Exploring Differences and Evolution of Coordination Level of the Industrial Structure, Economy and Ecological Environment Complex System in Beijing–Tianjin–Hebei Urban Agglomeration

Abstract

Industrial structure (IS), economy (EC), and ecological environment (EE) could influence each other and constitute a complex system (IS-EC-EE). This study is to explore the differences and evolution of the coordination level of the IS-EC-EE complex system of 13 cities in the Beijing–Tianjin–Hebei (BTH) urban agglomeration by coupling coordination degree model, Markov chain, GM (1,1) model, Dagum Gini coefficient, and Kernel density estimation method to provide a reference for regional sustainable development. The results show that the comprehensive evaluation index of IS, EC, and EE had significant differences among the 13 cities in the BTH region. The IS-EC-EE system of the whole BTH region was in a highly coupled and low-coordinated development state. And the coordination degrees of the 13 cities in the BTH region had spatial differences, which were mainly due to inter-regional differences, and the differences became larger. Furthermore, the coordinated development of the 13 cities had the probability of keeping high, moderate, and low coordination. It was predicted that the coordination degree of the IS-EC-EE system in the whole BTH region in 2020–2030 was roughly similar to the trend in 2009–2019. The coordination levels of Beijing and Tianjin were higher than in Hebei Province, so optimizations need to be considered for regional overall sustainable development.

1. Introduction

The regional coordinated development strategy is a major initiative proposed by China to achieve sustainable development. As one of the most developed and active regions in economic development, the coordinated development of the Beijing–Tianjin–Hebei (BTH) region is one of the major national strategies, which has an important role in Chinese development. A coupling coordination model could describe the coordinated development level. Coupling, a concept that comes from physics, is used to evaluate the mutual interaction and influence between two or more systems [1,2]. The coupling coordination degree model is applied to evaluate the coordinated development of complex systems, including the coupling degree and coordination degree. The coupling degree could quantify the degree of interaction between two or more subsystems in a complex system. The coordination degree could represent the synergistic effect and harmonious degree of a complex system [3,4]. The multidimensional interdependence could be reflected by the coupling coordination level of complex systems.

By adjusting the industrial structure of the BTH region, the noncapital functions of Beijing could be alleviated, thereby promoting the coordinated development of the region. Industrial structure (IS), economy (EC), and ecological environment (EE) are closely linked, constituting a complex system (IS-EC-EE) in which three subsystems interact with each other. Changes in one subsystem will affect other subsystems, thereby affecting the coordinated development of the entire system. The adjustment and optimization of industrial structures are the requirements for sustainable economic development and ecological environments. Sustainable economic development can also promote the transformation of industrial structures and contribute to the improvement of the ecological environment. The improvement of the ecological environment requires an adjustment of industrial structures and economic support. As one of the key urban agglomerations in China, the Beijing–Tianjin–Hebei (BTH) region includes 13 cities, Beijing, Tianjin, and 11 cities in Hebei Province, such as Shijiazhuang, Zhangjiakou, etc. To promote the coordinated development of the BTH region, the industrial structure of the region has undergone significant changes. At the same time, the economic development, demand, and consumption of energy in different industries and cities are significantly different. Therefore, it is of great theoretical and practical significance to study the adjustment of the industrial structure in the BTH region and the changes it brings to the ecological environment and economic development of the region.

While there is little research specifically focused on the coordinated development of the IS-EC-EE ternary complex system in the BTH region, the coupling coordination degree model has been used to study the relationship between industrial structure, ecological environment, and economy-related dual systems. As for the coupling and coordination relationship between industrial structure and ecological environment, studies were conducted between industrial structure and energy efficiency, atmospheric environment, and carbon emission, respectively [1,5,6,7,8]. Scholars widely studied the coupling coordination between the economy and the ecological environment [9,10,11,12,13]. Industrial structure, economy, and ecological-environment-related ternary systems, such as industrial economy–natural resources–environment, energy–economy–ecological environment, energy–economy–environment, economic development–resource utilization–environmental quality, economy–resource–environment, carbon emissions–industrial structure–economic system, were also investigated [14,15,16,17,18,19].

The research on the relationship between industrial structure, ecological environment, and economic development ignores that these three subsystems are a multilevel and comprehensive system. And only when the internal elements of the system are integrated with each other can system optimization be achieved. At the same time, there is less analysis of the spatiotemporal dynamic evolution of the relationship between industrial structure adjustment and environmental and economic development in the BTH region. A Markov chain could describe the transfer probability and evolution trend by discrete computation. The Markov chain method can be used to investigate the evolution of the coordination degree of the IS-EC-EE complex system in the BTH region over time. The GM (1,1) model, Dagum Gini coefficient, and Kernel density estimation method are conducted to predict and analyze the regional differences and distribution dynamics of the IS-EC-EE complex system coordination levels in the BTH region. This study is specifically focused on the coordinated development of the IS-EC-EE ternary complex system in the BTH region. The differences in and evolution of the coordination level of the IS-EC-EE complex system from 2009 to 2019 in the 13 cities in the BTH urban agglomeration were explored by the coupling coordination degree model, Markov chain, GM (1,1) model, Dagum Gini coefficient, and Kernel density estimation method to provide a reference for regional sustainable development.

The structure of this article is as follows: Section 1 introduces the research background and significance of the coordinated development of the IS-EC-EE ternary complex system in the BTH region. Section 2 shows the materials and methods, including the evaluation indicators of the IS-EC-EE, coupling coordination model, Markov chain, GM (1,1) model, Dagum Gini coefficient, and Kernel density estimation method. Section 3 describes the results analysis, the coordinated development, differences, and evolution of the IS-EC-EE ternary complex system in the BTH region. Section 4 provides the discussion, summarizes the main viewpoints and conclusions, and provides some policy recommendations.

2. Materials and Methods

2.1. Evaluation Indicators

The IS-EC-EE complex system (S) consists of three subsystems, i.e., S_IS subsystem, S_EC subsystem, and S_EE subsystem. The evaluation indicators of IS-EC-EE are shown in

Table 1. Evaluation indicators of the IS-EC-EE complex system.

Subsystem	Index Layer and Description	Unit	Index Attribute
IS subsystem	The proportion of primary industry	%	−
	The proportion of secondary, tertiary industry	%	+
	The specific gravity of primary industry	RMB 10,000	−
	The specific gravity of secondary, tertiary industry	RMB 10,000	+
	The proportion of employees in primary industry	%	−
	The proportion of employees in secondary, tertiary industry	%	+
	Industrial agglomeration (proportion of regional gross industrial output value in GDP relative to that of national)	1	+
	Industrial structure advancement (ratio of output value of the tertiary industry to that of the secondary industry)	1	+
	Industrial structure rationalization Theil index (${\mathrm{Y}}_{\mathrm{i}}$,${\mathrm{L}}_{\mathrm{i}}$ are the output value and employment number of the ith industry, respectively ${\displaystyle {\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}}\left(\frac{{\mathrm{Y}}_{\mathrm{i}}}{\mathrm{Y}}\right)\ln \left(\raisebox{1ex}{$\frac{{\mathrm{Y}}_{\mathrm{i}}}{\mathrm{Y}}$}\!\left/ \!\raisebox{-1ex}{$\frac{{\mathrm{L}}_{\mathrm{i}}}{\mathrm{L}}$}\right.\right)={\displaystyle {\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}}\left(\frac{{\mathrm{Y}}_{\mathrm{i}}}{\mathrm{Y}}\right)\ln \left(\raisebox{1ex}{$\frac{{\mathrm{Y}}_{\mathrm{i}}}{{\mathrm{L}}_{\mathrm{i}}}$}\!\left/ \!\raisebox{-1ex}{$\frac{\mathrm{Y}}{\mathrm{L}}$}\right.\right), \mathrm{n}=3)$	1	+
EC subsystem	Population	10,000 people	+
	GDP per capita	RMB 10,000	+
	Actual utilization level of foreign capital	USD 10,000	+
	Investment in fixed assets per capita	RMB 10,000	+
	Public financial expenditure per capita	RMB 10,000	+
	Per capita disposable income of urban residents	RMB	+
EE subsystem	Total energy consumption	Standard coal tons	−
	Growth rate of energy consumption	%	−
	GDP energy intensity	Ton/RMB 10,000	−
	Comprehensive utilization rate of industrial solid waste	%	+
	Industrial smoke and dust emission	ton	−
	Industrial SO2 emission	ton	−
	Total industrial wastewater discharge	10,000 tons	−

. The selection of indicators refers to previously published articles [1,2,8,10,12,20] and takes into account factors such as data availability and scientificity. The data from 2009 to 2019 in this paper are from the China Statistical Yearbook, China Urban Statistical Yearbook, China Environmental Statistical Yearbook, China Energy Statistical Yearbook, Hebei Economic Yearbook, Beijing Statistical Yearbook, and Tianjin Statistical Yearbook. The average interpolation method is used to fill in the missing values.

Table 2. Descriptive statistics of the data.

Variable	Obs	Mean	Std. Dev.	Min	Max
The specific gravity of primary industry	143	839,293.1	1,134,581	15,866	5,312,190
The specific gravity of secondary industry	143	10,400,000	13,300,000	812,724	76,100,000
The specific gravity of tertiary industry	143	18,600,000	39,200,000	478,431	295,000,000
The proportion of primary industry	143	4.151049	4.102382	0.32	20.26
The proportion of secondary industry	143	45.30552	12.26229	16.16	66.23
The proportion of tertiary industry	143	50.54301	12.58269	29.64	83.52
The proportion of employees in primary industry	143	0.7185315	0.8213446	0.01	4.06
The proportion of employees in secondary industry	143	37.36322	18.39597	10.58	165.47
The proportion of employees in tertiary industry	143	64.64224	45.40601	18.92	576.25
Industrial structure advancement	143	1.537832	0.8381772	0.59	5.17
Industrial agglomeration	143	1.618322	2.721588	0.1	11.34
Theil index	143	13.08161	14.45865	0.01	64.72
GDP per capita	143	50,233.75	30,029.12	15,174	164,220
Investment in fixed assets per capita	143	1.866993	1.928644	0.16	8.7
Public financial expenditure per capita	143	9957.236	10,466.66	1734.26	54,270
Population	143	759.0912	323.1529	287.24	1397
Per capita disposable income of urban residents	143	27,109.1	11,103.34	1173	73,849
Actual utilization level of foreign capital	143	252,139.6	513,200.9	1610	3,082,563
Total energy consumption	143	2646.039	2658.747	264.87	8260
Growth rate of energy consumption	143	0.590979	2.3551	−5.03	16.07
GDP energy intensity	143	122.7205	155.7709	17.63	800
Total industrial wastewater discharge	143	8816.362	6297.723	615	31,058
Industrial SO2 emission	143	76,106.58	70,403.89	1132	331,863
Industrial smoke and dust emission	143	81,273.86	180,082.3	3349	1,859,866
Comprehensive utilization rate of industrial solid waste	143	75.29042	25.18788	4.74	100

shows the descriptive statistics of the data in the article. There are no outliers, and there are significant differences in evaluation indicators. So, the state of the IS, EC, and EE of the 13 cities in the BTH urban agglomeration are analyzed in the following section.

2.2. Processing Data

The entropy method, which is an objective valuation method to reduce the influence of subjective factors, is used to determine the weight of indicators in the evaluation system [14,20]:

First, data standardization. ${\mathrm{X}}_{\mathrm{ij}}^{\prime }$ is the normalized index value, and ${\mathrm{X}}_{\mathrm{ij}}$ is the value of the ith index of j city (i = 1, 2…, m; j = 1, 2,…, n)

$${\mathrm{X}}_{\mathrm{ij}}^{\prime }=\left\{\begin{array}{c}\frac{{\mathrm{X}}_{\mathrm{ij}}-{\mathrm{minX}}_{\mathrm{ij}}}{{\mathrm{maxX}}_{\mathrm{ij}}-{\mathrm{minX}}_{\mathrm{ij}}}, \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{p}\mathrm{o}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e} \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{o}\mathrm{r}\\ \frac{{\mathrm{maxX}}_{\mathrm{ij}}-{\mathrm{X}}_{\mathrm{ij}}}{{\mathrm{maxX}}_{\mathrm{ij}}-{\mathrm{minX}}_{\mathrm{ij}}}, \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{n}\mathrm{e}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e} \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{o}\mathrm{r}\end{array}\right.$$

(1)

Second, the entropy value of index i:

$${\mathrm{e}}_{\mathrm{i}}=-\mathrm{k}{{\displaystyle \sum }}_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{P}}_{\mathrm{ij}}\ln {\mathrm{P}}_{\mathrm{ij}}$$

(2)

in which, $\mathrm{k}=1/\ln \left(\mathrm{n}\right)$ > 0 and ${\mathrm{e}}_{\mathrm{i}}$ ≥ 0,${ \mathrm{P}}_{\mathrm{ij}}=\frac{{\mathrm{X}}_{\mathrm{ij}}^{\prime }}{{{\displaystyle \sum }}_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{X}}_{\mathrm{ij}}^{\prime }}$;

Third, the weight of indicators:

$${ \mathrm{w}}_{\mathrm{i}}=\frac{{\mathrm{g}}_{\mathrm{i}}}{{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{g}}_{\mathrm{i}}}$$

(3)

in which ${\mathrm{g}}_{\mathrm{i}}$ is the difference coefficient: ${\mathrm{g}}_{\mathrm{i}}=\frac{1-{\mathrm{e}}_{\mathrm{i}}}{\mathrm{m}-{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{e}}_{\mathrm{i}}}$.

Fourth, the comprehensive evaluation index of the three subsystems:

$${\mathrm{S}}_{\mathrm{i}}={{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{w}}_{\mathrm{i}}{\ast \mathrm{X}}_{\mathrm{ij}}^{\prime } ({\mathrm{S}}_{\mathrm{i}}={\mathrm{S}}_{\mathrm{IS}},{\mathrm{S}}_{\mathrm{EC}},{\mathrm{S}}_{\mathrm{EE}})$$

(4)

2.3. Coupling Coordination Model

The calculation formula of coupling degree C and coordination degree D are [20]

$$\mathrm{C}=\sqrt[3]{\frac{S_{\mathrm{IS}}\ast S_{\mathrm{EC}}\ast S_{\mathrm{EE}}}{{\left(\frac{S_{\mathrm{IS}}+S_{\mathrm{EC}+S_{\mathrm{EE}}}}{3}\right)}^3}}$$

(5)

$$\mathrm{T}=\alpha S_{\mathrm{IS}}+\beta S_{\mathrm{EC}}+\mathsf{\gamma }{S}_{\mathrm{EE}}$$

(6)

$$\mathrm{D}=\sqrt{C\ast \mathrm{T}}$$

(7)

T is the comprehensive evaluation index of the complex system. Since every subsystem (IS, EC, EE) is equally important to the coordination of the complex system, set α = β = γ = 1/3.

Coupling degree C and coordination degree D ∈ [0–1]. The closer to 1, the higher the coupling degree and coordination degree, and the connection between the subsystems would be closer and the complex system would be more coordinated. Referring to the references [10,21], the classification standards of C and D are 0 ≤ C < 0.3 (disorder coupled), 0.3 ≤ C < 0.5 (low coupled), 0.5 ≤ C < 0.8 (moderate coupled), 0.8 ≤ C ≤ 1 (highly coupled); 0 ≤ D < 0.5 (incoordination), 0.5 ≤ D < 0.6 (low coordination), 0.6 ≤ D < 0.7 (moderate coordination), and 0.7 ≤ D ≤ 1 (high coordination).

2.4. Markov Chain

Markov chain is used to analyze the evolution of the coordination degree in terms of stability, upward transfer, and downward transfer over time [22,23].

$${\mathrm{P}}_{\mathrm{ij}=}\frac{{\mathrm{n}}_{\mathrm{ij}}}{{\mathrm{n}}_{\mathrm{i}}}$$

(8)

${\mathrm{P}}_{\mathrm{ij}}$ is the probability of i-coordination-type cities transferring to j type in the next year;${ \mathrm{n}}_{\mathrm{ij}}$ is the sum of the number of i-coordination-type cities transfer to j type in the next year; ${\mathrm{n}}_{\mathrm{i}}$ is i-coordination-type cities.

2.5. GM (1,1) Prediction Model

GM (1,1) model is applied to predict to coordination degree using accumulated generating operation [24,25,26].

The original sequence is

$$x^{\left(0\right)}=\left\{x^{\left(0\right)}\left(1\right),x^{\left(0\right)}\left(2\right),x^{\left(0\right)}\left(3\right),\dots ,x^{\left(0\right)}\left(\mathrm{n}\right)\right\}$$

(9)

and uses the accumulation generation method to convert the original data into an increasing sequence, obtaining a new sequence

$$x^{\left(1\right)}\left(\mathrm{i}\right)={{\displaystyle \sum }}_{j=1}^i{x}^{\left(0\right)}\left(j\right),i=1,2\dots ,n$$

(10)

let $x^{\left(1\right)}$ satisfy the first-order ordinary differential equation

$$\frac{d{x}^{\left(1\right)}}{dt}+a{x}^{\left(1\right)}=\mathrm{b}$$

(11)

a represents the coefficient, b represents gray control coefficient and is fitted using the least square method to obtain $\left[\begin{array}{c}a\\ b\end{array}\right]={\left(B^{T}B\right)}^{-1}{B}^{T}Y; \mathrm{Y}={\left[x^{\left(0\right)}\left(2\right) x^{\left(0\right)}\left(3\right) \dots x^{\left(0\right)}\left(\mathrm{n}\right)\right]}^T$, and B is the construction of the data matrix, $\mathrm{B}=\left[\begin{array}{c}\begin{array}{c}-1/2\left[x^{\left(1\right)}\left(1\right)+x^{\left(1\right)}\left(2\right)\right]\\ -1/2\left[x^{\left(1\right)}\left(2\right)+x^{\left(1\right)}\left(3\right)\right]\end{array}\\ \begin{array}{c}\dots \\ -1/2\left[x^{\left(1\right)}\left(n-1\right)+x^{\left(1\right)}\left(n\right)\right]\end{array}\end{array} \begin{array}{c}\begin{array}{c}1\\ 1\end{array}\\ \begin{array}{c}\dots \\ 1\end{array}\end{array}\right]$.

The time response function corresponding to the differential equation is

$${\mathrm{x}}^{\left(1\right)}\left(\mathrm{t}+1\right)=\left({\mathrm{x}}^{\left(0\right)}\left(1\right)-\frac{\mathrm{b}}{\mathrm{a}}\right){\mathrm{e}}^{-\mathrm{at}}+\frac{\mathrm{b}}{\mathrm{a}}, \mathrm{t}=1, 2, 3\dots$$

(12)

The result of the time response function is an accumulation sequence. Further calculation of

$${ \mathrm{x}}^{\left(0\right)}\left(\mathrm{k}+1\right)={\mathrm{x}}^{\left(1\right)}\left(\mathrm{k}+1\right)-{\mathrm{x}}^{\left(1\right)}\left(\mathrm{k}\right)$$

(13)

Yields the predicted value of coupling coordination degree ${\mathrm{x}}^{\left(0\right)}\left(\mathrm{k}+1\right)$.

2.6. Dagum Gini Coefficient

The Dagum Gini coefficient [23,27,28] is used to measure the regional relative differences and sources of coordination degree of the IS-EC-EE complex system.

Overall Gini coefficient:

$$\mathrm{G}=\frac{{{\displaystyle \sum }}_{j=1}^k{{\displaystyle \sum }}_{h=1}^k{{\displaystyle \sum }}_{i=1}^{n_j}{{\displaystyle \sum }}_{r=1}^{n_h}\left|Y_{ji}-Y_{jr}\right|}{2n^2\overline{Y}}$$

(14)

k is the number of regions, n is number of cities, ${\mathrm{Y}}_{\mathrm{ji}}\left({\mathrm{Y}}_{\mathrm{hr}}\right)$ is the coordination degree of the IS-EC-EE complex system of i(r) province in j (h) region, ${\mathrm{n}}_{\mathrm{j}}\left({\mathrm{n}}_{\mathrm{h}}\right)$ is the number of cities in j (h) region, and $\overline{\mathrm{Y}}$ is the average coordination degree of the IS-EC-EE complex system in all cities.

$$G_{jj}=\frac{\frac{1}{2\overline{Y_j}}{{\displaystyle \sum }}_{i=1}^{n_j}{{\displaystyle \sum }}_{r=1}^{n_h}\left|Y_{ji}-Y_{jr}\right|}{n_j^2}$$

(15)

$$G_{jh}=\frac{{{\displaystyle \sum }}_{i=1}^{n_j}{{\displaystyle \sum }}_{r=1}^{n_h}\left|Y_{ji}-Y_{hr}\right|}{n_j{n}_h\left(\overline{Y_j}+\overline{Y_h}\right)}$$

(16)

${\mathrm{G}}_{\mathrm{jj}}$ is Gini coefficient of j region, ${\mathrm{G}}_{\mathrm{jh}}$ is Gini coefficient between jh regions, and $\overline{{\mathrm{Y}}_{\mathrm{j}}}\left(\overline{{\mathrm{Y}}_{\mathrm{h}}}\right)$ is the average value of regional coordination degree of j(h) region.

Defined variables: ${\mathrm{P}}_{\mathrm{j}}=\frac{{\mathrm{n}}_{\mathrm{j}}}{\mathrm{n}}$; ${\mathrm{S}}_{\mathrm{j}}=\frac{{\mathrm{n}}_{\mathrm{j}}\overline{{\mathrm{Y}}_{\mathrm{j}}}}{\mathrm{n}\overline{\mathrm{Y}}}$;

$${\mathrm{M}}_{\mathrm{jh}}={{\displaystyle \int }}_0^{\infty }{\mathrm{dF}}_{\mathrm{j}}\left(\mathrm{Y}\right){{\displaystyle \int }}_0^{\mathrm{Y}}\left(\mathrm{Y}-\mathrm{x}\right){\mathrm{dF}}_{\mathrm{h}}\left(\mathrm{x}\right)$$

(17)

$${\mathrm{N}}_{\mathrm{jh}}={{\displaystyle \int }}_0^{\infty }{\mathrm{dF}}_{\mathrm{h}}\left(\mathrm{Y}\right){{\displaystyle \int }}_0^{\mathrm{Y}}\left(\mathrm{Y}-\mathrm{x}\right){\mathrm{dF}}_{\mathrm{j}}\left(\mathrm{x}\right)$$

(18)

$${ \mathrm{D}}_{\mathrm{jh}}=\frac{{\mathrm{M}}_{\mathrm{jh}}-{\mathrm{N}}_{\mathrm{jh}}}{{\mathrm{M}}_{\mathrm{jh}}+{\mathrm{N}}_{\mathrm{jh}}}$$

(19)

Variable ${\mathrm{D}}_{\mathrm{jh}}$ is the relative impact of inter-regional coordination between j and h regions; ${\mathrm{F}}_{\mathrm{j}}\left({\mathrm{F}}_{\mathrm{h}}\right)$ is cumulative density distribution function of j(h) region; ${\mathrm{M}}_{\mathrm{jh}}$ is the difference in coordination between regions, i.e., the mathematical expectation for the sum of all ${\mathrm{Y}}_{\mathrm{ji}}-{\mathrm{Y}}_{\mathrm{hr}}>0 in regions j and h$; and ${\mathrm{N}}_{\mathrm{jh}}$ is the first-order moment of hypervariable, and all samples are with ${\mathrm{Y}}_{\mathrm{hr}}-{\mathrm{Y}}_{\mathrm{ji}}>0$ $\mathrm{in} \mathrm{j} \mathrm{and} \mathrm{h}$.

The Gini coefficient is decomposed into intraregional gap ${\mathrm{G}}_{\mathrm{w}}$, inter-regional gap ${ \mathrm{G}}_{\mathrm{nb}}$ and hypervariable density ${\mathrm{G}}_{\mathrm{t}}$ (representing the impact of regional sample overlap on regional disparity), $\mathrm{G}={\mathrm{G}}_{\mathrm{w}}+{\mathrm{G}}_{\mathrm{nb}}+{\mathrm{G}}_{\mathrm{t}}$

$$G_w={{\displaystyle \sum }}_{j=1}^k{G}_{jj}{P}_j{S}_j$$

(20)

$$G_{nb}={{\displaystyle \sum }}_{j=2}^k{{\displaystyle \sum }}_{h-1}^{j-1}{G}_{jh}{D}_{jh}\left(P_j{S}_h+P_h{S}_j\right)$$

(21)

$$G_t={{\displaystyle \sum }}_{j=2}^k{{\displaystyle \sum }}_{h=1}^{j-1}{G}_{jh}\left(P_j{S}_h+P_h{S}_j\right)\left(1-D_{jh}\right)$$

(22)

2.7. Kernel Density Estimation

Kernel density estimation method is applied to describe the distribution dynamics of the coordination of the IS-EC-EE complex system. As a nonparametric estimation method, the Kernel density estimation method uses continuous density curves to describe the distribution of random variables [23,27].

f (x) is the density function of the coordination degree x of the NU and ECER system

$$\mathrm{f}\left(\mathrm{x}\right)=\frac{1}{\mathrm{Nh}}{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{N}}\mathrm{K}\left(\frac{{\mathrm{X}}_{\mathrm{i}}-\mathrm{x}}{\mathrm{h}}\right)$$

(23)

N is the number of observations,${ \mathrm{X}}_{\mathrm{i}}$ is independent, identically distributed observations; x is the mean of x-observations, K is kernel density function, and h is bandwidth, and the smaller the h, the higher the estimation accuracy.

The Gaussian kernel density function is

$$\mathrm{K}\left(\mathrm{x}\right)=\frac{1}{\sqrt{2\mathsf{\pi }}}\exp \left(-\frac{{\mathrm{x}}^2}{2}\right)$$

(24)

3. Results Analysis

3.1. Comprehensive Evaluation Index of the IS-EC-EE Complex System in BTH

3.1.1. Comprehensive Evaluation of the IS Subsystem

The comprehensive evaluation index is used to describe the development situation of IS, EC, EE, and the IS-EC-EE complex system of the 13 cities in the BTH region. The BTH region includes Beijing, Tianjin, and 11 cities in Hebei Province. The figures are to demonstrate the overall development of IS-EC-EE in the BTH region and the differences among the three major regions of Beijing, Tianjin, and Hebei province. The comprehensive evaluation index of the IS subsystem of the 13 cities in the BTH region from 2009 to 2019 is shown in Figure 1.

As shown in Figure 1, overall, the IS comprehensive evaluation index of the BTH region showed an upward trend from 2009 to 2017. The average evaluation index of the IS subsystem in the BTH region in 2009 was 0.37, and the average in 2017 was 0.40, with a growth rate of 7.6%. In 2018 and 2019, except for Beijing, which showed an increase (0.94 in 2019) and Tianjin which showed an initial increase (0.64) and then a decrease (0.51), the evaluation index of the IS subsystem in the other 11 cities of Hebei Province decreased, with evaluation index values ranging from 0.1 to 0.3 in 2018 and 2019. The average in the BTH region in 2019 was 0.27. From the overall average, Beijing’s average was 0.75; Tianjin’s was 0.55, ranking third in Shijiazhuang (0.36), fourth in Hengshui (0.32), and last in Tangshan (0.25). The IS index of other cities was 0.28–0.31. The above results indicate that there are significant differences in the development level of IS among the 13 cities in the BTH region. The IS values in Beijing and Tianjin are higher, but the IS of the other 11 cities in Hebei Province still needs further optimization.

3.1.2. Comprehensive Evaluation of the EC Subsystem

The comprehensive evaluation index of the EC subsystem of the 13 cities in the BTH region is shown in Figure 2.

As shown in Figure 2, the comprehensive evaluation index of the EC subsystem of the 13 cities in the BTH region was divided into two large tiers. The first tier was Beijing (with an average value of 0.84) and Tianjin (with an average of 0.76), while the 11 cities of Hebei Province were in the second tier, with an average index of 0.03–0.3. The economic development level of each city in the BTH region varies greatly. The average comprehensive index of the economic subsystem in the BTH region in 2009 was 0.25, and the average in 2019 was 0.22. The economic comprehensive evaluation index of Tianjin has undergone significant changes, with values above 0.8 from 2009 to 2016 and dropping to 0.5–0.6 from 2017 to 2019. The comprehensive evaluation index of the EC subsystem of Beijing and Tianjin rank in the top two, with Tangshan (0.30) and Shijiazhuang (0.23) ranking third and fourth and Chengde (0.064), Xingtai (0.058), and Hengshui (0.038) ranking last. The economic indices of other cities range from 0.1 to 0.2, indicating that there is still a lot of room for economic development.

3.1.3. Comprehensive Evaluation of the EE Subsystem

The comprehensive evaluation index of the EE subsystem of the 13 cities in the BTH region is shown in Figure 3.

As shown in Figure 3, except for Tianjin (0.371), Tangshan (0.390), and Beijing (0.598), the three cities had lower ecological environment indices and ranked in the bottom three, while the other 10 cities had higher EE indices, with values ranging from 0.6 to 1. The EE index of Qinhuangdao City, Baoding City, Chengde City, and Cangzhou City in the BTH region decreased from 2009 to 2019, while the other cities fluctuated and increased. The ecological environment index of Tianjin, Tangshan, and Chengde had a relatively large fluctuation range, while the fluctuation range of the other 10 cities was not significant. The average index of the EE subsystem in the BTH region in 2009 was 0.69, and the average in 2019 was 0.71, with a small increase and staying relatively stable throughout the evaluation period. Hengshui City and Langfang City ranked in the top two places, with an average of 0.92. The average EE index of the BTH urban agglomeration from 2009 to 2019 was around 0.7, indicating that the EE subsystem of the BTH region has developed well.

3.1.4. Comprehensive Evaluation of the IS-EC-EE System

From 2009 to 2019, the comprehensive evaluation index of the IS-EC-EE complex system of the 13 cities in the BTH region is shown in Figure 4.

As shown in Figure 4, the comprehensive evaluation index of the IS-EC-EE complex system in the BTH region changed little. The value of the BTH region in 2009 was 0.47, and the average in 2019 was 0.40. The average value of the complex system of Beijing and Tianjin ranked as the first two, with average values of 0.730 and 0.560. The comprehensive evaluation index of the 11 cities in Hebei Province was 0.32–0.47.

From the comprehensive development index of the IS, EC, and EE subsystems and the IS-EC-EE ternary complex system, the 13 cities in the BTH region have different characteristics of IS, EC, and EE. The value of the comprehensive development index of IS, EC, and the complex system in Beijing is the highest, and the average value of the EE subsystem is 0.60, which needs to be improved. The BTH region is an economic and ecological circle that influences and interacts with itself. Adjusting the IS of Beijing could affect the IS, EC, and EE subsystems and the IS-EC-EE complex system in the BTH region. During the evaluation period, the average comprehensive development index of the complex system in the BTH region is between 0.4 and 0.5. The comprehensive development of the complex system in the BTH region still needs further improvement.

3.2. Analysis of the Coupling Degree of the IS-EC-EE System in BTH

Using coupling degrees to measure the strength relationship of system connections, the coupling degrees of dual systems, i.e., IS-EC, IS-EE, and EC-EE systems, are also investigated to reflect the relationship between pairwise subsystems. The coupling degrees of the IS-EC, IS-EE, and EC-EE system and the IS-EC-EE complex system in the 13 cities of the BTH region from 2009 to 2019 are shown in Figure 5, Figure 6, Figure 7 and Figure 8.

As shown in Figure 5, the IS-EC systems of Zhangjiakou, Chengde, Xingtai, and Hengshui of Hebei Province were in a moderately coupled stage, while the other nine cities were highly coupled. The average of the IS-EC system in the BTH region was 0.89, highly coupled. The IS-EE systems of all cities in the BTH region were highly coupled (Figure 6). The coupling degree of the EC-EE system had the greatest numerical difference. The EC-EE systems of the other 12 cities were highly and moderately coupled, except Hengshui of Hebei Province, which was lowly coupled (Figure 7). The EC-EE system in the BTH region was moderately coupled (average 0.72). It can be seen from Figure 8 that the IS-EC-EE complex systems of most of the cities in the BTH region were basically in the highly and moderately coupled stages. The average of the IS-EC-EE complex system of the 13 cities in the BTH region was 0.78, highly coupled.

3.3. Analysis of Coordinated Level of the IS-EC-EE Complex System in BTH

The coordination degree is used to measure the coordination effect between systems. Coordination degrees of dual systems were also researched. The coordination degrees of the IS-EC, IS-EE, and EC-EE system and the IS-EC-EE complex system in the 13 cities of the BTH region from 2009 to 2019 are shown in Figure 9, Figure 10, Figure 11 and Figure 12.

As shown in Figure 9, the IS-EC systems of Beijing and Tianjin were highly coordinated, Shijiazhuang and Tangshan were lowly coordinated, and the other eight cities were incoordinated. The coordination degree of the IS-EC system in Hengshui was the lowest (0.30–0.40). The IS-EC system of the BTH region was lowly coordinated (0.50). As displayed in Figure 10, the coordination degree of the IS-EE system of Beijing was the highest, ranging from 0.79 to 0.84, which is high coordination, except for Tangshan, which had the lowest (average of 0.56) in the low coordination stage. The IS-EE systems of the other 12 cities were all in moderate and high coordination stages. The average coordination degree of IS-EE in the BTH region was 0.63–0.73, the moderate coordination stage. As represented in Figure 11, the coordination degree of EC-EE in Beijing, followed by Tianjin, Langfang, and Shijiazhuang had high coordination, except for Hengshui (mean 0.43), Xingtai (0.46), Chengde (0.47), and Zhangjiakou (0.49), which were on the brink of being incoordinated. The remaining five cities, Tangshan, Qinhuangdao Handan, Cangzhou, and Baoding are in low coordination state. From Figure 12, it can be seen that the coordination level of the IS-EC-EE complex systems in Beijing (mean 0.85) and Tianjin (mean 0.73) were in the high coordination stage; Shijiazhuang (mean 0.62) and Langfang (mean 0.60) of Hebei Province were in the moderate coordination stage; Xingtai City (mean 0.49), Chengde City (mean 0.49), and Hengshui City (mean 0.47) of Hebei Province were on the verge of being in the incoordination stage. The coordination degree of the other six cities of Hebei Province was basically low coordination. From 2009 to 2019, the average coordination degree of the IS-EC-EE system in the BTH region was 0.58, which is low coordination. Overall, the coordination degree of the ternary composite system in the BTH region is increasing. The coordination degree of the IS-EC-EE ternary system is higher than that of the dual system, indicating the coordination relationship of the ternary system is better.

3.4. Evolution of the Coordination Degree of the IS-EC-EE Complex System

Markov chain was applied to study the coordination level evolution and the probability distribution of the IS-EC-EE complex system of the 13 cities in the BTH region, i.e., to measure the probability of the coordination level of every city transitioning from one state to another. The values of the coordination degree were discretized by dividing into four levels: incoordination, low coordination, moderate coordination, and high coordination. We use 1, 2, 3, and 4 to code the four coordination states of incoordination, low coordination, moderate coordination, and high coordination, respectively. The larger the code is, the greater the coordination degree is, and the more coordinated the complex system is. And the stability, upward transfer, and downward transfer coordination levels correspond to unchanged, transitioning from lower to higher and transitioning from higher to lower coordination levels, respectively.

Table 3. Transfer probability of coordination degree of complex systems.

	1	2	3	4
1	0.5000	0.5000	0.0000	0.0000
2	0.0714	0.7714	0.1286	0.0286
3	0.0000	0.1667	0.6250	0.2083
4	0.0000	0.0000	0.0000	1.0000

displays the state transition probability matrix of the coordination degree of the IS-EC-EE complex system of the 13 cities in the BTH region from 2009 to 2019.

The probability that the coordination state of a complex system remains unchanged is represented by the numerical value on the diagonal of the Markov transition probability matrix. The transition probability between different coordination states is reflected by the value on the nondiagonal line.

Table 2 shows that (1) there were nonzero numbers distributed on both sides of the diagonal on the nondiagonal line, which meant that the coordination degree of the complex system would change to a higher or lower level of coordinated development in the adjacent years. And the coordination evolution has continuity; there will be no leaped transformation. (2) The values on the diagonal were greater than those on the nondiagonal, indicating that the coordinated development level of the 13 cities in the BTH region was more likely to remain unchanged in high, moderate, and low coordination. And the possibility of maintaining high coordination was 100%. Beijing (average 0.85) and Tianjin (average 0.73) would maintain highly coordinated development. (3) The probability of keeping incoordination unchanged was 50%, and the probability of ascending to low coordination was 50%. This was true for Hengshui (average 0.47), Xingtai (average 0.49), and Chengde (average 0.49) of Hebei Province. (4) The probability of maintaining low coordination was 77.14%, the probability of transferring to incoordination was 7.14%, and the probability of changing to moderate coordination was 12.86%, such as in Tangshan (average 0.55), Qinhuangdao (average 0.56), Handan (average 0.56), Baoding (average 0.56), Zhangjiakou (0.50), and Cangzhou (average 0.57) of Hebei Province. (5) The probability of maintaining moderate coordination was 62.50%, the probability of transforming to high coordination was 20.83%, and the probability of transforming to low coordination was 16.67%, such as in Shijiazhuang (average 0.62) and Langfang (average 0.60) of Hebei Province. It can be seen that Beijing and Tianjin would maintain highly coordinated development, while the 11 cities of Hebei Province would maintain moderate and low coordination.

3.5. Prediction of Coordination Degree of the IS-EC-EE Complex System

The GM (1.1) model is used to predict the coordination of the IS-EC-EE complex system of the 13 cities in the BTH region from 2020 to 2030, as shown in Figure 13.

As shown in Figure 13, the coordination degree of the IS-EC-EE complex system in the BTH region from 2020 to 2030 was roughly similar to the trend from 2009 to 2019. Among them, the coordination degrees of Beijing and Tianjin would be basically the same, with an average of 0.88 and 0.71, respectively, which still belong to high coordination. Except for Langfang of Hebei Province, the average coordination degree increased from 0.60 to 0.66, and the coordination degree of the remaining 10 cities of Hebei Province decreased slightly. In addition, Shijiazhuang (average 0.60) and Langfang (average 0.66) of Hebei Province would be in moderate coordination, and Tangshan (average 0.53) and Cangzhou City (average 0.54) of Hebei Province would be in low coordination. The average coordination degree of the remaining seven cities of Hebei Province will be between 0.40 and 0.49, which would be incoordinated. From the above analysis, it could be seen that when adjusting the IS of the BTH urban agglomeration, it is necessary to combine the local economy and ecological environment to avoid excessive transfer of polluting industries and affecting the local economy and ecological environment. The coordination degree of the IS-EC-EE complex system is related to the coordinated development of the entire BTH region.

3.6. Regional Differences in the IS-EC-EE Complex System Coordination

The previous analysis shows that there are significant differences in the coordination degree of the IS-EC-EE complex system in the BTH region. The relative regional differences and the sources of coordination degrees of IS-EC-EE in the BTH region are analyzed by the Dagum Gini coefficient. The Gini coefficient can describe the intraregional differences, inter-regional differences, and the sources of the differences. The results are shown in Figure 14, Figure 15 and Figure 16.

Figure 14 displays that the Gini coefficient of the IS-EC-EE complex system coordination within Beijing and Tianjin was 0. No segmentation was conducted within the two cities, so the intraregional differences are 0. The intraregional Gini coefficients of Hebei Province and the BTH region as a whole were less than 0.15, indicating that the internal differences were very small. And the internal differences in Hebei Province were smaller than those in BTH as a whole. The coordination degree of the 11 cities in Hebei Province fluctuated around 0.5, which is at the levels of incoordination and low coordination. Only a few cities had a coordination degree higher than 0.6 in some years. The Gini coefficient increased in 2018 because the coordination degree of Beijing had been increasing, while the coordination degree of cities in Hebei Province had shown a downward trend, resulting in an increase in the coordination level gap. In 2019, the Gini coefficient decreased because the coordination degree rebounded and the gap decreased compared with 2018.

Figure 15 shows the inter-regional differences in the IS-EC-EE complex system coordination between Beijing, Tianjin, and Hebei Province. The regional differences in complex system coordination in the BTH region are Beijing–Hebei Province > Tianjin–Hebei Province > Tianjin–Beijing because Beijing has always been at a high coordination level, and the coordination degree has been increasing in recent years. Tianjin’s coordination degree has always been lower than Beijing’s, fluctuating around 0.7. And Tianjin’s coordination was higher than that of the 11 cities in Hebei Province.

From Figure 16, it can be seen that the regional differences in the IS-EC-EE complex system coordination in the BTH region were mainly due to inter-regional differences, with an average contribution rate of 62% and an average contribution rate of 38% for intraregional differences, while the hypervariable density of 0 indicates that there was no cross-overlap between regional samples. In order to reduce the difference in coordination level between Beijing, Tianjin, and Hebei, it was necessary to reduce the differences among the three regions.

3.7. Distribution Dynamic Evolution of the IS-EC-EE System Coordination

Although the Dagum Gini coefficient could quantitatively analyze the relative differences and sources of the IS-EC-EE complex system coordination in the BTH region, it could not reflect the absolute differences and dynamic evolution of IS-EC-EE coordination in the BTH region. Therefore, this article uses Kernel density estimation to study the dynamic evolution process of the IS-EC-EE complex system coordination in the BTH region.

Figure 17 displays the Kernel density of the IS-EC-EE complex system coordination level in the BTH region from 2009 to 2019 to further study the dynamic evolution. The height of the main peak shows a trend of “rising–falling–rising–falling”. The Kernel density curve in 2018 and 2019 moved to the left and kurtosis reached the lowest, indicating that the coordination level of some cities in these two years was declining and the coordination was concentrated between 0.4 and 0.5, while the coordination level in the BTH region before 2018 concentrated between 0.5 and 0.6. And there was a polarization phenomenon because the coordination degree of Beijing and Tianjin fluctuated around 0.8, and the coordination level of the two cities was far higher than that of the 11 cities in Hebei Province, which was also the reason why the curve side peak was at 0.8. In addition, the widening of the Kernel density curve indicated that the differences in the coordination level of the 13 cities in the BTH region became larger, which was consistent with the analysis of Gini coefficient results.

4. Discussion and Conclusions

4.1. Discussion

Industrial structure, economy, and ecological environment could interact and constrain each other. The 13 cities in the BTH region have different industrial structure, economy, and ecological environment characteristics. The comprehensive evaluation index was applied to assess the IS, EC, and EE development in the BTH region. There are significant differences in the IS and EC comprehensive evaluation index among the 13 cities in the BTH region. In addition to the higher IS and EC values in Beijing and Tianjin, the IS and EC of the other 11 cities in Hebei Province still need further optimization. The EE of the BTH region has developed well in recent years. The comprehensive evaluation index of the IS-EC-EE complex system in the BTH region changed little, and Beijing and Tianjin ranked in the top two. The comprehensive development index of the IS, EC, and IS-EC-EE complex system in Beijing is the highest in the BTH region, while the EE subsystem needs to be improved. The BTH region is an economic and ecological circle that interacts with itself. The comprehensive development of the IS-EC-EE complex system in the BTH region needs to be further improved.

The coupling coordination model has been widely used to study the coordinated development level [12,29,30]. There is limited research specifically focused on the compatibility between the IS-EC-EE ternary complex system in the BTH region. So, this paper studies the adjustment of the industrial structure in the BTH region and the changes it brings to the ecological environment and economic development of the region. Furthermore, this paper analyzes the regional differences and distribution dynamics of the IS-EC-EE complex system coordination levels in the BTH region to provide a reference for regional sustainable development. The coupling degree is used to measure the strength relationship of system connections. The IS-EC and IS-EE systems in the BTH region were highly coupled. The EC-EE system in the BTH region was moderately coupled. The IS-EC-EE complex system of the BTH region was highly coupled. This indicates that the IS, EC, and EE subsystems have close relationships and could affect each other, forming a complex system with coupling characteristics, while there are still noticeable coupling differences in the 13 cities of the BTH region. It can be seen that the industrial structure adjustment of the BTH region must be carried out in combination with the local economy and ecological environment.

The coordination degree is used to measure the coordination effect between systems. The IS-EC, IS-EE, and EC-EE systems in the entire BTH were in the low, moderate, and low coordination stages, respectively. The coordination levels of the IS-EC-EE complex system in Beijing and Tianjin were in the high coordination stage. The average coordination of the IS-EC-EE system in the BTH region was low coordination. So, there are clear gaps in the coordination degrees of the IS-EC-EE complex system between the 13 cities of the BTH region. Thus, the research findings of the Dagum Gini coefficient and Kernel density estimation method indicate the differences in the coordinated development level of the IS-EC-EE system in the BTH region are mainly attributed to intercity differences and the differences in coordination level among the 13 cities increasing. In order to reduce the difference in coordination level between Beijing, Tianjin, and Hebei Province, it was necessary to reduce the difference between the three regions. Furthermore, the evolution of regional coordination is continuous, and the differences in the coordination levels of the 13 cities in the BTH region have become larger. The GM (1.1) model predicted the level of coordinated development of the IS-EC-EE system in the BTH region would remain similar. As the prediction periods extend, the prediction deviation of the GM (1.1) model would increase, so the prediction stability should be considered in future studies [29,31]. The transition probability matrix of the Markov chain shows the evolution of regional coordination is continuous, and incorporating the concept of spatial lag into the traditional model is recommended. The spatial Markov chain, which contains spatial lag, could be applied in following research [22].

The coordinated development of the IS-EC-EE complex system is related to the coordinated development of the entire BTH region. Therefore, in order to prompt the coordinated development of the BTH urban agglomeration as the capital economic circle, the actual situation in various cities should be paid more attention to. When formulating regional sustainable policies, IS adjustment should consider the industrial structure, economy, and ecological status of the receiving cities. When adjusting the IS of the BTH urban agglomeration, it is necessary to combine the local economy and ecological environment to avoid excessive transfer of polluting industries that may affect the local economy and ecological environment. The IS, EC, and EE are interrelated and mutually influential, forming a complex system with coupling characteristics. The coordination degree of the IS-EC-EE complex system is related to the coordinated development of the entire BTH region. The adjustment and optimization need to be considered from the perspective of the system and regional overall sustainable development.

This article specifically studies the development level and coordination relationship of the IS, EC, and EE in the 13 cities in the BTH region from 2009 to 2019. The characteristics of the IS, EC, and EE in other regions are different from those in the BTH region. The conclusions obtained might not be directly applicable to other regions but could provide a reference for regional coordinated development.

4.2. Conclusions

This paper constructed a comprehensive evaluation index system of the IS-EC-EE system. The coupling coordination model was constructed to analyze the spatiotemporal differences in the coordinated development of the IS-EC-EE system of the 13 cities in the BTH region from 2009 to 2019. At the same time, the Markov chain and GM (1,1) model were used to analyze and predict the evolution of the IS-EC-EE complex system coordinated development. The Dagum Gini coefficient and Kernel density estimation method were conducted to research the regional differences and distribution dynamics of the IS-EC-EE complex system coordination levels in the BTH region.

There were significant regional differences in the IS and EC levels of the 13 cities in the BTH region, with Beijing and Tianjin ranking in the top two. The average EE index of the BTH region was around 0.7, indicating that the overall EE development of the region was relatively good. The comprehensive evaluation indicators of the IS-EC-EE system in the BTH region had not changed much, with significant differences among the 13 cities. The average indexes of the IS-EC and IS-EE systems in the BTH region were highly coupled. The EC-EE system in the BTH region was moderately coupled. The IS-EC-EE complex system of most cities in the BTH region was basically in the highly and moderately coupled stage. The IS-EC system of the BTH region was low coordination. The IS-EE in BTH was 0.63–0.73, moderate coordination stage. The average coordination degree of the EC-EE system in the BTH region was 0.56–0.61, indicating a low coordination stage. The average coordination degree of the IS-EC-EE system in the BTH region was 0.58, which is a low coordinated development state. The coordination degrees of Beijing and Tianjin were both high coordination and significantly higher than those of the 11 cities in Hebei Province.

There was a high probability that the coordinated development level of the 13 cities in the BTH region would remain in high, moderate, and low coordination. The coordinated development level of the IS-EC-EE system in the BTH urban agglomeration from 2020 to 2030 was roughly similar to the trend from 2009 to 2019.

The economic and ecological circles composed of the 13 cities in the BTH region interact with each other, and the evolution of the IS-EC-EE complex is continuous and delayed. When formulating and implementing corresponding industrial structure adjustments, economic development, and environmental policies, it is necessary to recognize the differences and functionalities between cities from the perspective of the coordinated development of the 13 cities in the BTH region as a whole to achieve the overall optimization of the region.