Comprehensive Evaluation of the High-Quality Development of the Ecological and Economic Belt along the Yellow River in Ningxia

Abstract

The measurement of high-quality economic development and regional research plays a crucial role in achieving ecologically led high-quality development. This paper aims to establish a comprehensive evaluation index system for the high-quality level of development in Ningxia’s Yellow River ecological and economic zone, focusing on five dimensions: innovation, coordination, green, openness, and shared. By employing a factor analysis, this study estimates the level of high-quality development along the Yellow River Ecological Economic Belt in Ningxia for the year 2020. A multi-index panel data factor analysis and entropy weight TOPSIS method are employed to analyze the spatiotemporal evolution characteristics of high-quality development across 10 counties from 2014 to 2020. The empirical findings reveal that, in 2020, the overall level of high-quality development in the region remained relatively low. Among the contributing factors, shared and coordinated development demonstrated the highest impact on high-quality development, while open, green, and innovative development showed limited influence. Only Xingqing District, Litong District, and Helan County in the 10 counties have a composite factor score of greater than 0 on the level of economic development, while the other 7 counties have a composite score of less than 0. The study also identified a significant spatial heterogeneity in the quality of high-quality development along Ningxia’s Yellow River Ecological Economic Belt. Through a cluster analysis, the regions along the Yellow River Ecological Economic Belt in Ningxia are classified into categories of high, medium, and low levels of quality development. Over the period from 2014 to 2020, each county exhibited a steady increase in high-quality development, while the differences in development levels among the 10 counties gradually diminished. Based on these findings, practical suggestions are provided to guide Ningxia in leading the path of high-quality development through ecological civilization construction along the Yellow River ecological economy.

1. Introduction

The Yellow River Basin is an important ecological barrier and economic zone in China, playing a crucial role in ecological protection and rapid economic development. In June 2020, after inspecting the Ningxia Autonomous Region, General Secretary Xi Jinping pointed out the need to solidly promote the ecological governance, protection, and high-quality development of the Yellow River Basin, in order to promote the steady improvement in the “quantity” and “quality” of economic and social development. If the construction of an ecological civilization leads to high-quality development, the focus is to optimize the ecological security pattern and improve regional ecosystem services. Therefore, comprehensively measuring the high-quality development level of the ecological and economic belt along the Yellow River in Ningxia is of great significance for building a leading area for high-quality development in the Yellow River Basin.

The nature and connotation of the quality of economic development have been thoroughly studied by domestic and foreign scholars from different perspectives. Kamaev [1] first defined the meaning of the quality of economic development, pointing out that economic development is composed of two dimensions, speed and quality, and it is unscientific to focus only on the quantity of economic development, while efficiency is the quality of economic growth. North [2] pointed out that institutional factors are key elements of economic growth and development, and efficient institutions can promote economic growth and development. Barro [3] further defined the quantity and quality of national economic development and pointed out that the level of economic development in a country or region is not simply identified by the quantity of the GDP. The level of national economic development should cover a very rich range, and the quality of economic development is closely related to economic culture, education, health care, social systems, and other aspects. Thomas [4] pointed out that the quality of development is a supplement to the speed of development, and it is also an important part of the development structure, such as job allocation, environmental protection capacity, social governance structure, and national risk management. Lozano [5] defines sustainability in three dimensions, combining the concept of TBL with an intergenerational perspective that seeks a balance between the economic, environmental, and social dimensions across different time horizons (short-, long-, and longer-future term). Mishan [6] studied the relationship between economic development and social welfare, and pointed out that although economic development is indeed beneficial for the improvement of social income, it does not necessarily mean that the corresponding status, income, or welfare index of the economy and society increase simultaneously. Tian et al. [7] believe that high-quality development is a brand new development concept, model, and strategy, with a quality-oriented measurement and evaluation standard, and a new development concept that highly integrates innovation, coordination, green, openness, and shared dimensions. Li et al. [8] closely linked the connotation of high-quality development and the changes in the main contradictions of the new-era society and constructed a high-quality evaluation index system from five aspects: economic vitality, innovation efficiency, green development, people’s lives, and social harmony. Han et al. [9] proposed that high-quality economic development is an efficient economic cycle, that is, the reproduction link of four cycles of production, distribution, exchange, and consumption.

In the construction of the evaluation index system for economic development quality, experts and scholars at home and abroad have adopted various methods. Some experts and scholars use a single indicator, while others use composite indicators: Agbola [10] focused on foreign direct investment and human capital; Ghosh et al. [11] focused on factors such as banking globalization and information and communication technology; Mohammad et al. [12] studied factors such as the Land Surface Ecological Status. Bai et al. [13] pointed out that there is a significant spatial spillover effect in China’s economic growth by creating an entropy calculation program, using a spatial econometric model and the Moran index. David [14] used a nonparametric method to explore the quality of economic development in Central and Eastern European countries, and used the Moran index and the spatial econometric economic model to show the unbalanced dynamic evolution trend of per capita income. Papalia et al. [15] used a program to determine the entropy to understand the convergence characteristics of economic growth rates in various developed countries, while some researchers constructed indicators to measure the quality of economic development. For example, Niebel [16], Zeira [17], and Aisen et al. [18] used various econometric models to explore the specific effects of economic development drivers on the quantity of economic growth, including education level, labor quality, globalization openness, and modern communication technology, The degree of political stability was included in the evaluation index. Xu et al. [19] constructed a five-dimensional evaluation index system and used the entropy weight method to measure the high-quality development level of nine provinces and regions in the Yellow River Basin. The results showed that the overall high-quality development level of the Yellow River Basin showed an upward trend. Wang et al. [20] used the input–output model to analyze the water resource carrying capacity of eight provinces in the Yellow River Basin. The results showed that there is a strong spatial heterogeneity in the water resource carrying capacity of the upper, middle, and lower reaches of the Yellow River Basin. Huang et al. [21] constructed an evaluation index system for high-quality development in the Yellow River Basin based on the “Five Development Concepts”. The results showed that the overall level of high-quality development in the Yellow River Basin is not high, and the level of high-quality development in the upstream, middle, and downstream regions is gradually increasing.

The Ningxia Yellow River Economic Belt plays an important role in the economic and social construction and ecological security system of the entire region and is of great significance for local ecological conservation, economic coordination, and social culture [22]. At present, the comprehensive evaluation of the high-quality development of the regional ecological economy is mostly based on provinces and cities [23], while the county economy is the basic unit of the national economy that connects with the urban economy, supports each other, and runs relatively independently. Exploring the current situation of the high-quality development of the county economy is crucial for the effective implementation of the high-quality development of the regional economy. Therefore, based on the essence and connotation of high-quality development, this paper constructs an evaluation system of the high-quality development level of the Ningxia Ecological Economic Belt along the Yellow River from five dimensions: innovation, coordination, green, openness, and shared. A factor analysis and cluster analysis were used to comprehensively evaluate the high-quality development level of 10 counties along the Yellow River Ecological Economic Belt in Ningxia in 2020, and the spatiotemporal evolution characteristics of the high-quality development level of each county during 2014–2020 were explored by combining a multi-indicator panel data factor analysis and entropy weight TOPSIS method, in order to provide future development direction and countermeasure suggestions for the ecological and economic belt along the Yellow River in Ningxia to enhance the level of high-quality development.

2. Materials and Methods

2.1. Study Area

The Ningxia ecological and economic zone along the Yellow River is located in an important position in the domestic section of the new Eurasian Land Bridge, and it is a strategic place connecting the east to the west and the northwest of China, with obvious location advantages. Ningxia’s ecological and economic belt along the Yellow River takes the Yellow River as the hub, the Yellow River diversion irrigation area as the support, and Yinchuan, the capital city with close geographical proximity, convenient transportation, and high economic correlation, as the center; the scope includes Zhongwei City (Shapotou District, Zhongning County), Wuzhong City (Litong District, Qingtongxia City), Yinchuan City (Lingwu City, Yongning County, Xingqing District, Helan County), and Shizuishan City (Pingluo County, Huinong District). The region covers Tengger Desert, Helan Mountains, Yinchuan Plain, lakes, and wetlands. The total area of the region is 28,700 km², accounting for 43% of the total area of Ningxia, but 80% of the cities and towns in Ningxia are concentrated. In 2021, the total population of the region was 3.661 million, accounting for 50% of the total population of Ningxia, creating over 60% of Ningxia’s GDP and fiscal revenue. It is a strategic highland and main growth pole for Ningxia’s economic development. The distribution map of counties along the Yellow River Ecological Economic Belt in Ningxia is shown in Figure 1.

2.2. Construction of Evaluation Index System

The evaluation indicators of this paper mainly draw on the research of Ren et al. on the high-quality development of the Yellow River Basin [24]. Based on the “Five Development Concepts”, a total of 17 indicators from five dimensions of innovative development, coordinated development, green development, open development, and shared development are used to build the high-quality development evaluation system of the Ningxia Ecological Economic Belt along the Yellow River. Among them, innovation and development is the core driving force for high-quality economic development; coordinated development is an inherent requirement for high-quality development; green development is a prerequisite for high-quality development, development that is in line with nature and promotes the harmonious coexistence of human beings and nature, and development that achieves the greatest economic and social benefits at the cost of the least amount of resources and the environment; open development is the sine qua non condition for the realization of high-quality development; and shared development is the fundamental pursuit of high-quality development. The specific evaluation indicator system is shown in

Table 1. Evaluation index system for the high-quality development of the ecological and economic belt along the Yellow River in Ningxia.

	Primary Indicators	Secondary Indicators	Sign	Indicator Attribute
High-quality development level	Innovation development	Science and technology expenditure/General public budget expenditure	X11	+
		R&D expenditure as a percentage of the GDP	X12	+
		Number of invention patents per 10,000 people	X13	+
	Coordinated development	Proportion of urban population to total population	X21	+
		Coefficient of variation in resident income	X22	+
		Value ratio of primary industry to tertiary industry	X23	+
	Green development	Energy conservation and environmental protection expenditure/General public budget expenditure	X31	+
		Utilization rate of industrial solid waste	X32	+
		Industrial wastewater treatment rate	X33	+
		Comprehensive Index of Environmental Air Quality	X34	−
	Open development	Total imports and exports	X41	+
		The proportion of utilizing foreign investment in the GDP	X42	+
		Information transmission and information technology service industry/General public budget expenditure	X43	+
	Shared development	Number of beds in health facilities per thousand people	X51	+
		Per capita consumption expenditure of urban residents/Per capita consumption expenditure of rural residents	X52	−
		Per capita disposable income growth rate/GDP growth rate of residents	X53	+
		The proportion of social security and employment expenditure to local fiscal budget expenditure	X54	+

.

2.3. Data Sources

The data mainly come from the Ningxia Statistical Yearbook from 2015 to 2021, as well as statistical yearbooks, statistical bulletins, and other statistical data from 10 counties along the Yellow River Ecological Economic Belt in Ningxia.

2.4. Research Methods

A total of five research methods are used in this paper and the step-by-step flowchart is shown in Figure 2.

2.4.1. Factor Analysis Method

Factor analysis [25] involves representing multivariate variables as salient variables with strong correlations. By constructing a factor model with a single hidden variable to represent the information of multiple variables, the analysis of multidimensional indicators becomes clearer and clearer. The factor analysis model is as follows:

$$\{\begin{array}{c}{x}_1={\mu }_1+{\alpha }_{11}{f}_1+\dots +{\alpha }_{1m}{f}_m+{\epsilon }_1\\ x_2={\mu }_2+{\alpha }_{21}{f}_1+\dots +{\alpha }_{2m}{f}_m+{\epsilon }_2\\ ⋮\\ x_p={\mu }_p+{\alpha }_{p1}{f}_1+\dots +{\alpha }_{pm}{f}_m+{\epsilon }_p\end{array}$$

(1)

In the formula, μ₁, μ₂, …, μ_p is the mean of x₁, x₂, …, x_p; f₁, f₂, …, f_m is the common factor; and ε₁, ε₂, …, ε_p is a special factor.

2.4.2. Multi-Indicator Panel Data Factor Analysis

In order to explore the spatiotemporal evolution characteristics of the high-quality economic development in the counties along the Yellow River in Ningxia from 2014 to 2020, a traditional factor analysis is obviously not applicable, so a multi-indicator panel data factor analysis is used for this research [26]. Due to the differences in data among different indicator units in the indicator system, which do not have comparability and uniformity, in order to avoid bias in the evaluation results, data standardization is carried out before analyzing the data. This article adopts the Z-score standardization method, and the formula is as follows:

$$Z_{ij}=\frac{X_{ij}-{\mu }_j}{{\sigma }_j}(i=1,2,\mathrm{3...10})$$

(2)

In the formula, Z is the value of the jth indicator of the ith county after dimensionless processing; X_ij is the original value of the corresponding county; μ_j is the average of the corresponding ten counties; and δ_j is the data difference of the corresponding index.

After standardizing the data, an SPSS factor analysis was also used to process the data and calculate the scores of each factor.

2.4.3. Cluster Analysis

Cluster analysis, also known as group analysis, is essentially a concept of “birds of a feather flock together” [27]. It is a multivariate statistical analysis technique that classifies samples and indicators. For complex multidimensional information, cluster analysis can be used to aggregate information, thereby standardizing complex data structures.

2.4.4. Entropy Weight TOPSIS Method

The current research on the TOPSIS method has been relatively in-depth, and some scholars have improved it by combining other methods, such as TOPSIS-OPA, entropy weight TOPSIS, etc. [28,29], which have improved the TOPSIS method from different perspectives and made the research method more reliable. The entropy weight TOPSIS method combines the advantages of the entropy weight method and TOPSIS method [29], which can effectively avoid the subjective influence of the weight setting, thus making the weight setting of indicators more objective and reasonable. The following is a five-step introduction to the application process of the entropy weight TOPSIS method.

(1) Standardized processing.

$$\mathrm{Positive} \mathrm{indicators}: x_{ij}^{\prime }=\frac{x_{ij}-\min (x_j)}{\max (x_j)-\min (x_j)}$$

(3)

$$\mathrm{Negative} \mathrm{indicator}: x_{ij}^{\prime }=\frac{\max (x_j)-x_{ij}}{\max (x_j)-\min (x_j)}$$

(4)

(2) Based on the standardized indicator value P_ij, the information entropy e_j of each indicator is calculated.

$$P_{ij}=\frac{x_{ij}^{\prime }}{{\displaystyle \sum _{\alpha }{\displaystyle \sum _i{x}_{ij}^{\prime }}}}$$

(5)

$$e_j=-k{\displaystyle \sum _i{P}_{ij}\ln P_{ij}}$$

(6)

In the formula, k > 0, making e_j ≥ 0.

(3) Based on the information entropy e_j, the weight W_j of each index is calculated, and the weighting matrix of the measurement index is constructed.

$$w_j=\frac{1-e_j}{{\displaystyle \sum _j(1-e_j)}}$$

(7)

$$X_{ij}=x_{ij}^{\prime }{w}_j$$

(8)

In the formula, m represents the number of indicators.

(4) Calculate the Euclidean distance, determine the optimal and worst solutions, and the corresponding Euclidean distances D_i⁺ and D_i⁻.

$$D_i^{+}=\sqrt{{\displaystyle \sum _j{(Z_{ij}-Z_j^{\zeta +})}^2}}$$

(9)

$$D_i^{-}=\sqrt{{\displaystyle \sum _j{(Z_{ij}-Z_j^{\zeta -})}^2}}$$

(10)

(5) Calculate the proximity Ci between each measurement scheme and the ideal scheme.

$$C_j=\frac{D_i^{-}}{D_i^{+}+D_i^{-}}$$

(11)

2.4.5. Kernel Density Estimation

Kernel density estimation is a nonparametric test method [30]. The calculation formula is as follows:

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

(12)

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

(13)

In the formula, f(x) is the density function of a random variable; K(x) is the kernel function; N is the sample size; x_i is the sample value, which follows an independent and identically distributed distribution; x is the mean; and h is the bandwidth.

3. Results and Analysis

3.1. Analysis of the Measurement of the Level of High-Quality Development in 2020

3.1.1. Factor Analysis

Before the factor analysis, a KMO and Bartlett’s sphere test are first conducted. Only when the KMO test coefficient is >0.5 and the p-value is <0.05 can the evaluation index data have reliability (the significance probability of the statistical values in the Bartlett’s sphere test). According to the test results, the Bartlett’s spherical test statistic p = 0.000 (<0.050), and the KMO test value is 0.713. Based on the evaluation principle, it is believed that the variables selected in this article are suitable for the requirements of factor analysis modeling. When the variance contribution rate of the common factor is higher than 85%, the selected factor can be selected. When the fifth common factor is extracted, the cumulative variance contribution rate is 87.469%. According to the extraction principle, the first five factors are selected as evaluation indicators, and the results are shown in

Table 2. Factor extraction results.

Component	Initial Eigenvalue			Extract the Sum of Squares of the Load			Sum of Squares of the Rotational Load
	Total	Variance Contribution Rate (%)	Accumulated Contribution Rate (%)	Total	Variance Contribution Rate (%)	Accumulated Contribution Rate (%)	Total	Variance Contribution Rate (%)	Accumulated Contribution Rate (%)
1	4.869	28.641	28.641	4.869	28.641	28.641	4.617	27.158	27.158
2	3.542	20.833	49.474	3.542	20.833	49.474	3.643	21.432	48.590
3	2.443	14.370	63.844	2.443	14.370	63.844	2.436	14.330	62.920
4	2.321	13.654	77.497	2.321	13.654	77.497	2.349	13.818	76.738
5	1.695	9.971	87.469	1.695	9.971	87.469	1.824	10.731	87.469

.

After obtaining the factor extraction results, in order to obtain factors with stronger explanatory power, the maximum variance method was used for the orthogonal rotation of factor loads to obtain the rotation component matrices of five factors, as shown in

Table 3. Rotating component matrix.

Variable	Component
	Principal Component 1	Principal Component 2	Principal Component 3	Principal Component 4	Principal Component 5
X11	−0.806	0.229	0.247	0.010	0.908
X12	0.142	0.071	0.320	0.189	0.812
X13	−0.775	0.164	0.496	−0.202	0.106
X21	0.871	−0.359	0.070	−0.499	−0.052
X22	0.813	−0.153	−0.373	0.168	0.098
X23	0.024	0.090	−0.277	0.676	0.145
X31	0.506	−0.517	0.088	−0.145	−0.125
X32	0.166	−0.102	0.085	0.816	0.312
X33	0.828	0.119	−0.022	0.888	0.089
X34	0.041	0.029	−0.770	0.211	0.256
X41	−0.400	0.380	0.775	0.011	0.112
X42	−0.514	0.263	0.862	0.166	−0.235
X43	−0.451	0.494	0.309	−0.185	0.066
X51	0.257	0.630	−0.570	−0.023	0.212
X52	−0.224	0.916	0.099	−0.056	0.154
X53	0.236	0.412	−0.453	0.156	0.661
X54	0102	−0.236	0.367	0.465	−0.653

.

According to the results in Table 2 and Table 3, the variance contribution rate of the first common factor principal component extracted is 27.158%, mainly reflecting the proportion of urban population to the total population, the coefficient of variation in residents’ income, and the industrial wastewater treatment rate. Therefore, it can be named as the coordinated development factor. For the second common factor, the principal component variance contribution rate is 21.432%, mainly reflecting two indicators: the number of beds in health institutions per thousand people and the proportion of education funds to the GDP. Therefore, it can be named the shared development factor. The third common factor, with a principal component variance contribution rate of 14.33%, mainly reflects three indicators: total import and export volume, utilization of foreign investment, and the environmental air quality index. Therefore, it can be named as the open development factor. The fourth common factor, with a variance contribution rate of 13.819%, mainly reflects two indicators: the industrial solid waste utilization rate and industrial wastewater treatment rate. Therefore, it can be named as the green development factor. The fifth common factor variance contribution rate is 10.731%, which mainly reflects the proportion of scientific and technological expenditure to general public budget expenditure and the proportion of R&D expenditure to the GDP. Therefore, it can be named the innovation development factor.

We use A₁, A₂, A₃, A₄, and A₅ to represent the principal components of the five common factors of coordination, shared, openness, green, and innovation, respectively. The contribution percentages of the five common factors can be used as their respective weights. Then, through weighted averaging, the evaluation model for the high-quality development level of the ecological and economic belt along the Yellow River in Ningxia is obtained as follows:

$$A=(27.158\%A_1+21.43\%A_2+14.33\%A_3+13.819\%A_4+10.731\%A_5)/87.469\%$$

(14)

By constructing an evaluation model for the high-quality development level of the ecological and economic belt along the Yellow River in Ningxia using the above formula, the scores of various factors and the comprehensive scores of the high-quality development level of 10 counties were obtained. The specific results are shown in

Table 4. Ranking results of the comprehensive evaluation of the high-quality development of the Ningxia Ecological Economic Belt along the Yellow River component matrix.

County Level	Coordinated Development		Shared Development		Open Development		Green Development		Innovation-Driven Development		Comprehensive Evaluation
	Score	Ranking	Score	Ranking	Score	Ranking	Score	Ranking	Score	Ranking	Score	Ranking
Xingqing	0.191	5	2.029	1	1.253	1	−0.523	6	1.549	1	0.869	1
Yongning	0.753	2	−1.430	10	0.047	6	−1.818	10	−0.898	7	−0.506	10
Helan	1.061	1	−0.957	9	1.005	2	−0.583	7	−0.286	4	0.132	3
Lingwu	−0.619	8	−0.595	8	−1.040	8	1.112	1	0.319	2	−0.294	7
Huinong	−0.632	9	−0.379	7	0.967	3	0.014	5	0.157	3	−0.109	4
Pingluo	−0.654	10	0.663	3	0.528	4	0.699	2	−2.198	10	−0.113	5
Litong	0.526	4	0.941	2	0.129	5	−0.683	9	−1.254	8	0.153	2
Qingtongxia	−0.398	7	−0.092	6	−1.109	9	0.283	4	−1.369	9	−0.451	9
Shapotou	0.575	3	−0.027	5	−0.856	7	−0.616	8	−0.755	6	−0.158	6
Zhongning	−0.279	6	0.193	4	−1.811	10	0.322	3	−0.660	5	−0.366	8

and Figure 3.

From Table 4 and Figure 3, it can be seen that the overall level of the economic development quality in the 10 counties along the Yellow River ecological and economic belt in Ningxia is relatively low. Among the 10 counties, only Xingqing District, Litong District, and Helan County had comprehensive factor scores greater than 0, and no city and county had comprehensive factor scores greater than 1, and the other 7 counties had comprehensive factor scores less than 0. Xingqing District ranks first in innovative development, open development, and shared development, and its scores are significantly higher than those of the other nine counties. However, it belongs to the middle level in coordinated development and green development. The comprehensive scores of Litong District and Helan County are similar, with the difference being that Helan County places more emphasis on coordinated and open development, while Litong District’s main advantage lies in shared development. The comprehensive evaluation scores of Huinong District, Pingluo County, Shapotou District, and Lingwu City are all less than 0, indicating that the comprehensive level of high-quality development in these areas is lower than the average level. Zhongning County, Qingtongxia City, and Yongning County scored less than 0 in both the individual and comprehensive evaluations of the five factors, indicating significant differences in the level of high-quality development compared to the other seven counties. This indicates that there are significant deficiencies in the level of high-quality development in these regions.

Through the factor analysis, the score of each factor in 10 counties was obtained. Positive and negative values represent that the score is higher or lower than the average level, and the variance contribution rate of each common factor represents the influence of this common factor on the dependent variable. In order to compare the level of high-quality development in each dimension of the 10 counties, the scores of five factors were normalized using SPSS, and the results were obtained as shown in Figure 4.

(1) Coordinated development level dimension: From Figure 4a, it can be seen that in the measurement of the coordinated development level, Helan County and Yongning County are in the leading position, Shapotou District and Litong District are in the middle, and the other six counties are relatively backward, indicating that the coordinated development level of the cities along the Yellow River ecological economic belt in Ningxia varies greatly and is uneven. Helan County has the largest single high-quality dairy farm in Ningxia and is an important high-quality milk source in China. As early as 2018, Helan County introduced relevant policies to promote the high-quality development of the dairy industry, supported the establishment of livestock- and poultry-breeding farms and a variety of quality-testing centers, and focused on building an important national breeding base for high-quality dairy cows, achieving breakthroughs in high-quality breeding. Not only that, but Helan County is also the “first county and district in northwest fisheries”. Based on the advantages of fishery resources, Helan County vigorously develops a green aquaculture industry, adjusts aquaculture structure, accelerates the transformation and upgrading of fisheries, increases the proportion of famous and high-quality varieties of aquaculture, and gradually realizes the optimization of fishery industry structure and the improvement in aquatic quality and efficiency. The coordinated development level of Pingluo County, Huinong District, and Lingwu City is relatively low; the reason is that the value-added development of these three cities in primary industries is relatively low, the urban population growth rate has slowed down, and the urbanization process has slowed down.

(2) Shared development level dimension: It can be seen from Figure 4b that there is a large gap in the sharing development level of the 10 counties. Xingqing District has focused on promoting the high-quality and balanced development of compulsory education and has achieved remarkable results in promoting the quality and upgrade of education work. It pays attention to the data sharing of medical institutions, policy implementation, and leading the healthy development of the medical industry. In 2019, Litong District was identified as an “Internet and Education” demonstration county. With this opportunity, Litong District has implemented an education quality improvement plan, strengthened the information construction of education, created high-quality classrooms, and greatly improved the level and quality of education. There are some problems in Yongning County, such as an insufficient publicity of public health services, shortage of township health personnel, low management level, inadequate infrastructure of community health stations, and low service quality, which seriously limit the improvement in the shared development level of Yongning County.

(3) Open development level dimension: From Figure 4c, it can be seen that the measurement values of the open development level in Xingqing District, Helan County, and Huinong District are relatively high, located in the first gradient. Pingluo County, Litong District, and Yongning County are located in the second gradient, while Shapotou District, Lingwu City, Qingtongxia City, and Zhongning County are located in the third gradient. Xingqing District and Helan County have fully utilized their excellent geographical location to build a new type of cordial and clean relationship between government and business and commercial interests, optimize the investment environment, attract more foreign capital injection, and increase the total import and export trade volume. Shapotou District has a national-level desert ecological nature reserve, but its progress in foreign trade is relatively slow, and it still needs to fully utilize its own advantages to further promote the process of opening up and developing.

(4) Green development level dimension: From Figure 4d, it can be seen that Lingwu City has the highest measure of the green development level, followed by Pingluo County, Zhongning County, Qingtongxia City, Huinong District, Xingqing District, Helan County, Shapotou District, Litong District, and Yongning County. Lingwu City has certain advantages in energy conservation and environmental protection expenditure, sewage treatment, and urban ecological greening compared to the other nine counties. It indicates that Lingwu City is at a high level in the ecological and economic belt along the Yellow River in Ningxia in terms of green and low-carbon production, and the concept of green development benefiting the people. Pingluo County is close to the Helan Mountain Range, transitioning from “relying on mountains to eat” to “creating scenery based on mountains”. In the past two years, through vigorous afforestation and urban greening, not only has the ecological environment been greatly improved but the county has also embarked on a green development path, becoming a beautiful background for high-quality development. Yongning County and Litong District mainly focus on industry and agriculture. How to effectively solve industrial wastewater, exhaust gas, and agricultural nonpoint source pollution has become the primary task for them to overcome the insufficient level of green development.

(5) Innovation development level dimension: From Figure 4e, it can be seen that Xingqing District, as the main urban area of Yinchuan City, has abundant higher education resources and relatively complete high-tech industries. It places scientific and technological innovation in a prominent position, enabling technology to empower high-quality economic development in its jurisdiction. Compared to the other nine counties, there are significant differences in the level of innovation and development. In the future, it is necessary to vigorously implement an innovation-driven development strategy and increase funding and personnel investment in scientific research and the technology service industries.

3.1.2. Cluster Analysis

In order to further analyze the differences in the high-quality development level of the 10 counties along the Yellow River in Ningxia in 2020, a cluster model was constructed. Combined with the comprehensive scores of the factor analysis of each county, they were divided into regions with a high economic quality development level and regions with a medium and low level. The results are shown in

Table 5. Classification of high-quality development levels in the ecological and economic belt along the Yellow River in Ningxia.

Category	City/County/District
Higher regions	Xingqing District
General regions	Litong District, Huinong District, Pingluo County, Helan County, Lingwu City, Shapotou District
Lower regions	Yongning County, Qingtongxia City, Zhongning County

. In order to more intuitively display the distribution differences of the high-quality development levels among different counties, ArcGIS maps were used for classification, and the results are shown in Figure 5.

Xingqing District is the only area with a high level of high-quality economic development along the Yellow River in Ningxia, and its score in the five evaluation factors is relatively high, ranking first in innovative development, open development, and shared development. Xingqing District is the science and technology, culture, education, economy, finance, and trade logistics center of Yinchuan City and even Ningxia Hui Autonomous Region. It can be seen that Xingqing District focuses on technological innovation, open cooperation, and people’s livelihood services to lead high-quality development.

Areas with a medium level of high-quality economic development comprise six counties, including Litong District, Huinong District, Pingluo County, Helan County, Lingwu City, and Shapotou District. Litong District ranks second in the comprehensive factor score, with its shared development and coordinated development ranking relatively high. This is closely related to Litong District’s efforts to build public service facilities and effectively promote the high-quality and balanced development of compulsory education. Huinong District and Helan County both scored and ranked relatively high on the open development factor. Both regions have always been mainly developing agriculture. In recent years, by expanding the opening-up of characteristic agriculture to the outside world and promoting the scale of open emerging industries, they have ultimately achieved an increase in total import and export volume and the inflow of foreign investment. Pingluo County ranks second, third, and fourth in terms of green, shared, and open development, respectively. This is due to the effective implementation of the Pingluo County Ecological County Construction Plan. At the same time, Pingluo County is also a national demonstration county for comprehensive tourism. The development of tourism not only makes it a breakthrough in open development but also brings more opportunities for local development. Lingwu City ranks first out of the 10 counties in terms of green development, and its scores are far ahead of the other 9 counties. It has advantages in environmental pollution control and resource sustainable development, but its scores in coordinated and open development are relatively backward, and further improvement is needed in terms of people’s livelihoods.

Areas with low levels of high-quality economic development include Yongning County, Qingtongxia City, and Zhongning County. These three regions have relatively low and basically less than 0 scores in both the individual and comprehensive evaluations of the five factors, indicating that these three cities and counties are lower than the average level in innovation, coordination, green, open, and shared development. Yongning County ranks 10th in both green and shared development. Yongning County mainly focuses on industrial development and achieving green industrial development is currently the primary goal of improving the level of high-quality development in Yongning County. Zhongning County ranks 3rd and 4th in green and shared development, respectively, but 10th in open development. It can be seen that the main obstacle to high-quality development in Zhongning County is to promote open development. As the “hometown of Chinese goji berries”, Zhongning County should fully utilize its industrial advantages to promote Zhongning goji berries to the world.

3.2. Temporal and Spatial Evolution Characteristics of the High-Quality Development Level

3.2.1. Multi-Indicator Panel Data Factor Analysis

In order to explore the spatiotemporal evolution characteristics of the high-quality development level along the Yellow River Ecological Economic Belt of Ningxia, based on the index system constructed above, a multi-indicator panel data factor analysis was used to measure the economic high-quality level of each county during 2014–2020. In order to make the measurement results more reasonable, a traditional factor analysis was first used to obtain the variance contribution rate and cumulative variance contribution rate of the common factors extracted in each year, and the components of the common factors were determined, as shown in

Table 6. Annual factor variance contribution rate and cumulative variance contribution rate.

Component	Year
	2014	2015	2016	2017	2018	2019	2020
F1 (%)	27.254	26.594	29.561	27.564	28.121	26.264	27.158
F2 (%)	19.856	20.633	20.512	20.645	21.698	20.598	21.432
F3 (%)	14.252	15.261	13.325	17.465	14.621	15.654	14.330
F4 (%)	13.018	12.642	12.956	11.563	12.854	11.569	13.818
F5 (%)	10.796	11.011	9.888	11.328	9.521	8.965	10.731
Accumulated contribution rate (%)	85.176	86.141	86.242	88.565	86.815	83.050	87.469

.

From Table 6, it can be seen that the number of common factors extracted each year is five, and the cumulative variance contribution rate reaches over 80%. This indicates that the five common factors extracted can well reflect all evaluation indicators. Based on the measurement results of the high-quality development of the ecological and economic belt along the Yellow River in Ningxia in 2020 and the principal component contribution rates of other common factors in various years, the five common factors are also named as the five factors: coordination, shared, openness, green, and innovation. Due to the consistency of the 17 evaluation indicators selected from 2014 to 2020, the variance contribution rates and cumulative variance contribution rates of the five common factors remained at a relatively stable level. In order to explore the changes in the time dimension of the high-quality development level of the 10 counties along the Yellow River ecological and economic belt in Ningxia, the variance contribution rates of the five common factors for each year from 2014 to 2020 were averaged as the common factors for the high-quality development level of each year. From this, it can be concluded that the evaluation model for the high-quality development level of the ecological and economic belt along the Yellow River in Ningxia from 2014 to 2020 is as follows:

$$F=(27.502\%F_1+20.768\%F_2+14.987\%F_3+12.631\%F_4+10.32\%F_5)/86.208\%$$

(15)

By constructing an evaluation model for the high-quality development level of the ecological and economic belt along the Yellow River in Ningxia from 2014 to 2020 using the above formula, the comprehensive scores of the high-quality development level of the economy in the 10 counties and regions in each year were obtained. The specific results are shown in

Table 7. Factor analysis and measurement results of the high-quality development level in counties along the Yellow River Economic Belt in Ningxia from 2014 to 2020.

Number	County	Factor Score	2014	2015	2016	2017	2018	2019	2020	Mean Value
1	Xingqing	Score	0.679	0.691	0.726	0.742	0.786	0.814	0.852	0.756
		Sort	1	1	1	1	1	1	1	1
2	Yongning	Score	−0.656	−0.633	−0.612	−0.592	−0.568	−0.532	−0.484	−0.582
		Sort	10	10	10	10	10	10	10	10
3	Helan	Score	0.071	0.081	0.095	0.102	0.126	0.131	0.141	0.107
		Sort	2	2	2	3	3	3	3	2
4	Lingwu	Score	−0.346	−0.365	−0.378	−0.376	−0.351	−0.321	−0.289	−0.347
		Sort	7	7	7	7	7	7	7	7
5	Huinong	Score	−0.254	−0.231	−0.222	−0.203	−0.185	−0.154	−0.112	−0.194
		Sort	4	4	5	4	5	4	4	4
6	Pingluo	Score	−0.268	−0.241	−0.215	−0.206	−0.182	−0.156	−0.119	−0.198
		Sort	5	5	4	5	4	5	5	5
7	Litong	Score	0.034	0.056	0.084	0.116	0.133	0.142	0.157	0.103
		Sort	3	3	3	2	2	2	2	3
8	Qingtong	Score	−0.612	−0.596	−0.584	−0.567	−0.534	−0.496	−0.453	−0.548
		Sort	9	9	9	9	9	9	9	9
9	Shapotou	Score	−0.294	−0.273	−0.256	−0.248	−0.223	−0.189	−0.161	−0.235
		Sort	6	6	6	6	6	6	6	6
10	Zhongning	Score	−0.549	−0.538	−0.512	−0.499	−0.426	−0.401	−0.369	−0.471
		Sort	8	8	8	8	8	8	8	8

and Figure 6.

3.2.2. Entropy Weight TOPSIS Method

Based on the indicator system constructed in Table 1, the entropy weight TOPSIS method is used to calculate the high-quality development level of each county in the ecological and economic belt along the Yellow River in Ningxia from 2014 to 2020. The results are shown in

Table 8. Entropy weight TOPSIS method measurement results of the high-quality development level in counties along the Yellow River Economic Belt in Ningxia from 2014 to 2020.

County Level	2014	2015	2016	2017	2018	2019	2020
Xingqing	0.7216	0.7689	0.7856	0.8216	0.8879	0.9215	0.9531
Yongning	0.4555	0.4612	0.4716	0.4951	0.5316	0.5561	0.5813
Helan	0.6791	0.6888	0.7012	0.7169	0.7655	0.7956	0.8144
Lingwu	0.4612	0.4766	0.4986	0.5179	0.5877	0.6185	0.6315
Huinong	0.5879	0.6215	0.6515	0.6749	0.7156	0.7749	0.7945
Pingluo	0.5412	0.5855	0.6136	0.6346	0.6845	0.7125	0.7298
Litong	0.5689	0.6122	0.6433	0.6518	0.7765	0.8122	0.8312
Qingtongxia	0.4619	0.4715	0.4812	0.5015	0.5578	0.5677	0.5766
Shapotou	0.5169	0.5549	0.5621	0.6045	0.6421	0.6948	0.7012
Zhongning	0.4712	0.4856	0.5088	0.5166	0.5679	0.5978	0.6122

and Figure 7.

According to the multi-index panel data factor analysis, Table 7 and Table 8 and Figure 6 and Figure 7 were obtained using the entropy weight TOPSIS method; the two methods analyzed the level of high-quality economic development along the Yellow River Ecological Economic Belt in Ningxia, and the ranking of each county was basically consistent. From 2014 to 2020, the change trend of the high-quality economic development level in the 10 counties along the Yellow River Ecological Economic Belt in Ningxia was also basically consistent, showing a steady rise on the whole. The difference lies in the fact that the positive or negative factor scores of the factor analysis of the multi-indicator panel data mean that the values are higher or lower than the whole research area. The higher the factor score, the higher the high-quality development level, which is similar to the entropy weight TOPSIS method, whose measure value is closer to 1, i.e., the higher the value, the higher the high-quality development level.

Overall, after 2017, the speed of the upgrading of the high-quality development level of Ningxia along the Yellow Ecological Economic Belt has gradually accelerated, mainly because the new expression of high-quality development was first put forward at the 19th National Congress of the Communist Party of China. The Ningxia region adheres to the new development concept, and supply-side structural reform is set as the main line, resolutely fights the “three tough battles”, vigorously implements the “three strategies”, and effectively promotes the high-quality economic development of the Ningxia region. In 2020, due to the impact of COVID-19, the way forward for high-quality development in all counties has been hindered, especially in Qingtongxia City, where the tourism industry has developed well, making it have a downward trend in coordinated development. However, with the effective implementation of epidemic prevention work, the high-quality development of the Ningxia Yellow River Ecological Economic Belt has slowed but has still maintained an upward trend. Xingqing District has always been in a leading position in terms of high-quality economic development, significantly higher than the other nine counties. This is mainly due to Xingqing District being the core area of the capital city of Yinchuan City, with its advantageous geographical location, the gathering of funds and research talents, and the accelerated development of emerging industries, continuously promoting its high-quality economic development. The high-quality economic development level of Huinong District and Pingluo County is in a relatively stable upward trend, mainly due to the continuous improvement in industrial transformation results, the good development of a real economy, increasing the contribution rate of the modern service industry to economic growth, and the accelerated growth rate of people’s income. Before 2017, the high-quality economic development level of Litong District was slightly lower than that of Huinong District, but the growth rate accelerated after 2017, surpassing Huinong District. This is consistent with the fact that since the 18th National Congress of the Communist Party of China, Litong District has continued to deepen supply-side structural reform and accelerate the pace of industrial transformation and development, resulting in a steady progress in the entire economy, a steady improvement in quality and efficiency, and continuous improvement in people’s quality of life.

3.2.3. Kernel Density Estimation

In order to further analyze the dynamic characteristics of the time evolution of the high level of development, a kernel density estimation was used to analyze the score of the factor analysis and the measurement of the multi-index panel data, and a distribution map of the high-quality development nuclear density of the ecological and economic belt along the Yellow River in Ningxia from 2014 to 2020 was obtained, as shown in Figure 8.

From Figure 8, it can be seen that over time, the nuclear density curve gradually shifts to the right, presenting a single-peak distribution feature. The peak height continues to rise, and the left tail of the curve concentrates toward the center. This indicates that the high-quality development level of the ecological and economic belt along the Yellow River in Ningxia is gradually improving, and cities with low levels of high-quality development are decreasing. Cities with high levels of high-quality development and economic development are increasing, and the high-quality development level of the urban economy is showing a decreasing trend. Specifically, the peak value of the kernel density estimation curve in 2016 is basically close to that in 2014. The center of the density function moves slightly to the right, the width shrinks, and the left tail shifts more to the right. On the one hand, it shows that the high-quality economic development level of Ningxia’s ecological and economic belt along the Yellow River has been greatly improved, and on the other hand, it also shows that regional differences are gradually narrowing. The curve of kernel density estimation shifted to the right in 2018 compared with that in 2016, and the peak value also increased slightly, which means that the difference of the high-quality development level in the study area is still further narrowing, and the overall high-quality development level still maintains an upward trend. Compared with 2018, the peak value in 2020 has significantly increased, and the center of the density function has significantly shifted to the right, with a narrower width. This indicates that the regional differences in the level of high-quality economic development at this stage are decreasing, and there is a significant acceleration in the improvement in high-quality economic development in some counties. After the concept of high-quality development was first proposed in 2017, various counties and regions have accelerated their pace toward high-quality development based on their own advantages. Huinong District, Helan County, and Pingluo County, which mainly focus on agriculture, have effectively created a new situation of agricultural transformation and upgrading, the integration of leisure agriculture and characteristic industries, and the development of agricultural product processing, Yongning County and Qingtongxia City, which focus on industry, continuously transform and enhance traditional industries, accelerate industrial transformation, increase investment in scientific and technological research and development, and build national high-tech enterprises. Each county has closely followed the pace of the times, followed the development of practice, and steadfastly pursued the path of high-quality development, achieving significant results. In June 2020, Ningxia took on the important task of building a leading area for ecological protection and high-quality development in the Yellow River Basin, which also provided an opportunity for high-quality economic development in the Ningxia region. With the comprehensive support of national policies, funds, manpower, and other aspects, the economy, society, ecology, and people’s livelihoods have been integrated, and coordinated development, guided by the construction of the leading area, has promoted the high-quality development of the ecological and economic belt along the Yellow River in Ningxia to achieve new breakthroughs.

4. Discussion

This paper used factor analysis and cluster analysis to estimate the high-quality development level of Ningxia along the Yellow River Ecological Economic Belt in 2020, combined with a multi-indicator panel data factor analysis and entropy weight TOPSIS method to compare and analyze the spatiotemporal evolution characteristics of the high-quality development of Ningxia along the Yellow River Ecological Economic Belt from 2014 to 2020. The research shows that the overall level of the high-quality development of Ningxia’s ecological and economic belt along the Yellow River is low, and there is a strong spatial heterogeneity. However, from 2014 to 2020, the level of high-quality development has maintained a steady upward trend, and the differences between the high-quality development levels in various counties have gradually decreased. This result is consistent with the research of Wang et al. on the high-quality development of the ecological and economic belt along the Yellow River in Ningxia in 2019 [22], but it did not analyze the high-quality development level of the ecological and economic belt along the Yellow River in Ningxia from the perspective of time evolution characteristics. Wen et al. [31] analyzed the differences in the high-quality development level of Ningxia’s economy from the five dimensions of innovation, coordination, green, openness, and shared development. They found that the fluctuation in the high-quality development level of Ningxia’s economy has been continuously improving since 2013, which is consistent with our research results. However, they found that regional differences have shown an expanding trend from the research on high-quality development in cities, which may be inconsistent with the research indicators and differences in the research objects adopted in this article.

The measurement results of the multi-indicator panel data can determine whether their level is higher or lower than the average level of the study area according to the positive or negative scores, but they cannot directly reflect the change in the growth amount, which can be supplemented by the comparative verification of the entropy weight TOPSIS method. Secondly, by using the same index to measure and analyze the research area, a single research method is avoided, and the credibility of the research results is enhanced.

However, there are still shortcomings in this study because the research object is a county. As the basic unit of the national economy in China, the county region has imperfect data acquisition, which also leads to the relatively small number of evaluation indicators selected in this paper, and there is a certain lack of representativeness. In the application of the multi-index panel data factor analysis and entropy weight TOPSIS method to analyze the spatiotemporal evolution characteristics of high-quality development in the study area, there is no analysis from multiple dimensions. Xu et al. [19] analyzed the temporal and spatial evolution characteristics of high-quality development levels in various provinces and regions of the Yellow River Basin from the five dimensions constructed, making the study of temporal and spatial evolution characteristics more comprehensive.

In the past period, Ningxia along the Yellow River ecological and economic zone has realized rapid economic development by relying mainly on the development model of high-intensity investment, resource, and energy development, but it has also paid a large price in terms of resource depletion and environmental pollution, and has accumulated many ecological risks and contradictions. Ningxia is an important industrial zone in the region, and its energy-based development model has the characteristics of structural homogenization, being heavy-duty and resource-based. Relying on an energy-based development model, Ningxia has not been fundamentally transformed, resulting in greater pressure on the ecological environment. In terms of agricultural development, the level of agricultural standardization is not high, the level of innovation is not enough, the quality of agricultural products is not high, the industrial chain is not long, the added value of agricultural products is low, and the rational use of agricultural fertilizers, as well as the overall level of production and operation in the livestock- and poultry-breeding areas, is low, which have all led to a more serious pollution of the surface sources of the rural environment. With the acceleration of industrialization and urbanization, the output per unit of industrial land in Ningxia’s cities along the Yellow River ecological and economic zone is significantly lower than the national average, the energy consumption per unit of the GDP is significantly higher than the national average, and the inefficiency of resource utilization makes the environmental quality of the Yellow River ecological and economic zone face serious challenges. As an economically underdeveloped region, Ningxia’s economic development is lagging behind, the ecological environment is more fragile, the cost of pollution control is relatively high, the introduction of high-level scientific and technological talents lacks attraction, and regional innovation and development momentum is insufficient. These have all led to an overall low level of high-quality economic development across the Ningxia region.

As the main theme of current social and economic development, high-quality development has profound implications that are worth exploring. The selection of evaluation indicators is still a question worth considering. Only by accurately identifying the problems in the development process can we effectively implement and solve the existing problems. This also highlights the importance of analyzing and studying the high-quality development level of a region from multiple dimensions. The selection of evaluation indicators still needs to be tailored to local conditions, and a reasonable evaluation indicator system should be constructed.

5. Conclusions

This paper constructs an evaluation index system for high-quality economic development along the Yellow River ecological and economic belt in Ningxia from five dimensions of innovation, coordination, green, openness, and sharing, with a total of 17 indicators, to comprehensively evaluate the high-quality economic development level of 10 counties along the Yellow River Ecological Economic Belt in Ningxia, dividing the study area into regions with high levels of high-quality economic development and those with medium and low levels with a cluster analysis method. We analyzed the spatiotemporal evolution characteristics of the high-quality economic development level of the 10 counties from 2014 to 2020 with a multi-index panel data factor analysis and entropy weight TOPSIS method. The conclusions are as follows:

(1) The overall level of high-quality development along the Yellow River Ecological Economic Belt in Ningxia in 2020 is not high, with only Xingqing District, Litong District, and Helan County in the 10 counties scoring greater than 0 on the comprehensive factor of the economic development level, with the other 7 counties scoring less than 0.

(2) Analyzing the five factors individually, it can be seen that the open and coordinated development factors score high among the counties, the green and shared development factors score in the middle, and the innovative development factor scores the lowest.

(3) The 10 counties are divided into three types of higher, general, and lower areas through a cluster analysis. Among them, only Xingqing District of Yinchuan City belongs to the higher-quality development area. Huinong District, Helan County, and Pingluo County, which mainly develop specialty agriculture and tourism, belong to the general high-quality development area, and Yongning County and Qingtongxia City, which mainly develop industry, belong to the lower high-quality development area, and the high-quality development level of the whole Ningxia Yellow River Ecological Economic Belt is characterized by strong spatial heterogeneity.

(4) The level of high-quality economic development in the 10 counties along the Yellow River Ecological Economic Belt in Ningxia from 2014 to 2020 shows a steady increase over time, and the regional variability in the level of high-quality economic development continues to decrease.

Based on the above research, the following countermeasures and suggestions are proposed for the high-quality development of the ecological economy along the Yellow River in Ningxia:

(1) Adhering to green development and vigorously developing the ecological industrial system: Green, circular, and low-carbon development should be vigorously promoted, green ecological industries should be vigorously developed, poverty alleviation and prosperity should be adopted as basic goals, and the integrated development of the primary, secondary, and tertiary industries in urban and rural areas should be promoted. The green, low-carbon, and circular development of industry should be promoted, the industrial structure should be optimized, the development of similar industrial clusters should be guided, product quality and efficiency should be improved, and the transformation and upgrading of large and small industries should be accelerated. The green and low-carbon modern service industry system should be vigorously built, a new situation of deep integration and mutual promotion between the modern service industry and various industries should be formed, and the overall quality and level of the service industry should be improved.

(2) Accelerating the implementation of an innovation-driven strategy and promoting industrial transformation and development: Technological innovation is the first driving force to promote economic growth, so we must adhere to the core position of innovation in the modernization drive, and a strategy of innovation-driven science and education should be implemented. A good development environment for innovation and entrepreneurship should be actively created, institutional barriers restricting innovation and development should be resolutely removed, scientific and technological innovation and model innovation should be promoted, an economic system with innovation as the main guide and support should be accelerated, and the internal driving force of high-quality development should be continuously enhanced, so as to achieve the high-quality development of Ningxia along the Yellow River Ecological Economic Belt.

(3) Continuously improving people’s livelihoods and enhancing people’s well-being: On the one hand, the growth of residents’ income should be synchronized with economic growth or be even slightly faster than economic growth. On the other hand, according to the level of economic and social development and local financial capacity, we should do our best and act within our capabilities to continuously improve the level of public services. We need to increase people’s income through multiple channels, further improve the income distribution system, increase the construction of supporting facilities such as community elderly care and medical security services, promote the construction of accessible environments in old urban communities and the aging adaptation of public facilities, and enhance the happiness of the people.

(4) Promoting the protection of natural forests and the afforestation of national territory, and strengthening soil erosion and desertification control: The ecological security barrier system should be improved, the integrity of the ecosystem, the continuity of geographical units, and the sustainability of economic and social development should be taken into account, and the construction and protection of important ecological corridors should be strengthened. The compensation mechanism for ecological protection should be improved, the transfer payment system for key regional ecological function areas should be improved, and transfer payments for key ecological function areas, major water source areas, and nature-protected areas should be intensified. The virtuous cycle of the ecosystem should be promoted, the quality and stability of the ecosystem should be continuously improved, and the mutual promotion and progress of the ecosystem and social and economic development should be realized.