# Evaluation of City–Industry Integration Development and Regional Differences under the New Urbanization: A Case Study of Sichuan

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## Abstract

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## 1. Introduction

## 2. Background

#### 2.1. Literature Review

#### 2.1.1. Indicators of City–Industry Integration Evaluation

#### 2.1.2. Methods of City–Industry Integration Evaluation

#### 2.1.3. Regional Differences of Urbanization

#### 2.2. Research Framework

## 3. Materials and Methods

#### 3.1. Evaluation Index System

#### 3.1.1. Web Crawler

Target Layer | Criteria Layer | Indicators | Indicator Sources |
---|---|---|---|

City integration production | Economy and industry | Per capita GDP (+) | 1. Literature research: Jia et al. [20]; Tang [28]; Zhou et al. [51] 2. Web crawler |

The proportion of the secondary industry output value to the GDP (+) | |||

The proportion of the tertiary industry output value to the GDP (+) | |||

The gross industrial output value of enterprises above scale (+) | |||

Habitat and employment | Household deposit balance (+) | 1. Literature research: Zhang and Shen [26]; Gan et al. [14]; Yan [52] 2. Web crawler | |

The proportion of employed persons in the secondary industry (+) | |||

Number of registered unemployed rate in urban areas (+) | |||

Per capita housing area in urban areas (+) | |||

Social services | Number of hospital beds per 10,000 people (+) | 1. Literature research: Li and Zhang [16]; Li and Zhang [18]; Zhou et al. [51] 2. Web crawler | |

Number of autobuses per 10,000 people (+) | |||

Culture sports and the media expenditure (+) | |||

Number of schools per 10,000 people (+) | |||

Urban construction | Investment in fixed assets in the construction of municipal public facilities (+) | 1. Literature research: He and Xia [21]; Yang and Fang [27]; Cong et al. [10] 2. Web crawler | |

The proportion of industrial land (−) | |||

The proportion of land used for public facilities (+) | |||

Per capita urban road area (+) | |||

Ecology and environment | Expenditure on energy saving (+) | 1. Literature research: Yang and Fang [27]; Yan [52] 2. Web crawler | |

The comprehensive utilization rate of industrial solid waste (+) | |||

Wastewater discharge (−) | |||

The reduction rate of energy consumption per unit of GDP (+) | |||

Science and technology | The number of people with a college degree or above per 10,000 people (+) | 1. Literature research: Zhen and Zhu [25]; Yan [52] 2. Web crawler | |

Science and technology expenditure accounts for the proportion of fiscal expenditures (+) | |||

R&D input intensity (+) | |||

Applications granted (+) | |||

The full-time equivalent of R&D personnel (+) |

#### 3.1.2. Theoretical Analysis

#### 3.2. Normalization

_{ij}is the value of indicator j in year i, y

_{ij}is the normalized value. $max\left({x}_{ij}\right)$ is the maximum value of indicator j, and $min\left({x}_{ij}\right)$ is the minimum value of indicator j. When the indicator generates positive or negative contributions to the system, Equations (1) and (2) are applied, respectively.

#### 3.3. Combination Empowerment

#### 3.3.1. Subjective Weight Definition (PSO-AHP)

- Establish a hierarchical model: target layer; criterion layer; indicator layer.
- Construct a comparison matrix A
_{k}. Using the nine-scale method for scaling, the values of the matrix elements are compared to reflect the relative importance of several factors. Get the comparison matrix.$$A=\left[\begin{array}{cccc}{a}_{11}& {a}_{12}& \cdots & {a}_{1n}\\ {a}_{21}& {a}_{22}& \cdots & {a}_{2n}\\ \vdots & \vdots & \vdots & \vdots \\ {a}_{n1}& {a}_{n2}& \cdots & {a}_{nn}\end{array}\right]$$ - Determination of objective function.
- For the judgment matrix A
_{k}, let the single ordering weight of each element be ${\omega}_{k}$, $k=1~n$. If the judgment matrix A_{k}satisfies ${a}_{ij}=\raisebox{1ex}{${\omega}_{i}$}\!\left/ \!\raisebox{-1ex}{${\omega}_{j}$}\right.\left(i,j=1~n\right)$, then A_{k}has complete consistency, that is, the consistency index is equal to 0.$$\sum}_{k=1}^{n}\left({a}_{ik}{\omega}_{k}\right)={\displaystyle \sum}_{k=1}^{n}(\frac{{\omega}_{i}}{{\omega}_{k}}){\omega}_{k}=n{\omega}_{i$$$$\sum}_{i=1}^{n}{\displaystyle \sum}_{k=1}^{n}\left({a}_{ik}{\omega}_{k}\right)-n{\omega}_{i}=0$$ - Obviously, the smaller the value at the left end of Equation (4), the higher the degree of the consistency of A
_{k}. When Equation (5) is established, A_{k}has complete consistency. Therefore, the weight calculation and optimization of each element can be transformed into the following optimization problem:$$\mathrm{The}\mathrm{objective}\mathrm{function}:Min{F}_{CI}\left({\omega}_{k}\right)={{\displaystyle \sum}}_{i=1}^{n}\left|{{\displaystyle \sum}}_{k=1}^{n}\left({a}_{ik}{\omega}_{k})\right)-n{\omega}_{i}\right|/n$$$$\mathrm{The}\mathrm{constraints}:\{\begin{array}{c}{\displaystyle \sum}_{k=1}^{n}{\omega}_{k}=1\hfill \\ {\omega}_{k}0\left(k=1~n\right)\end{array}$$ - In Equation (6), ${F}_{CI}\left(n\right)$ is the consistency index function, and when the minimum value is obtained, the corresponding weight value is the optimal weight corresponding to matrix A
_{k}.$${\omega}_{i}^{A}={\displaystyle \sum}_{k=1}^{n}{\omega}_{k}\u2022{w}_{i}^{k}$$$${F}_{CI}^{A}\left({\omega}_{i}^{A}\right)={\displaystyle \sum}_{k=1}^{n}{\omega}_{k}\u2022{F}_{CI}^{k}\left({\omega}_{i}^{k}\right)$$

- Determination of objective function. The algorithm flow is shown in Figure 3.
- Determine the parameters of the PSO algorithm, namely, the number of populations n, the maximum number of iterations N, and the learning factors c
_{1}and c_{2}; the variation range of v_{m}is the inertia coefficient $\left[-{v}_{max},{v}_{max}\right]$. In this article, n = 100, N = 500, c_{1}= c_{2}= 2, −v_{max}= −0.001, and v_{max}= 0.001. - Normalize the weights to generate the initial solution of the particles. The feasible solution is substituted into Equation (5) to calculate the fitness of the initial particles, and the global optimal particles are selected from them. Update iterates over particles with Equations (10) and (11).$${v}_{in}{}^{k+1}={v}_{in}{}^{k}+{c}_{1}{r}_{1}\left({p}_{in}{}^{k}-{x}_{in}{}^{k}\right)+{c}_{2}{r}_{2}\left({p}_{gn}{}^{k}-{x}_{in}{}^{k}\right)$$$${x}_{in}{}^{k+1}={x}_{in}{}^{k}+{v}_{in}{}^{k}+1\left(i=1,2\dots m;n=1,2,\dots N\right)$$
- After updating the fitness of the particles, compare and select the optimal position of the particles and the global. If the iteration termination condition is met, the iteration terminates, outputs the optimal solution obtained by the model, and moves on to the next step. If the iteration termination condition is not met, jump back to the first step until the iteration termination condition is met.
- Substitute the obtained optimal value into the objective function and calculate the consistency ratio value corresponding to the judgment matrix. If the consistency requirement is met, the next step is conducted; otherwise, adjust the judgment matrix and re-execute the loop. Finally, the global optimal position and corresponding weight value, and consistency indicator function value MinF
_{CI}are output.

#### 3.3.2. Objective Weight Definition (EM)

- Calculate the characteristic weight.$${p}_{ij}=\frac{{x}_{ij}}{{{\displaystyle \sum}}_{i=1}^{n}{x}_{ij}}$$
- Calculate the information entropy e
_{j}and difference coefficient g_{j}of the jth evaluation index.$${e}_{j}=-\frac{1}{\mathrm{ln}n}{\displaystyle \sum}_{i=1}^{n}{p}_{ij}\mathrm{ln}{p}_{ij}$$$${g}_{j}=1-{e}_{j}$$ - Calculate the weight of the jth index.$${\omega}_{j}=\frac{{g}_{j}}{{{\displaystyle \sum}}_{i=1}^{m}{g}_{j}}$$

#### 3.3.3. Blends Weighs

- Represent weights as a linear combination of subjective weights ${\omega}_{a}$ and objective weights ${\omega}_{e}$.$$\omega =\alpha {\omega}_{a}+\left(1-\alpha \right){\omega}_{e}$$
- The goal is to minimize the square sum of the deviation between the combined weight and the subjective weight, and the square sum of the deviation between the combined weight and the objective weight. Moreover, substitute the objective function into Equation (16).$$\mathrm{The}\mathrm{objective}\mathrm{function}:minz={{\displaystyle \sum}}_{i=1}^{n}\left[{\left(\omega -{\omega}_{a}\right)}^{2}+{\left(\omega -{\omega}_{e}\right)}^{2}\right]$$$$\underset{\alpha}{min}{\displaystyle \sum}_{i=1}^{n}\left\{{\left[\left(\alpha {\omega}_{a}+\left(1-\alpha \right){\omega}_{e}-{\omega}_{a}\right)\right]}^{2}+{\left[\left(\alpha {\omega}_{a}+\left(1-\alpha \right){\omega}_{e}-{\omega}_{e}\right)\right]}^{2}\right\}$$
- Take the derivative of Equation (18) with respect to $\alpha $ and let the first derivative be zero, then obtain $\alpha =0.5$, and substitute it into Equation (16).$$\omega =0.5\times {\omega}_{a}+0.5\times {\omega}_{e}$$

#### 3.4. Evaluation Method (GRA-TOPSIS)

- Determine the positive ideal solution Z
^{+}and negative ideal solution Z^{−}of the weighted normalized matrix.$${Z}^{+}=\left({z}_{1}{}^{+},{z}_{2}{}^{+},\dots ,{z}_{n}{}^{+}\right)=\omega $$$${Z}^{-}=\left({z}_{1}{}^{-},{z}_{2}{}^{-},\dots ,{z}_{n}{}^{-}\right)=0$$ - Calculate the Euclid distance to the positive ideal solution di
^{+}and negative ideal solution di^{−}for each scheme.$$d{i}^{+}=\sqrt{{{\displaystyle \sum}}_{j=1}^{n}{(zij-z{j}^{+})}^{2}}\mathrm{and}d{i}^{-}=\sqrt{{{\displaystyle \sum}}_{j=1}^{n}{(zij-z{j}^{-})}^{2}}$$ - Calculate the gray correlation coefficient matrix between each scheme and the positive R
^{+}and negative ideal solution R^{−}.$${R}^{+}={({r}_{ij}{}^{+})}_{m\times n}\mathrm{and}{R}^{-}={({r}_{ij}{}^{-})}_{m\times n}$$$${r}_{ij}{}^{+}=\frac{\underset{i}{min}\underset{j}{min}\left|{z}_{j}{}^{+}-{z}_{ij}\right|+\epsilon \underset{i}{max}\underset{j}{max}\left|{z}_{j}{}^{+}-{z}_{ij}\right|}{\left|{z}_{j}{}^{+}-{z}_{ij}\right|+\epsilon \underset{i}{max}\underset{j}{max}\left|{z}_{j}{}^{+}-{z}_{ij}\right|}$$$${r}_{ij}{}^{-}=\frac{\underset{i}{min}\underset{j}{min}\left|{z}_{j}{}^{-}-{z}_{ij}\right|+\epsilon \underset{i}{max}\underset{j}{max}\left|{z}_{j}{}^{-}-{z}_{ij}\right|}{\left|{z}_{j}{}^{-}-{z}_{ij}\right|+\epsilon \underset{i}{max}\underset{j}{max}\left|{z}_{j}{}^{-}-{z}_{ij}\right|}$$ - Calculate the gray correlation degree of each scheme with the positive ideal solution r
^{+}and the negative ideal solution r^{−}.$${r}^{+}=\frac{1}{n}{{\displaystyle \sum}}_{j=1}^{n}{r}_{ij}{}^{+}\mathrm{and}{r}^{-}=\frac{1}{n}{{\displaystyle \sum}}_{j=1}^{n}{r}_{ij}{}^{-}$$ - Dimensionless processing is performed on Euclid distance and correlation degree.$${D}_{i}{}^{+}=\frac{{d}_{i}{}^{+}}{max{d}_{i}{}^{+}}\mathrm{and}{D}_{i}{}^{-}=\frac{{d}_{i}{}^{-}}{max{d}_{i}{}^{-}}$$$${R}_{i}{}^{+}=\frac{{r}_{i}{}^{+}}{max{r}_{i}{}^{+}}\mathrm{and}{R}_{i}{}^{-}=\frac{{r}_{i}{}^{-}}{max{r}_{i}{}^{-}}$$
- Combine dimensionless distance and dimensionless correlation. The larger the values of D
_{i}^{−}and R_{i}^{+}, the closer the scheme is to the positive ideal solution, and so the combined formula can be determined as Equation (30).$${P}_{i}{}^{+}=\alpha {D}^{-}+\beta {R}^{+}\mathrm{and}{P}_{i}{}^{-}=\alpha {D}^{+}+\beta {R}^{-}$$$$Q{i}^{+}=\frac{{P}_{i}{}^{+}}{{P}_{i}{}^{+}+{P}_{i}{}^{-}}$$

#### 3.5. GINI

- Suppose X represents a non-negative random variable with a probability distribution function of $F\left(X\right)=P\left(X\le x\right)$, then there is a corresponding quantile function ${F}^{-1}\left(p\right)=\mathrm{inf}\left\{x:F\left(X\right)\ge P\right\}$. If the form of the quantile function is determined as ${G}_{F}\left(p\right)=1-\frac{{L}_{F}\left(p\right)}{p}$, the functional form of the Gini coefficient is obtained.$${G}_{F}={{\displaystyle \int}}_{0}^{1}\left(1-\frac{{L}_{F}\left(p\right)}{p}\right)\phi \left(p\right)dp$$
- Equation (31) is the continuous function form of the Gini coefficient, but the city–industry integration degree of each city from 2010 to 2019 calculated in this paper is discrete. Therefore, Fan’s calculation formula is adopted [65]:$$G=\frac{2}{k}{\displaystyle \sum}_{i=1}^{k}i{e}_{i}-\frac{k+1}{k}$$
_{i}represents the composite score of the ith region; and e_{i}represents the overall score of the ith region as a share of the population, in order from lowest to highest.

#### 3.6. Theil Exponential Decomposition

_{i}represents the city–industry integration degree of each city, y is the sample average, y

_{k}is the integration degree of each region, and g

_{k}represents the number of groups. Decomposing the Theil index into inter-group difference T

_{w}and intra-group difference T

_{b}can more intuitively reflect the uneven characteristics of city–industry integration in various regions.

## 4. Case Study

#### 4.1. Research Case Area

#### 4.2. Data Acquisition and Result

#### 4.2.1. Result of Indicator Data Processing

#### 4.2.2. Result of the Development of City–Industry Integration

- Overall results and discussion of city–industry integration.

- 2.
- Evaluation results and discussion of each subsystem.

#### 4.2.3. Differences in the Development Level of City–Industry Integration

#### 4.3. Policy Suggestions

- (1)
- The development level of city–industry integration is not only affected by a single factor. In the development of urbanization, we should pay attention to the comprehensive coordination of industry, city, residents, environment, and science. People are an intermediate bridge connecting industries and cities, and the development level of people’s lives is an important measure of the quality of city–industry integration. Therefore, urbanization development should pay attention to increasing population welfare expenditure and improving the medical and health environment. At the same time, urbanization development should abandon the extensive industrial development mode with high pollution and high energy consumption and instead pay attention to the construction and protection of the urban ecological environment. The development concept of innovation, coordination, green, openness, and sharing is the fundamental idea for promoting the high-quality development of new urbanization in an all-around way.
- (2)
- Given the imbalance of regional development, it is necessary for the government to fundamentally play a role in resource allocation and provide a fair development environment for different cities, strengthening regional cooperation, cultivating urban agglomeration and support, and coordinating cross-regional development in order to weaken the regional differences of city–industry integration development. We took intraregional cooperation as a breakthrough in the reduction of the imbalance within the Chengdu Plain Economic Zone. The South Sichuan Economic Zone will further build a multi-center urban agglomeration integrated innovation and development pilot zone to improve its strategic position. Moreover, the Northeast Sichuan Economic Zone will provide full play to its eco-tourism strengths for industrial transformation, improving the scientific and technological innovation ability of south Sichuan and northeast Sichuan economic zones, establishing a cross-regional coordinated development mechanism, and realizing sustainable development.

## 5. Conclusions and Prospects

#### 5.1. Conclusions

#### 5.2. Shortcomings and Prospects

#### 5.2.1. Study Limitations

- (1)
- Since the indicator data are characteristic of Sichuan Province, and the development of city–industry integration in each area has its unique characteristics, if the indicator system is applied to other regions or extended to the whole country, the evaluation indicators and corresponding data may need to be adjusted according to the conditions of the study area.
- (2)
- Although the evaluation system constructed in this paper is comprehensive, the evaluation indicators only select typical indicators, which simplify the complexity of the problem. At the same time, given the accessibility and validity of the data, this paper temporarily does not consider the data of the three autonomous prefectures in Sichuan Province, which has a certain impact on completely reflecting the overall level of Sichuan.

#### 5.2.2. Future Research

- (1)
- With the updating and supplementation of statistical data, as well as the enrichment and refinement of statistical indicators, the index system of city–industry integration in Sichuan Province can be further improved. On the basis of refining the evaluation indexes, it will be of more reference value to measure the city–industry integration in areas with the same stage and type of urbanization development.
- (2)
- At present, there are few studies on regional differences for new urbanization, and less on city–industry integration. Therefore, in the future, more methods for regional differences can be introduced into the study of city–industry integration. This paper adopted mathematical statistics methods and spatial measurement methods such as the Moran index that can be introduced in the future, providing further development of the combined use of these two types of methods for spatial imbalance analysis.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

Data-Bind | Metric Classification | The Number of Indicators |
---|---|---|

Urban development | People | 14 |

National accounts | 12 | |

Labor employment | 50 | |

Investment in fixed assets | 20 | |

Industry | 35 | |

Finance | 16 | |

Foreign economic relations and trade | 6 | |

Total retail sales of consumer goods | 14 | |

Tourism | 4 | |

Scientific and technological innovation | 4 | |

Resource environment | 24 | |

Public service | 50 | |

Infrastructure | 45 |

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Authors | Main Method |
---|---|

Zhang and Shen, etc. [26] | AHP |

Su et al., etc. [31] | Fuzzy comprehensive analysis |

Wang et al., etc. [32] | Factor analysis |

Yang and Fang, etc. [27] | Principal component analysis |

Zhang et al., etc. [33] | EM |

Tang, etc. [22] | GRA |

Gan and Mao, etc. [34] | TOPSIS |

Model | Judgment Matrix | W1 | W2 | W3 | W4 | W5 | W6 | Consistency Indicator Function Value |
---|---|---|---|---|---|---|---|---|

PSO-AHP | B | 0.2661 | 0.1944 | 0.1681 | 0.1650 | 0.1328 | 0.0736 | 0.0455 |

C1 | 0.3623 | 0.1453 | 0.2974 | 0.1950 | 0.0242 | |||

C2 | 0.2326 | 0.1534 | 0.3478 | 0.2662 | 0.0367 | |||

C3 | 0.2495 | 0.1236 | 0.3990 | 0.2279 | 0.0322 | |||

C4 | 0.3623 | 0.1092 | 0.1966 | 0.3319 | 0.0288 | |||

C5 | 0.4113 | 0.1526 | 0.1076 | 0.3285 | 0.0228 | |||

C6 | 0.0978 | 0.3761 | 0.1972 | 0.1666 | 0.1623 | 0.0225 | ||

AHP | B | 0.2490 | 0.1917 | 0.1645 | 0.1604 | 0.1221 | 0.1123 | 0.0625 |

C1 | 0.3539 | 0.1642 | 0.2918 | 0.1902 | 0.0552 | |||

C2 | 0.2263 | 0.1825 | 0.3322 | 0.2589 | 0.0777 | |||

C3 | 0.2427 | 0.1517 | 0.3910 | 0.2147 | 0.0804 | |||

C4 | 0.3507 | 0.1345 | 0.1935 | 0.3214 | 0.0800 | |||

C5 | 0.4012 | 0.1498 | 0.1274 | 0.3216 | 0.0682 | |||

C6 | 0.1169 | 0.3690 | 0.1936 | 0.1642 | 0.0450 |

Criteria Layer | Indicators * | PSO-AHP | EM | Comprehensive Weight |
---|---|---|---|---|

Economy and industry | PG | 0.0964 | 0.0231 | 0.0598 |

PSOVG | 0.0387 | 0.0104 | 0.0245 | |

PTOVG | 0.0791 | 0.0181 | 0.0486 | |

GIOVEAS | 0.0519 | 0.0340 | 0.0430 | |

Habitat and employment | HDB | 0.0452 | 0.0802 | 0.0627 |

PES | 0.0298 | 0.0107 | 0.0202 | |

PUTP | 0.0676 | 0.0221 | 0.0449 | |

PCHA | 0.0517 | 0.0111 | 0.0314 | |

Social services | NB | 0.0419 | 0.0285 | 0.0352 |

NA | 0.0208 | 0.0391 | 0.0300 | |

CSME | 0.0671 | 0.0680 | 0.0676 | |

NS | 0.0383 | 0.0201 | 0.0292 | |

Urban construction | IFAC | 0.0598 | 0.0947 | 0.0773 |

PII | 0.0180 | 0.0134 | 0.0157 | |

PBF | 0.0324 | 0.0552 | 0.0438 | |

PURA | 0.0548 | 0.0095 | 0.0322 | |

Ecology and environment | EEA | 0.0546 | 0.0271 | 0.0408 |

CURISW | 0.0203 | 0.0054 | 0.0128 | |

WD | 0.0143 | 0.0139 | 0.0141 | |

RRECPG | 0.0436 | 0.0196 | 0.0316 | |

Science and technology | NP | 0.0072 | 0.0382 | 0.0227 |

SEAPUFE | 0.0277 | 0.0270 | 0.0283 | |

RDII | 0.0145 | 0.0614 | 0.0379 | |

AG | 0.0123 | 0.1718 | 0.0921 | |

FERDP | 0.0119 | 0.0975 | 0.0547 |

Region | 2010 | 2011 | 2015 | 2018 | 2019 | |||||
---|---|---|---|---|---|---|---|---|---|---|

Score | Ranking | Score | Ranking | Score | Ranking | Score | Ranking | Score | Ranking | |

Chengdu | 0.6282 | 1 | 0.5970 | 1 | 0.6228 | 1 | 0.6390 | 1 | 0.6300 | 1 |

Zigong | 0.3835 | 4 | 0.3745 | 7 | 0.3790 | 6 | 0.3682 | 13 | 0.3718 | 9 |

Panzhihua | 0.3975 | 3 | 0.3963 | 4 | 0.3910 | 4 | 0.4062 | 2 | 0.3917 | 3 |

Luzhou | 0.3529 | 16 | 0.3395 | 17 | 0.3570 | 13 | 0.3657 | 14 | 0.3639 | 14 |

Deyang | 0.3827 | 6 | 0.3755 | 6 | 0.3793 | 5 | 0.3851 | 5 | 0.3976 | 2 |

Mianyang | 0.4186 | 2 | 0.4077 | 3 | 0.4164 | 3 | 0.3891 | 3 | 0.3786 | 7 |

Guangyuan | 0.3634 | 12 | 0.3494 | 13 | 0.3498 | 15 | 0.3864 | 4 | 0.3912 | 4 |

Suining | 0.3548 | 14 | 0.3523 | 11 | 0.3707 | 8 | 0.3719 | 10 | 0.3547 | 18 |

Neijiang | 0.3559 | 13 | 0.3468 | 15 | 0.3539 | 14 | 0.3352 | 18 | 0.3612 | 16 |

Leshan | 0.3832 | 5 | 0.3770 | 5 | 0.3764 | 7 | 0.3698 | 11 | 0.3714 | 10 |

Nanchong | 0.3784 | 7 | 0.3749 | 9 | 0.3671 | 11 | 0.3724 | 8 | 0.3711 | 11 |

Meishan | 0.3644 | 11 | 0.3677 | 2 | 0.4251 | 2 | 0.3730 | 7 | 0.3759 | 8 |

Yibin | 0.712 | 8 | 0.3558 | 10 | 0.3680 | 10 | 0.3739 | 6 | 0.3797 | 6 |

Guang’an | 0.3665 | 10 | 0.3749 | 16 | 0.3432 | 16 | 0.3624 | 16 | 0.3669 | 12 |

Dazhou | 0.3446 | 17 | 0.3677 | 14 | 0.3366 | 18 | 0.3697 | 12 | 0.3609 | 17 |

Ya’an | 0.3708 | 9 | 0.3558 | 8 | 0.3689 | 9 | 0.3720 | 9 | 0.3908 | 5 |

Bazhong | 0.3414 | 18 | 0.3735 | 18 | 0.3378 | 17 | 0.3646 | 15 | 0.3643 | 13 |

Ziyang | 0.3540 | 15 | 0.3735 | 12 | 0.3597 | 12 | 0.3482 | 17 | 0.3616 | 15 |

Region | Chengdu Plain Economic Zone | South Sichuan Economic Zone | Northeast Sichuan Economic Zone | Panzhihua | |
---|---|---|---|---|---|

Economic industry | 2010 | 2.8091 | 1.2005 | 1.3908 | 0.4190 |

2019 | 2.9977 | 1.2744 | 1.4264 | 0.4108 | |

Change | 0.1886 | 0.0739 | 0.0356 | −0.0082 | |

Habitat and employment | 2010 | 3.5818 | 1.5358 | 2.0731 | 0.3790 |

2019 | 3.6058 | 1.6039 | 2.2388 | 0.4060 | |

Change | 0.0240 | 0.0681 | 0.1657 | 0.0270 | |

Social services | 2010 | 3.0621 | 1.3298 | 1.6086 | 0.3882 |

2019 | 2.9938 | 1.3373 | 1.6316 | 0.6313 | |

Change | −0.0683 | 0.0075 | 0.0230 | 0.2431 | |

Urban construction | 2010 | 3.1851 | 1.3388 | 1.7931 | 0.3819 |

2019 | 3.2248 | 1.3354 | 1.8320 | 0.3837 | |

Change | 0.0397 | −0.0034 | 0.0389 | 0.0018 | |

Ecology and environment | 2010 | 3.1164 | 1.6486 | 2.0382 | 0.3973 |

2019 | 2.9344 | 1.8503 | 1.9834 | 0.3614 | |

Change | −0.1820 | 0.2017 | −0.0548 | −0.0359 | |

Science and technology | 2010 | 2.6773 | 1.0293 | 1.0989 | 0.2893 |

2019 | 2.7376 | 1.0297 | 1.0845 | 0.2644 | |

Change | 0.0603 | 0.0004 | −0.0144 | −0.0249 |

**Table 7.**Theil index and structural decomposition of development of city–industry integration in each region.

Year | Overall Difference | Inter-Regional Differences | Intra-Regional Differences | Chengdu Plain Economic Zone | South Sichuan Economic Zone | Northeast Sichuan Economic Zone |
---|---|---|---|---|---|---|

2010 | 0.0047 | 0.0005 (10.73%) | 0.0042 (89.27%) | 0.0041 (87.80%) | 0.0000 (0.40%) | 0.0001 (1.07%) |

2011 | 0.0052 | 0.0006 (11.32%) | 0.0046 (88.68%) | 0.0045 (86.45%) | 0.0001 (1.62%) | 0.0000 (0.61%) |

2012 | 0.0067 | 0.0014 (20.69%) | 0.0053 (79.31%) | 0.0052 (77.55%) | 0.0000 (0.66%) | 0.0001 (1.10%) |

2013 | 0.0065 | 0.0013 (20.51%) | 0.0051 (79.49%) | 0.0051 (78.69%) | 0.0000 (0.38%) | 0.0000 (0.42%) |

2014 | 0.0053 | 0.0014 (26.23%) | 0.0039 (73.77%) | 0.0038 (71.99%) | 0.0000 (0.67%) | 0.0001 (1.11%) |

2015 | 0.0061 | 0.0010 (16.91%) | 0.0050 (83.09%) | 0.0049 (80.63%) | 0.0001 (1.49%) | 0.0001 (0.97%) |

2016 | 0.0074 | 0.0015 (19.67%) | 0.0060 (80.33%) | 0.0058 (78.37%) | 0.0001 (1.24%) | 0.0001 (0.72%) |

2017 | 0.0072 | 0.0015 (20.24%) | 0.0057 (79.76%) | 0.0056 (78.23%) | 0.0001 (1.26%) | 0.0001 (0.27%) |

2018 | 0.0052 | 0.0014 (26.76%) | 0.0038 (73.24%) | 0.0037 (71.17%) | 0.0001 (1.32%) | 0.0000 (0.74%) |

2019 | 0.0052 | 0.0009 (14.65%) | 0.0044 (83.35%) | 0.0043 (82.55%) | 0.0001 (1.07%) | 0.0001 (1.74%) |

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**MDPI and ACS Style**

Gan, L.; Wei, L.; Huang, S.; Lev, B.; Jiang, W. Evaluation of City–Industry Integration Development and Regional Differences under the New Urbanization: A Case Study of Sichuan. *Appl. Sci.* **2022**, *12*, 4698.
https://doi.org/10.3390/app12094698

**AMA Style**

Gan L, Wei L, Huang S, Lev B, Jiang W. Evaluation of City–Industry Integration Development and Regional Differences under the New Urbanization: A Case Study of Sichuan. *Applied Sciences*. 2022; 12(9):4698.
https://doi.org/10.3390/app12094698

**Chicago/Turabian Style**

Gan, Lu, Lihong Wei, Shan Huang, Benjamin Lev, and Wen Jiang. 2022. "Evaluation of City–Industry Integration Development and Regional Differences under the New Urbanization: A Case Study of Sichuan" *Applied Sciences* 12, no. 9: 4698.
https://doi.org/10.3390/app12094698