High-Quality Development Evaluation and Spatial Evolution Analysis of Urban Agglomerations in the Middle Reaches of the Yangtze River

Abstract

Urban agglomeration in the middle reaches of the Yangtze River is key to the rise of central China, and an important engine for the high-quality development of the Yangtze River economic belt. Research on urban agglomeration in the middle reaches of the Yangtze River focuses mainly on economic development, ecological environment, and innovation. Few studies focus on evaluation combining development levels. This study uses the entropy weight TOPSIS method to build an urban high-quality development level indicator system with “innovation, coordination, greenness, openness, and sharing” as the indicators, comprehensively measuring the high-quality development level of 31 cities in the middle reaches of the Yangtze River from 2010 to 2019 and analyzing the evolution of spatial distribution and autocorrelation. The results show that the high-quality development level of the urban agglomeration in the middle reaches of the Yangtze River varies greatly, and fluctuations are significant. The spatial distribution also shows an evolution from belt-shaped agglomeration to point-shaped diffusion distribution, and the spatial autocorrelation shows a negative correlation. Finally, this paper proposes suggestions to promote high-quality development from three aspects: system coordination, industry coordination, and sustainability.

1. Introduction

The World Development Report of the World Bank in 2009 raises a new question about why world economic activities tend to cluster [1]. Scholars found that urban agglomeration brings different cities together and become a new driving force and platform for national economic growth [2]. The first theoretical research on high-quality development was quality economics, followed by the concept of “quality of economic growth” proposed by Soviet scholar Kamayev, who believed that efficiency is quality [3]. Barro’s research is the closest to the current high-quality development. He believes that the real development level of a country is not only reflected in the level of economic growth, but also health, education, and law [4]. Internationally, the social development evaluation system [5], urban ecological environment indicator system [6], population development evaluation indicator system [7], and global innovation index [8] are related to high-quality development evaluation. However, these indicators can only reflect part of the content of high-quality urban development. The world reached a consensus on high-quality development in the Paris Agreement on Climate Change in 2016 [9]. The world expects a sustainable development environment. The concept of high-quality development in China is more inclined to sustainable development, and constantly explores the evaluation system of high-quality development. The evaluation of urban agglomeration has changed from a focus on economic benefits to the characteristics of sustainability and diversity, such as the environment, vitality, and culture. In the new era, China is facing a situation of large-scale economic development, economic/industrial weakness, and an increasingly prominent quality disadvantage. To adapt to changes in major social contradictions, high-quality development has become a long-term strategy that must be followed in national modernization [10]. As a new growth pole of China’s economy, the urban agglomeration in the middle reaches of the Yangtze River has increasingly prominent strategic significance [11]. As such, this paper takes urban agglomeration in the middle reaches of the Yangtze River as an example to evaluate the level of high-quality urban development, which can more accurately reflect all aspects of the achievements of urban agglomeration construction, identify the weak links for the future, and provide new ideas for high-quality development.

Research on the measurement of the development of urban agglomerations in the middle reaches of the Yangtze River has attracted scholarly attention. The existing research mainly focuses on the measurement of different aspects of urban agglomerations in the middle reaches of the Yangtze River. For example, Qiu et al. found that the density of the innovation spatial connection network of the urban agglomerations in the middle reaches of the Yangtze River is not high, and suggested strengthening the innovation connection [12]. Li used the Theil index method to measure the equalization level of public services in urban agglomerations in the middle reaches of the Yangtze River and concluded that the level was moderate but not stable, and predicted that the “14th five-year plan” period would be its rising period [13]. Zhou et al. evaluated the comprehensive bearing capacity of the urban agglomerations in the middle reaches of the Yangtze River and found that it showed a W-shaped change. The authors believed that the weak points should be actively solved and coordination should be strengthened [14]. Huang et al. analyzed the economic connection network of the urban agglomerations in the middle reaches of the Yangtze River by using the flow empirical analysis method and the modified gravity model. They found that the economic connection network presented a pole diffusion effect and expressed the view that the isolation should be broken to strengthen the economic connection [15]. Chen et al. studied the relationship between urban resilience and the scale of urban agglomerations in the middle reaches of the Yangtze River. They found that resilience decreased slightly, and the relationship type was mainly low-level coordination, which evolved in the same direction. They believed that the focus of large city construction was to enhance the ability to cope with disasters [16].

Comprehensive measurement research aiming at high-quality development has become the focus of academic circles. Scholars have conducted measurement research on high-quality development levels from different research perspectives. First, Xu et al. conducted measurement research on the high-quality development of different industries and regions. The measurement results show that the high-quality development of the manufacturing industry in the east is superior to that in the central and western regions. The authors have proposed strengthening technological innovation and transforming and upgrading integration innovation [17]. Zhang et al. measured the high-quality development of agriculture in 13 provinces in China. They found that the elements of high-quality development of agriculture are replaceable, and suggested supplementing the easily replaced technical and human capital elements [18]. Tang et al. measured the data from China’s provinces from 2013 to 2018 and found that the high-quality economic development level of more than half of the provinces fluctuated and increased, and there were obvious differentiation patterns among regions. The authors believed that labor, science, technology, and finance were the main reasons for the high-quality development differentiation of the provinces [19]. For example, Ling et al. measured the high-quality development level of the Guangdong Hong Kong Macao Bay area and found that it showed the characteristics of volatility, agglomeration, and imbalance. The authors proposed the high-quality development of the Guangdong Hong Kong Macao Bay area with five major targeted paths [20]. Zhou et al. measured the high-quality development of the Yangtze River economic belt and found that there was a growing regional imbalance, increasing year by year. It was high in the east and low in the west. The authors suggested changing the energy development conditions, the coordinated development of energy and production, and clean low-carbon energy development. The second aspect was to use different models and methods to build a comprehensive index system to measure high-quality development [21]. For example, Wang et al. used the PCA-EM measurement model and CVM measurement method to study the high-quality development of China’s coastal urban agglomeration [22]. Gao et al. used dynamic factor analysis to measure the green development level of the Yangtze River economic belt industry [23]. Shi et al. used the Gini coefficient and kernel density estimation method to measure the high-quality development of cities in the Yellow River Basin [24]. The comprehensive indicator system could include public service, green development, open development, coordinated development, and other indicators in the same evaluation system, thereby conducting a more comprehensive evaluation of the level of high-quality development.

In general, the research on the urban agglomerations in the middle reaches of the Yangtze River in the existing literature mainly focuses on a single measurement index. Research on the measurement of the high-quality development level of the urban agglomerations in the middle reaches of the Yangtze River from the two dimensions of time and space is relatively rare. Therefore, previous research could not objectively reflect the real situation of the high-quality development level of the urban agglomerations in the middle reaches of the Yangtze River. To measure the high-quality development of the urban agglomerations in the middle reaches of the Yangtze River, this paper takes 31 cities in the urban agglomerations in the middle reaches of the Yangtze River as the research object and tries to address the following three issues: (1) the composition of a high-quality urban development level indicator system; (2) the measurement of change in the high-quality development level of urban agglomerations in the middle reaches of the Yangtze River in both time and space; (3) the improvement of the high-quality development level of the urban agglomerations in the middle reaches of the Yangtze River. Through the examination and analysis of these three issues, the key nodes restricting the high-quality development of the urban agglomeration in the middle reaches of the Yangtze River are identified, and ways of improving the situation are explored.

2. Materials and Methods

2.1. Research Materials

The urban agglomeration in the middle reaches of the Yangtze River connects the east to the west and the south to the north. It is the second-largest urban agglomeration in China. Its area is second only to the urban agglomeration in the Yangtze River Delta. It is the main component of the Yangtze River economic belt and plays an important role in the regional development pattern [25]. Wuhan is the center of the urban agglomeration in the middle reaches of the Yangtze River. The mainland area includes the Wuhan urban circle, the urban agglomeration around Poyang Lake, and the urban agglomeration around Changsha, Zhuzhou, and Xiangtan [26]. This paper selects 31 cities in the middle reaches of the Yangtze River as the research object, comprehensively reflecting the high-quality development level of the cities in the urban agglomeration.

The research data are from the China Statistical Yearbook, the China Urban Statistical Yearbook, the China Regional Economic Statistical Yearbook, the Statistical Bulletins, the government work reports, and the environmental work reports of Hubei, Hunan, and Jiangxi provinces and their 31 cities. However, because the data on various indicators in 2020 were greatly affected by the COVID-19 pandemic, survey data from 2010 to 2019 were selected. For the data from those years, the moving average method is adopted to compensate for missing data in individual years.

2.2. Research Methods

Entropy-based TOPSIS is an objective weighting method [27]. Its advantages include simple calculation and reasonable results. Specifically, by comparing the difference between the best scheme and the worst scheme of each measurement object, quantitative ranking is carried out. The measurement steps are as follows: first, the extreme value method is used to deal with the original dimensionless index data, so that the original indexes of different orders of magnitude and dimensions are comparable; second, a single weighting method may bring uncertainty. To avoid this uncertainty, the coefficient of variation method and the entropy weight method are used to assign the index weights, respectively. Then, the average value of the obtained weights is taken as the final weight. Finally, TOPSIS is used to rank the results [28]. The specific implementation steps are as follows:

The first step is to standardize.

$$P_{ij}=\frac{X_{ij}-\min (X_{ij})}{\max (X_{ij})-\min (X_{ij})}$$

(1)

$$P_{ij}=\frac{\max (X_{ij})-X_{ij}}{\max (X_{ij})-\min (X_{ij})}$$

(2)

The original index data in the measurement system are standardized by the extreme value method, whereby i (I = 1, 2,…, n) represents a city; j (j = 1, 2,…, m) represents the year; X_ij represents the original index, P_ij represents the measurement index value after standardization processing; max(X_ij), min(X_ij) represent the maximum value and the minimum value of X_ij, respectively. If the indicator attribute X_ij is a positive indicator, Equation (1) applies; if the indicator attribute X_ij is a negative indicator, Equation (2) applies.

The second step is to calculate the weight.

First, the coefficient of variation C_j is calculated according to the mean P_j and standard deviation Q_j of each measurement index P_ij, and its weight Y_1j is calculated. The calculation formula is:

$${\overline{P}}_j=\frac{1}{n}{\displaystyle \sum _{i=1}^n{P}_i}$$

(3)

$$Q_j=\sqrt{\frac{1}{n-1}{\displaystyle \sum _{i=1}^n{(P_{ij}-\overline{P_j})}^2}}$$

(4)

$$C_j=\frac{Q_j}{{\overline{P}}_j}$$

(5)

$$Y_{1j}=\frac{C_j}{{\displaystyle \sum _{j=1}^m{C}_j}}$$

(6)

Second, the information entropy W_j and its weight Y_2j are calculated according to the data distribution dispersion of each measurement index P_ij. The calculation formula is:

$$W_j=\ln \frac{1}{n}{\displaystyle \sum _{i=1}^n\left[\left(\frac{P_{ij}}{{\displaystyle \sum _{i=1}^n{P}_{ij}}}\right)\ln \left(\frac{P_{ij}}{{\displaystyle \sum _{i=1}^n{P}_{ij}}}\right)\right]}$$

(7)

$$Y_{2\mathrm{j}}=\frac{\left(1-W_j\right)}{{\displaystyle \sum _{j=1}^m\left(1-W_j\right)}}$$

(8)

The third step is to construct the weighting matrix, calculate the weight P_ij of each measurement index, and construct the weighting matrix T. The calculation formula is:

$$Y_j=\frac{1}{2}\left(Y_{1j}+Y_{2j}\right)$$

(9)

$$T=\left(t_{ij}\right)n\times m,(t_{ij}=Y_j\times P_j)$$

(10)

The fourth step is to determine the scheme. This involves determining the optimal scheme and the worst scheme according to the weighting matrix T, and calculating its Euclidean distance:

$$Z_j^{+}=\left(\max r_{i1},\max r_{i2},\cdots ,\max r_{im}\right)$$

(11)

$$Z_j^{-}=\left(\min r_{i1},\min r_{i2},\cdots ,\min r_{im}\right)$$

(12)

$$r_i^{+}=\sqrt{{\displaystyle \sum _{j=1}^m{\left(Z_j^{+}-t_{ij}\right)}^2}}$$

(13)

$$r_i^{-}=\sqrt{{\displaystyle \sum _{j=1}^m{\left(Z_j^{-}-t_{ij}\right)}^2}}$$

(14)

where Z_j⁺ and Z_j⁻ are the best measure scheme and the worst measure scheme, respectively, and r is the Euclidean distance.

The fifth step is to calculate the relative proximity and determine the high-quality development level.

$$R_i=\frac{r_i^{-}}{r_i^{+}+r_i^{-}}$$

(15)

where R_i is the relative proximity between the measurement scheme Z and the ideal scheme. When 0 < R_i < 1, the greater the relative proximity value R_i of the city and the higher the high-quality development level of the city. Conversely, the smaller the R_i, the lower the high-quality development level of the city.

2.3. Indicator Selection

There are many evaluation indicators that measure the level of high-quality development, and different indicator systems can be built based on different research dimensions [29]. Wang et al., from the perspective of a high-quality economy, summarized the high-quality development indicators of urban agglomeration into five aspects: structure, efficiency, stability, sustainability, and environmental protection [30]. Shen et al. summarized the high-quality development indicators of urban agglomeration into three aspects: comprehensive economic development, coordinated social development, and environmentally friendly development from the perspective of multiple factor flows [31]. Nie et al. built an evaluation index system for China’s inter-provincial high-quality development from five aspects: product and service quality, economic benefits, social benefits, ecological benefits, and economic operation status [32]. At the Fifth Plenary Session of the 18th CPC Central Committee, the five development concepts of innovation, coordination, greenness, openness, and sharing were put forward [33] based on the five development concepts and existing research [34,35,36]. In this line, considering the scientific validity, operability, and data integrity of the evaluation, this paper identifies the five first-class indicators of high-quality urban development and 18 s-class indicators [37]. In terms of innovation indicators, “Proportion of R&D expenditure to GDP” represents innovation investment, “Number of R&D employees per 10,000 people” represents the innovation environment, “Number of invention patents per 10,000 people” represents innovation capability, and “Proportion of high-tech enterprises in China” represents innovation competitiveness [38]. In terms of the coordinated development index, from the perspective of industrial structure and urban–rural gap, the proportion of the output value of the secondary and tertiary industries in the total output value and the comparison of the consumption level of urban and rural residents were identified [39]. The green development index is measured from the three dimensions of pollution, environmental protection, and green coverage, including waste gas emission per unit industrial added value, smoke (dust) emission per unit industrial added value, sewage treatment rate, domestic garbage treatment rate, and green coverage rate of built-up areas [40]. Openness involves the inclusive level of high-quality urban development. The open development index was calculated on three aspects: the proportion of total foreign trade imports and exports in the country, the proportion of foreign direct investment in the country, and the proportion of inbound tourists in the country [41]. Sharing includes social security, employment, medical care, income, and other indicators, representing the development of people’s living standards, reflecting the universality of high-quality urban development [42] (

Table 1. Evaluation index system for the high-quality development of urban agglomerations in the middle reaches of the Yangtze River.

Level 1 Indicator	Level 2 Indicator	Unit	Indicator Attribute
Innovation-driven development	Proportion of R&D expenditure to GDP	%	Positive
	Number of R&D employees per 10,000 people	Person/10,000	Positive
	Number of invention patents per 10,000 people	Pieces/10,000 people	Positive
	Proportion of high-tech enterprises in China	%	Positive
Coordinated development	Proportion of output value of secondary and tertiary industries in total output value	%	Positive
	Comparison of consumption levels of urban and rural residents	%	Negative
Green development	Waste gas emission per unit industrial added value	T/10,000	Negative
	Smoke (powder) dust emission per unit industrial added value	10,000 ton	Negative
	Sewage treatment rate	%	Positive
	Domestic waste treatment rate	%	Positive
	Green coverage rate of built-up area	%	Positive
Development for global progress	Proportion of total foreign trade imports and exports in the country	%	Positive
	Proportion of foreign direct investment in China	%	Positive
	Proportion of inbound tourists in China	%	Positive
Development for the benefit of all	Proportion of social security and employment expenditure in general public budget expenditure	%	Positive
	Number of doctors per thousand	Person/thousand	Positive
	Per capita GDP	RMB	Positive
	Registered unemployment rate	%	negative

).

3. Results

3.1. Evaluation Index

The data on urban agglomeration in the middle reaches of the Yangtze River were collected and processed. Then, the high-quality development level and its changes in 31 cities in the 10 years from 2010 to 2019 were calculated, as shown in

Table 2. High-quality development index of urban agglomerations in the middle reaches of the Yangtze River from 2010 to 2019.

City	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	Ranking in 2019
Wuhan	0.586	0.887	0.735	0.611	0.669	0.726	0.667	0.805	0.802	0.858	1
Huangshi	0.197	0.142	0.336	0.275	0.271	0.272	0.247	0.294	0.12	0.236	4
Ezhou	0.193	0.111	0.277	0.235	0.23	0.239	0.206	0.272	0.302	0.345	3
Huanggang	0.081	0.052	0.115	0.095	0.109	0.116	0.106	0.056	0.068	0.043	28
Xiaogan	0.221	0.141	0.059	0.052	0.055	0.061	0.059	0.082	0.061	0.072	18
Xianning	0.171	0.1	0.229	0.208	0.206	0.227	0.182	0.074	0.089	0.066	19
Xiantao	0.041	0.037	0.045	0.046	0.058	0.069	0.067	0.042	0.042	0.033	30
Qianjiang	0.124	0.116	0.17	0.128	0.151	0.138	0.122	0.207	0.22	0.177	6
Tianmen	0.055	0.048	0.124	0.071	0.068	0.069	0.082	0.038	0.042	0.027	31
Xiangyang	0.112	0.088	0.154	0.13	0.135	0.109	0.093	0.1	0.095	0.108	9
Yichang	0.091	0.059	0.187	0.156	0.17	0.203	0.206	0.206	0.198	0.163	7
Jingzhou	0.038	0.023	0.04	0.035	0.034	0.082	0.097	0.093	0.099	0.046	25
Jingmen	0.057	0.042	0.083	0.067	0.064	0.09	0.107	0.121	0.129	0.09	14
Changsha	0.655	0.32	0.606	0.674	0.678	0.634	0.505	0.492	0.436	0.461	2
Zhuzhou	0.118	0.078	0.163	0.17	0.18	0.148	0.089	0.103	0.1	0.102	12
Xiangtan	0.12	0.065	0.14	0.114	0.14	0.116	0.091	0.092	0.09	0.103	11
Yueyang	0.136	0.08	0.179	0.203	0.229	0.265	0.112	0.106	0.105	0.107	10
Yiyang	0.07	0.032	0.077	0.043	0.041	0.041	0.057	0.04	0.045	0.048	24
Changde	0.115	0.119	0.136	0.144	0.153	0.177	0.078	0.08	0.073	0.081	17
Hengyang	0.105	0.058	0.134	0.135	0.111	0.095	0.073	0.08	0.074	0.085	15
Loudi	0.076	0.038	0.081	0.084	0.088	0.06	0.046	0.039	0.036	0.045	26
Nanchang	0.204	0.135	0.264	0.244	0.269	0.233	0.191	0.201	0.191	0.209	5
Jiujiang	0.105	0.075	0.146	0.134	0.149	0.144	0.142	0.141	0.131	0.128	8
Jingdezhen	0.091	0.06	0.107	0.089	0.092	0.073	0.103	0.101	0.079	0.059	23
Yingtan	0.115	0.064	0.119	0.113	0.1	0.086	0.084	0.11	0.068	0.063	20
Xinyu	0.128	0.072	0.123	0.092	0.082	0.069	0.067	0.087	0.075	0.062	21
Yichun	0.049	0.036	0.063	0.056	0.061	0.061	0.07	0.066	0.061	0.06	22
Pingxiang	0.043	0.03	0.061	0.058	0.052	0.048	0.056	0.049	0.046	0.041	29
Shangrao	0.073	0.052	0.094	0.084	0.095	0.1	0.113	0.104	0.094	0.083	16
Fuzhou	0.031	0.021	0.042	0.038	0.038	0.039	0.042	0.041	0.043	0.043	27
Ji’an	0.063	0.047	0.091	0.085	0.095	0.106	0.103	0.095	0.087	0.092	13

. The table shows that in 2019, the high-quality development index of the urban agglomeration in the middle reaches of the Yangtze River was distributed between 0.027 and 0.858. The index of Wuhan, the first city in the high-quality development index, and Changsha, the second city, differed by 0.397, reflecting a relatively clear difference in the high-quality development level of the 31 cities in the urban agglomeration in the middle reaches of the Yangtze River. Among the cities, Wuhan City has the highest index, and Tianmen City has the lowest. The average value of the high-quality development index of the 31 cities is 0.133, the standard deviation is 0.162, and the coefficient of variation is 1.211. This shows that the high-quality development level of the urban agglomerations in the middle reaches of the Yangtze River varies greatly among cities, and the absolute and relative gaps in the high-quality development level among the urban agglomerations in the middle reaches of the Yangtze River are relatively significant.

In addition, from the time-span perspective, the high quality of urban agglomeration in the middle reaches of the Yangtze River shows a dynamic fluctuation trend. The high-quality development level of cities is clearly graded. According to the ascending and descending order, cities can be classified into stable, rising, and falling cities, as

Table 3. High-quality development level ranking of the 31 cities in 2019.

Type	City
Stable type	Wuhan/Changsha/Nanchang/Huangshi/Zhuzhou/Xiangtan/ Hengyang/Yueyang/Yiyang/Xiantao/Pingxiang
Ascending type	Yichang/Jingmen/Ji’an/Jiujiang/Xiangyang/Shangrao/Jingzhou/Jichun/Qianjiang/Fuzhou/Ezhou
Descending type	Xiaogan/Xianning/Xinyu/Huanggang/Yingtan/Tianmen/Loudi/ Jingdezhen/Changde

shows. Among them, Nanchang, the capital of Jiangxi Province, ranks fifth in the urban agglomeration in the middle reaches of the Yangtze River in terms of its high-quality development level. Wuhan and Changsha are relatively stable in terms of their high-quality development level, always ranking in the top two. However, it is clear that the high-quality development levels of Xiaogan City, Xianning City, and Xinyu City have decreased significantly, while those of Yichang City, Jingmen City, and Ji’an City have increased by 11 places, respectively.

The mean value of the high-quality development index of the urban agglomerations in the middle reaches of the Yangtze River is ±0.5 standard deviation. This allows the high-quality development level of the 31 cities in 2019 to be divided into three levels. As

Table 4. Classification of high-quality development level of 31 cities in 2019.

Level	City
First-level city (index ≥ 0.214)	Wuhan/Changsha/Ezhou/Huangshi
Second-level city (0.052 ≤ index ≤ 0.214)	Nanchang/Qianjiang/Yichang/Jiujiang/Xiangyang/Yueyang/Xiangyang/Yueyang/Xiangtan/Zhouzhou/Ji’an/Jingmen/Hengyang/Shangrao/Change/ Xiaogan/Xianning/Yingtan/Xinyu/Yichun/Jingdezhen
Third-level city (index ≤ 0.052)	Yiyang/Jingzhou/Loudi/Huanggang/Fuzhou/Pingxiang/Xiantao/Tianmen

shows, the high-quality development index of the first-level cities is higher than 0.214 (index ≥ mean + 0.5 standard deviation), a total of four cities accounting for 13%, indicating that these cities have advantages over other cities in the middle reaches of the Yangtze River. The high-quality development index of the second-level cities is between 0.052 and 0.214, with 19 cities accounting for 61%. It has formed a hierarchical structure of an olive-shaped urban agglomeration, which is large in the middle and small at both ends, indicating that these cities attach great importance to the high-quality development level. However, there is still a large development space compared with the first-level cities. The high-quality development index of the third-level cities is lower than 0.052 (index ≤ mean −0.5 standard deviation) for a total of eight cities, accounting for 26%, indicating that these cities urgently need to change their development concepts and attend to the promotion of high-quality development level while attending to the promotion of economic growth.

In the urban high-quality development index system constructed using the five development concepts, the development level indexes of the five subsystems of the 31 cities in 2019 are obtained through the calculation of the five first-class indicators.

Table 5. Horizontal ranking of five subsystems of 31 urban agglomerations in 2019.

City	High-Quality Development Index Ranking	Innovative Development Level	Sequencing	Coordinated Development Level	Sequencing	Green Development Level	Sequencing	Open Development Level	Sequencing	Shared Development Level	Sequencing
Wuhan	1	0.257	1	0.023	1	0.047	14	0.423	1	0.103	2
Changsha	2	0.143	4	0.022	2	0.052	9	0.239	2	0.109	1
Ezhou	3	0.159	2	0.013	12	0.052	10	0.065	4	0.052	13
Huangshi	4	0.158	3	0.018	4	0.035	27	0.004	27	0.053	10
Nanchang	5	0.043	13	0.021	3	0.058	5	0.111	3	0.070	6
Qianjiang	6	0.117	5	0.012	15	0.032	28	0.001	30	0.050	14
Yichang	7	0.086	6	0.013	14	0.046	15	0.039	10	0.076	3
Jiujiang	8	0.022	21	0.016	8	0.057	6	0.063	5	0.052	11
Xiangyang	9	0.071	7	0.013	13	0.046	16	0.024	15	0.064	7
Yueyang	10	0.029	18	0.012	16	0.044	19	0.046	7	0.050	15
Xiangtan	11	0.053	10	0.017	5	0.049	13	0.037	11	0.072	4
Zhuzhou	12	0.064	8	0.016	11	0.052	11	0.030	13	0.063	8
Ji’an	13	0.009	30	0.011	18	0.051	12	0.049	6	0.024	27
Jingmen	14	0.062	9	0.009	24	0.046	18	0.011	23	0.059	9
Hengyang	15	0.020	24	0.010	23	0.031	29	0.040	8	0.049	16
Shangrao	16	0.019	25	0.010	20	0.043	22	0.039	9	0.021	29
Changde	17	0.025	20	0.010	21	0.057	7	0.030	12	0.070	5
Xiaogan	18	0.051	11	0.007	27	0.044	20	0.010	24	0.036	21
Xianning	19	0.036	15	0.008	25	0.042	23	0.005	26	0.046	17
Yingtan	20	0.030	17	0.016	9	0.058	4	0.023	16	0.038	20
Xinyu	21	0.041	14	0.017	6	0.068	1	0.015	19	0.033	23
Yichun	22	0.027	19	0.010	22	0.044	21	0.027	14	0.017	30
Jingdezhen	23	0.046	12	0.017	7	0.061	2	0.015	20	0.032	24
Yiyang	24	0.033	16	0.003	29	0.046	17	0.013	22	0.045	18
Jingzhou	25	0.021	23	0.001	31	0.039	25	0.008	25	0.024	28
Loudi	26	0.018	26	0.011	19	0.038	26	0.015	18	0.052	12
Fuzhou	27	0.016	28	0.006	28	0.060	3	0.017	17	0.013	31
Huanggang	28	0.021	22	0.001	30	0.019	31	0.004	29	0.041	19
Pingxiang	29	0.016	29	0.016	10	0.054	8	0.014	21	0.028	26
Xiantao	30	0.018	27	0.012	17	0.025	30	0.004	28	0.032	25
Tianmen	31	0.006	31	0.007	26	0.040	24	0.001	31	0.034	22
Average value		0.055		0.012		0.046		0.046		0.049

lists the results.

(1)

Innovation and development level. Wuhan City (0.257) has the highest score for innovation and development level, and Tianmen City (0.006) has the lowest score. The difference between the two is 0.251, indicating that there is a significant gap in innovation and development levels in the urban agglomerations in the middle reaches of the Yangtze River. Specifically, some cities rank high in the high-quality development level, but their innovation development level is relatively low. For example, Nanchang ranks fifth in the high-quality development level, but its innovation development level only ranks 13th. Jiujiang ranks eighth in the high-quality development level, but its innovation development level only ranks 21st. However, some cities rank low in the high-quality development level, but their innovation development level is relatively high. For example, Jingdezhen only ranks 23rd in the high-quality development level, but its innovation development level ranks 12th.

(2)

Coordinated development level. As the core cities in the urban agglomeration of the middle reaches of the Yangtze River, Wuhan, Changsha, and Nanchang are relatively consistent in their regional status and high-quality development level, ranking first, second, and third, respectively. Xinyu is the sixth city with an outstanding coordinated development level, and its high-quality development level only ranks 21st; Jingdezhen city ranks 7th, and its high-quality development level only ranks 23rd; Yingtan City ranks 9th, and its high-quality development level is only 20th. The cities with the coordinated development level ranking behind the high-quality development level include Ezhou City, Qianjiang City, Yichang City, Xiangyang City, and Yueyang City. The high-quality development level ranks third, sixth, seventh, ninth, and tenth, respectively, while the coordinated development level ranks 12th, 15th, 14th, 13th, and 16th, respectively. The average value of the coordinated development level is low, indicating that the urban agglomeration in the middle reaches of the Yangtze River is insufficient in coordinated development, and the overall coordination level needs to be improved.

(3)

Green development level. The lowest green development level is for Huanggang City (0.019), and the highest is for Xinyu City (0.068). Among the top ten cities with a high-quality development level, only Nanchang and Jiujiang have the same green development level as their high-quality development level. The green development level of the other eight cities is significantly lower than the high-quality development level. For example, Wuhan, Changsha, Ezhou, Huangshi, Qianjiang, Yichang, Xiangyang, and Yueyang rank 14th, 9th, 10th, 27th, 28th, 15th, 16th, and 19th. Therefore, the high-quality development of some cities is not matched by their green development, and the concept of green development needs to be constantly improved during high-quality development.

(4)

The level of openness and development. The average score for open development level is 0.046. The highest score is for Wuhan City (0.423), and the lowest score is for Tianmen City (0.001). The difference between the two is 0.422, and there is a large gap. Only seven cities exceeded the average score, namely Wuhan (0.423), Changsha (0.239), Nanchang (0.111), Ezhou (0.065), Jiujiang (0.063), Ji’an (0.049), and Yueyang (0.046).

(5)

Shared development level. The average score for shared development level is 0.049, and the shared development level of 16 cities exceeds the average value, indicating that the overall shared development level of the urban agglomeration in the middle reaches of the Yangtze River is relatively high. The city with the highest level of shared development is Changsha (0.109), and the city with the lowest level is Fuzhou (0.013). The difference between the two is 0.096, and there is a large gap. The level of shared development includes social security, employment, medical care, per capita GDP, and other aspects. It is the cornerstone of high-quality development. Only by effectively improving the level of equalization of public services and reducing the level of shared development of cities of different sizes can the overall high-quality development level of urban agglomerations in the middle reaches of the Yangtze River be improved.

3.2. Spatial Feature Analysis

3.2.1. Spatial Evolution Analysis

Spatial evolution analysis can reflect the dynamic evolution process of the functional structure, urban hierarchy, and the connections formed between cities in urban agglomerations. It is a regional system covering points, lines, networks, and areas [43]. Studying the spatial evolution of urban agglomerations is conducive to clarifying the spatial structure, development context, and trend of urban agglomerations, summarizing existing problems in the development of urban agglomerations, and formulating feasible high-quality development planning [44]. To further reflect the spatial distribution and evolution characteristics of the high-quality development level of cities in the middle reaches of the Yangtze River since 2010, ArcGIS10.2 software was used to draw the spatial evolution map of the high-quality development level of the middle reaches of the Yangtze River in China from 2010 to 2019, using the natural breakpoint method [45], as Figure 1 shows. The time sections were selected in 2010, 2015, and 2019. Specifically, in 2010, the high-quality development level of Xiaogan City, Wuhan City, Ezhou City, Huangshi City, Xianning City, Yueyang City, Changsha City, Xiangtan City, and Zhuzhou City was greater than 0.118, showing obvious belt-shaped agglomeration space distribution characteristics. In 2015 and 2019, it gradually changed to point-like diffusion distribution characteristics. From the perspective of the evolution patterns, urban spaces with high-quality development levels change from belt-shaped agglomeration distribution to point-shaped diffusion distribution. This is because the continuous development of transportation lines promoted the high-quality development of cities along the line. For example, in 2010, the Beijing Guangzhou railway, the Wuhan Guangzhou section of the Beijing Guangzhou high-speed railway, and other trunk lines were used as channels to form a high-quality development belt distribution for Wuhan Changsha (Xiaogan City, Wuhan City, Ezhou City, Huangshi City, Xianning City, Yueyang City, Changsha City, Xiangtan City, Zhuzhou City, and Hengyang City). With the construction of the Shanghai Kunming expressway, the Shanghai Kunming high-speed railway, the Wuhan Jiujiang high-speed railway, and other trunk lines, Wuhan Nanchang (Wuhan, Ezhou, Huangshi, Jiujiang, and Nanchang), gradually formed a point-like connection. Meanwhile, the original Wuhan Changsha exhibition belt has been transformed into a point-like diffusion distribution in Wuhan, Changsha, Nanchang, and some surrounding cities.

The performance in the three years shows that the difference in high-quality development level was the most significant in 2010. From the provincial perspective, the high-quality development level of Hunan Province was relatively high, and the high-quality development level of Jiangxi Province was relatively low. Among them, the difference between the highest and lowest urban high-quality development index is 0.624. Five cities have an index lower than 0.055, namely Yichun City, Pingxiang City, Xiantao City, Jingzhou City, and Fuzhou City. Four cities have an index higher than 0.197, namely Changsha, Wuhan, Xiaogan, and Nanchang. The other three cities, except for Xiaogan, are provincial capitals. The high-quality development index of the other 22 cities is between 0.055 and 0.197. In 2015, the high-quality development level was significantly improved, and there were seven cities with an index exceeding 0.203: Wuhan, Changsha, Huangshi, Yueyang, Ezhou, Nanchang, and Xianning. There were six cities with an index lower than 0.069: Xiaogan City, Yichun City, Loudi City, Pingxiang City, Yiyang City, and Fuzhou City, reflecting that the overall high-quality development level of the urban agglomerations in the middle reaches of the Yangtze River improved to a certain extent during this period. In 2019, the city’s high-quality development level continued to improve, but the gap between the high-quality development levels widened. The index gap increased to 0.831, and the high-quality development index of Changsha dropped most significantly, from 0.655 in 2010 to 0.461 in 2019. The retrospective evaluation index system found that its innovation development level dropped significantly. However, in terms of the relative gap, the standard deviation of the high-quality development index from 2010 to 2019 continued to increase, from 0.137 to 0.162, reflecting the gradual widening of the gap in the level of high-quality development among cities in the urban agglomerations in the middle reaches of the Yangtze River.

3.2.2. Spatial Autocorrelation Analysis

Spatial autocorrelation analysis (Moran’s I) [46] was used to study whether the high-quality development level of neighboring cities has similar attribute values [47]. The calculation formulas are:

$$I=\frac{n}{S_0}\frac{{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{G}_{ij}{H}_i{H}_j}}}{{\displaystyle \sum _{i=1}^n{H}_i^2}}$$

(16)

$$S_0={\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{G}_{ij}}}$$

(17)

$$H_I=\frac{I-E\left[I\right]}{\sqrt{V\left[I\right]}}$$

(18)

where H_j represents the deviation between the attribute of city i and the average value, G_ij represents the spatial weight between cities i and j, n = 31—that is, 31 cities—S₀ is the aggregation of all city weights, and I is the global Moran’s index of the urban agglomerations in the middle reaches of the Yangtze River. The value range of Moran’s index is −1~1. When Moran’s I = 0, it indicates that the space presents randomness; Moran’s I > 0 indicates a positive spatial correlation. The larger the value, the more obvious the spatial correlation. Moran’s I < 0 indicates a negative spatial correlation. The smaller the value, the greater the spatial difference. The results should be interpreted in combination with z-scores and p-values [46].

Table 6. Moran’s I index.

Year	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
Moran’s I	−0.049	−0.034	−0.035	−0.047	−0.047	−0.029	−0.052	−0.080	−0.042	−0.019
z-score	−0.163	−0.012	−0.018	−0.140	−0.134	0.042	−0.199	−0.520	−0.100	0.162
p-value	0.435	0.495	0.493	0.444	0.447	0.483	0.421	0.301	0.460	0.436

shows that the p-value from 2010 to 2019 is greater than 10%, which fails to pass the significance level test, and the Moran’s I of each year is less than zero, indicating that the high-quality development level of the urban agglomerations in the middle reaches of the Yangtze River presented a negative spatial correlation in the decade from 2010 to 2019. Moran’s index shows that the high-quality development level of urban agglomerations in the middle reaches of the Yangtze River has not yet formed a spatial agglomeration effect.

Figure 2 shows an analysis of the Moran scatter map of 31 cities in the urban agglomeration in the middle reaches of the Yangtze River in 2019. The first quadrant and the third quadrant represent the positive spatial autocorrelation and the concentration of similar values. The first quadrant has a high value in itself, and other surrounding areas, such as Ezhou City and Huangshi City, also have high values. The third quadrant has a low value, and the high-quality development level of surrounding cities is also low. The second quadrant and the fourth quadrant show negative spatial correlation, indicating spatial anomaly. The second quadrant indicates low value, while the surrounding cities (Huanggang City, Xianning City, Xiantao City, Xiaogan City, Xiangtan City, Zhuzhou City, Yiyang City, Yichun City, Pingxiang City, and Loudi City) have high value. The fourth quadrant shows high value, but the surrounding cities (Wuhan, Changsha, Yichang, Nanchang, and Qianjiang) have low value.

4. Discussion

The main contribution of this paper is to provide a new thinking dimension for urban development evaluation, a dimension based on sustainable high-quality development. At present, the development of the world’s urban agglomerations has achieved remarkable results. There are six world-class urban agglomerations recognized globally [48]. These urban agglomerations have effectively weakened administrative barriers, improved the efficiency of transportation infrastructure and market transactions, and enabled the entire market environment system to evolve into a higher division of labor system. However, as far as the evaluation indicators are concerned, the international evaluation indicators for cities mainly focus on efficiency and potential indicators such as the Global City Comprehensive Ranking and the Global City Potential Ranking issued by the international management consulting company Colney [49]. In the context of the global ecological crisis, the evaluation system with high-quality development as its core can promote the sustainability of urban development. Therefore, this paper takes 31 cities in the middle reaches of the Yangtze River in China as examples to build a high-quality development evaluation index system for urban agglomerations.

Urban agglomeration development is an important power source for high-quality development [50]. It involves strategic deployment made by drawing on international development experience in combination with China’s current conditions to achieve interactive and coordinated development among regions, forming the driving force for China’s sustained economic growth, and ultimately promoting the high-quality development of various regions and cities. Therefore, the goal of economic development should move from quantity to quality through high-quality development [51]. This paper proposes the following research-based suggestions: based on the phenomenon that there are significant differences in the high-quality development levels of cities in the urban agglomeration in the middle reaches of the Yangtze River, it is proposed to form a standardized and institutionalized special mechanism for urban agglomeration in terms of institutional coordination to ensure the free flow of capital, labor, technology, and other production factors in the urban agglomeration. Given the characteristics of negative spatial correlation, this paper proposes suggestions for promoting the complementary development of industries among cities, activating the platform economy, promoting the division and dislocation of scientific and technological enterprises and the labor force among regions, and strengthening horizontal regional linkage and vertical industrial coordination in terms of industrial coordination. In light of the olive-shaped urban hierarchical structure, it is proposed to simultaneously promote synergy, green, openness, and sharing, especially synergy and green development in terms of sustainability.

5. Conclusions

Drawing on the concept of high-quality development, this study builds a high-quality development indicator system for urban agglomeration from the following five fields: innovation, coordination, greenness, openness, and sharing. The evaluation from five dimensions can scientifically, objectively, and comprehensively reflect the development level of urban agglomeration in the middle reaches of the Yangtze River. We used the entropy TOPSIS method to calculate the high-quality development evaluation value of the urban agglomerations in the middle reaches of the Yangtze River. The spatial evolution characteristics of the high-quality development level of the urban agglomerations in the middle reaches of the Yangtze River were studied using ArcGIS10.2 software, coefficient of variation method, Moran’s index, and scatter plot. The results are as follows.

The evaluation indicators show significant differences in the high-quality development level of cities in the urban agglomerations in the middle reaches of the Yangtze River. The ranking changes in the past ten years indicate that there are three types of cities: stable, rising, and falling. According to the classification of high-quality development levels in 2019, the second-level cities accounted for a large proportion of the cities. The measurement results of the five subsystems indicate that the development level of each city in the five subsystems is not balanced.

The analysis of the spatial evolution during the period studied revealed a change from belt-shaped agglomeration to point-shaped diffusion distribution. In terms of spatial autocorrelation, the high-quality development level has not yet formed a spatial agglomeration effect. It has formed a spatial negative correlation.

The limitations of data and space in this study result in shortcomings. First, the research data only extend to 2019. The data for 2020 and 2021 were not statistically analyzed due to the impact of COVID-19. Second, the connotation of high-quality development level has not been thoroughly analyzed. Follow-up work will carry out an in-depth analysis of these deficiencies to enhance the applicability of the high-quality development level indicator system in a wider range. Third, research on the high-quality development plan and policy system construction of the urban agglomeration in the middle reaches of the Yangtze River is not sufficiently far-ranging. These problems will be addressed in the next stage of our research.