface vegetation damage [10], whose further deterioration may also lead to serious social and economic problems. The depletion of mineral resources and the decline of leading industries in mining cities lead to the following problems, such as economic contraction, increased unemployment, and social upheaval. This further expands the ecological deterioration directly to population outflows and even the demise of mining cities [11]. Resource and environmental carrying capacity (RECC) refers to the maximum scale of social and economic activities that the resource and environment system can withstand within a certain time and region without affecting the reasonable development of natural resources and ecological environment protection [12,13]. In the field of ecology, Park and Burgess were the first to put forward the concept of carrying capacity which is used to study the maximum number of target species that can be normally supported in a region [14]. Later, William Allan first mentioned the concept of land bearing capacity in his research on the African economy [15]. Later, the research on bearing capacity gradually penetrated into the fields of economics, environment, society, etc. [16]. At present, the research methods on the RECC of cities mainly include the analytic hierarchy process [17,18,19], system dynamics models [20,21,22], fuzzy mathematics [23], the comprehensive index method [24], and the entropy weight topsis model method [25,26]. In addition, there are also some studies to measure RECC by constructing different analytical models, such as the DPSIR model [27], the niche width model [28], the unbalanced condition assessment model [29], and the three-dimensional tetrahedron model [30], which extend the research in this field in terms of technical methods. There are different studies of countries [31], regional urban clusters [28,32], individual cities [33], and even individual towns [27]. There are also studies that break down administrative boundaries and focus on natural areas [34] or landscape areas [35]. Most of the above literature is from the China National Knowledge Infrastructure (CNKI) database and WOS database from 2000 to 2022. From the above research, we can find that the current research is mainly concentrated in a national [15], provincial [16], municipal, or single coal mine area [12,18]. However, studies on the spatial-temporal dynamic analysis of the RECC of mining cities are still very rare, from the perspective of coal concentration areas. The particularity of resource-based mining cities provides new ideas and results for this study, which are different from general cities.

Based on the actual development process of seven mining cities in the Huaihai Economic Zone, this research analyzes the relevant data of natural resources, ecological environments, and social economies from 2012 to 2018. The research will comprehensively consider the temporal and spatial variation characteristics of resource and environment carrying capacity, so as to reveal the dynamic variation rules of RECC and analyze the most dominant factors driving the change. It is hoped that the research can provide reference for mining cities in the Huaihai Economic Zone to speed up the transformation to ecological economy and enhance the comprehensive carrying capacity of the region.

2. Materials and Methods

2.1. Case Study Area

The Huaihai Economic Zone is located in the junction area between East China and Central China, as well as the eastern core region of the Eurasian Continental Bridge, whose longitude is between 114°49′ and 119°48′, and latitude is between 33°08′ and 36°13′. Its military strategic position is very important, moreover it also occupies an important position in the national economic development pattern [36]. In 2020, the GDP of the ten cities in the core area of the Huaihai Economic Zone totaled 34,463 trillion yuan, accounting for 3.39% of the national GDP. It bears the responsibility of connecting China’s economy from east to west and from north to south, its north side is the Beijing–Tianjin–Hebei urban agglomeration, its south side is the Yangtze River Delta urban agglomeration, its west side is the Guanzhong urban agglomeration, and its east side is the outlet to the sea in Lianyungang (Figure 1). In the country’s mining cities, the Huaihai Economic Zone has a very important position, it occupies two of the country’s 14 coal bases, which are the southwest Shandong coal base and Lianghuai coal base [37] (Figure 2). According to the Huaihe River Ecological Economic Belt Development Plan released by the National Development and Reform Commission on 16 October 2018, seven of the ten core cities in the Huaihai Economic Zone are mining cities (Figure 3), whose name are Xuzhou (ZX), Huaibei (HB), Jining (JN), Zaozhuang (ZZ), Linyi (LY), Suzhou (SZ), and Shangqiu (SQ). According to the classification which comes from a China State Council notice in 2013 (

Table 1. Table of mining city types.

Growth Type	Mature Type	Decay Type	Regenerative Type
Shangqiu	Jining, Suzhou	Zaozhuang, Huaibei	Xuzhou, Linyi

), it is shown that mining resource-based cities are classified into four types, including growth type, mature type, decay type, and regenerative type. Within the Huaihai Economic Zone, these seven mining cities play an important role in energy supply during the development of the whole region. However, the mining and processing of minerals have produced environmental pollution problems, leading to the contradiction between environmental protection and economic development in the Huaihai Economic Zone. In order to save resources, improve ecology, and develop economy in this region, it is necessary to further analyze the spatio-temporal variation characteristics and leading factors of resource and environment carrying capacity.

2.2. Research Methodology

Firstly, this paper constructed the evaluation index system of RECC through related review. Secondly, the research used the entropy method to determine the index weight of resource and environment carrying capacity of each mining city. Then it took the average weight of seven mining cities as the final weight for a certain index. Finally, the weighted matrix is constructed, and then the TOPSIS model is used to analyze the RECC of mining cities in Huaihai Economic Zone. The entire analysis process is shown in the figure below (Figure 4).

2.3. Sources of the Data

The data used in this study are from seven prefecture-level statistical yearbooks, Jiangsu Statistical yearbooks, Anhui Statistical yearbooks, Shandong Statistical yearbooks, Henan Statistical yearbooks, China Economic and Social Big Data Research Platform, and China Urban Construction Statistical yearbooks. The above yearbooks and materials can be easily searched in the China National Knowledge Infrastructure (CNKI) database. The data mainly include ecological environment, natural resources, resource utilization, environmental protection, urban and rural construction, economic development, and social security.

2.4. Construction of Index System

In recent years, many scholars have conducted relevant studies on the construction of the evaluation index system of resource and environment carrying capacity. For example, Graymore Mlm divides resource and environment carrying capacity into three subsystems: resource, ecology, and social economy [38]. Some scholars believe that the concept of carrying capacity has evolved step by step from population carrying capacity, resource carrying capacity, environmental carrying capacity, and ecological carrying capacity [39]. Others believe that resources, environment, and social economy can better reflect the ability of resource environment systems to withstand various social and economic activities of human beings [40]. Therefore, the indicators selected in this paper refer to relevant studies, especially some indicators added to indicate the particularity of a mining city RECC study (

Table 2. Index system table.

System	First Level Index	Second Level Index	Third Level Index	Efficiency (+/−)	Resources of References
Resources and environmental carrying capacity	Natural resources	Water resources	Water resources per capita (t)	+	Jingjing Bai et al. [41]
			Annual water consumption per capita (t)	−	Yang Chen et al. [42]
		Cultivated land resource	Per capita sown area (km2)	+	Guiyou zhang et al. [43]
			Arable land (km2)	+	Liao Shiju et al. [44], Wei-Ling Hsu et al. [45]
		Forest resources	Total forest area (km2)	+	Jin Yue [46]
			Forest coverage rate (%)	+	Liao Shiju et al. [45], Jingjing Bai et al. [42]
		Mineral resources	Coal production (t)	−	An Qier et al. [47], Zheng Xin et al. [12]
			Raw coal consumption (t)	−	Bao Keyu [48]
		Resource utilization efficiency	Coal consumption per 10,000 yuan of GDP (t)	−	Zheng Xin et al. [12], Chen Dan et al. [49]
			Water consumption per 10,000 yuan of GDP (t)	−	Li Shaonan et al. [50], Zheng Xin et al. [12]
	Ecological environment	Green environment	Green rate of built-up area (%)	+	Liao Shiju et al. [45], Yi Xiao et al. [51]
			Green area per capita (m2)	+	Chen Dan et al. [50]
		Water environment	Discharge of sewage (t)	+	Yi Xiao et al. [52]
			Sewage treatment rate (%)	+	Liao Shiju et al. [45], Li Shaonan et al. [51]
		Atmospheric environment	Industrial SO2 emissions (t)	−	Yang Chen et al. [43], Yi Xiao et al. [52]
			Industrial smoke (powder) dust treatment rate (%)	−	Bao Keyu [49]
		Soil environment	Industrial solid waste production (t)	−	Zhaofeng Wang et al. [52]
			Industrial solid waste disposal rate (%)	+	Tan S et al. [53]
	Social economic environment	Living ability	Urban per capita housing area (m2)	+	Chen Dan et al. [50]
			Rural per capita housing area (m2)	+	Chen Dan et al. [50]
		Traffic capacity	Road network area (m2)	+	Tan S et al. [53]
		Education level	Number of students enrolled in higher education (kilo)	+	Jin Yue [47]
		Income and Consumption	Engel coefficient (%)	+	Jin Yue [47]
			Urban per capita disposable income (yuan)	+	Yi Xiao et al. [52], Zhaofeng Wang et al. [52]
		GDP	GDP of City (billion yuan)	+	Zhaofeng Wang et al. [52], Tan S et al. [53]
			GDP per capita (yuan)	+	Jingjing Bai et al. [42]
		Medical and health care	Ten thousand people have the number of doctors (kilo)	+	Bao Keyu [49]
			Number of health facilities	+	Zheng Xin et al. [12]

). According to the inclusion relationship of natural resources, this paper selects five indexes of water, arable land, forest, mineral resources, and utilization efficiency from the perspective of resource types and utilization efficiency to construct second-level indexes. In the ecological environment, four indicators of green space, water, atmosphere, and soil environment were selected from the perspective of ecological environment category to construct second-level indicators. In the social and economic environment, six indicators including living ability, transportation ability, education level, economic income, GDP status, and medical and health care were selected from the perspectives of residents’ life and urban development to construct the second-level indicators. In this paper, 3 first-level indicators, 15 second-level indicators, and 28 third-level indicators are selected for the RECC of the study area (Table 2).

2.5. Entropy Weight Method to Determine the Weight

Faced with the characteristics of data uncertainty, disorder, and large amount of information, this paper adopts the entropy weight TOPSIS model to evaluate the resource and environmental carrying capacity of mining cities in the Huaihai Economic Zone. The entropy method can effectively avoid the shortcoming of too strong a subjectivity in quantitative analysis, and the result is more reasonable [54]. The biggest advantage of TOPSIS algorithm is to find the nearest solution to the optimal solution by step. The calculation process of the improved entropy weight TOPSIS model is relatively simple, which can reflect the real weight relatively objectively, scientifically, and accurately, and can help to select the optimal solution more effectively [55].

2.5.1. Determination of Target Sequence

The original data matrix was constructed from 28 carrying capacity analysis indicators from 2012 to 2018. Due to the different dimensions of data, it is necessary to standardize the original data matrix.

$$\mathrm{Positive} \mathrm{indicators}: x_{ij}=\frac{X_{ij}-\min \left(X_{ij}\right)}{\max \left(X_{ij}\right)-\min \left(X_{ij}\right)}$$

(1)

$$\mathrm{Negative} \mathrm{indicators}: x_{ij}=\frac{\max \left(X_{ij}\right)-X_{ij}}{\max \left(X_{ij}\right)-\min \left(X_{ij}\right)}$$

(2)

In the formula, X_ij is the initial value of the i index in the j year; x_ij is the standardized value of the i index in the j year; i is the number of evaluation indicators, i = 1, 2, …, m; j is the number of evaluation years, j = 1, 2, …, n. The standardized matrix A was obtained by calculating the formula.

$$A=\left[\begin{array}{ccc}{\mathrm{x}}_{11}& \dots & {\mathrm{x}}_{1\mathrm{n}}\\ \dots & \dots & \dots \\ {\mathrm{x}}_{\mathrm{m}1}& \dots & {\mathrm{x}}_{\mathrm{mn}}\end{array}\right]$$

(3)

2.5.2. The Entropy Method Calculates the Weight

The entropy method can effectively take into account the variation degree of the evaluation index and objectively reflect its importance. Following is the entropy weight calculation formula.

$$w_i=\frac{1-e_i}{m-{\displaystyle \sum _{i=1}^m{e}_i}}$$

(4)

$$e_i=-\frac{1}{\ln n}{\displaystyle \sum _{j=1}^n{f}_{ij}}\ln f_{ij}$$

(5)

In the formula $f_{ij}=\frac{x_{ij}}{{\displaystyle \sum _{j=1}^n{x}_{ij}}}$, if $f_{ij}=0$, then $\underset{f_{ij}\to 0}{\lim }{f}_{ij}\ln f_{ij}=0$.

$w_i$ is the weight of carrying capacity index; $e_i$ is information entropy. The lower the entropy, the more orderly the system; the higher the entropy, the more chaotic or scattered the system. In order to reflect the objectivity and fairness of weights, the weights of 28 indicators of seven mining cities are averaged twice. The treatment formula is followed.

$$w_j=\overline{w_i}$$

(6)

2.5.3. Construction of TOPSIS Model

In order to increase the objectivity of the matrix, this paper creates the normalized carrying capacity analysis matrix Y according to the previously determined weights.

$$Y={\left|y_{ij}\right|}_{m\times n}={\left|w_j\times x_{ij}\right|}_{m\times n}$$

(7)

2.5.4. Determine Positive and Negative Ideal Solutions

The positive ideal solution is the optimal solution of each index in the bearing capacity analysis, and is the maximum value of the ith index in the evaluation data in j years. The negative ideal solution is the worst solution of each index in the carrying capacity analysis, and is the minimum value of the ith index in the evaluation data in j years. The specific calculation formula is as follows:

$$Y^{+}=\max \left\{y_{ij}\right\}$$

(8)

$$Y^{-}=\min \left\{y_{ij}\right\}$$

(9)

2.5.5. Distance Calculation

In this paper, Euclidean distance is used to calculate the distance between the indexes of resource and environmental carrying capacity of mining cities and the positive and negative ideal solutions. Let $D^{+}{{}_j}_{}$ denote the distance between the ith index and $y^{+}{}_i$, and $D^{-}{{}_j}_{}$ denote the distance between the ith index and $y^{-}{}_i$. The specific calculation formula is as follows:

$$D^{+}{}_j=\sqrt{{\displaystyle \sum _{i=1}^m(y^{+}{}_i-y_{ij}{)}^2}}$$

(10)

$$D^{-}{}_j=\sqrt{{\displaystyle \sum _{i=1}^m(y^{-}{}_i-y_{ij}{)}^2}}$$

(11)

2.5.6. Comprehensive Calculation Results of RECC

$C_j{}_{}$ is the comprehensive evaluation index of urban resources and environment carrying capacity in the j year, whose value range is between 0 and 1. When $C_j{}_{}=0$, it indicates that the carrying capacity of resources and environment in this region is the lowest; when $C_j{}_{}=1$, it indicates that the resource and environment carrying capacity of this region is optimal.

$$C_j=\frac{D^{-}{}_j}{D^{+}{}_j+D^{-}{}_j}$$

(12)

2.6. Calculation Results of Entropy Weight and TOPSIS Model

After standardizing the original data, the paper uses entropy weight method to calculate 28 index weights of each mining city. Then, by selecting the average weight, the unified index weight of the RECC of mining cities in the Huaihai Economic Zone can be obtained (

Table 3. Index weight table.

Third Level Index	XZ	HB	JN	ZZ	LY	SZ	SQ	Average
Water resources per capita	0.058	0.070	0.166	0.239	0.000	0.182	0.173	0.127
Annual water consumption per capita	0.000	0.000	0.001	0.000	0.000	0.000	0.000	0.000
Per capita sown area	0.001	0.002	0.014	0.056	0.006	0.001	0.142	0.032
Arable land	0.000	0.000	0.000	0.003	0.000	0.017	0.001	0.003
Total forest area	0.004	0.007	0.010	0.006	0.095	0.010	0.028	0.023
Forest coverage rate	0.004	0.017	0.003	0.009	0.097	0.014	0.024	0.024
Coal production	0.013	0.365	0.031	0.162	0.001	0.000	0.050	0.089
Raw coal consumption	0.150	0.017	0.474	0.020	0.002	0.001	0.022	0.098
Coal consumption per 10,000 yuan of GDP	0.000	0.000	0.000	0.001	0.000	0.000	0.000	0.000
Water consumption per 10,000 yuan of GDP	0.000	0.001	0.000	0.000	0.000	0.000	0.000	0.000
Green rate of built-up area	0.001	0.001	0.006	0.005	0.001	0.055	0.016	0.012
Green area per capita	0.017	0.007	0.074	0.033	0.014	0.044	0.084	0.039
Discharge of sewage	0.006	0.000	0.000	0.001	0.000	0.000	0.001	0.001
Sewage treatment rate	0.030	0.000	0.000	0.001	0.001	0.005	0.001	0.005
Industrial SO2 emissions	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
Industrial smoke (powder) dust treatment rate	0.000	0.001	0.000	0.001	0.001	0.000	0.000	0.001
Industrial solid waste production	0.002	0.001	0.001	0.000	0.005	0.001	0.000	0.001
Industrial solid waste disposal rate	0.002	0.000	0.001	0.001	0.008	0.013	0.003	0.004
Urban per capita housing area	0.012	0.018	0.014	0.019	0.044	0.032	0.026	0.024
Rural per capita housing area	0.008	0.007	0.005	0.024	0.041	0.031	0.034	0.021
Road network area	0.001	0.388	0.030	0.229	0.018	0.061	0.074	0.114
Number of students enrolled in higher education	0.002	0.004	0.056	0.044	0.114	0.086	0.012	0.045
Engel coefficient	0.056	0.013	0.030	0.021	0.176	0.122	0.026	0.063
Urban per capita disposable income	0.094	0.045	0.035	0.013	0.088	0.068	0.073	0.059
GDP of City	0.091	0.015	0.021	0.039	0.104	0.099	0.063	0.062
GDP per capita	0.098	0.015	0.015	0.046	0.101	0.129	0.115	0.074
Ten thousand people have the number of doctors	0.040	0.008	0.015	0.024	0.019	0.030	0.021	0.022
Number of health facilities	0.311	0.001	0.000	0.005	0.067	0.001	0.013	0.057

). After using standardized matrix calculation of the resource environmental bearing capacity value, the RECC values of seven mining cities in the Huaihai Economic Zone from 2012 to 2018 (

Table 4. Numerical table of RECC from 2012 to 2018.

	2012	2013	2014	2015	2016	2017	2018
XZ	0.180	0.203	0.213	0.219	0.232	0.242	0.245
HB	0.143	0.156	0.154	0.170	0.194	0.198	0.220
JN	0.219	0.227	0.225	0.227	0.240	0.243	0.253
ZZ	0.190	0.184	0.186	0.182	0.178	0.171	0.166
LY	0.199	0.205	0.204	0.207	0.210	0.212	0.212
SZ	0.221	0.218	0.218	0.219	0.225	0.224	0.224
SQ	0.214	0.214	0.213	0.211	0.211	0.213	0.214
SUM	1.365	1.406	1.412	1.436	1.489	1.503	1.535

) and the values of carrying capacity subsystems (

Table 5. Numerical table of RECC subsystem of the area from 2012 to 2018.

	2012	2013	2014	2015	2016	2017	2018
RCC	0.623	0.587	0.524	0.505	0.493	0.476	0.412
ECC	0.309	0.340	0.401	0.406	0.439	0.465	0.521
SECC	0.434	0.480	0.487	0.525	0.557	0.562	0.602

and

Table 6. Numerical table of RECC subsystem of each city from 2012 to 2018.

	2012	2013	2014	2015	2016	2017	2018
XZ	0.180	0.203	0.213	0.219	0.232	0.242	0.245
RCC	0.093	0.117	0.106	0.113	0.108	0.113	0.066
ECC	0.035	0.024	0.047	0.040	0.058	0.051	0.085
SECC	0.051	0.062	0.059	0.066	0.066	0.077	0.093
HB	0.143	0.156	0.154	0.170	0.194	0.198	0.220
RCC	0.055	0.044	0.040	0.040	0.055	0.044	0.045
ECC	0.030	0.055	0.052	0.061	0.044	0.074	0.080
SECC	0.059	0.057	0.062	0.069	0.095	0.081	0.095
JN	0.219	0.227	0.225	0.227	0.240	0.243	0.253
RCC	0.126	0.120	0.103	0.088	0.088	0.093	0.083
ECC	0.019	0.020	0.042	0.054	0.063	0.060	0.081
SECC	0.073	0.088	0.079	0.085	0.088	0.089	0.089
ZZ	0.190	0.184	0.186	0.182	0.178	0.171	0.166
RCC	0.081	0.075	0.068	0.067	0.053	0.043	0.039
ECC	0.054	0.049	0.042	0.038	0.050	0.056	0.055
SECC	0.055	0.061	0.076	0.078	0.075	0.072	0.072
LY	0.199	0.205	0.204	0.207	0.210	0.212	0.212
RCC	0.071	0.066	0.058	0.056	0.054	0.052	0.050
ECC	0.062	0.071	0.076	0.073	0.075	0.079	0.077
SECC	0.065	0.068	0.069	0.078	0.081	0.082	0.085
SZ	0.221	0.218	0.218	0.219	0.225	0.224	0.224
RCC	0.107	0.085	0.082	0.078	0.074	0.071	0.070
ECC	0.077	0.077	0.075	0.073	0.079	0.076	0.072
SECC	0.037	0.056	0.061	0.068	0.071	0.077	0.083
SQ	0.214	0.214	0.213	0.211	0.211	0.213	0.214
RCC	0.089	0.081	0.068	0.064	0.061	0.059	0.059
ECC	0.031	0.044	0.065	0.067	0.069	0.069	0.070
SECC	0.093	0.089	0.080	0.081	0.081	0.084	0.085
Total	1.365	1.403	1.412	1.436	1.489	1.512	1.534
RCC	0.623	0.587	0.524	0.505	0.493	0.476	0.412
ECC	0.309	0.340	0.401	0.406	0.439	0.465	0.521
SECC	0.434	0.480	0.487	0.525	0.557	0.562	0.602

) were obtained.

3. Results

According to the results of the entropy weight method, among all first-level indicators, socio-economic environment has the greatest impact on the overall resource and environment carrying capacity level, with its weight reaching 0.542. Moreover, the natural resources impact a lot, with a weight of 0.395. The ecological environment has the least impact on the overall resource and environment carrying capacity level, with a weight of 0.063. Among the three-level indexes, the total amount of water resources has the greatest impact on the level of RECC, with the index weight reaching 0.127. The second is highway mileage, with an index weight reaching 0.114. The third index weight is raw coal consumption, whose weight reached 0.098. The top 10 indexes in descending order are: Water resources per capita > Road network area > Raw coal consumption > Coal production > Per capita GDP > Engel coefficient > GDP of City > Urban per capita disposable income > Number of health facilities > Number of students enrolled in higher education (Table 3).

The research results show that the RECC of mining cities in the Huaihai Economic Zone rises from 1.3651 to 1.5353, showing an overall upward trend from 2012 to 2018, with an increase of 12.46% growth rate. In the initial period, the improvement of RECC was relatively slow from 2012 to 2015, the overall level increased by 5.20%, and the average annual increase rate was 1.73%. From 2015 to 2018, the environmental carrying capacity improved faster, with the overall level increasing by 6.91%, with an average annual increase of 2.30%. The RECC of seven cities in 2012 ranked from high to low, respectively, is: SZ > JN > SQ > LY > ZZ > XZ > HB; however, in 2018 it became: JN > XZ > SZ > HB > SQ > LY > ZZ (Table 4). The overall ranking changes greatly, especially in Xuzhou City and Huaibei City, respectively, up to fourth and third rank. Jining City rose from second place to first place; the other cities all fell two places. Among all of them, the rank of Huaibei City improved fastest, with an overall increase of 53.8% RECC in the six years, while the level of Zaozhuang City improved the slowest, with a decrease of 12.77% in the six years. The speed of horizontal improvement from high to low is: HB > XZ > JN > LY > SZ > SQ > ZZ.

3.1. Variation Characteristics of RECC in Each City

The RECC of Xuzhou City increased from 0.180 in 2012 to 0.245 in 2018, with an overall increase of 36.11% and an average annual increase of 6.1%. During 2012–2013, the resource and environment carrying capacity of Xuzhou increased by 13.0% in a single year. During this period, the overall output and consumption of coal resources in Xuzhou decreased by a large margin, while the GDP situation developed well and increased by 12.52%, which also led to a significant increase in urban per capita income and urban finance, making the single-year carrying capacity level show a rapid rise trend. Within six years, Xuzhou’s carrying capacity level has increased from the sixth to the second, with a very significant increase (Figure 5 (XZ)).

The RECC of Huaibei City increased from 0.143 in 2012 to 0.220 in 2018, with an overall increase of 53.85% and an average annual increase of 8.97%. From the bottom level in 2012, it has rapidly risen to the middle level in the region, and the momentum is very strong. It is expected that, in the future, it will surpass Suzhou and become one of the top three levels in the region. In six years, the level of carrying capacity improved rapidly. During 2017–2018, it increased by 14.42%, which was mainly due to the improvement of water resources in 2018. During 2013–2014, it decreased by 1.4%, which was mainly due to the fact that coal mining in Huaibei City increased rather than decreased during the period, leading to the overall decline in the level (Figure 5 (HB)).

The RECC of Jining City increased from 0.218 in 2012 to 0.253 in 2018, with an overall increase of 15.9% and an average annual increase of 2.6%. The overall development of Jining city is relatively stable. Because the starting point of environmental carrying capacity was relatively high, the development momentum is still excellent, so by 2018, the RECC of Jining City is still at the first level in the region (Figure 5 (JN)).

The RECC of Zaozhuang decreased from 0.190 in 2012 to 0.166 in 2018, with an overall reduction of 12.7 percent and an average annual decrease of 2.1 percent. Zaozhuang is the only city in the region with a decrease in RECC. The main reason for the decrease is that the annual water consumption, coal consumption, and industrial soot emissions are increasing, but the agricultural sown area is decreasing. Zaozhuang in comparison has a low urbanization level and to improve the level of urbanization in the process, there was an inevitable need to consume water and coal smoke, and to release more of the urban construction land expansion which also led to agricultural planting area decreasing. A variety of these reasons together led to the resource environmental bearing capacity of Zaozhuang dropping significantly (Figure 5 (ZZ)).

The resource and environmental carrying capacity of Linyi City increased from 0.199 in 2012 to 0.212 in 2018, with an overall increase of 6.9% and an average annual increase of 1.2%. The overall development of Linyi city is relatively flat. From 2012 to 2018, the overall level of resources and environment carrying capacity in Suzhou and Shangqiu increased by 1.5% and 0.1%, respectively, and the level of carrying capacity increased very slowly and basically remained unchanged.

3.2. Spatial Variation Characteristics of RECC

In 2012, the resource and environmental carrying capacity of mining cities in the Huaihai Economic Zone showed a spatial trend of strong in the surrounding areas and weak in the middle. Jining and Linyi in the north and Shangqiu and Suqian in the south had a higher level of environmental carrying capacity, while Xuzhou, Huaibei, and Zaozhuang in the middle of the region had a lower level of carrying capacity (Figure 6 (2012)). This is because Xuzhou, Huaibei, and Zaozhuang are three cities in which the situation of coal resources depletion appeared earlier, and the proportion of coal-related secondary industry in these three cities is high, so the environmental pollution problem is more prominent, ultimately leading to their general low resource and environmental carrying capacities in 2012. In 2014, regional resources environmental bearing capacity began to appear in spaces to the west, whereas the east showed a weak trend. Higher levels of bearing capacity are located in the western cities of Jining, Shangqiu, and Suzhou, whereas in the eastern parts of Linyi, Zaozhuang, and Xuzhou levels are weaker. However, in Xuzhou, which is beyond Zaozhuang and Linyi, and in Jining, which is beyond suzhou, there were changes in spatial characteristics (Figure 6 (2014)). Cities represented by Xuzhou have experienced accelerated urban transformation and industrial upgrading, and gradually got rid of the dependence on coal resources. A better economic development situation and industrial development strategy have reduced the disturbance to the environment and the demand for resources, and the resource and environmental carrying capacity has gradually improved. In 2016, Xuzhou further surpassed Shangqiu and Suzhou, and the momentum of resource and environmental carrying capacity improvement was still strong (Figure 6 (2016)). The characteristics of being strong in the west and weak in the east were destroyed, and a strong region centered on Jining, Xuzhou, and Suzhou gradually began to form, while cities such as Linyi, Huaibei, and Shangqiu showed a weak performance. In 2018, the environmental carrying capacity of the central region was further enhanced, and Huaibei completed the overtaking of Shangqiu, thus forming an outward-decreasing distribution map of resource and environmental carrying capacity with Jining and Xuzhou as the core (Figure 6 (2018)).

3.3. Variation Characteristics of RECC of Subsystem

The carrying capacity of the subsystem is divided into resources carrying capacity (RCC), ecological carrying capacity (ECC), and social economic carrying capacity (SECC). The ECC and SECC increased from 0.410 and 0.412 in 2012 to 0.540 and 0.564 in 2018, with an overall increase of 31.7% and 36.8%, respectively. The RCC decreased from 0.543 in 2012 to 0.431 in 2018, an overall decrease of 20.54% (Table 5).

Table 5 shows the RECC subsystem scores of prefecture-levels in seven cities. Except Xuzhou, RCC shows a downward trend on the whole. This is because some natural resources are non-renewable, so it is difficult to realize the improvement of RCC. According to the data of ECC, almost all cities have realized an improvement of their ecological environments, which has led to the promotion of ECC. According to SECC, except for Shangqiu, all the other cities have achieved steady improvement (Figure 7).

4. Discussion

4.1. Changes about RECC of Mining Cities

Among all the indicators that have a great impact on RECC are water resources per capita, road network area, raw coal consumption, coal production, and per capita GDP. This is because water resources directly affect the survival of human beings, coal is an important source of energy for human beings, and highway mileage and GDP are both barometers of economic development, so their heavy weight is consistent with normal cognition. These cities are improving their RECC because they are becoming more ecological. Most mining cities have gradually realized the intensive use of resources, and their coal mining and consumption have steadily decreased. The urban economy has developed rapidly, and the road network density and green land rate have been rapidly improved. As we all know, changes in coal mining and coal consumption are mutually reinforcing, which are key factors affecting the changes of RECC. In the past ten years, the Huaihai Economic Zone has strengthened the use of photovoltaic and wind energy, reducing the dependence on coal energy. In coal mining, green mining and clean production technologies have been applied on a large scale, reducing environmental damage. Linking to coal consumption, with the change in energy structure, the demand for coal decreases, which directly reduces carbon emissions and pollutant emissions, which further reduces the environmental impact, thus improving the RECC. The ecological carrying capacity (ECC) of other mining cities, such as Huainan City [56], experienced a process of first decline and then increase, which is not completely consistent with the characteristics of this study. The main reason is that Huainan city was studied earlier, when mining activities and urban construction activities were more intense. After 2010, the ECC changes in Huainan have shown consistent characteristics with this study. This is because the intensity of urban development has decreased and the city has accelerated its ecological transformation. This indicates that the spatio-temporal variation of RECC of mining cities is similar.

4.2. The Influence of Mining City Classification on RECC

From

Table 7. Table of different RECC types about mining cities.

Type of Mining Cities	Growth Type	Mature Type		Decay Type		Regenerative Type
Name	Shangqiu	Jining	Suzhou	Zaozhuang	Huaibei	Xuzhou	Linyi
Type of RECC	Stable	Steady growth	Stable	Decline	Rapid growth	Rapid growth	Steady growth

, it can be found that different types of mining cities have different process of changes in RECC. Regenerative mining cities belongs to growth type, including rapid growth and steady growth. The RECC of mature mining cities is relatively stable, generally in a stable state, or with a steady growth trend. Shangqiu is a representative growth type mining city, whose RECC showed a stable development trend. However, the decay type of mining city has two exactly opposite results. The RECC of Zaozhuang city has declined a lot, while Huaibei showed a rapid growth trend in RECC, which demonstrates that the worst type of city still has huge potential for RECC improvement.

4.3. Correlation Analysis of RECC Subsystem Changes

At the overall level, the three subsystem changes have a strong correlation. The correlation between RCC and ECC was −0.992, showing a significance level of 0.01, indicating a significant negative correlation between RCC and ECC. The correlation value between RCC and SECC was −0.964, showing a significance level of 0.01, indicating that there was a significant negative correlation between RCC and SECC (

Table 8. Correlation table of sub-system RECC of mining city in Huaihai Economic Zone.

		XZ			HB			JN			ZZ			LY			SZ			SQ			Total
		RCC	ECC	SECC	RCC	ECC	SECC	RCC	ECC	SECC	RCC	ECC	SECC	RCC	ECC	SECC	RCC	ECC	SECC	RCC	ECC	SECC	RCC	ECC	SECC
RCC	Pearson correlation	1			1			1			1			1			1			1			1
	sig. (double tail)
ECC	Pearson correlation	0.684	1		−0.319	1		−0.937 **	1		−0.418	1		−0.909 **	1		0.702	1		−0.997 **	1		−0.992 **	1
	sig. (double tail)	0.090			0.485			0.002			0.350			0.005			0.078			0.000			0.000
SECC	Pearson correlation	0.797 *	0.796 *	1	−0.068	0.923 **	1	−0.950 **	0.985 **	1	−0.560	−0.490	1	−0.933 **	0.735	1	−0.991**	−0.740	1	0.878 **	−0.911 **	1	−0.964 **	0.969 **	1
	sig. (double tail)	0.032	0.032		0.885	0.003		0.001	0.000		0.191	0.265		0.002	0.060		0.000	0.057		0.009	0.004		0.000	0.000

* p < 0.05 ** p < 0.01.

). The correlation value between ECC and RECC was 0.969, showing a significance level of 0.01, indicating that there was a significant positive correlation between RCC and SECC. From the changes of the carrying capacity of the three subsystems, it can be seen that the improvement of ECC and SECC have a positive correlation, whose growth change trajectory are basically the same. In fact, the rapid development of the social economy can promote the progress of the construction of ecological civilization. The phenomenon is obvious that more manpower, capital, and technology are invested in the field of ecological and environmental protection. However, due to the limitation and non-renewability of resources, RCC is often negatively correlated with SECC, which indicates that the improvement of SECC in this region is achieved to some extent through the consumption of natural resources.

There are two positive correlations, which are between Xuzhou’s RCC and SECC and Shangqiu’s RCC and SECC. In addition, there are two significant negative correlations, including RCC and SECC in Suzhou and RCC and ECC in Shangqiu. The three subsystems of Jining and Linyi both have a strong positive or negative correlation. The results indicate that there is a universal phenomenon that the subsystems of mining cities have strong correlations. Through the consumption of certain natural resources, mining cities achieve socio-economic and ecological environment improvements, and finally show the results of decreasing RCC and increasing ECC and SECC.

5. Conclusions and Suggestions

Based on the analysis of the spatio-temporal variation characteristics of the RECC of mining cities in the Huaihai Economic Zone from 2012 to 2018, the following conclusions are drawn: From the perspective of time series, the RECC of mining cities in the Huaihai Economic Zone showed a steady increase from 2012 to 2018, and the increase was relatively slow from 2012 to 2015. Then the speed increased rapidly from 2015 to 2018. In comparison, cities with more successful industrial transformations, such as Xuzhou, Huaibei, and Jining, are more rapid in improving the speed of their RECCs. In the stage of industrial structure transformation, Linyi, Suzhou, Shangqiu, and Zaozhuang are four cities of which the environmental carrying capacity improvement speed is relatively flat, and has even decreased. From the variation characteristics of the sub-system carrying capacity, the SECC capacity of the region increased year by year, and the increasing speed was first fast and then slow. The ECC has a similar trend as the SECC. The overall trend of RCC is opposite to that of the social economy, showing a downward trend year by year, and the rate of decline is originally fast and then slow. It reached the bottom in 2017 and began to increase slowly in 2018. From the perspective of spatial distribution, the resource and environmental carrying capacity of each city gradually showed the characteristics of spatial agglomeration. From the strong surroundings and weak center in 2012, it gradually developed into a strong center and weak surroundings in 2018. Xuzhou and Jining gradually became the polarization center of RECC, and they will continue to strengthen in the future. This also shows, to a certain extent, that the level of economic level is closely related to the development of regional environmental carrying capacity, and economic advantages can gradually penetrate into the field of environmental construction, so as to improve regional resources and environmental carrying capacity.

In order to better promote the transformation and development of mining cities in the Huaihai Economic Zone and improve the resource and environment carrying capacity, the following improvement suggestions are put forward: in terms of natural resources, mining cities should pay attention to the protection and utilization of water sources, appropriately reduce the dependence on coal resources, reduce the amount and consumption of coal mining, accelerate the transformation of energy structure from coal-based to diversified, and accelerate the transformation of energy development impetus from traditional energy to new energy [57]. In terms of ecological environment, we should pay attention to the construction of parks and the promotion of green land rates, which complement each other. Urban green parks can provide recreation sites, conserve water and soil, improve air quality, and reduce the urban heat island effect [58], so they play a great role in improving RECC. In terms of SECC, mining cities should pay attention to road infrastructure construction, because road infrastructure construction is the most closely related to economic development and has the greatest stimulating effect on economic development [59]. The study suggests that mining cities in the Huaihai Economic Zone should enhance road infrastructure construction, especially focusing on upgrading road infrastructure in rural areas.