# Coupling Coordination Degree of City-Industry Integration in Shanghai Based on Entropy Evaluation Method

Authors: Lin Su; Jingjing Jia
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

With the quality of urbanization getting more attention, city-industry integration has increasingly become a hot topic in academia. This paper is based on the traditional quantitative research of city integration, introducing the measuring model of ternary coupling coordination degree of city-industry integration in Shanghai and highlighting the ecological environment has become an important factor in the process of integration. Through the entropy method and coupling coordination model to measure the integration of Shanghai, a reasonable conclusion was reached within data analyses.


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## Body

## 1. Literature Review

In the process of urban development, uncoordinated industrialization and urbanization appear frequently. The degree of fusion between industrial development and urban function is of great significance to the sustainability of a city. Therefore, it is necessary to measure the integration degree between the city and industry. Researchers in academia have measured the integration from different aspects and through different research methods. Zhang [1], in 2004–2018, used F city’s statistics as the research object and constructed the evaluation system, including urban development, industry support, and population development as three dimensions of the 18 index. With the objective entropy method to calculate the weight of 18 evaluation indicators, he established the ternary coupling model of F city (production, city, and human). As a result, the city integration coupling degree and coupling coordination degree was evaluated. Shi [2], starting from the perspective of industrial economic development, urban construction, and ecological level, then, the dynamic coupling index system of ecological integration was constructed. With 13 typical resource-based cities as the research objects, the coupling coordination degree model was used to study the coupling relationship among the three. The consequences showed that the ecological coupling of the resource-based city is well coupled and primary coordinated, far from high-quality coupling. There was also an inseparable relationship between industrial economy, urban construction, and resource endowment. There was a positive correlation between industrial economy and ecological development level, while a negative between industrial economy and urbanization level. Finally, the paper put forward feasible countermeasures with the ecological construction from the industry, city, and human development. Zou [3], selected 15 indicators to measure the degree of city-industry integration, using 31 provinces as research objects and based on the relative data from 2000 to 2015. Through the entropy evaluation method, the coupling coordination, and GIS, the differences of space-time structure were conducted and analyzed. He also explored the impact indicators on the coordinated development based on panel data and classification data of city-industry coordination types. Wang [4], focused on urbanization and industrial cluster in Shandong Province and explored a feasible way to build an integrated system. By constructing the coupling model of an urbanization-industrial cluster, the coupling coordination index of 17 cities in Shandong Province was calculated, and the coupling development degree of city-industry coupling in Shandong Province was scientifically evaluated. According to the empirical results, the author came up with suggestions. Gu [5], based on the interactive mechanism, influencing factors of “city-industry integration,” the two subsystems of industrial function and urban function, analyzed the coupling and coordination of evaluation indicators by the entropy method. The consequences showed that the entropy method can better depict the “city integration” of the Kunshan Development Zone, which fits with reality; that is, relying on the early development of the old days. But with the massive influx of the migrant population, the “city integration” of Kunshan maintained a steady development and even decreased. Wu [6], took Yangquan City as an example, and the main component analysis method was used to reveal the development law of city-industry integration from 2004 to 2015. Through the coupling coordination model, countermeasures and suggestions were further compatible to the integration. The results presented that before 2009, the industrial development level of Yangquan City was faster than the urban development level, after which the industrial development level lagged behind the urban. Fan [7] constructed the evaluation system of the industrial development index, and used the entropy method to calculate the comprehensive industrial development index of Shandong Province. The urbanization coupling coordination degree model was established, which was used to calculate the coordination degree, and the spatial differences were analyzed. Bian [8], since constructing the evaluation index system of urbanization and ecological environment system, he used the coupling and coordination model to measure, trend, and analyze the comprehensive development level of urbanization and the ecological environment from 2009 to 2018 in Nanchang. Li [9] evaluated the new urbanization of 13 cities in the Beijing-Tianjin-Hebei region through constructing a novel evaluated system. He used coupling research to explore the coordinated development level of “production-living-ecological” space among cities. The conclusion mentioned that the development of this region was generally stable while severely polarized. The superiorities were not strong enough to drive peripheral cities [10–12].It can be seen from the existing research literature that there are quantities of quantitative studies on the integration of industry and city, yet the idea is mainly to decompose the integration into industrialization and urbanization. Therefore, study the relationship between the two. On this basis, few researchers have introduced population and other factors to build a multilevel city-industry integration index system, and some researchers pay more attention to the quality of new urbanization. To sum up, the connotation of city-industry integration is rich, and the index system and methods are comprehensive. With ecological and environmental factors becoming crucial in recent years, it is necessary to introduce these two factors into the city-industry integration measurement index system. Building a ternary city-industry integration coupling and coordination measurement system, and further improving the research and city-industry integration measurement content.


## 2. Index System

Most of the previous city-industry integration measurement index system were divided into two aspects: urbanization and industrialization. With the increasing importance of the ecological environment to cities, this paper introduced the ecological environment-related indicators as a separate dimension and constructed the ternary city-industry integration coupling coordination measurement system, based on the indicators of urbanization and industrialization. When measuring the coupling and coordination degree of city-industry integration, the coordination between urbanization, industrialization, and the ecological environment was considered, and more emphasis on the importance of environmental protection. Therefore, the index system is keeping pace with the times (Table1).Table 1
City-industry integration coupling coordination degree measure index system of Shanghai.
MetricunitUrbanizationpeople’s lifePer capita GDPYuan per personTotal retail sales of social consumer goodsWan YuanUrban sizeGDPWan YuanThe natural growth rate of the registered population‰InfrastructureTotal gas supply to LPG householdsTen thousand tonsRoad areaMillion square metersLength of drainage pipekilometerGreen areahectarePark green areahectareDomestic water supply quantityOne hundred million cubic metersReal estate development and investmentWan YuanPublic serviceThe number of ordinary middle schoolsTheThe number of ordinary primary schoolsTheNumber of full-time teachers in ordinary middle schoolsHuman beingNumber of full-time teachers in ordinary primary schoolsHuman beingNumber of students in ordinary middle schoolsThousands of peopleNumber of students enrolled in ordinary primary schoolsThousands of peopleThe total amount of public librariesThousands of booksNumber of health personnel in hospitals and health centersHuman beingNumber of urban taxi operating vehiclesA carTotal passenger transport of urban buses and tramsTen thousand person-timeGeneral public budget revenueWan YuanGeneral public budget expenditureWan YuanGeneral public budget expenditure (education)Wan YuanGeneral public budget expenditure (science and technology)Wan YuanIndustrializationIndustrial supportShare of the added value of the tertiary industry in GDP _%Actual amount of foreign capital utilized (USD)Ten thousand dollarsThe proportion of people employed in the tertiary industry in all urban units%Ecological conditionEcological conditionIndustrial waste gas treatment facilitiesCoverOperation cost of industrial waste gas treatment facilitiesWan YuanNumber of nature reservesIndividual


## 3. Entropy Method to Calculate the Comprehensive Score of Shanghai’s Urbanization Subsystem, Industrialization Subsystem, and Ecological Environment Subsystem

### 3.1. Calculation Method: Entropy Method

Entropy is a physical measurement unit; the greater means, the more chaotic the data, the less information, the less utility value, and thus, the less weight. The entropy law is a method of combining the information value provided by entropy. The entropy method is calculated as follows [13, 14]:(1)
Selectn years and m indexes, then the value of j in the year is i (i = 1, 2, …, n; j = 1,2, …, m).(2)
Normalization treatment of the indicators [15]: Homogeneity of heterogeneous indicators because the units of measurement of various indicators are not unified, so standardize them before using and solve the problem of homogenization of different quality indicators. Moreover, because the positive indicators and negative indicators represent different meanings (the higher the positive indicators, the better, and the lower the negative indicators, the better), we use different algorithms for high and low indicators. The specific methods are as follows:(1)Positive indicator:xij′=xij−minxij,…,xnjmaxx1j,…,xnj−minx1j,…,xnj,Negative indicator:xij′=maxx1j,…,xnj−xijmaxx1j,…,xnj−minxij,…,xnj.xij is the value of the jth index in the ith year (i = 1, 2, …, n; j = 1, 2, …, m) and the normalized data is xij'.(3)
Calculate the proportion of theith country in item j indicator as follows:(2)pij=xij∑i=1nxij,i=1,2,3,…,n,j=1,2,3,…,m.(4)
Calculate the entropy of the itemj index:(3)ej∑i=1npijlnpij=−k,j=1,2,3,…,m.Usually,k is valued as follows:(4)k=1lnn,0≤ej1.(5)
Calculation of information entropy redundancy:(5)dj=1−ej.(6)
Calculate the weights of each indicator:(6)wj=dj∑j=1mdj.(7)
Calculate the composite score for each year:(7)Si=∑j=1mwjpij.The index data are multiplied by the corresponding weight, which is the “composite score.”


### 3.2. Calculation Results

The data source of this paper is selected from Shanghai Statistical Yearbook from 2001 to 2018. The calculation results of the specific index weight and the comprehensive score of the three subsystems are as follows:


#### 3.2.1. The Results of Urbanization Subsystem Calculation

In this paper, the entropy method is used to calculate the weight of 25 items including per capita GDP. As can be seen from Table2, there is a certain difference in the weight of each index. General public budget expenditure (science and technology) has the highest weight of 0.168, followed by public budget revenue of 0.0957, public budget expenditure of 0.0939, and public budget expenditure (education) of 0.0907, indicating that the city’s public budget revenue and expenditure factors present high weight. It is of great significance to promote the development and promotion of urbanization.Table 2
Summary of weight calculation results calculated by the entropy method of urbanization index.
MetricInformation entropy valueeInformation utility valuedWeight coefficientw (%)Per capita GDP0.97480.02523.28Total retail sales of social consumer goods0.93660.06348.27GDP0.95260.04746.19The natural growth rate of the registered population0.95010.04996.51Total gas supply to LPG households0.99610.00390.51Road area0.98160.01842.40Length of drainage pipe0.95510.04495.86Green area0.92510.07499.77Park green area0.98550.01451.90Domestic water supply quantity0.99570.00430.57Real estate development and investment0.95080.04926.42The number of ordinary middle schools0.99950.00050.06The number of ordinary primary schools0.99860.00140.18Number of full-time teachers in ordinary middle schools0.99930.00070.10Number of full-time teachers in ordinary primary schools0.99550.00450.59Number of students in ordinary middle schools0.99730.00270.36Number of students enrolled in ordinary primary schools0.99560.00440.58Total amount of public libraries0.99790.00210.27Number of health personnel in hospitals and health centers0.99090.00911.19Number of urban taxi operating vehicles0.99960.00040.05Total passenger transport of urban buses and trams0.99890.00110.14General public budget revenue0.92670.07339.57General public budget expenditure0.92810.07199.39General public budget expenditure (education)0.93050.06959.07General public budget expenditure (science and technology)0.87130.128716.80


#### 3.2.2. Calculation Results of the Industrialization Subsystem

Through the weight calculation of the added value of the tertiary industry in GDP, it can be seen from Table3 that the proportion of the added value of the tertiary industry in GDP, the amount of foreign investment utilized (USD), and the proportion of the employment personnel in the tertiary industry in all urban units are 0.0622, 0.8862, and 0.0515, respectively. There are significant differences in the weight of each index, among which the weight of the actual amount of foreign capital utilized (USD) is the highest at 0.886.Table 3
Summary of weight results calculated by entropy method of industrialization index.
MetricInformation entropy valueeInformation utility valuedWeight coefficientw (%)Share of the added value of the tertiary industry in GDP _0.99670.00336.22The actual amount of foreign capital utilized (USD)_0.95360.046488.62Proportion of employed personnel in the tertiary industry in all urban units0.99730.00275.15


#### 3.2.3. Results of the Ecological Environment Subsystem Calculation

Through the weight calculation of a total of 3 industrial waste gas treatment facilities, it can be seen from Table4: The weights of industrial waste gas treatment facilities, operation cost, and number of nature reserves are 0.209, 0.776, and 0.015, respectively. There are significant differences among the indicators, among which the highest operating weight of industrial waste gas treatment facilities is 0.7762, followed by industrial waste gas treatment facilities is 0.2092, and finally the lowest weight of nature reserves is 0.0146.Table 4
Summary of weight results by entropy method.
MetricInformation entropy valueeInformation utility valuedWeight coefficientw (%)Industrial waste gas treatment facilities0.98260.017420.92The operation cost of industrial waste gas treatment facilities0.93550.064577.62Number of nature reserves0.99880.00121.46


#### 3.2.4. Results of the Comprehensive Score Calculation for the Three Subsystems

After multiplying the index data with the corresponding weight, the comprehensive score of the system is obtained as follows (Table5):Table 5
Comprehensive scores of the three subsystems of urbanization, industrialization, and ecological environment in Shanghai.
A particular yearThe urbanization subsystem,U1 comprehensive scoreIndustrial subsystematizationU2 comprehensive scoreEco environment subsystemU3 comprehensive score20016069465.255277042288030.707635148.8003420026847851.752261051665574.562564602.0269720038003557.583150507426125.808364940.8187820049581896.351504287322408.808572557.1512620051.1594391653341206270107.904786979.7821320061.2960416753937133388665.4071147126.125120071.5854396212812783443915.1551140714.690620081.8107000088566244517683.3243133506.285220092.022367529536539579322.4914172279.294120102.3216090097987592606405.5765224993.154520112.6381771796085086629850.1579324050.765620122.827606545987435701870.7762315366.628320133.0701497179833263893718.8493366977.297320143.39852693263683933961.8638314499.548920153.79384749172154985636.8879336985.906920164.2861495825655411116722.792346316.673820174.61347681849316951345726.521409082.040220184.9511122836761331487087.421414786.87


## 3.1. Calculation Method: Entropy Method

Entropy is a physical measurement unit; the greater means, the more chaotic the data, the less information, the less utility value, and thus, the less weight. The entropy law is a method of combining the information value provided by entropy. The entropy method is calculated as follows [13, 14]:(1)
Selectn years and m indexes, then the value of j in the year is i (i = 1, 2, …, n; j = 1,2, …, m).(2)
Normalization treatment of the indicators [15]: Homogeneity of heterogeneous indicators because the units of measurement of various indicators are not unified, so standardize them before using and solve the problem of homogenization of different quality indicators. Moreover, because the positive indicators and negative indicators represent different meanings (the higher the positive indicators, the better, and the lower the negative indicators, the better), we use different algorithms for high and low indicators. The specific methods are as follows:(1)Positive indicator:xij′=xij−minxij,…,xnjmaxx1j,…,xnj−minx1j,…,xnj,Negative indicator:xij′=maxx1j,…,xnj−xijmaxx1j,…,xnj−minxij,…,xnj.xij is the value of the jth index in the ith year (i = 1, 2, …, n; j = 1, 2, …, m) and the normalized data is xij'.(3)
Calculate the proportion of theith country in item j indicator as follows:(2)pij=xij∑i=1nxij,i=1,2,3,…,n,j=1,2,3,…,m.(4)
Calculate the entropy of the itemj index:(3)ej∑i=1npijlnpij=−k,j=1,2,3,…,m.Usually,k is valued as follows:(4)k=1lnn,0≤ej1.(5)
Calculation of information entropy redundancy:(5)dj=1−ej.(6)
Calculate the weights of each indicator:(6)wj=dj∑j=1mdj.(7)
Calculate the composite score for each year:(7)Si=∑j=1mwjpij.The index data are multiplied by the corresponding weight, which is the “composite score.”


## 3.2. Calculation Results

The data source of this paper is selected from Shanghai Statistical Yearbook from 2001 to 2018. The calculation results of the specific index weight and the comprehensive score of the three subsystems are as follows:


### 3.2.1. The Results of Urbanization Subsystem Calculation

In this paper, the entropy method is used to calculate the weight of 25 items including per capita GDP. As can be seen from Table2, there is a certain difference in the weight of each index. General public budget expenditure (science and technology) has the highest weight of 0.168, followed by public budget revenue of 0.0957, public budget expenditure of 0.0939, and public budget expenditure (education) of 0.0907, indicating that the city’s public budget revenue and expenditure factors present high weight. It is of great significance to promote the development and promotion of urbanization.Table 2
Summary of weight calculation results calculated by the entropy method of urbanization index.
MetricInformation entropy valueeInformation utility valuedWeight coefficientw (%)Per capita GDP0.97480.02523.28Total retail sales of social consumer goods0.93660.06348.27GDP0.95260.04746.19The natural growth rate of the registered population0.95010.04996.51Total gas supply to LPG households0.99610.00390.51Road area0.98160.01842.40Length of drainage pipe0.95510.04495.86Green area0.92510.07499.77Park green area0.98550.01451.90Domestic water supply quantity0.99570.00430.57Real estate development and investment0.95080.04926.42The number of ordinary middle schools0.99950.00050.06The number of ordinary primary schools0.99860.00140.18Number of full-time teachers in ordinary middle schools0.99930.00070.10Number of full-time teachers in ordinary primary schools0.99550.00450.59Number of students in ordinary middle schools0.99730.00270.36Number of students enrolled in ordinary primary schools0.99560.00440.58Total amount of public libraries0.99790.00210.27Number of health personnel in hospitals and health centers0.99090.00911.19Number of urban taxi operating vehicles0.99960.00040.05Total passenger transport of urban buses and trams0.99890.00110.14General public budget revenue0.92670.07339.57General public budget expenditure0.92810.07199.39General public budget expenditure (education)0.93050.06959.07General public budget expenditure (science and technology)0.87130.128716.80


### 3.2.2. Calculation Results of the Industrialization Subsystem

Through the weight calculation of the added value of the tertiary industry in GDP, it can be seen from Table3 that the proportion of the added value of the tertiary industry in GDP, the amount of foreign investment utilized (USD), and the proportion of the employment personnel in the tertiary industry in all urban units are 0.0622, 0.8862, and 0.0515, respectively. There are significant differences in the weight of each index, among which the weight of the actual amount of foreign capital utilized (USD) is the highest at 0.886.Table 3
Summary of weight results calculated by entropy method of industrialization index.
MetricInformation entropy valueeInformation utility valuedWeight coefficientw (%)Share of the added value of the tertiary industry in GDP _0.99670.00336.22The actual amount of foreign capital utilized (USD)_0.95360.046488.62Proportion of employed personnel in the tertiary industry in all urban units0.99730.00275.15


### 3.2.3. Results of the Ecological Environment Subsystem Calculation

Through the weight calculation of a total of 3 industrial waste gas treatment facilities, it can be seen from Table4: The weights of industrial waste gas treatment facilities, operation cost, and number of nature reserves are 0.209, 0.776, and 0.015, respectively. There are significant differences among the indicators, among which the highest operating weight of industrial waste gas treatment facilities is 0.7762, followed by industrial waste gas treatment facilities is 0.2092, and finally the lowest weight of nature reserves is 0.0146.Table 4
Summary of weight results by entropy method.
MetricInformation entropy valueeInformation utility valuedWeight coefficientw (%)Industrial waste gas treatment facilities0.98260.017420.92The operation cost of industrial waste gas treatment facilities0.93550.064577.62Number of nature reserves0.99880.00121.46


### 3.2.4. Results of the Comprehensive Score Calculation for the Three Subsystems

After multiplying the index data with the corresponding weight, the comprehensive score of the system is obtained as follows (Table5):Table 5
Comprehensive scores of the three subsystems of urbanization, industrialization, and ecological environment in Shanghai.
A particular yearThe urbanization subsystem,U1 comprehensive scoreIndustrial subsystematizationU2 comprehensive scoreEco environment subsystemU3 comprehensive score20016069465.255277042288030.707635148.8003420026847851.752261051665574.562564602.0269720038003557.583150507426125.808364940.8187820049581896.351504287322408.808572557.1512620051.1594391653341206270107.904786979.7821320061.2960416753937133388665.4071147126.125120071.5854396212812783443915.1551140714.690620081.8107000088566244517683.3243133506.285220092.022367529536539579322.4914172279.294120102.3216090097987592606405.5765224993.154520112.6381771796085086629850.1579324050.765620122.827606545987435701870.7762315366.628320133.0701497179833263893718.8493366977.297320143.39852693263683933961.8638314499.548920153.79384749172154985636.8879336985.906920164.2861495825655411116722.792346316.673820174.61347681849316951345726.521409082.040220184.9511122836761331487087.421414786.87


## 3.2.1. The Results of Urbanization Subsystem Calculation

In this paper, the entropy method is used to calculate the weight of 25 items including per capita GDP. As can be seen from Table2, there is a certain difference in the weight of each index. General public budget expenditure (science and technology) has the highest weight of 0.168, followed by public budget revenue of 0.0957, public budget expenditure of 0.0939, and public budget expenditure (education) of 0.0907, indicating that the city’s public budget revenue and expenditure factors present high weight. It is of great significance to promote the development and promotion of urbanization.Table 2
Summary of weight calculation results calculated by the entropy method of urbanization index.
MetricInformation entropy valueeInformation utility valuedWeight coefficientw (%)Per capita GDP0.97480.02523.28Total retail sales of social consumer goods0.93660.06348.27GDP0.95260.04746.19The natural growth rate of the registered population0.95010.04996.51Total gas supply to LPG households0.99610.00390.51Road area0.98160.01842.40Length of drainage pipe0.95510.04495.86Green area0.92510.07499.77Park green area0.98550.01451.90Domestic water supply quantity0.99570.00430.57Real estate development and investment0.95080.04926.42The number of ordinary middle schools0.99950.00050.06The number of ordinary primary schools0.99860.00140.18Number of full-time teachers in ordinary middle schools0.99930.00070.10Number of full-time teachers in ordinary primary schools0.99550.00450.59Number of students in ordinary middle schools0.99730.00270.36Number of students enrolled in ordinary primary schools0.99560.00440.58Total amount of public libraries0.99790.00210.27Number of health personnel in hospitals and health centers0.99090.00911.19Number of urban taxi operating vehicles0.99960.00040.05Total passenger transport of urban buses and trams0.99890.00110.14General public budget revenue0.92670.07339.57General public budget expenditure0.92810.07199.39General public budget expenditure (education)0.93050.06959.07General public budget expenditure (science and technology)0.87130.128716.80


## 3.2.2. Calculation Results of the Industrialization Subsystem

Through the weight calculation of the added value of the tertiary industry in GDP, it can be seen from Table3 that the proportion of the added value of the tertiary industry in GDP, the amount of foreign investment utilized (USD), and the proportion of the employment personnel in the tertiary industry in all urban units are 0.0622, 0.8862, and 0.0515, respectively. There are significant differences in the weight of each index, among which the weight of the actual amount of foreign capital utilized (USD) is the highest at 0.886.Table 3
Summary of weight results calculated by entropy method of industrialization index.
MetricInformation entropy valueeInformation utility valuedWeight coefficientw (%)Share of the added value of the tertiary industry in GDP _0.99670.00336.22The actual amount of foreign capital utilized (USD)_0.95360.046488.62Proportion of employed personnel in the tertiary industry in all urban units0.99730.00275.15


## 3.2.3. Results of the Ecological Environment Subsystem Calculation

Through the weight calculation of a total of 3 industrial waste gas treatment facilities, it can be seen from Table4: The weights of industrial waste gas treatment facilities, operation cost, and number of nature reserves are 0.209, 0.776, and 0.015, respectively. There are significant differences among the indicators, among which the highest operating weight of industrial waste gas treatment facilities is 0.7762, followed by industrial waste gas treatment facilities is 0.2092, and finally the lowest weight of nature reserves is 0.0146.Table 4
Summary of weight results by entropy method.
MetricInformation entropy valueeInformation utility valuedWeight coefficientw (%)Industrial waste gas treatment facilities0.98260.017420.92The operation cost of industrial waste gas treatment facilities0.93550.064577.62Number of nature reserves0.99880.00121.46


## 3.2.4. Results of the Comprehensive Score Calculation for the Three Subsystems

After multiplying the index data with the corresponding weight, the comprehensive score of the system is obtained as follows (Table5):Table 5
Comprehensive scores of the three subsystems of urbanization, industrialization, and ecological environment in Shanghai.
A particular yearThe urbanization subsystem,U1 comprehensive scoreIndustrial subsystematizationU2 comprehensive scoreEco environment subsystemU3 comprehensive score20016069465.255277042288030.707635148.8003420026847851.752261051665574.562564602.0269720038003557.583150507426125.808364940.8187820049581896.351504287322408.808572557.1512620051.1594391653341206270107.904786979.7821320061.2960416753937133388665.4071147126.125120071.5854396212812783443915.1551140714.690620081.8107000088566244517683.3243133506.285220092.022367529536539579322.4914172279.294120102.3216090097987592606405.5765224993.154520112.6381771796085086629850.1579324050.765620122.827606545987435701870.7762315366.628320133.0701497179833263893718.8493366977.297320143.39852693263683933961.8638314499.548920153.79384749172154985636.8879336985.906920164.2861495825655411116722.792346316.673820174.61347681849316951345726.521409082.040220184.9511122836761331487087.421414786.87


## 4. Analysis of the Coupling Coordination Degree of City-Industry Integration in Shanghai

### 4.1. Coupling Coordination Degree Model

The key to the accurate mechanism lies in the synergy between the order parameters within the system. The coupling degree is the measure of this synergy. In the comparative analysis of the coupling degree, merely relying on the coupling degree model cannot reflect the coordination of urbanization, industrialization, and the ecological environment [16–18]. As a supplement to the coupling degree, there is the concept of coordination, which is used to reflect the benign interactions between the subsystems. The two concepts (coupling degree and coordination) generally measure the harmony degree among the internal subsystems in the process of the coupling coordination degree, which reflects the transition trend of the system orderly [19]. The advantage of the coupling coordination model is that it can measure not only the coordination degree between the subsystems but also the coordination degree between all the factors within the subsystem. Therefore, the coupling coordination model measures the ternary coordination relationship among urbanization, industrialization, and the ecological environment.(1)
Degree of couplingC. The calculation formula is as follows:(8)C=u1∗u2∗u3u1+u2+u3/333.In the formulau1 is the comprehensive score of the urbanization subsystem, u2 is the comprehensive score of the industrialization subsystem, and u3 is the comprehensive score of the ecological environment subsystem.(2)
Integrated Coordination Index. The calculation formula is as follows:(9)T=β1U1+β2U2+β3U3.In the formula,β1, β2, and β3 represent the weight of the subsystem, as set up in this article, β1 = β2 = β3.(3)
The value of the coupling coordination degreeD. The calculation formula is as follows:(10)D=C∗T.


### 4.2. Calculation Results

In this paper, the index data is first interval so that all the data are between 0 and 1, and then the processed data are used for a formal coupling coordination study. Data internalization is to \compress the data between [0.01, 0.99] by a + (b − a)∗ (X − Min)/(Max − Min), where b is 0.99 and a is 0.01, and max and min indicate the corresponding maximum and minimum values of a term, respectively [19]. The calculation results of city-industry fusion coupling coordination degree are as follows:As can be seen from Tables6 and 7, the curve rose from 2001 to 2002 before decreasing until 2005. There was a constant rise in the coupling coordination degree of the city-industry integration of Shanghai to 2018. It increased from 0.116 (2001) to 0.995 in 2018; therefore, it belongs to the quality coordination level (level 10). Hence, the coordination between urbanization, industrialization, and the ecological environment increases annually in Shanghai. Not only are the people’s happiness, urban scale, infrastructure, public services, and industrial support between two factors and internal factors of higher but also the relationship between these factors and ecological environmental factors is more harmonious.Table 6
Calculation results of city-industry integration in Shanghai.
yearC value of coupling degreeCoordination IndexT valueCoupling coordination degreeD valueCoordination levelThe degree of coupling coordination2001.00.9090.0150.1162Major maladjustment2002.00.6240.1470.3034Mild dysregulation2003.00.9320.0920.2933Moderate dysregulation2004.00.9580.0830.2813Moderate dysregulation2005.00.6020.0960.2413Moderate dysregulation2006.00.9130.1900.4165On the verge of dysregulation2007.00.9670.2210.4625On the verge of dysregulation2008.00.9920.2520.5005On the verge of dysregulation2009.00.9900.3170.5616Forced coordination2010.00.9730.3930.6187Primary coordination2011.00.9320.5080.6887Primary coordination2012.00.9580.5340.7158Intermediate coordination2013.00.9730.6480.7948Intermediate coordination2014.00.9930.6380.7968Intermediate coordination2015.00.9920.7010.8349Good coordination2016.00.9960.7820.8839Good coordination2017.00.9990.9220.96010Quality coordination2018.01.0000.9900.99510Quality coordinationTable 7
Classification criteria of coupling coordination degree.
Coupled coordination degreeD value intervalCoordination levelThe degree of coupling coordination(0.0∼0.1)1Extreme disorder[0.1∼0.2)2Major maladjustment[0.2∼0.3)3Moderate dysregulation[0.3∼0.4)4Mild dysregulation[0.4∼0.5)5On the verge of dysregulation[0.5∼0.6)6Forced coordination[0.6∼0.7)7Primary coordination[0.7∼0.8)8Intermediate coordination[0.8∼0.9)9Good coordination[0.9∼1.0)10Quality coordinationBy Figure1, Shanghai city-industry integration coupling coordination---D value curve trend can be seen: in addition to 2003 to 2005, the trend is overall rising in the statistical period (from 2001 to 2018), especially from 2005 to 2006. Shanghai’s urbanization, industrialization, and the ecological environment from 2005 to 2018 has been steadily improved: city-industry integration has become higher and urban function has performed more perfectly with a well-managed ecological environment.Figure 1
D value of the city-industry integration coupling coordination degree in Shanghai.


## 4.1. Coupling Coordination Degree Model

The key to the accurate mechanism lies in the synergy between the order parameters within the system. The coupling degree is the measure of this synergy. In the comparative analysis of the coupling degree, merely relying on the coupling degree model cannot reflect the coordination of urbanization, industrialization, and the ecological environment [16–18]. As a supplement to the coupling degree, there is the concept of coordination, which is used to reflect the benign interactions between the subsystems. The two concepts (coupling degree and coordination) generally measure the harmony degree among the internal subsystems in the process of the coupling coordination degree, which reflects the transition trend of the system orderly [19]. The advantage of the coupling coordination model is that it can measure not only the coordination degree between the subsystems but also the coordination degree between all the factors within the subsystem. Therefore, the coupling coordination model measures the ternary coordination relationship among urbanization, industrialization, and the ecological environment.(1)
Degree of couplingC. The calculation formula is as follows:(8)C=u1∗u2∗u3u1+u2+u3/333.In the formulau1 is the comprehensive score of the urbanization subsystem, u2 is the comprehensive score of the industrialization subsystem, and u3 is the comprehensive score of the ecological environment subsystem.(2)
Integrated Coordination Index. The calculation formula is as follows:(9)T=β1U1+β2U2+β3U3.In the formula,β1, β2, and β3 represent the weight of the subsystem, as set up in this article, β1 = β2 = β3.(3)
The value of the coupling coordination degreeD. The calculation formula is as follows:(10)D=C∗T.


## 4.2. Calculation Results

In this paper, the index data is first interval so that all the data are between 0 and 1, and then the processed data are used for a formal coupling coordination study. Data internalization is to \compress the data between [0.01, 0.99] by a + (b − a)∗ (X − Min)/(Max − Min), where b is 0.99 and a is 0.01, and max and min indicate the corresponding maximum and minimum values of a term, respectively [19]. The calculation results of city-industry fusion coupling coordination degree are as follows:As can be seen from Tables6 and 7, the curve rose from 2001 to 2002 before decreasing until 2005. There was a constant rise in the coupling coordination degree of the city-industry integration of Shanghai to 2018. It increased from 0.116 (2001) to 0.995 in 2018; therefore, it belongs to the quality coordination level (level 10). Hence, the coordination between urbanization, industrialization, and the ecological environment increases annually in Shanghai. Not only are the people’s happiness, urban scale, infrastructure, public services, and industrial support between two factors and internal factors of higher but also the relationship between these factors and ecological environmental factors is more harmonious.Table 6
Calculation results of city-industry integration in Shanghai.
yearC value of coupling degreeCoordination IndexT valueCoupling coordination degreeD valueCoordination levelThe degree of coupling coordination2001.00.9090.0150.1162Major maladjustment2002.00.6240.1470.3034Mild dysregulation2003.00.9320.0920.2933Moderate dysregulation2004.00.9580.0830.2813Moderate dysregulation2005.00.6020.0960.2413Moderate dysregulation2006.00.9130.1900.4165On the verge of dysregulation2007.00.9670.2210.4625On the verge of dysregulation2008.00.9920.2520.5005On the verge of dysregulation2009.00.9900.3170.5616Forced coordination2010.00.9730.3930.6187Primary coordination2011.00.9320.5080.6887Primary coordination2012.00.9580.5340.7158Intermediate coordination2013.00.9730.6480.7948Intermediate coordination2014.00.9930.6380.7968Intermediate coordination2015.00.9920.7010.8349Good coordination2016.00.9960.7820.8839Good coordination2017.00.9990.9220.96010Quality coordination2018.01.0000.9900.99510Quality coordinationTable 7
Classification criteria of coupling coordination degree.
Coupled coordination degreeD value intervalCoordination levelThe degree of coupling coordination(0.0∼0.1)1Extreme disorder[0.1∼0.2)2Major maladjustment[0.2∼0.3)3Moderate dysregulation[0.3∼0.4)4Mild dysregulation[0.4∼0.5)5On the verge of dysregulation[0.5∼0.6)6Forced coordination[0.6∼0.7)7Primary coordination[0.7∼0.8)8Intermediate coordination[0.8∼0.9)9Good coordination[0.9∼1.0)10Quality coordinationBy Figure1, Shanghai city-industry integration coupling coordination---D value curve trend can be seen: in addition to 2003 to 2005, the trend is overall rising in the statistical period (from 2001 to 2018), especially from 2005 to 2006. Shanghai’s urbanization, industrialization, and the ecological environment from 2005 to 2018 has been steadily improved: city-industry integration has become higher and urban function has performed more perfectly with a well-managed ecological environment.Figure 1
D value of the city-industry integration coupling coordination degree in Shanghai.


## 5. Conclusion

This paper focuses on the measurement of Shanghai city-industry integration, based on urbanization and industrialization. Then the paper introduces the importance of the ecological environment, emphasizing the crucial integration in the process of ecological civilization. So that builds the ternary coupling coordination measurement model and uses entropy method to determine the weight and comprehensive score. And finally, by utilizing the coupling coordination measurement, we reached the following conclusion: (1) Shanghai’s public budget revenue and expenditure factors have significant meaning to promote the development and promotion of urbanization. (2) The operation cost of Shanghai industrial waste gas treatment facilities and their input is crucial for the protection of ecological environment. (3) The degree of coordination between urbanization, industrialization, and ecological environment in Shanghai was relatively low from 2001 to 2005. (4) From 2006 to 2018, coordination among urbanization, industrialization, and the ecological environment in Shanghai has continuously improved until 2017 and 2018, when reached the highest level of coordination. Now, people’s happiness, urban scale, infrastructure, public services, industry support, and ecological environment factors as well as all other factors within the system are highly coordinated.



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*Source: 7985899-2022-05-19.xml*