System Dynamics Theory Applied to Differentiated Levels of City–Industry Integration in China

Abstract

The development of city–industry integration is crucial for modern cities and is a core element of city competitiveness enhancement and sustainable development. This study considers system dynamics theory to examine city–industry integration and constructs an index system to measure the degree of integration. For this purpose, 31 regions in China (including provinces, autonomous regions, and municipalities directly under the central government) are considered as research samples. Objective evaluation methods such as factor analysis and entropy methods are applied to evaluate the target value. The research results reveal a wide gap in the levels of city–industry integration in various regions of China. Furthermore, the Middle East outperforms the Western and Northeastern regions. Accordingly, the advantages of the Central and Eastern regions should be combined, and a leading and radiation-driven role should be played. Moreover, capital investment in the Western and Northeastern regions should be increased, and emphasis should be placed on local characteristics. Moreover, urban economic development, industrial transformation, and industrial upgrading should be promoted, and the sustainable development capacity of cities should be enhanced.

1. Introduction

The urbanization level of developed countries has exceeded 75% and that of developing countries is approximately 50%. The urbanization level of China reached 63.89% in 2020, with the number of cities reaching 687. Cities, the main body of the national economy, facilitate the modernization of a country. Moreover, cities are the economic centers of regional development, thereby enhancing regional economic development and economic levels as well as the development of cities. With the acceleration of the modernization of urban construction since the 21st century, urban industries are transforming and upgrading to enhance their industrial economy and thereby promote urban development [1].

The industrial economy is an indispensable foundation of cities, and urban development plays a decisive role in industrial economic development. Furthermore, city–industry integration relates to the rational allocation of urban resources, the quality of urban residents’ life, sustainable urban development, and other major issues of modern urban management. “City–industry integration” is the simultaneous and coordinated development of urbanization and industrialization, including the integration of social, economic, cultural, industrial, spatial, and other aspects. It is a new model based on the city, with industry as the guarantee. It augments urban renewal, improves service support, and enhances the spatial value of urban areas to achieve benign development among industry, city, and residents. Currently, the development model of “promoting the city with industry and promoting production with the city” is crucial for urban management as well as to realize the mutual coordination between industrial development and urban function improvement.

Cities are large human agglomerations with administratively defined boundaries and are composed of extensive housing, transportation, and communication systems. Industries are the product of the continuous development of productivity and social division of labor. Since the reform and opening up, people’s lifestyles have changed dramatically. According to the National Bureau of Statistics, the urbanization rate in the eastern part of China was as high as 63.89% in 2021, 45.99% higher than the urbanization rate of 17.9% in 1978. Along with the continuous increase in the urbanization rate, problems such as the rapid reduction of arable land, urban diseases, ruination, and bubbling have also emerged. Accordingly, the concept of city–industry integration has emerged to solve the problems in the urbanization process.

City–industry integration is a strategic initiative proposed by the state to execute deep urbanization, switch the regional development mode, and prevent the phenomenon of “empty cities” and “sleeping cities”. It aims to realize the return of cities from “function-oriented” to “people-oriented”. The industry is the basis of urban development, and the city is the guarantee of industrial upgrading. City–industry integration can organically combine urban functions with industrial development to enhance the efficiency of urban resource allocation, clarify urban positioning, and promote industrial transformation and upgrading, thereby facilitating urban vitality and competitiveness.

Lewis developed a model of urban–rural “binary economic structure theory”. The model posits that the urban–rural divide has contributed to the transfer of surplus rural labor to cities and towns, thereby augmenting the urbanization process [1]. Chenery analyzed the relationship between economic development and urbanization in more than 100 countries from 1950 to 1970 based on World Bank Statistics. The author proposed a “two-way law of mutual promotion of urbanization and economic development” [2]. Jacobs asserted in Urban Economics that relying on the development of urban agriculture substantially increased productivity. Industrialization causes urbanization, and urbanization results from industrialization [3].

City–industry integration is a complex systemic project [4], which can be interpreted as a return from urban functionalism to a humanistic orientation [5]. City–industry integration is mainly reflected in the unification of layout and function, symbiosis of city and industry, integration of residence and employment, interaction of production and service, and coordination of economy and environment. [6]. The “industry” of city–industry integration refers to industry and the competitiveness of the industry and the radiation-driven effect of integration with cities [7]. The “city” of city–industry integration needs to be further developed in accordance with the development of industries that promote local economic development [8]. The city–industry integration can be realized in different ways by different entities. The problem of city–industry integration is more prominent in the construction of new urban areas, which usually include high-tech development zones, economic and technological development zones, and industrial parks in cities. The construction of new urban areas has undergone the following development stages from a simple factory industrial park focusing only on spatial concentration to an industrial park focusing on industrial concentration, providing support and service to industry rather than residence. Thereafter, it has transformed into a technology park focusing on talent concentration and service for science and technology, focusing on the human residence and living facilities. Finally, it has transformed into an industrial park integrated with the city that attracts high-end industries to settle there. Simultaneously, it creates a livable environment for city–industry integration. In many cities in China, the government’s pursuit of city–industry integration generally includes three levels of meaning: the establishment of a new city district with complete functions, the selection of industries that meet the future positioning and planning nature of the city, and organic integration of the new city district with the old city [9]. For some specific forms of parks, different scholars have conducted studies. Wang et al. [10] examined the level of city–industry integration of high-tech zones in major cities in China. The authors determined that high-tech zones with a higher degree of city–industry integration performed better in terms of scientific and technological innovation and economic scale. Jiang [11] compared the degree of city–industry integration of provincial-level development zones in Jiangsu Province. The authors reported that the level of economic development, economic pulling power to the city, and industrial structure of the development zones affected the degree of city–industry integration.

Zheng et al. [12] examined the National Independent Innovation Demonstration Zone in Jiangsu Province. The authors determined that high-tech zones performed better in terms of livelihood protection to promote city–industry integration but failed to attract highly educated talents to reside in the zones. Sun et al. [13] revealed three paths of city–industry integration around the plain compensation approach of value loss and judged the rationality of city–industry integration for three types of suburban development zones.

Different researchers have differently summarized the problems in the implementation of city–industry integration and have proposed corresponding suggestions. In the process of city–industry integration, new urban areas face issues such as weak service facilities, serious separation of jobs and residences, insufficient interaction between industries and cities, and imperfect functions [9]. The relevant provinces also have the problems of unscientific industrial space layouts, an insufficient supply of park infrastructure, and weak economic strength, which are not conducive to gathering industrial elements when implementing city–industry integration [14]. Accordingly, the country should strive to explore a people-centered path [15]; focus on planning, leading, and coordination; take the initiative to conduct industrial transformation and upgrading [16]; and continuously promote the integration of elements, functions, and space in the development of city–industry integration [17] to achieve the co-prosperity of industry and city in space and the symbiosis of residents and environment in function [18].

Scholars have used different methods to examine the degree of city–industry integration and have established an evaluation index system. The more commonly used methods include the analytic hierarchy process (AHP) [19,20], factor analysis and principal component analysis [21,22,23], entropy method [24,25,26], integration, coordination, and coupling degree models [27,28,29], four-grid quadrant method [30], and fuzzy comprehensive evaluation method [31]. Liu examined the development of the port economy and city integration in Suzhou, China, based on gray correlation analysis [32]. Given that city–industry integration implies the organic integration of industry and city in different dimensions; it is based on the system theory condition. Therefore, it is more scientific to examine the level of city–industry integration under the system theory condition and using the system dynamics approach. Currently, few studies have focused on city–industry integration by using the system theory, and most of them are only limited to exploring the operation mechanism of the city–industry integration system. The present study uses Vensim PLE Software to simulate the city–industry integration system. In addition, it uses the city–industry integration evaluation index system to empirically evaluate the city–industry integration level of 31 provinces, autonomous regions, and municipalities directly under the central government in China.

2. Methodology

2.1. Construction of the System Dynamics Model

Bertalanffy considered a system as an organic whole with certain functions formed by several elements linked in a certain structural form [33]. Systems are generally characterized by wholeness, structure, hierarchy, and relatedness. The city–industry integration system constitutes diverse, complex, and dynamic subsystems, and each subsystem is influenced by many elements. The city–industry integration system can be developed at two levels: industry and city. The industrial level is reflected by the industrial organization, structure, layout, and agglomeration, and the city level is reflected by urban resources, scale, life, governance, and so on. City–industry integration constitutes the population, environment, policy, and service systems. Through the intersection of these systems with related elements, a city–industry integration system with the characteristics of factor, function, system, population, and cultural integration is formed. In the city–industry integration system, urban development and industrial transformation and upgrading are mutually influential and jointly promote improvement. The city–industry integration system is crucial to solving the problems of “urban diseases”, such as traffic congestion, housing tension, water supply shortage, energy shortage, environmental pollution, disorder, energy flow, and imbalance of input and output of material flow. Furthermore, it helps to promote and inherit the city’s characteristic culture and enhance the industrial development and infrastructure construction in the city. Accordingly, city–industry integration can promote the double-driven development of the city economy and city construction. Figure 1 depicts the system diagram of “city–industry integration”.

“Causal chains” helps examine the complexity of the city–industry integration system, given that it can reflect the relation between events. Causal networks are systems constituting several “causal chains” [34]. In this study, the Vensim PLE Software was used to simulate the cause–effect relationships among industries, cities, and the whole system to reflect the cause–effect relation between its elements. The paper was combined with related studies [35,36]. The proportion of value added by secondary and tertiary industries was selected to reflect the industrial structure. In addition, the assets of industrial enterprises above the scale and real estate fixed asset investment were selected to reflect the industrial economic situation. Moreover, the number of scientific and technological innovation personnel and the number of patents granted were selected to reflect the scientific and technological innovation situation. Several industrial enterprises above the designated size and the proportion of industrial employment were selected to reflect the industrial scale. Figure 2 presents the causality diagram at the industrial level.

Urban development is the premise of the process of the development of city–industry integration. Combined with the variables in related studies [37,38], GDP per capita, disposable income per capita, and average wage of employees were selected to reflect people’s living standards. Population density was selected to reflect the city scale. Furthermore, the number of physicians per 10,000 people, number of passenger cars per capita, postal service per capita, public library collection per capita, and education expenses were selected to reflect the city’s function and service level. The target of educational expenditure refers to the actual expenditure on education in the budgets of the central and local financial departments, including the personnel expenditure and public expenditure on schools of various types and at various levels, as well as the expenditure on the construction of schools and the purchase of large-scale teaching equipment. This index can well reflect the city’s service function in the field of education. In addition, environmental protection expenditure was selected to reflect urban sustainable development ability. As depicted in Figure 3, the cause–effect diagrams at the city level were constructed using the aforementioned indicators.

Furthermore, the cause–effect diagrams at the industry and city levels were combined to obtain the cause–effect diagram of the city–industry integration system, reflecting the effect of each element at the industry and city levels in the city–industry integration system. Figure 4 depicts the causality diagram of the city–industry integration system.

2.2. Construction of an Index System

Following the scientific principles, structure, hierarchy and relevance, a multidimensional index system was constructed by considering the connotation of city–industry integration and the causal correlation between key elements of each internal subsystem, such as population, policy, environment and service systems.

Indicators were selected from industrial development and urban development to develop the indicator system. The index system comprised two primary indicators and 18 secondary indicators. Of these, eight secondary indicators were selected to reflect industrial structure, industrial economic status, industrial science and technology innovation status, and industrial scale. Moreover, 10 secondary indicators were selected to reflect people’s living standards, urban scale, urban function and service level, and urban sustainable development capability.

Table 1. The index system of the level of city–industry integration.

First-Level Index	Second-Level Indicators	Unit	Characteristic	Source and Reference
Industry Development Level	The proportion of value added to secondary industry	%	Positive	CMD [39]
	The proportion of value added to tertiary industry	%	Positive	CMD [39]
	Assets of industrial enterprises above the scale	Billion	Positive	CIED [40]
	Real estate fixed assets investment	Billion	Positive	CRD [40]
	Number of scientific and innovative personnel	People	Positive	CRD [41]
	Number of patents granted	Pieces	Positive	CRD [41]
	Above-scale industry number of business units	Individual	Positive	CIED [30]
	The proportion of industrial employees in active population	%	Positive	NBS [30]
Ubran Development Level	GDP per capita	Yuan	Positive	CMD [28]
	Disposable income per capita	Yuan	Positive	CMD [28]
	Population density	People/km2	Centering	CRD [42]
	Number of physicians per 10,000 people	People/10,000	Positive	CRD [42]
	Number of passenger cars per capita	Vehicle/person	Positive	CRD [43]
	Postal business per capita	Billion Yuan/10,000 people	Positive	CRD [43]
	Education expenses	Million Yuan	Positive	CRD [28]
	Environmental protection expenditure	Million Yuan	Positive	CRD [44]
	Public libraries per capita book collection	Books/10,000 people	Positive	CRD [45]
	Average wage of employees	Yuan	Positive	NBS [28]

displays the specific indicators. The relevant data are disclosed in the Statistical Yearbook of China and the Statistical Yearbook of each province. Most of the data are from the China Macro Database (CMD), China Regional Database (CRD) and China Industrial Enterprise Database (CIED), while the rest are released by the National Bureau of Statistics (NBS).

2.3. Data Processing

Dimensionless treatment for each positive indicator and consistent treatment for negative and centered indicators using the following formulas were considered to prevent the influence of different magnitudes, units, and nature of indicators.

For the positive indicator, the formula is as follows:

$$z=\frac{x-x_{min}}{x_{max}-x_{min}}$$

(1)

For the inverse indicator, the formula is:

$$z=\frac{x_{max}-x}{x_{max}-x_{min}}$$

(2)

For the centering indicator, the formula is as follows:

$$z=\frac{1}{\left|x-\overline{x}\right|}$$

(3)

where $z$ denotes the standardized index value. The population density in the index system is the central indicator, and the rest are positive indicators.

After the standardization of indicators, the values were shifted to the right by 0.0001 overall to eliminate the effect of the 0 value.

Moreover, weights are essential to combine data into one indicator according to their importance. Thus, a preferable method should be chosen to calculate the weights of each indicator. Since subjective assignment methods such as AHP need to determine the weights based on expert scores, which are somewhat subjective, the objective assignment method was chosen to determine the weights of each indicator [46]. Furthermore, a combination of factor analysis and the entropy method in the objective assignment method was selected to avoid the subjectivity of the subjective assignment method and the limitation of selecting only one objective assignment method. This approach was considered to assign weights to the indicators of the city–industry integration index system and evaluate the degree of city–industry integration in 31 provinces, autonomous regions, and municipalities directly under the central government.

Using SPSS 25.0 software, factor analysis was applied to determine the weights of seven indicators of industrial development level and 10 indicators of city level in the index system of city–industry integration to obtain the scores of the two indicators in each province. The specific steps are as follows: (1) conduct a factor analysis appropriateness test; (2) conduct a common factor ANOVA; (3) conduct factor rotation and naming; (4) and calculate the factor scores of industrial development and city development levels in each province.

3. Results and Discussion

3.1. Results of Factor Analysis

The KMO statistic of the industrial development level was 0.760, implying suitability for factor analysis. As indicated in

Table 2. Explanation of total variance of industrial development level and urban development level.

Ingredients	Initial Eigenvalue			Extraction of the Sum of Squares of Loads			Sum of Squared Rotating Loads
	Total	Percentage of Variance	Cumulative %	Total	Percentage of Variance	Cumulative %	Total	Percentage of Variance	Cumulative %
Industry Development Level
1	4.754	59.428	59.428	4.754	59.428	59.428	4.698	58.728	58.728
2	1.773	22.157	81.584	1.773	22.157	81.584	1.816	22.699	81.427
3	1.013	12.664	94.248	1.013	12.664	94.248	1.026	12.821	94.248
4	0.195	2.435	96.683
5	0.163	2.042	98.725
6	0.053	0.661	99.386
7	0.035	0.436	99.822
8	0.014	0.178	100.000
Urban Development Level
9	4.950	49.505	49.505	4.950	49.505	49.505	4.378	43.782	43.782
10	1.805	18.053	67.557	1.805	18.053	67.557	2.317	23.165	66.948
11	1.479	14.789	82.347	1.479	14.789	82.347	1.540	15.399	82.347
12	0.646	6.462	88.808
13	0.426	4.260	93.069
14	0.237	2.373	95.441
15	0.181	1.808	97.250
16	0.141	1.411	98.661
17	0.100	0.999	99.659
18	0.034	0.341	100.000

Extraction method: The principal component analysis was performed; components 1–8 represent industrial development level indicators, and components 9–18 represent urban development level indicators.

, the characteristic roots of the first two principal components were greater than 1, and the cumulative contribution rate reached 94.248% (generally, it is considered better to have a cumulative contribution rate of more than 80%). Combined with the gravel plot, it is appropriate to take the first three principal components. Since the indicators did not indicate significant differentiation among the principal components, they were rotated. After the rotation, the first principal component had a large load on the assets of industrial enterprises, investment in real estate fixed assets, number of scientific and technological innovation personnel, number of patent authorizations, and number of industrial enterprises above the designated size. These are referred to as industrial development factors. The second principal component had a large load on the proportion of value added in the secondary industry and the proportion of value added in the tertiary industry, referred to as the industrial structure factor. The third principal component had a large load on the proportion of total industrial employment. This is referred to as the industrial employment situation factor. The seven variables were reduced to three, and the scores of each factor in each province were calculated. The three common factors were denoted as $F_1, F_{2,}$ and $F_3$. Combined with the component score coefficient matrix in

Table 3. Component score coefficient matrix.

	Ingredients
	1	2	3
Industry Development Level
The proportion of secondary industry value added	0.009	0.501	0.027
The proportion of value added to tertiary industry %	0.065	−0.541	0.067
Assets of industrial enterprises above the scale	0.210	−0.011	−0.096
Real estate fixed assets investment	0.199	0.019	0.073
Number of scientific and innovative personnel	0.215	−0.095	−0.015
Number of patents granted	0.211	−0.048	−0.041
Number of industrial enterprise units above the scale	0.195	0.076	0.041
The proportion of industrial employees in active population	−0.023	−0.028	0.979
Urban Development Level
GDP per capita	0.214	−0.012	−0.030
Disposable income per capita	0.222	−0.030	−0.016
Population density	−0.109	0.018	0.639
Number of physicians per 10,000 people	0.077	−0.047	0.499
Number of passenger cars per capita	0.136	0.092	−0.037
Postal business per capita	0.100	0.191	−0.006
Education business expenses	−0.094	0.463	−0.054
Environmental protection expenditure	−0.097	0.446	0.045
Public library collections per capita	0.213	−0.081	−0.018
Average wage of employees	0.252	−0.168	−0.042

, three linear equations were established.

$$F_1=0.009X_1+0.065X_2+0.210X_3+0.199X_4+0.215X_5+0.211X_6+0.195X_7-0.023X_8$$

(4)

$$F_2=0.501X_1-0.541X_2-0.011X_3+0.019X_4-0.095X_5-0.048X_6+0.076X_7-0.028X_8$$

(5)

$$F_3=0.027X_1+0.067X_2-0.096X_3+0.073X_4-0.015X_5-0.041X_6+0.041X_7+0.979X_8$$

(6)

$X_1$ denotes the proportion of value added in the secondary sector, $X_2$ denotes the proportion of value added in the tertiary industry, $X_3$ denotes the assets of industrial enterprises above the scale, $X_4$ denotes the fixed asset investment in real estate, $X_5$ denotes the number of scientific and technological innovation personnel, $X_6$ denotes the number of patents granted, $X_7$ denotes the number of industrial enterprise units above the scale, and $X_8$ denotes the proportion of industrial employees in active population.

Based on the cumulative contribution of the three common factors in Table 2, the final factor score equation at the level of industrial development was derived.

$$F_I=0.594F_1+0.222F_2+0.127F_3$$

(7)

The same method applies to the urban development level indicators. According to the aforementioned steps, among the 10 indicators of urban development level, the principal components with the first three characteristic roots exceeding 1 and with a cumulative contribution rate exceeding 82.347% were selected, as indicated in Table 2. After rotation, the first principal component was loaded on the GDP per capita, disposable income per capita, number of passenger cars per capita, postal service per capita, public library collection per capita, and the average wage of employees. Therefore, it was referred to as the urban quality of life factor. The second principal component is loaded on education expenditure and environmental protection expenditure. It is called the urban sustainable development factor. The third principal component is loaded on population density and the number of physicians per 10,000 people. It is referred to as the urban residents’ health security factor. The three public factors are denoted as $F_4$, $F_5$, and $F_6$. The component score coefficient matrix in Table 3 was considered to establish three linear equations.

$$F_4=0.214X_9+0.222X_{10}-0.109X_{11}+0.077X_{12}+0.136X_{13}+0.100X_{14}-0.094X_{15}-0.097X_{16}+0.213X_{17}+0.252X_{18 }$$

(8)

$$F_5=-0.012X_9-0.030X_{10}+0.018X_{11}-0.047X_{12}+0.092X_{13}+0.191X_{14}+0.463X_{15}+0.446X_{16}-0.081X_{17}-0.168X_{18}$$

(9)

$$F_6=-0.030X_9-0.016X_{10}+0.639X_{11}+0.499X_{12}-0.037X_{13}-0.006X_{14}-0.054X_{15}+0.045X_{16}-0.018X_{17}-0.042X_{18}$$

(10)

$X_9$ denotes the GDP per capita, $X_{10}$ denotes the disposable income per capita, $X_{11}$ denotes the population density, $X_{12}$ denotes the number of physicians per 10,000 people, $X_{13}$ denotes the number of passenger cars per capita, $X_{14}$ denotes the number of postal services per capita, $X_{15}$ denotes education expenses, $X_{16}$ denotes the expenditure on environmental protection, $X_{17}$ denotes the number of books in public libraries per capita, and $X_{18}$ denotes the average wage of employees.

Based on the cumulative contribution of the three public factors, the final factor score equation at the level of urban development was derived as follows:

$$F_C=0.495F_4+0.181F_5+0.148F_6$$

(11)

Accordingly, the data of each province, autonomous region, and municipality directly under the central government were incorporated into the aforementioned formula to derive the industrial development level and city development level

In terms of industrial development level, a wide gap was observed between the scores of each province. Guangdong and Jiangsu were far ahead of other provinces in terms of industrial development, with a difference of more than 0.5 points from the provinces with weaker industrial development levels. Specifically, with regard to industrial development, industrial structure, and industrial employment factors, provinces with weaker industrial development levels had a larger gap in the industrial development factor. Thus, when developing industries, provinces with weaker industrial development levels should focus on investment in fixed assets and science and technology innovation, as well as actively support enterprises in their provinces to improve their overall economic strength and scale.

In terms of urban development levels, Beijing, Zhejiang, and Shanghai outperformed all other provinces. Although the difference in urban development levels among provinces was not as major as the difference in the industry, the gap was still relatively obvious and the difference in some provincial scores exceeded 0.5 points. Specifically, with regard to the urban quality of life factor, urban sustainable development factor, and urban residents’ health protection factor, provinces with lower scores had a larger gap than those with higher scores in the urban sustainable development factor. Accordingly, when building cities, more emphasis should be placed on investment in education and environmental protection to improve the city’s talent pool and environmental foundation to promote the city’s stable and sustainable development.

3.2. Results of Entropy Weight

The entropy method is an objective weighting method. The method can more objectively avoid the interference of human factors and can reflect the importance of each evaluation index in the comprehensive index system [47]. The steps to determine the index weights using the entropy method are as follows.

First, calculate the contribution of the i-th individual under the j-th indicator $ij$.

$$P_{ij}=\frac{x_{ij}}{{{\displaystyle \sum }}_{i=1}^n{x}_{ij}}$$

(12)

Second, calculate the entropy value of the j-th indicator.

$$e_j=-\frac{1}{lnn}{\displaystyle \sum }_{i=1}^n{p}_{ij}\ln \left(p_{ij}\right),0\le e_j\le 1$$

(13)

Third, calculate the coefficient of variability of each indicator.

$$g_j=1-e_j$$

(14)

Finally, determine the weights of each indicator W_j.

$$W_j=\frac{g_j}{{{\displaystyle \sum }}_{i=1}^m{g}_j},j=1,2,3\dots m$$

(15)

In this paper, according to the aforementioned steps and the composition of each index, the entropy method was used to calculate the two main indexes of industrial development and urban development levels, resulting in $F_I$ and $F_C$ as weights in

Table 4. Weighting table of industrial development level and urban development level.

Tier 1 Indicators	Weighting w
Industry Development Level $F_I$	0.540
Level of urban development $F_C$	0.460

.

The formula for calculating the final score of the level of city–industry integration for each province, autonomous region, and municipality directly under the central government was obtained.

$$F=0.540F_I+0.460F_C$$

(16)

3.3. Results of the Integration Level

The level of city–industry integration for each province, autonomous region, and municipality directly under the central government was calculated and ranked, as presented in Figure 5. In addition, the specific values for each region are provided in

Table A1. Ranking of provinces, autonomous regions, and municipalities directly under the central government on the level of city–industry integration.

Province	$\mathbf{Industry} \mathbf{Development} \mathbf{Level} {\mathit{F}}_{\mathit{I}}$	$\mathbf{Level} \mathbf{of} \mathbf{Urban} \mathbf{Development} {\mathit{F}}_{\mathit{C}}$	Level of City–Industry Integration F	Ranking
Guangdong	0.669	0.348	0.521	1
Jiangsu	0.640	0.344	0.504	2
Zhejiang	0.494	0.440	0.469	3
Beijing	0.101	0.601	0.331	4
Shandong	0.390	0.227	0.315	5
Fujian	0.354	0.229	0.296	6
Anhui	0.351	0.190	0.277	7
Shanghai	0.142	0.420	0.270	8
Henan	0.360	0.140	0.259	9
Hubei	0.272	0.162	0.222	10
Tianjin	0.172	0.274	0.219	11
Sichuan	0.251	0.121	0.192	12
Hebei	0.202	0.175	0.190	13
Shaanxi	0.206	0.154	0.182	14
Hunan	0.205	0.132	0.171	15
Jiangxi	0.226	0.106	0.171	16
Liaoning	0.157	0.172	0.164	17
Inner Mongolia	0.139	0.185	0.160	18
Shanxi	0.164	0.131	0.149	19
Yunnan	0.178	0.114	0.148	20
Chongqing	0.168	0.119	0.145	21
Tibet	0.159	0.120	0.141	22
Jilin	0.125	0.122	0.124	23
Guangxi	0.142	0.092	0.119	24
Guizhou	0.129	0.105	0.118	25
Ningxia	0.110	0.123	0.116	26
Qinghai	0.104	0.113	0.108	27
Xinjiang	0.093	0.107	0.100	28
Gansu	0.123	0.062	0.095	29
Heilongjiang	0.077	0.101	0.088	30
Hainan	0.039	0.124	0.078	31

of Appendix A.

The results of the city–industry integration level analysis indicate that Guangdong, Jiangsu, Zhejiang, Beijing, Shandong, Fujian, Anhui, Shanghai, Henan, Hubei, and Tianjin had better degrees of city–industry integration. However, Guizhou, Ningxia, Qinghai, Xinjiang, Gansu, Heilongjiang, and Hainan had large gaps compared with other provinces in terms of city–industry integration. However, the score of each province was not very high, indicating more room for improvement. Further, Figure 6 indicates that the level of city–industry integration is jointly determined by regional industrial development and urban development. On the one hand, the bar chart of different regions indicates that urban development and industrial development levels in China exhibited regional heterogeneity. On the other hand, industrial development within the same region was not consistent with the level of urban development, and one-third of the regions had a large gap between the two, further affecting the coordination of industrial and urban integrated development. Specifically, the level of industrial development in most areas was higher than that in cities. Of the regions with a higher level of urban development, Beijing, Shanghai, Tianjin, Hainan, and other regions had greater differences in the level of urban development compared with the level of industrial development. The urban development of such regions was more regulated by national policies. Although their natural resource endowment conditions were not dominant, urban development was more developed and attracted groups of talent and capital.

The level of city-industry integration for each province, autonomous region, and municipality directly under the central government was calculated and ranked, as presented in Figure 7. In addition, the specific values for each region are provided in Appendix A.

In order to show the results more clearly, Figure 8 displays the geographical heat map of the development level of city–industry integration.

In terms of the geographical location of provinces with different degrees of city–industry integration, on the whole, city–industry integration in China indicated obvious spatial distribution characteristics. Figure 8 depicts that the darker the color, the higher the fusion level. Figure 6 and Figure 8 depict a similar distribution. However, the level of integrated development in most regions was lower than their industrial development level but higher than their urban development level. Specifically, most provinces with higher degrees of city–industry integration were concentrated in the Central and Eastern regions, and the city–industry integration in the eastern coastal regions was generally higher. The degree of city–industry integration was generally higher in the eastern coastal region, and the level of city–industry integration was generally lower in the Western region and the Northeastern region, forming a gradient spatial distribution pattern. The main reason for this pattern is that the economic development of the Central and Eastern coastal regions was generally higher. The level was generally better with a better external development pattern, and the industrial structure, scientific and technological innovation, sustainable development, and people’s living standard were generally in a better state. The Western and Northeastern regions were less efficient in industrial transformation and upgrading, and the economic development was affected by the geographical location, thereby resulting in a lower level of city–industry integration. Undoubtedly, city–industry integration in the eastern coastal cities will definitely take the vanguard position in the integrated development of industry and cities in China. The eastern coastal areas will lead the in-depth development of the city–industry integration in the country in the future, given the implementation of relevant strategies for the further integrated development of domestic cities and the in-depth implementation of policies and institutions.

4. Conclusions

Based on the system dynamic model, this study identifies the causal relationship between the factors in the industrial and urban systems. For this purpose, this paper constructs an evaluation system for city–industry integration in China and measures it by combining factor analysis and entropy value methods. The development level of industrial and urban integration in China is affected by industrial development and urban development, exhibiting a decreasing east-middle-west distribution. Accordingly, the integration level needs to be improved. To sum up, industry augments urban development, and city is the platform of industrial development. The integrated development of city and industry promotes industrial optimization and upgrading and healthy urban development under the joint action of industrial production factors, economic strength, urbanization level, development environment, and other dimensions. The government should formulate different policies and strategies to improve the level of city–industry integration in each region. For the better-developed Central and Eastern regions, their advantages should be further stabilized and effective systems and economic zones should be established in city clusters or metropolitan areas to bring into play their industrial agglomeration and diffusion-driving effects, thereby leading the development direction for the surrounding areas. For the more slowly developing Western and Northeastern regions, the transformation and upgrading of industries should be actively promoted to bring into play the characteristics of regional resource endowments and low labor costs as well as to give full play to the local characteristics and ensure the development of local industries. Moreover, emphasis should be placed on the local characteristics, supply of capital should be ensured, talents in various fields should be introduced, the city should be augmented by industry, the flow and concentration of population should be promoted, the attractiveness of the region should be enhanced, and the development of city–industry integration should be promoted.

This paper combines causal chain analysis in system dynamics with factor analysis and entropy method in objective weighting method to measure and analyze the development level of industrial integration in China. Given the complexity of industry, city, and other systems, the application of a causal chain will help scholars to sort out the relation between various factors within the system. The objective weighting method can avoid the one-sidedness and subjectivity of subjective weighting, thereby reflecting the internal relationship of data. We believe that the methods applied in this paper can provide some value in the research on complex systems, especially in the research fields of innovation ecosystems, digital economic systems, and energy–economy–environment (3E). For instance, the integration of industrial digitalization and digital industrialization in the development of digital economy, as well as the integration and development of each subsystem in the 3E system.

Notably, this paper endeavors to incorporate more factors on the basis of theoretical analysis. However, many factors affect industrial development and urban development. With economic development and social progress, more factors will be included in the future. In addition, most indicators are only disclosed at the provincial level, and there is a lack of data at the township, district, and county levels. Therefore, based on the availability of data, this paper focuses on the analysis of the development level of inter-provincial city–industry integration. In the future, we aim to expand the research direction of this study and explore the factors impacting and driving city–industry integration from a mathematical perspective by building econometric models.