Open AccessArticle

The Spatio-Temporal Evolution and Driving Factors of High-Quality Development in the Yellow River Basin during the Period of 2010–2022

Mengna Zhang

and

Shanzhong Qi

College of Geography and Environment, Shandong Normal University, Jinan 250358, China

Author to whom correspondence should be addressed.

Sustainability 2023, 15(18), 13512; https://doi.org/10.3390/su151813512

Submission received: 13 August 2023 / Revised: 7 September 2023 / Accepted: 7 September 2023 / Published: 9 September 2023

Download

Browse Figures

Figure 1
The evaluating framework of HQD level. "> Figure 2
Location map of study area. "> Figure 3
The HQD index of the YRB. "> Figure 4
Boxes map of <math display="inline"><semantics> <mrow> <msubsup> <mi>D</mi> <mi>i</mi> <mo>+</mo> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mi>D</mi> <mi>i</mi> <mo>−</mo> </msubsup> </mrow> </semantics></math>, and Ci in the YRB. "> Figure 5
The HQD index of five subsystems in 2010 ((a) Drivers, (b) Pressures, (c) Response, (d) State, (e) Impact). "> Figure 6
The HQD index of five subsystems in 2014 ((a) Drivers, (b) Pressures, (c) Response, (d) State, (e) Impact). "> Figure 7
The HQD index of five subsystems in 2018 ((a) Drivers, (b) Pressures, (c) Response, (d) State, (e) Impact). "> Figure 8
The HQD index of five subsystems in 2022 ((a) Drivers, (b) Pressures, (c) Response, (d) State, (e) Impact). "> Figure 9
Spatial differentiation of HQD index in (a) 2010, (b) 2014, (c) 2018, and (d) 2022. "> Figure 10
Elliptic distribution of standard deviation and center moving trajectory of HQD index in the YRB from 2010 to 2022. "> Figure 11
Spatial differentiation of GTWR model’s regression coefficient in (a) 2010, (b) 2014, (c) 2018 and (d) 2020. ">

Versions Notes

Abstract

The Yellow River Basin is an important ecological barrier and economic development area in China, but it faces some problems such as the degradation of its ecological quality and a lagging economic level. Promoting the high-quality development of the Yellow River Basin is the only way for China’s economic construction to enter into high-quality development, and an objective evaluation of the development quality of the study area is the premise for effectively improving this development quality. Based on panel data during the period of 2010–2022, a framework of drivers, pressures, state, impact, and a response model was used to build an index system. The index of high-quality development for each province in the Yellow River Basin was calculated using the entropy TOPSIS model. Further, the descriptive statistics method and standard deviation ellipse were applied to analyze the spatio-temporal characteristics of high-quality development in the study area, and the geographical detector and spatio-temporal geographical weighted regression model were employed to reveal the driving factors for this high-quality development in the Yellow River Basin. The results showed that (1) the high-quality development index of the Yellow River Basin was steadily improved over the study period, with an average annual growth rate of 3.024%. (2) The high-value area of the high-quality development level in the study area was distributed from northwest to southeast, with the high values of each subsystem tending to be spatially stable, as well as the spatial differences of the subsystems increasing. (3) The proportion of tertiary industry, per capita disposable income, rural–urban income ratio, per capita GDP, per capita highway mileage, and population were the main factors affecting the spatio-temporal evolution of high-quality development level in the Yellow River Basin, with average q values of 0.867, 0.938, 0.852, 0.781, 0.842, and 0.763, respectively. (4) Except for the negative effect of per capita GDP, the other five driving factors all had positive effects on the high-quality development level, with average values of 0.044, 0.068, 0.227, 0.064, and 0.215, respectively.

Keywords:

high-quality development; geographic detector; spatio-temporal geographical weighted regression; the Yellow River Basin

1. Introduction

The Yellow River Basin (YRB) plays an important role in China’s economic and social development and ecological security [1,2,3]. With the rapid development of urbanization and industrialization, the economic level of the YRB has been promoted rapidly and society has progressed continuously [4]. However, since 2010, some problems, such as large urban area expansion, an excessive population density, ecological deterioration, and resource shortage, as well as unbalanced and uncoordinated development, have been becoming increasingly serious and have become important factors in restricting the realization of high-quality development (HQD) in the YRB in the new era [5,6]. Moreover, the problem of carbon emission has also gradually restricted the development of the YRB and attracted extensive attention from many scholars. For example, based on night light data, Du et al. (2021) discussed the spatio-temporal evolution of carbon emissions and its influencing factors in the Yellow River Basin [7]. Wu et al. (2023) used the STIRPAT model to predict the peak carbon emissions of the provinces in the YRB and proposed differentiated peak carbon emission paths [8]. Wang et al. (2023) used the super-efficiency SBM model to measure the carbon emission efficiency of the YRB and analyzed the spatial-temporal differentiation of this carbon emission efficiency using the spatial autoregressive model [9]. Rong et al. (2023) analyzed the status quo and changes in land use carbon emissions in the YRB using a carbon emission coefficient method based on land use and socio-economic data [10].

In the context of these issues, the HQD in the YRB has attracted great attention from the Party and government. In short, HQD is a coordinated and sustainable development that combines economic benefits, social benefits, and ecological benefits. At present, this is the key development direction in the YRB. Specifically, on the basis of meeting the reasonable needs of economic and social development, HQD pays more attention to infrastructure construction and ecological and environmental protection which meet the material and spiritual needs of people in the YRB. In September 2019, President Xi emphasized that the YRB has great influence content for China’s economic and social development and ecological security, and profoundly clarified the significance of ecological protection and HQD in the YRB. Further, he made major plans to strengthen the management and protection and promote the HQD of the YRB [11]. The Outline of the Plan for Ecological Protection and High-Quality Development of the Yellow River Basin was issued and implemented in 2021, which would help the YRB to enter into a critical period of economic and social development transformation. As a turning point for the HQD of the YRB, the year of 2022 had important theoretical and practical significance. Therefore, the period of 2010–2022 is the time scale for this study, which aims to provide recommendations for the implementation of ecological protection and HQD strategies in the YRB.

HQD is the main way of promoting the coordinated development of the YRB, and it is also an important symbol of establishing a sound green, low-carbon, and circular economic system [12,13]. In this study, the green space coverage rate of built-up areas mainly represents the environmental quality level of the YRB. The greater the green space coverage rate, the less greenhouse gas emissions, especially in regard to CO₂ emissions. The green space coverage rate and CO₂ emissions have a significant negative correlation. In addition, some studies have shown that CO₂ emissions are closely related to energy consumption [14]. Therefore, the number of buses per 10,000 people is selected as the measurement index of CO₂ emissions, and the two are also significantly negatively correlated. On the other hand, the evaluation index system for HQD in the YRB is a multi-indicator evaluation system, which requires clarifying the weights of each indicator before a comprehensive evaluation and then synthesizing them into a single index for calculation. TOPSIS is a commonly used multi-index decision-making method. The traditional TOPSIS model was proposed by Hwang and Yoon in 1981. It mainly calculates and sorts the relative distance between the evaluation object and the ideal target, so as to obtain the relative merits and demerits of the evaluation object, which is regarded as an efficient tool for comparing the HQD levels between regions [15,16]. The entropy weight method can use the information entropy principle in information theory to determine the weight objectively according to the discrete degree of data [17,18]. In this study, the entropy weight method and TOPSIS model are combined to further study the dynamic trend of the HQD level in the YRB.

On the basis of exploring the spatio-temporal evolution characteristics of HQD in the YRB, identifying the main factors is the prerequisite to promoting the implementation of national strategy [19]. A regression analysis is a common method for identifying these dominant factors [20]. Although a traditional global regression analysis can analyze whether the correlation between explanatory variables and dependent variables is significant, it faces the problem of multicollinearity and cannot identify the importance of driving factors [21]. As a new statistical method for spatial anisotropy measurement and factor detection, a geographical detector can better avoid the multicollinearity problem in regression analysis, further evaluate the importance of influencing factors, and identify the leading factors more scientifically, which has been effectively employed in HQD driving factors [12,22]. Since the influence of each driving factor on HQD is not equal, it is necessary to use an econometric model to analyze the spatio-temporal difference after identifying the dominant factor. The ordinary least squares method (OLS) is widely used to explore the quantitative relationship between independent and dependent variables [23]. However, it assumes that regression parameters have nothing to do with the spatial location of samples, ignores the non-stationary law of data, and fails to reflect the spatial heterogeneity between dependent variables and their influencing factors in geographical phenomena [24,25]. Based on the summary of local regression analyses and variable parameter research, Fortheringham and Brunsdon (1999) [26] proposed a geographically weighted regression model (GWR) using the idea of local smoothing. By establishing the local regression equation of each point within the space range, the GWR model explores the spatial changes and related driving factors of the research object at a certain scale, fully considers the local effects of spatial objects, and has higher accuracy compared to OLS [27,28]. However, GWR does not consider the correlation of panel data in the time dimension, and cannot conduct a spatio-temporal econometric analysis of data [27,29]. Unlike GWR, the spatio-temporal geographical weighted regression (GTWR) model, with a time dimension, can analyze the effect intensity and change direction of driving factors from the spatio-temporal dimension, and is widely used in geography and related disciplines, including spatial model analyses [30,31,32].

Previous studies have provided a strong theoretical basis and methodological support for HQD in the YRB. In theory, HQD is an extension of the concept of sustainable development based on the actual situation of China’s development. According to Chen et al. (2021), the HQD model emphasizes the coupling and coordinated growth of the economy, society, people’s livelihood, and the environment, including scientific and technological innovation, regional coordination, social income sharing, ecological and environmental sustainability, and the openness of economic development [33]. Wan et al. (2023) pointed out that HQD is a fusion of multi-dimensional development, including innovation, coordination, green, open, and shared concepts [12]. Based on the DPSIR model, Zhao et al. (2022) argued that HQD is composed of five logical criteria, including drivers, pressures, state, impact, and response [34]. In view of the advantages of the DPSIR model, such as its strong comprehensiveness, wholeness, and systematization, and according to the characteristics of the YRB, this study constructs a HQD evaluation index system of the YRB composed of five logical criteria, such as drivers, pressures, state, impact, and response, based on the DPSIR model. However, most studies have analyzed the level measurement, spatial differences, and evolution trends of HQD, while there are few studies on the spatio-temporal evolution characteristics and driving factors of HQD [35]. As the main front of the national key strategy, it is of great theoretical and practical significance to comprehensively investigate the current situation of the HQD in the YRB to promote the regional coordinated development strategy and form a new pattern of HQD.

In view of this, firstly, an index framework was constructed based on the drivers, pressures, state, impact, and response (DPSIR) model, and the entropy weight TOPSIS model was used to estimate the HQD index of the YRB during 2010–2022. Secondly, descriptive statistics and standard deviation ellipse were combined to analyze the spatio-temporal characteristics and evolution of the HQD in the YRB. Finally, a geographical detector and the GTWR model were employed to analyze the driving factors of the spatio-temporal differentiation of the HQD quality. This study is different from static, single-province, single-city, or single-level research, and is a supplement to the existing research content, breaking through the limitations of existing HQD-related research that only focus on the “ability” and “level” of development. Based on the spatio-temporal evolution results of the HQD and the analysis of the driving factors, the current level of HQD in the YRB can be obtained in this study. According to the scores of each subsystem, the shortcomings faced by HQD in the YRB can be found, so as to provide guidance for the implementation of ecological protection and high-quality development strategies in the YRB, which has practical application value in the study area. The research framework of this study is shown in Figure 1.

2. Materials and Methods

2.1. Study Area

The YRB spans the western and eastern regions, with a total length of 5464 km. The Yellow River originates from the Bayanhara Mountains in the Qinghai Province of China, flows through the Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan, and Shandong provinces, and finally drains into the Bohai Sea from the Kenli District, Dongying City, Shandong Province, covering an area of about 3,597,600 km² (Figure 2). Its terrain is high in the west and low in the east. Its precipitation is mainly concentrated in summer, with an uneven distribution and great inter-annual variation. The study area has sufficient sunlight and strong solar radiation. In 2022, the GDP of the YRB region was 30.70 trillion yuan, accounting for about 25.37% of the national GDP.

2.2. Data Source

This study examined the nine provinces in the YRB from 2010 to 2022. The data for calculating the HQD index were mainly obtained from the China Statistical Yearbook and the Statistical Yearbook of Nine Provinces of the Yellow River Basin during the study period “https://data.cnki.net/ (accessed on 23 January 2023)”, and some of the data were from the Statistical Bulletin of National Economic and Social Development of Nine Provinces of the Yellow River Basin “https://www.tjcn.org/ (accessed on 26 January 2023)”.

2.3. Methodology

2.3.1. Establishment of HQD Index System Based on DPSIR Model

The DPSIR model was first modified by OECD based on the Pressure State Response (PSR) model and DSR model. It is a model built by an evaluation index system, including drivers (D), pressures (P), state (S), impact (I), and response (R) [36]. Based on the premise of satisfying the principles of orientation, objectivity, scientificity, and operability, and referring to previous studies [37,38], 20 indicators were selected within the framework of the DPSIR model to build a HQD evaluation system for the YRB (Table 1).

The screening steps for the evaluation indicators were as follows: (1) The original evaluation indicators were standardized, and then a normal test was carried out. If all of them had a normal distribution, then a person correlation analysis was carried out. (2) A Pearson correlation analysis was used for screening to eliminate indicators with repeated information. (3) A principal component analysis was carried out on the evaluation indicators, and the indicators with small factor loads were deleted to obtain the final indicators of HQD in the YRB.

2.3.2. Entropy Weight TOPSIS Model

The entropy weight TOPSIS method is an improved TOPSIS model [13] that was used for determining the weights in the evaluation indicator system of the HQD in the YRB, and was applied to sort the scheme. It is detailed as follows.

(1): The processing direction was determined according to the index attributes, and the range method was used to standardize each index:

X_ij is a positive indicator:

Y_{i j} = \frac{X_{i j} - X_{\min}^{i j}}{X_{\max}^{i j} - X_{\min}^{i j}} (i = 1, 2, \dots, m; j = 1, 2, \dots, n)

X_ij is a negative indicator:

Y_{i j} = \frac{X_{\max}^{i j} - X_{i j}}{X_{\max}^{i j} - X_{\min}^{i j}} (i = 1, 2, \dots, m; j = 1, 2, \dots, n)

where i is the city, j is the measure index, X_ij and Y_ij are the original and standardized index values, respectively, X_max and X_min are the maximum and minimum values of the j index, respectively, m is the number of sample cities, and n is the number of high-quality development indicators.

(2): Calculate the information entropy of each indicator:

P_{ij} = \frac{Y_{i j}}{\sum_{i = 1}^{m} Y_{i j}} e_{j} = - k \sum_{i = 1}^{m} P_{i j} \ln P_{i j}

where P_ij indicates the proportion of city i in index j, e_j indicates information entropy, and k is a constant, k = 1/lnm.

(3): Calculate the entropy and weight value of each indicator:

W_{j} = \frac{(1 - e_{j})}{\sum_{j = 1}^{n} (1 - e_{j})}

where W_j is the weight of j index and the range of W_j is from 0 to 1.

(4): Construct a weighted standardized matrix according to the product of w_j and y_ij:

{(z_{i j})}_{m \times n} = {(w_{j} \times y_{i j})}_{m \times n}

where z_ij indicates the weighted standardized index value, w_j indicates the index weight, and y_ij indicates the standardized index value.

(5): Determine the positive and negative ideal solutions of the evaluation scheme. The positive ideal solution (Z⁺) and the negative ideal solution (Z⁻) represent the optimal and worst scheme in the index vector, respectively. It is detailed as follows:

Z^{+} = {\max z_{ij} | i \in [1, m]; j \in J^{+}}

Z^{-} = {\min z_{ij} | i \in [1, m]; j \in J^{-}}

where Z⁺ is a positive ideal solution, Z⁻ is a negative ideal solution, and J⁺ and J⁻ are positive and negative indicator sets, respectively.

(6): Calculate the distance $D_{i}^{+}$ and $D_{i}^{-}$ between the evaluation object and the optimal solution and worst solution. The smaller $D_{i}^{+}$ is, the closer it is to the optimal solution; the larger $D_{i}^{-}$ is, the farther it is from the worst solution. It can be calculated by the following equations.

D_{i}^{+} = \sqrt{\sum_{j = 1}^{n} (z_{i j} - z_{j}^{+}})^{2}, i \in [1, m]

D_{i}^{-} = \sqrt{\sum_{j = 1}^{n} (z_{i j} - z_{j}^{-}})^{2}, i \in [1, m]

where

D_{i}^{+}

and

D_{i}^{-}

are the Euclidean distances from the positive and negative ideal solutions of each evaluation scheme, respectively.

(7): Calculate the relative proximity C_i. C_i represents the good or bad result of the evaluation object. In this study, it refers to the comprehensive score value of the HQD. The basic equation can be expressed as:

C_{i} = \frac{D_{i}^{-}}{D_{i}^{+} + D_{i}^{-}}, i \in [1, m]

where C_i indicates the relative proximity, namely the comprehensive index of HQD.

In order to intuitively express the HQD level of the provinces in the YRB, this study divided HQD into five grades, namely very poor (0, 0.2), poor (0.2, 0.4), medium (0.4, 0.6), good (0.6, 0.8), and excellent (0.8, 1) (Table 2).

2.3.3. Ellipse of Standard Deviation

The ellipse of standard deviation is used to describe the distribution and diffusion of each sample in different spaces [39], and the movement of the average center point can reflect the overall migration direction [40].

Average center:

{\bar{X}}_{w} = \frac{\sum_{i = 1}^{n} w_{i} x_{i}}{\sum_{i = 1}^{n} w_{i}}; {\bar{Y}}_{w} = \frac{\sum_{i = 1}^{n} w_{i} y_{i}}{\sum_{i = 1}^{n} w_{i}}

Azimuth angle:

\tan θ = \frac{(\sum_{i = 1}^{n} w_{i}^{2} {\tilde{x}}_{i}^{2} - \sum_{i = 1}^{n} w_{i}^{2} {\tilde{y}}_{i}^{2}) + {\sqrt{(\sum_{i = 1}^{n} w_{i}^{2} {\tilde{x}}_{i}^{2} - \sum_{i = 1}^{n} w_{i}^{2} {\tilde{y}}_{i}^{2})}}^{2} + 4 \sum_{i = 1}^{n} w_{i}^{2} {\tilde{x}}_{i}^{2} {\tilde{y}}_{i}^{2}}{2 \sum_{i = 1}^{n} w_{i}^{2} \tilde{x} \tilde{y}}

x axis standard deviation:

σ_{x} = \sqrt{\frac{\sum_{i = 1}^{n} {(w_{i} {\tilde{x}}_{i} \cos θ - w_{i} {\tilde{y}}_{i} \sin θ)}^{2}}{\sum_{i = 1}^{n} w_{i}^{2}}}

y axis standard deviation:

σ_{y} = \sqrt{\frac{{\sum_{i = 1}^{n} (w_{i} {\tilde{x}}_{i} \sin θ - w_{i} {\tilde{y}}_{i} \cos θ)}^{2}}{\sum_{i = 1}^{n} w_{i}^{2}}}

where (x_i,y_i) represents the spatial location, w_i is the weight, and (

{\bar{X}}_{w}, {\bar{Y}}_{w}

) represents the weighted average center. θ represents the azimuth angle of the ellipse.

{\tilde{x}}_{i}

and

{\tilde{y}}_{i}

represent the coordinate deviation from the spatial location of each research object to the average center. σ_x and σ_y are the standard deviations of the x and y axes, respectively. The major axis of the ellipse represents the distribution direction of the HQD, and the short axis represents the distribution range.

2.3.4. Geographical Detector

Geographical detector is an advanced method based on spatial statistics and autocorrelation [41], which can explore spatial differentiation, reveal the influence and significance of each driving factor, detect the interaction intensity between detection factors, and carry out risk detection [42]. In this study, the factor detection tool of this method was used to rank the driving factors of the HQD in the YRB.

Factor detection is usually devoted to detecting the spatial differentiation of dependent variables and the influence of the independent variables of dependent variables, measured by a q value. It is detailed as follows.

q = 1 - \frac{1}{N σ_{}^{2}} \sum_{k = 1}^{L} N_{h} σ_{h}^{2} = 1 - \frac{S S W}{S S T}

S S W = \sum_{h = 1}^{L} N_{h} σ_{h}^{2}

S S T = N σ_{}^{2}

where h is the number of classification items of the independent variable X, h = 1, 2... L, and the range of q is from 0 to 1. The larger the value of q, the stronger the influence of X on the spatial differentiation of Y. N_h and N are the number of units in category h and the whole region, respectively. σ_h and 2σ_h are the variances of Y in class h and region, respectively. SSW and SST are the sum of variances and total regional variances of class L, respectively.

2.3.5. GTWR

On the basis of the GWR model, the GTWR model repeatedly determines the bandwidth of each event, and then repeatedly calculates the regression equation of each sample point [33]. The formula is as follows:

Y_{i} = β_{0} (u_{i}, v_{i}, t_{i}) + \sum_{k = 1}^{p} β_{k} (u_{i}, v_{i}, t_{i}) X_{i k} + ε_{i} (i = 1, 2, 3 \dots n)

where Y_i represents the observed value, u_i and v_i are the latitude and longitude of the observation point i, respectively, and t_i represents the time sequence of i. (u_i,v_i,t_i) represents the space–time coordinates and β₀(u_i,v_i,t_i) represents the regression constant. β_k(u_i,v_i,t_i) represents the regression coefficient of the kth independent variable at the ith observation point, X_ik represents the value of the kth independent variable at the ith point, and i represents the model residual.

2.4. Driving Factors of HQD in YRD

HQD not only contains the connotation of sustainable development, but also reflects its own uniqueness to a certain extent, which has the characteristics of being all-round, multi-dimensional, and wide-ranging [43]. On the basis of the sustainable development concept and drawing from related studies [12,27,44,45], seven factors were finally selected to construct an index system of HQD based on the availability of data and regional actual conditions (Table 3).

3. Results

3.1. Temporal Evolution of HQD in YRB

3.1.1. Subsystem Development Level

The improvement in the high-quality development level of the Yellow River Basin was not the result of a single factor, but the coordination of the five sub-systems with driving force, pressure, response, state, and influence. At the same time, the change trends of the five subsystems were also different (Table 4). From 2010 to 2022, the driving force index increased from 0.224 to 0.634, with an average annual growth rate of 9.031%, which was relatively high on the whole, indicating that the high-quality development of cities in the Yellow River Basin continued to improve. During the period of 2010–2022, the stress index decreased from 0.693 to 0.634, and the subsystem score showed a downward trend. From 2010 to 2016, the response index increased from 0.363 to 0.415. From 2017 to 2020, the score of the response subsystem decreased somewhat, but it was still at a good level, demonstrating a normal fluctuation. After 2020, it maintained an upward trend. During 2010–2022, the state index increased from 0.214 to 0.410, and the subsystem score showed an increasing trend. During 2010–2022, the impact index increased from 0.240 to 0.463, and the comprehensive score of the impact subsystem kept rising.

3.1.2. Total HQD Level

The high-quality development indexes of the Yellow River Basin during 2010–2022 were 0.324, 0.343, 0.350, 0.357, 0.370, 0.380, 0.388, 0.403, 0.406, 0.413, 0.417, 0.449, and 0.463, respectively, with an average of 0.389. The minimum value was 0.324 in 2010 and the maximum was 0.463 in 2022 (Table 5). In terms of the overall change trend, the HQD index of the provinces and regions in the YRB showed an increasing trend year by year (Figure 3), and the level of high-quality development continuously improved, with an average annual growth rate of 3.024%.

From the perspective of phased trend, the HQD of the YRB could be divided into three stages. From 2010 to 2016, the high-quality development index grew rapidly, and Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan, and Shandong increased by 0.08, 0.05, 0.02, 0.10, 0.10, 0.06, 0.06, 0.07, and 0.04, respectively. From 2016 to 2020, this growth rate slowed down, and the quality development index changed little and remained stable. From 2020 to 2022, the high-quality development indexes showed a rapid growth trend, increasing by 0.03, 0.03, 0.03, 0.07, 0.05, 0.04, 0.06, 0.04, and 0.05, respectively.

The D⁺ value reflected the close degree of the HQD in the YRB to the optimal plan, which was negatively correlated with the C_i value. On the contrary, the

D_{i}^{-}

value reflected the close degree of the HQD in the YRB to the worst scheme, which was positively correlated with the C_i value. As shown in Table 3 and Figure 3, the

D_{i}^{+}

values in the YRB decreased over the study period, being 0.194, 0.188, 0.185, 0.183, 0.178, 0.177, 0.175, 0.171, 0.169, 0.168, 0.167, 0.160, and 0.158, respectively. Meanwhile, the

D_{i}^{-}

values showed an increasing trend, which were 0.093, 0.099, 0.100, 0.102, 0.106, 0.109, 0.112, 0.116, 0.117, 0.119, 0.121, 0.131, and 0.137, respectively. It was obvious that the HQD level in the YRB improved steadily. In conclusion, the gap between the HQD status and optimal plan gradually decreased, and the distance between the HQD status and worst plan gradually increased. In particular, the change was most obvious during 2020–2022, with the

D_{i}^{+}

and

D_{i}^{-}

values changing by 0.009 and 0.016, respectively. The ranking result of the C_i value was the best HQD level in 2022 and the worst in 2010.

According to Figure 4, the values of the

D_{i}^{+}

median decreased from 2010 to 2022, while the

D_{i}^{-}

and C_i median values showed an upward trend. In addition, from to 2016 and 2020, there were different numbers of outliers in

D_{i}^{-}

, with the length of the boxes increasing, indicating that the gap between the

D_{i}^{-}

values of nine provinces in the YRB gradually widened. Moreover, abnormal values of C_i only occurred in 2010 and 2011, and the values of

D_{i}^{+}

were within the normal range, with small fluctuations in different regions. Further research found that the

D_{i}^{+}

value distribution was concentrated from 2010 to 2017, but the gap increased after 2017. Generally speaking, the HQD level in the YRB is on the rise.

3.2. Spatial Evolution of HQD in YRB

3.2.1. Subsystem Spatial Characteristics

In order to analyze the spatial evolution characteristics, the natural breakpoint method in Arcgis10.8 software was used to divide the HQD index into five levels. The HQD index of the five subsystems is shown in Figure 5, Figure 6, Figure 7 and Figure 8. The pressures subsystem had the highest level of HQD, followed by the drivers and state subsystems. On the contrary, the impact subsystem had a relatively low level of HQD, and the response subsystem had the lowest level. In terms of spatial distribution, the high values of the drivers index were distributed in the Shandong Province and Inner Mongolia Autonomous Region; the high values of the pressure index were distributed in Gansu and the Sichuan Province; the high values of the response index were distributed in the Shandong, Shaanxi, Henan, and Sichuan Provinces; the high values of the state index were distributed in the Qinghai Province and Inner Mongolia Autonomous Region; and the high values of the influence index were distributed in the Shandong Province. In addition, the HQD index of the five subsystems in the Ningxia Hui Autonomous Region was at a low level.

In terms of spatial differentiation, the greatest spatial differences were observed in the response and state subsystems, with the development indexes of the west and east not at the same level, followed by the pressures and impact subsystems; the spatial distribution of the drivers subsystem was more balanced, with the least regional differences. In terms of temporal evolution, the high-value areas of the HQD index between the five subsystems did not change significantly. Except for the pressures subsystem, the HQD level of the other four subsystems increased steadily, while the spatial differences in all the five subsystems gradually increased.

In general, the HQD level of the drivers, response, and impact subsystems showed a spatial characteristic of being high in the east and low in the west; the pressures subsystem showed a spatial characteristic of being high in the middle and low in the periphery; and the state subsystem roughly showed a spatial characteristic of being high in the west and low in the east. Among them, the responses subsystem had the greatest spatial differences, while the drivers subsystem had the least. Combined with the temporal evolution, the overall spatial differences in the five subsystems increased gradually, and the areas with high values of each subsystem development index were stable in space. In addition, the HQD indexes of the five subsystems were relatively higher, among which, pressure had the highest development index and responses the lowest.

3.2.2. Total Spatial Characteristics

The spatial characteristics of the HQD level in the YRB are shown in Figure 9. In the past decade, the high values of the HQD in the YRB were distributed from northwest to southeast, with the Gansu Province and Ningxia Hui Autonomous Region showing a low-quality development index, while Shanxi Province and Henan Province were at a medium level. From 2010 to 2018, the high-value areas were distributed in the Shandong and Qinghai Provinces, followed by the Sichuan Province, Shaanxi Province, and Inner Mongolia Autonomous Region, and the high-value areas evolved to the east after 2018, with the Shandong Province being the first in terms of HQD level.

3.2.3. Standard Deviation Ellipse

The four study points of 2010, 2014, 2018, and 2022 were selected to identify the basic parameters of the standard deviation ellipse of the HQD. The results of the standard deviation ellipse are shown in Table 6.

The rotation angle θ of the HQD index decreased from 80.074° to 78.436° over the study period, weakening the northeast–southwest spatial pattern, indicating that the HQD of the Shandong Province in the southeast had a certain pulling effect on the low development level of the Shanxi Province and Inner Mongolia Autonomous Region in the northeast. The standard deviation of the major axis decreased from 933.882 km to 904.683 km from 2010 to 2022, indicating that the “northeast–southwest” pattern of the HQD index was polarized. On the contrary, the standard deviation of the minor axis expanded from 512.074 km to 521.048 km over the study period, indicating that the HQD index was dispersed in a “northwest–southeast” pattern.

In this study, the spatial movement trajectory of the average center was plotted to better understand the spatial movement direction of the HQD index (Figure 10). According to the spatial movement of the average center, the HQD index of the YRB showed a northwest to southeast trend. The distance of the average center in the east–west direction was greater than that in the north–south direction, with the speed of the center in 2018–2022 being faster than that in 2010–2014 and 2014–2018.

3.3. Driving Factors of HQD in YRB

3.3.1. Dominant Factors Selection

In this study, geographic detector was applied to analyze the explanatory power of each factor to the HQD in the YRB. The detection results of the factor detector are shown in Table 7. On the whole, the p-values of the seven factors were all 0 in 2010, 2014, 2018, and 2022, indicating that the impact of these seven factors on the HQD was significant. In 2010, the influence ranking of each factor on the HQD of the YRB, in the order of strong to weak, was as follows, that is, per capita disposable income (0.97), proportion of tertiary industry (0.86), rural–urban income ratio (0.81), per capita highway mileage (0.73), population (0.58), per capita GDP (0.57), and the number of authorized invention patents (0.53). In 2014, the ranking was per capita disposable income (0.957), per capita highway mileage (0.864), rural–urban income ratio (0.862), per capita GDP (0.857), proportion of tertiary industry (0.840), population (0.812), and the number of authorized invention patents (0.412). In 2018, the ranking was per capita disposable income (0.965), per capita highway mileage (0.894), proportion of tertiary industry (0.863), per capita GDP (0.845), population (0.830), rural–urban income ratio (0.829), and the number of authorized invention patents (0.432). The ranking in 2022 was rural–urban income ratio (0.908), proportion of tertiary industry (0.905), per capita highway mileage (0.878), per capita disposable income (0.861), per capita GDP (0.853), population (0.831), and the number of authorized invention patents (0.277).

In the context of importance change, per capita disposable income and the number of authorized invention patents tended decrease, while the other factors all increased to varying degrees. It is necessary to explore the spatio-temporal differentiation characteristics of the driving factors due to the obvious differentiation between the HQD level and the driving factors. Compared to the other six factors, the q value of the number of authorized invention patents was relatively low. Therefore, x1, x2, x3, x4, x5, and x7 were selected as the dominant factors of the HQD in the YRB, with the GTWR model being further applied to explore the spatio-temporal heterogeneity of each factor.

3.3.2. Multicollinear Test

In this study, the GTWR model was adopted, with the HQD index as the dependent variable and the proportion of tertiary industry, per capita disposable income, rural-urban income ratio, per capita GDP, per capita highway mileage, and population as the explanatory variables. In order to avoid a deviation in the estimation results caused by the mutual influence of the independent variables, a multicollinearity test of each driving factor was carried out first before exploring the influence of the driving factors on the HQD. The results of the multicollinear test are shown in Table 8. It is obvious that the variance inflation factor (VIF) of all the indicators was less than 10, and the tolerance value was greater than 0.1, which indicated that there was no multicollinearity among the independent variables.

3.3.3. Regression Coefficient Analysis

The descriptive statistics of the regression coefficients of each driving factor in the YRB are summarized in Table 9. The maximum regression indexes of x1, x2, x3, x5, and x7 with HQD were 0.892, 0.890, 0.849, 0.428, and 0.661, respectively, and the minimum values were −0.843, −0.673, −0.238, −0.256, and −0.274, respectively, which indicate that the effects of x1, x2, x3, x5, and x7 on the HQD level of the YRB were both positive and negative. Moreover, x1, x2, x3, x5, and x7 played positive roles in the HQD level according to the average values (0.044, 0.068, 0.227, 0.064, and 0.215). On the contrary, the effect of x4 was negative, with the maximum value of the regression coefficient being positive (0.978) and the minimum value being negative (−1.330), but the average value being negative (−0.028). In general, it can be found that x3 had the greatest impact on the HQD level, followed by x7, according to the absolute value of the mean value of regression coefficients of each driving factor. In this study, the regression coefficients are visualized in order to visually compare and analyze the relationship between the driving factors with the HQD of the YRB under different spatio-temporal dimensions.

3.3.4. Regression Coefficient Spatial-Temporal Characteristic

The results of the parameters of the GTWR model are shown in Table 10. In terms of the goodness of fit, the R² and corrected R² were both higher than 0.99, indicating that the GTWR regression model could better measure the influence of the explanatory variables on the dependent variables.

The regression coefficients of the driving factors are visualized in Figure 11. The results showed that spatial differences existed among the influences of each factor on the HQD in the YRB, and the impact degree was significantly different.

(1): Proportion of tertiary industry. The influence of the proportion of tertiary industry on the HQD weakened gradually from 2010 to 2018, while it increased from 2018 to 2022. Furthermore, the positive high-value areas were distributed in the Ningxia Hui Autonomous Region during 2010–2014, while they were concentrated in eastern and central regions such as the Shandong Province and Shaanxi Province after 2014. From 2010 to 2014, the negative high-value areas were concentrated in the eastern region, and then gradually shifted to the western region. On the whole, the intensity and range of the positive influence gradually increased, while the negative influence decreased. In general, the proportion of tertiary industry played a positive role in the HQD level of the YRB.
(2): Per capita disposable income. The impact of per capita disposable income on the HQD of the YRB was extremely positive. It can be found that the positive high-value areas shifted from the Gansu Province to the central and eastern regions, and the negative high-value areas shifted from the Inner Mongolia Autonomous Region to the Sichuan Province. In conclusion, the scope and intensity of the positive influence of per capita disposable income on the HQD increased gradually from 2010 to 2018, while it decreased from 2018 to 2022.
(3): Rural–urban income ratio. The results showed that the positive high-value areas were concentrated in the Shandong and Henan Provinces and other eastern regions from 2010 to 2014, and gradually shifted to the central and western regions after 2014, including the Gansu, Qinghai, and Sichuan Provinces. The positive influence of rural–urban income ratio on the HQD of the YRB expanded gradually from 2010 to 2018, but the influence intensity gradually weakened. On the contrary, the range of the positive influence gradually narrowed from 2018 to 2022, while the intensity increased steadily.
(4): Per capita GDP. It can be found that the per capita GDP exhibited a mainly negative impact on the HQD of the YRB, with the absolute value of the negative regression coefficient being much larger than that of the positive regression coefficient. Over the years, the areas with positive high values migrated from the Inner Mongolia Autonomous Region to the western areas, including the Ningxia Hui Autonomous Region, Qinghai Province, and Gansu Province, while the areas with negative high values were concentrated in eastern regions, such as the Shandong Province. The scope of the positive influence changed little over time, mainly distributed in the Qinghai province, Gansu Province, Inner Mongolia Autonomous Region, Sichuan Province, and Ningxia Hui Autonomous Region, but its influence intensity gradually weakened. In general, the overall change amplitude of per capita GDP on the HQD was not large, so a relatively stable trend was maintained.

(5): Per capita highway mileage. The results of per capita highway mileage had little influence on the HQD, with the absolute values of the positive and negative regression coefficients being at a low level. The areas with positive high values were mainly distributed in the Ningxia Hui Autonomous Region and Gansu Province over the study period. However, the areas with negative high values showed obvious changes, which shifted from the Inner Mongolia Autonomous Region to the Shandong Province from 2010 to 2014, transferred to the Sichuan Province from 2014 to 2018, and then shifted back to the Shandong Province after 2018. There were positive and negative regression coefficients, and the proportion of both was more balanced. In addition, the range of positive influence gradually expanded over time, and the influence intensity remained stable.
(6): Population number. The results showed that population number exhibited a positive effect on the HQD of the YRB on the whole, and it showed a fluctuating trend over past decades. Because the occupation and degree of social and economic development were slightly different, the influence degree also had a great difference in the spatial and temporal distribution. The effects of population on the HQD were positive from 2010 to 2018, while in 2022, this was opposite. Moreover, the positive high-value areas shifted from the Inner Mongolia Autonomous Region to the Sichuan Province from 2010 to 2018, and then shifted back to the Inner Mongolia Autonomous Region from 2018 to 2022. In 2022, the negative high-value areas were concentrated in the Qinghai and Shaanxi provinces. In conclusion, the scope of the positive influence reduced gradually, and the intensity of the influence weakened at the same time.

3.4. Sensitivity Analysis of Entropy Weight TOPSIS Model

A sensitivity analysis is a quantitative analysis method for studying the influence intensity of an indicator when relevant factors change. Its essence is to explain the law of the influence magnitude of key indicators through numerical changes in the relevant variables on the basis of determining the influence effect of the analysis. In this study, the relevant variable was the input factor of the entropy weight TOPSIS model, and the key indicator was the HQD index. Through a sensitivity analysis, the influence of different indicators on the HQD level could be judged. The sensitivity coefficient S was divided into four levels, namely insensitive index (0 ≤ |S| < 0.05), medium sensitive index (0.05 ≤ |S| < 0.2), sensitive index (0.2 ≤ |S| < 1), and highly sensitive index (|S| ≥ 1).

The sensitivity analysis results are shown in Table 11. It can be seen that the sensitivity ranking of the HQD level indicators in the YRB was as follows: Per capita highway mileage > Economic opening structure > Urban university ratio > Population density > Resident income > Public transport > Per capita fixed asset investment > Labor productivity > Innovation output > Productivity of capital > Consumption rate > Green space coverage in built-up areas > Per capita GDP > Innovation input > Per capita electricity consumption > Per capita water consumption > Open environment > Urban–rural income gap > Proportion of tertiary industry > Wastewater discharge per unit of GDP. All the indicators were medium sensitive and sensitive indicators, and none were insensitive indicators. Moreover, the sensitivity coefficients of the four indexes contained in the pressure subsystem were all negative, and the sensitivity analysis results of the entropy weight TOPSIS model were basically consistent with the above research results.

4. Discussion

Considering that the YRB is trying to implement the important national strategy of ecological protection and high-quality development, this study systematically discusses the spatio-temporal evolution characteristics and driving factors of HQD in the YRB.

4.1. Temporal Characteristic of HQD in YRB

In terms of temporal evolution, the variation trend of the five subsystems was a bit different. With rapid economic development and social progress, people’s living standards and income status have been greatly improved, and the urban income gap has also been gradually narrowed [46]. Therefore, the comprehensive score of the drivers subsystem continues to increase. On account of the continuous development of urbanization, the population has increased sharply, and the discharge of various pollutants and energy consumption has increased continuously [47,48], which caused the score of the pressures subsystem to decrease gradually. Therefore, relevant policies in terms of resource utilization, environmental quality, ecological protection, and other aspects should be formulated to alleviate the pressure of the ecological environment and improve the comprehensive score of the pressures subsystem [49].

From 2010 to 2016, the comprehensive score of the response subsystem increased significantly, indicating that the YRB is attaching more and more importance to innovation development, with increasing innovation input, the continuous output of innovation achievements, a large increase in the number of invention patents granted, and certain achievements in the transformation of “old and new driving forces” [42]. The state index increased from 0.214 to 0.410 over time, indicating that the ecological condition of the YRB has improved to some extent. Moreover, the green spaces and road mileage have increased significantly, the tertiary industry has developed rapidly, and the proportion of output value has increased significantly, which is consistent with Zhou et al. (2022) [3] and Zhang et al. (2022) [2].

From 2010 to 2022, the comprehensive score of the impact subsystem increased continuously, indicating that the HQD strategy of the YRB has played a positive role in promoting social and economic development, and the influence degree is getting higher and higher. In general, the gap between the subsystems narrowed gradually, indicating that the development of subsystems in the YRB is becoming more balanced, which is consistent with the study of Wan et al. (2023) in the Sichuan Province [12]. However, the spatial differences in the five subsystems increased gradually, in which, since the implementation of the strategy for ecological protection and HQD in the YRB, the lower reaches of the Yellow River, such as the Shandong Province, have developed rapidly, while the provinces of Gansu Province and the Ningxia Hui Autonomous Region have developed slowly. Hence, these spatial differences are further increasing, which is consistent with Zhang et al. (2022) [2].

The HQD of the YRB can be divided into three stages over the study period. From 2010 to 2016, in the context of social progress and technological development, inter-city economic ties and intra-regional cooperation were significantly strengthened, and the HQD showed a steady upward trend, which is consistent with Jiang et al. (2021) [35]. From 2016 to 2020, with the background of increasing downward economic pressure, the HQD index increased a little, but evolved in a better direction on the whole. From 2020 to 2022, with the introduction of the major strategy for ecological protection and high-quality development in the Yellow River Basin, the transformation of old and new driving forces in the YRB continued to accelerate, and economic growth entered a phase of quantitative and qualitative renewal [50]. Moreover, industrial strength, innovation capacity, and social welfare level greatly improved, and the level of HQD significantly improved.

It can be seen that the D⁺ values of the regions fluctuated significantly from 2010 to 2022, and the data distribution was relatively dispersed. This was mainly because Shandong, Sichuan, Shanxi, and other provinces actively promoted the transformation of old and new driving forces, with strategic emerging industries and high-tech industries maintaining rapid growth. Moreover, new driving forces have made increasingly stable contributions to the HQD in the YRB, which has become an important driving force for HQD [12]. However, Qinghai, Gansu, Ningxia, and other regions are still dominated by fossil energy consumption, with the HQD index being relatively low.

4.2. Spatial Characteristic of HQD in YRB

From 2010 to 2018, the spatial distribution of the HQD index in the YRB remained stable. Over the period, the HQD level of the Qinghai Province continually increased, which was related not only to building a conservation-oriented society and accelerating the development of circular economy, but also the rapid development of high-tech industries in the 21st century, including a complete scientific research system and diversified financing channels.

From 2018 to 2022, the regions with high values of HQD moved in the direction of the east, and the Shandong Province ranked first in terms of HQD level, which was attributed to the implementation of ecological protection and HQD strategy in the YRB. In particular, the Shandong Province has innovated its operating system and mechanism, encouraged scientific and technological innovation, promoted industrial upgrading, and supported the expansion of opening-up and cooperation. Therefore, remarkable results have been achieved and its level of HQD has been greatly improved. Furthermore, the overall trend of the standard deviation ellipse will move eastward, driving the center of gravity of the ellipse to move eastward and southward, which is consistent with Zhang et al. (2022) [2]. This is due to the continuous optimization of industrial structures, relatively developed green and low-carbon economy and policy support, and the gap between the HQD of the eastern, central, and western regions widening continuously. The average center of the YRB HQD index moved faster in 2018–2022 than in 2010–2014 and 2014–2018, mainly because the major strategy for ecological protection and high-quality development in the Yellow River Basin was put forward in 2019. Since the implementation of this strategy, the Shandong Province and other provinces in the lower reaches of the Yellow River have taken a series of measures to actively implement the strategy. In conclusion, the HQD level has been greatly improved compared to that of 2010–2014 and 2014–2018.

4.3. Driving Factors of HQD in YRB

The range and intensity of the positive influence of the tertiary industry proportion gradually increased, while that of the negative influence decreased, which is consistent with Zhang et al. (2022) [12]. As is universally acknowledged, the proportion of tertiary industry is an important sign for measuring the developed degree of a region, which can reflect its economic structure to a certain extent [51]. In addition, tertiary industry plays a vital role in providing social services and job opportunities, which can create better conditions for optimizing economic structures and achieving high development [33]. Over the study period, especially since the implementation of the strategy for ecological protection and HQD in the YRB in 2019, the YRB focused on the positive role of tertiary industry in high-quality economic development, further optimizing production structures, promoting production specialization, reducing pollution, and promoting the sustainable and healthy development of the entire economy.

The range and intensity of the positive impact of per capita disposable income increased gradually from 2010 to 2018, while they decreased from 2018 to 2022. It can be seen that this increase in per capita disposable income played an important positive role in promoting regional high-quality coordination, which is consistent with the study of Li et al. (2023) [52]. However, in the later period of the study, the increase in per capita disposable income resulted in a significant rise in living costs and prices, which may aggravate the pressure of the social economy, thus bringing about adverse effects on HQD.

The positive impact of rural–urban income ratio decreased gradually from 2010 to 2018, while it increased from 2018 to 2022. This may have been due to the relatively low level of rural–urban income ratio in the early stage of the study, which was a limited driving force for HQD in the YRB. With the rapid development of urbanization and the improvement in per capita GDP, the urban–rural income coordination level was relatively high, with the gap between urban and rural income being narrowed gradually, thus enhancing the promoting role of HQD.

The range of the positive impact of per capita GDP did not change a lot, mainly being concentrated in the central and western provinces. Due to historical and geographical reasons, the proportion of tertiary industry in the central and western provinces is small and the efficiency of economic development is low [33]. Therefore, the per capita GDP exhibited a positive impact. In contrast, the economies of eastern regions such as the Shandong Province are dominated by tertiary industries, including technology, finance, and services [53]. Moreover, the industrial layout is mainly green and sustainable in most regions, which caused that the positive impact of per capita GDP to be weakened. Over the study period, the intensity of the positive influence of per capita GDP gradually decreased. On the one hand, there is no inevitable correlation between HQD and economic development, and an improvement in economic level alone cannot significantly promote the level of HQD [12]. On the other hand, it indicates that the current requirement for economic development has changed from improving the total quantity to ensuring quality. In the long run, a guarantee of economic quality has a more far-reaching impact on HQD than an improvement in the total quantity of the economy.

Over the study period, the positive influence range of per capita highway mileage on HQD gradually expanded, and the influence intensity remained stable, which is somewhat different from the results of other research [12,54]. Per capita highway mileage is an important infrastructure, and its rapid improvement can improve the level of the HQD in the YRB, in which a rapid improvement in infrastructure speeds up the internal flow of factor resources, which is conducive to the level of HQD [55].

From 2010 to 2022, the range of positive population influence gradually reduced, and the intensity of the influence weakened. The main reason is that overpopulation will put pressure on the regional environment, resources, and society, leading to a decline in the level of HQD [56]. In addition, there were many outliers in the population regression coefficient, indicating that there was a spatial imbalance in the HQD of the YRB, and the population coefficient of some areas was significantly different from that of other areas.

5. Conclusions and Prospects

5.1. Conclusions

Based on panel data in the YRB from 2010 to 2022, the contribution of this study, compared with the existing literature, is manifested in three aspects. Firstly, this study used the DPSIR model and entropy TOPSIS model to evaluate the HQD index, which will enrich the research content of HQD. Secondly, the use of standard deviation ellipses to examine the spatial differentiation of HQD helped to identify the spatial characteristics of the HQD in the YRB. Thirdly, the use of geographic detector and the GTWR model to reveal the driving factors of HQD in the YRB was helpful in systematically summarizing the impact mechanism of HQD.

(1): Through the temporal evolution, it was found that the level of HQD in the YRB showed a trend of rapid improvement from 2010 to 2022, with an average annual growth rate of 3.024%. It could be divided into three stages, namely, the HQD index increased rapidly from 2010 to 2016; from 2016 to 2020, the growth rate slowed down and the HQD index remained stable; and the HQD index in 2020–2022 witnessed rapid growth. The gap between the five subsystems was gradually narrowed, and the HQD of each subsystem in the YRB was more balanced.
(2): Through the spatial differentiation, the areas with high values of HQD level in the YRB were distributed from northwest to southeast over the study period. The development levels of the driver subsystem, response subsystem, and impact subsystem presented spatial characteristics with a distribution of being high in the east and low in the west; the pressures subsystem presented spatial characteristics with a distribution of being high in the middle and low in the periphery; and the state subsystem presented spatial differentiation characteristics with a distribution of being high in the west and low in the east. The spatial differences of the five subsystems in the provinces of the YRB increased gradually, and the regions with a high development index of each subsystem tended to be spatially stable.
(3): Through the standard deviation ellipse, it was found that the standard deviation ellipse was distributed in a “northeast–southwest” pattern; the length of the x-axis was gradually shortened from 933.882 km to 904.683 km and the length of the y-axis was gradually increased from 512.074 km to 521.048 km, indicating that the “northeast–southwest” pattern of HQD index was polarized.
(4): Through the moving trajectory of the center of gravity, it was found that the center of gravity of the HQD tended to move southward and eastward, suggesting that the growth rate of HQD in the southern and eastern provinces was higher than the average level, and HQD tended to develop to the south.
(5): Through geographic detector, it was found that the proportion of tertiary industry, per capita disposable income, rural–urban income ratio, per capita GDP, per capita highway mileage, and population jointly affected the spatio-temporal evolution of the HQD level in the YRB, with average q values of 0.867, 0.938, 0.852, 0.781, 0.842, and 0.763, respectively. The q value of the number of invention patents granted was relatively low compared to the other six factors, with an average value of 0.413.
(6): Through GTWR, except for the negative effect of per capita GDP, the other five driving factors all had positive effects on the HQD level, with average values of 0.044, 0.068, 0.227, 0.064, and 0.215, respectively.

5.2. Prospects

Due to the limitations of data acquisition, the content involved in this study was limited, and the constructed evaluation system failed to fully reflect the HQD of the YRB. The content of the index system needs to be further expanded in future studies. Moreover, this paper only explored the HQD assessment of the YRB and its temporal and spatial characteristics, and failed to put forward a relatively perfect evaluation standard for the national HQD. In the future research, we hope to further supplement and improve its theoretical connotation and evaluation system on the basis of the existing research in this paper, and extend it to a broader scope.

Author Contributions

Conceptualization, methodology, and writing—original draft preparation, M.Z.; writing—review and editing, S.Q. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflict of interest.

References

Wang, Z.H.; Liu, B.; Wang, L.S.; Shao, Q. Measurement and temporal & spatial variation of urban eco-efficiency in the Yellow River Basin. Phys. Chem. Earth 2021, 122, 102981. [Google Scholar]
Zhang, H.; Duan, Y.; Wang, H.; Han, Z.L.; Wang, H.Y. An empirical analysis of tourism eco-efficiency in ecological protection priority areas based on the DPSIR-SBM model: A case study of the Yellow River Basin, China. Ecol. Inform. 2022, 70, 101720. [Google Scholar]
Zhou, Z.X.; Sun, X.R.; Zhang, X.T.; Wang, Y. Inter-regional ecological compensation in the Yellow River Basin based on the value of ecosystem services. J. Environ. Manag. 2022, 322, 116073. [Google Scholar] [CrossRef]
Zhang, K.Z.; Dong, Z.C.; Guo, L.; Boyer, E.W.; Liu, J.Z.; Chen, J.; Fan, B.H. Coupled coordination spatiotemporal analyses inform sustainable development and environmental protection for the Yellow River Basin of China. Ecol. Indic. 2023, 151, 110283. [Google Scholar] [CrossRef]
Gong, W.F.; Fan, Z.Y.; Wang, C.H.; Wang, L.P.; Li, W.W. Spatial spillover effect of carbon emissions and its influencing factors in the Yellow River Basin. Sustainability 2022, 14, 3608. [Google Scholar] [CrossRef]
Wang, X.; Zhang, Q.X.; Chang, W.Y. Does economic agglomeration affect haze pollution? Evidence from China’s Yellow River basin. J. Clean. Prod. 2022, 335, 130271. [Google Scholar] [CrossRef]
Du, H.B.; Wei, W.; Zhang, X.Y.; Ji, X.P. Spatial-temporal pattern evolution and influencing factors of carbon emissions from energy consumption in the Yellow River Basin: Based on DMSP/OLS and NPP/VIIRS night light data. J. Geogr. Res. 2021, 40, 2051–2065. [Google Scholar]
Wu, H.; Yang, Y.; Li, W. Analysis of spatiotemporal evolution characteristics and peak forecast of provincial carbon emissions under the dual carbon goal: Considering nine provinces in the Yellow River basin of China as an example. Atmos. Pollut. Res. 2023, 14, 101828. [Google Scholar] [CrossRef]
Wang, X.P.; Shen, Y.S.; Su, C. Spatial—Temporal evolution and driving factors of carbon emission efffciency of cities in the Yellow River Basin. Energy Rep. 2023, 9, 1065–1070. [Google Scholar] [CrossRef]
Rong, T.Q.; Zhang, P.Y.; Li, G.H.; Wang, Q.X.; Zheng, H.T.; Chang, Y.H.; Zhang, Y. Spatial correlation evolution and prediction scenario of land use carbon emissions in the Yellow River Basin. Ecol. Indic. 2023, 154, 110710. [Google Scholar] [CrossRef]
Xi, J.P. Speech at the Symposium on ecological protection and quality development of the Yellow River basin China. Water Resour. 2019, 20, 1–3. (In Chinese) [Google Scholar]
Wan, J.; Wang, Z.; Ma, C.; Su, Y.; Zhou, T.; Wang, T.; Zhao, Y.; Sun, H.; Li, Z.; Wang, Y.; et al. Spatial-temporal differentiation pattern and influencing factors of high-quality development in counties: A case of Sichuan, China. Ecol. Indic. 2023, 148, 110132. [Google Scholar] [CrossRef]
Chen, X.L.; Di, Q.B.; Jia, W.H.; Hou, Z.W. Spatial correlation network of pollution and carbon emission reductions coupled with high-quality economic development in three Chinese urban agglomerations. Sustain. Cities Soc. 2023, 94, 104552. [Google Scholar] [CrossRef]
Yapıcıoğlu, P.; Demir, Ö. Environmental performances of the wastewater treatment plants: Green Index. Int. J. Glob. Warm. 2020, 21, 1–19. [Google Scholar] [CrossRef]
Yang, T.; Zhang, Q.; Wan, X.H.; Li, X.P.; Wang, Y.Y.; Wang, W. Comprehensive ecological risk assessment for semi-arid basin based on conceptual model of risk response and improved TOPSIS model-a case study of Wei River Basin, China. Sci. Total Environ. 2020, 719, 137502. [Google Scholar] [CrossRef] [PubMed]
Xiao, H.L.; Zhang, J.P.; Xu, M.; Zhang, H. Study on spatial variability evaluation of hydrometeorological elements based on TOPSIS model. J. Hydrol. 2023, 619, 129359. [Google Scholar] [CrossRef]
Cunha-Zeri, G.; Guidolini, J.F.; Branco, E.A.; Ometto, J.P. How sustainable is the nitrogen management in Brazil? A sustainability assessment using the Entropy Weight Method. J. Environ. Manag. 2022, 316, 115330. [Google Scholar] [CrossRef]
Ding, S.; Li, R.J.; Guo, J.H. An entropy-based TOPSIS and optimized grey prediction model for spatiotemporal analysis in strategic emerging industry. Eepert Syst. Appl. 2023, 213, 119169. [Google Scholar] [CrossRef]
Yuan, X.L.; Guo, Y.L.; Wang, H.X. The spatial and temporal differentiation of urban development quality in China and its driving factors. Human Geogr. 2022, 37, 129–138+170. (In Chinese) [Google Scholar]
Liang, L.F.; Yu, L.F.; Wang, Z.G. Identifying the dominant impact factors and their contributions to heatwave events over mainland China. Sci. Total Environ. 2022, 848, 157527. [Google Scholar] [CrossRef]
Lim, J.; Ooka, R.; Lim, H. Multicollinearity issue for the parameterization of urban ventilation potential with urban morphology. Sustain. Cities Soc. 2022, 87, 104218. [Google Scholar] [CrossRef]
Zhao, F.; Zhang, S.J.; Du, Q.D.; Ding, J.Y.; Luan, G.Z.; Xie, Z.Q. Assessment of the sustainable development of rural minority settlements based on multidimensional data and geographical detector method: A case study in Dehong, China. Socio-Econ. Plan Sci. 2021, 78, 101066. [Google Scholar] [CrossRef]
Wang, L.Y.; Hou, H.; Weng, J.X. Ordinary least squares modelling of urban heat island intensity based on landscape composition and configuration: A comparative study among three megacities along the Yangtze River. Sustain. Cities Soc. 2020, 62, 102381. [Google Scholar] [CrossRef]
Zhu, C.M.; Zhang, X.L.; Zhou, M.M.; He, S.; Gan, M.Y.; Yang, L.X.; Wang, K. Impacts of urbanization and landscape pattern on habitat quality using OLS and GWR models in Hangzhou, China. Ecol. Indic. 2020, 117, 106654. [Google Scholar] [CrossRef]
Wang, K.L.; Zhang, F.Q.; Xu, R.Y.; Miao, Z.; Cheng, Y.H.; Sun, H.P. Spatiotemporal pattern evolution and influencing factors of green innovation efficiency: A China’s city level analysis. Ecol. Indic. 2023, 146, 109901. [Google Scholar] [CrossRef]
Fotheringham, A.S.; Brunsdon, C. Local Forms of Spatial Analysis. Geogr. Anal. 1999, 31, 340–358. [Google Scholar] [CrossRef]
Hu, J.Y.; Zhang, J.X.; Li, Y.Q. Exploring the spatial and temporal driving mechanisms of landscape patterns on habitat quality in a city undergoing rapid urbanization based on GTWR and MGWR: The case of Nanjing, China. Ecol. Indic. 2022, 143, 109333. [Google Scholar] [CrossRef]
Han, B.; Jin, X.B.; Zhao, Q.L.; Chen, H.F. Spatiotemporal patterns and mechanisms of land-use conflicts affecting high-quality development in China. Appl. Geogr. 2023, 155, 102972. [Google Scholar] [CrossRef]
Ma, X.L.; Zhang, J.Y.; Ding, C.; Wang, Y.P. A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership. Comput. Environ. Urban 2018, 70, 113–124. [Google Scholar] [CrossRef]
Fotheringham, A.S.; Crespo, R.; Yao, J. Geographical and temporal weighted regression (GTWR): Geographical and temporal weighted regression. Geogr. Anal. 2015, 47, 431–452. [Google Scholar] [CrossRef]
Zhang, X.L.; Jia, W.X.; Wu, S.N. Research on the Temporal-spatial Transition and Driving Mechanism of High-quality Development in the Yellow River Basin. Chin. J. Popul. Sci. 2022, 36, 72–85. (In Chinese) [Google Scholar]
Jiang, F.M.; Chen, B.J.; Li, P.H.; Jiang, J.W.; Zhang, Q.Y.; Wang, J.N.; Deng, J.S. Spatio-temporal evolution and influencing factors of synergizing the reduction of pollution and carbon emissions—UTILIZING multi-source remote sensing data and GTWR model. Environ. Res. 2023, 229, 115775. [Google Scholar] [CrossRef]
Chen, Y.; Tian, W.T.; Zhou, Q.; Shi, T. Spatiotemporal and driving forces of ecological carrying capacity for high-quality development of 286 cities in China. J. Clean. Prod. 2021, 293, 126186. [Google Scholar] [CrossRef]
Zhao, J.H.; Xiu, H.R.; Wang, M.; Zhang, X.K.; Zhang, L.J. Evaluation of high-quality development in the Yellow River Basin based on improved DPSIR model. Yellow River People 2022, 44, 16–20. (In Chinese) [Google Scholar]
Jiang, L.; Zuo, Q.T.; Ma, J.X.; Zhang, Z.Z. Evaluation and prediction of the level of high-quality development: A case study of the Yellow River Basin, China. Ecol. Indic. 2021, 129, 107994. [Google Scholar] [CrossRef]
Malmir, M.; Javadi, S.; Moridi, A.; Neshat, A.; Razdar, B. A new combined framework for sustainable development using the DPSIR approach and numerical modeling. Geosci. Front. 2021, 12, 101169. [Google Scholar] [CrossRef]
Weng, Q.; Qin, Q.; Li, L. A comprehensive evaluation paradigm for regional green development based on “five-circle model”: A case study from Beijingtianjin-Hebei. J. Clean. Prod. 2020, 277, 124076. [Google Scholar] [CrossRef]
Bresciani, S.; Puertas, R.; Ferraris, A.; Santoro, G. Innovation, environmental sustainability and economic development: Dea-bootstrap and multilevel analysis to compare two regions. Technol. Forecast. Soc. Chang. 2021, 172, 121040. [Google Scholar] [CrossRef]
Liu, K.; Xue, Y.T.; Chen, Z.F.; Miao, Y. The spatiotemporal evolution and influencing factors of urban green innovation in China. Sci. Total Environ. 2023, 857, 159426. [Google Scholar] [CrossRef]
Zheng, X.; Yang, Z.P.; Zhang, X.Y.; Wang, T.; Chen, X.D.; Wang, C.R. Spatiotemporal evolution and influencing factors of provincial tourism ecological security in China. Ecol. Indic. 2023, 148, 110114. [Google Scholar] [CrossRef]
Peng, W.C.Y.; Fan, Z.X.; Duan, J.; Gao, W.J.; Wang, R.; Liu, N.; Li, Y.G.; Hua, S.E. Assessment of interactions between influencing factors on city shrinkage based on geographical detector: A case study in Kitakyushu, Japan. Cities 2022, 131, 103958. [Google Scholar] [CrossRef]
Liu, L.; Yang, Y.R.; Liu, S.; Gong, X.J.; Zhao, Y.T.; Jin, R.F.; Duan, H.Y.; Jiang, P. A comparative study of green growth efficiency in Yangtze River Economic Belt and Yellow River Basin between 2010 and 2020. Ecol. Indic. 2023, 150, 110214. [Google Scholar] [CrossRef]
Gong, M.Y.; Zhang, N. Drivers of China’s high-quality development: The role of intangible factors. Econ. Model. 2023, 124, 106294. [Google Scholar] [CrossRef]
Sun, Y.; Ma, A.; Su, H.; Su, S.; Chen, F.; Wang, W.; Weng, M. Does the establishment of development zones really improve industrial land use efficiency? Implications for China’s high-quality development policy. Land Use Policy 2020, 90, 104265. [Google Scholar] [CrossRef]
Sgambati, S.; Gargiulo, C. The evolution of urban competitiveness studies over the past 30 years. A bibliometric analysis. Cities 2022, 128, 103811. [Google Scholar] [CrossRef]
Pan, W.; Wang, J.; Lu, Z.; Liu, Y.S.; Li, Y.R. High-quality development in China: Measurement system, spatial pattern, and improvement paths. Habitat. Int. 2021, 118, 102458. [Google Scholar] [CrossRef]
Cherniwchan, J. Economic growth, industrialization, and the environment. Resour. Energy Econ. 2012, 34, 442–467. [Google Scholar] [CrossRef]
Lou, B.; Ulgiati, S. Identifying the environmental support and constraints to the Chinese economic growth: An application of the emergy accounting method. Energy Policy 2013, 55, 217–233. [Google Scholar] [CrossRef]
Khan, S.U.; Cui, Y.; Khan, A.A.; Ali, M.A.S.; Khan, A.; Xia, X.L.; Liu, G.B.; Zhao, M.J. Tracking sustainable development efficiency with human-environmental system relationship: An application of DPSIR and super efficiency SBM model. Sci. Total Environ. 2021, 783, 146959. [Google Scholar] [CrossRef]
Luo, L.; Wang, Y.N.; Liu, Y.C.; Zhang, X.W.; Fang, X.L. Where is the pathway to sustainable urban development? Coupling coordination evaluation and configuration analysis between low-carbon development and eco-environment: A case study of the Yellow River Basin, China. Ecol. Indic. 2022, 144, 109473. [Google Scholar] [CrossRef]
Li, J.F. Industrial structure upgrading and tertiary industry modernization. J. Sun Yatsen Univ. 2005, 45, 124–130, 144. (In Chinese) [Google Scholar]
Li, M.X.; Liu, J.H.; Chen, Y.; Yang, Z.J. Can sustainable development strategy reduce income inequality in resource-based regions? A natural resource dependence perspective. Resour. Policy 2023, 81, 103330. [Google Scholar] [CrossRef]
Rodrik, D. Where are we in the economics of industrial policies? Front. Econ. China 2019, 14, 329–335. [Google Scholar]
Hua, X.Y.; Jin, X.R.; Lyu, H.P.; Ye, Y.F.; Shao, Y.H. Spatial-temporal pattern evolution and influencing factors of high quality development coupling coordination: Case on counties of Zhejiang Province. Sci. Geogr. Sin. 2021, 41, 223–231. (In Chinese) [Google Scholar]
Liang, X.; Li, P. Empirical study of the spatial Spillover effect of transportation infrastructure on green total factor productivity. Sustainability 2021, 13, 326. [Google Scholar] [CrossRef]
Pradhan, R.P.; Arvin, M.B.; Nair, M. Urbanization, transportation infrastructure, ICT, and economic growth: A temporal causal analysis. Cities 2021, 115, 103213. [Google Scholar] [CrossRef]

Figure 1. The evaluating framework of HQD level.

Figure 2. Location map of study area.

Figure 3. The HQD index of the YRB.

Figure 4. Boxes map of

D_{i}^{+}

D_{i}^{-}

, and C_i in the YRB.

Figure 4. Boxes map of

D_{i}^{+}

D_{i}^{-}

, and C_i in the YRB.

Figure 5. The HQD index of five subsystems in 2010 ((a) Drivers, (b) Pressures, (c) Response, (d) State, (e) Impact).

Figure 6. The HQD index of five subsystems in 2014 ((a) Drivers, (b) Pressures, (c) Response, (d) State, (e) Impact).

Figure 7. The HQD index of five subsystems in 2018 ((a) Drivers, (b) Pressures, (c) Response, (d) State, (e) Impact).

Figure 8. The HQD index of five subsystems in 2022 ((a) Drivers, (b) Pressures, (c) Response, (d) State, (e) Impact).

Figure 10. Elliptic distribution of standard deviation and center moving trajectory of HQD index in the YRB from 2010 to 2022.

Table 1. The HQD evaluation index.

Target	Second Grade Indexes	Third Grade Indexes	Index Measurement	Unit	Index Attribute
The HQD in the YRB	Drivers	Resident income	Per capita disposable income	Yuan/person	positive
		Per capita GDP	GDP/population	Yuan/person	positive
		Consumption rate	Retail sales of consumer goods/GDP	%	positive
		Urban–rural income gap	Rural–urban income ratio	%	negative
	Pressures	Per capita electricity consumption	Annual electricity consumption/population	kw·h/person	negative
		Wastewater discharge per unit of GDP	Discharge of industrial wastewater/GDP	t/Yuan	negative
		Per capita water consumption	Annual water supply/population	t/person	negative
		Population density	Population/area	person/km²	negative
	State	Green space coverage in built-up areas	Green area/built-up area	%	positive
		Proportion of tertiary industry	Output value of tertiary industry/total output value	%	positive
		Per capita highway mileage	Highway mileage/population	km/person	positive
		Public transport	Number of buses per 10,000 people	Cars/10,000 people	positive
	Impact	Per capita fixed asset investment	Fixed asset investment/population	10,000 yuan/person	positive
		Economic opening structure	Total import and export volume/GDP	%	positive
		Labor productivity	GDP/Number of employees	Yuan/person	positive
		Productivity of capital	GDP/Total social investment in fixed assets	%	positive
	Response	Innovation input	R&D expenditure of society/GDP	%	positive
		Innovation output	Number of invention patents granted/Total number of patents granted	%	positive
		Urban university ratio	Number of urban universities/Total number of national universities	%	positive
		Open environment	Foreign investment/GDP	%	positive

Table 2. The HQD grading standard.

HQD	[0, 0.2)	[0.2, 0.4)	[0.4, 0.6)	[0.6, 0.8)	[0.8, 1]
Levels	very poor	poor	medium	good	excellent

Table 3. The driving factors of HQD in the YRB.

Variable	Symbol	Description	Measurement Method	Unit
Dependent variable	Y	HQD	Comprehensive measurement index
Independent variable	X1	Advanced industrial structure	Proportion of tertiary industry	%
	X2	Resident living standard	Per capita disposable income	Yuan
	X3	Income distribution measure	Rural–urban income ratio	%
	X4	Economic development level	Per capita GDP	Yuan
	X5	Basic service measure	Per capita highway mileage	km
	X6	Innovation level	Number of invention patents granted	item
	X7	Population size	Population	ten thousand people

Table 4. The HQD index in the YRB of subsystems from 2010 to 2022.

Year	HQD Index
Year	Drivers	Pressures	Response	State	Impact
2010	0.224	0.693	0.363	0.214	0.240
2011	0.247	0.683	0.404	0.222	0.270
2012	0.284	0.680	0.410	0.239	0.275
2013	0.318	0.674	0.401	0.256	0.285
2014	0.362	0.672	0.414	0.265	0.305
2015	0.404	0.671	0.422	0.287	0.303
2016	0.443	0.672	0.415	0.309	0.311
2017	0.483	0.666	0.407	0.329	0.347
2018	0.512	0.657	0.381	0.327	0.380
2019	0.553	0.654	0.373	0.338	0.383
2020	0.566	0.650	0.368	0.349	0.386
2021	0.618	0.641	0.381	0.394	0.440
2022	0.634	0.634	0.386	0.410	0.463

Table 5. The overall level of HQD in the YRB from 2010 to 2022.

	$D_{i}^{+}$	$D_{i}^{-}$	C_i	Sort
2010	0.194	0.093	0.324	13
2011	0.188	0.099	0.343	12
2012	0.185	0.100	0.350	11
2013	0.183	0.102	0.357	10
2014	0.178	0.106	0.370	9
2015	0.177	0.109	0.380	8
2016	0.175	0.112	0.388	7
2017	0.171	0.116	0.403	6
2018	0.169	0.117	0.406	5
2019	0.168	0.119	0.413	4
2020	0.167	0.121	0.417	3
2021	0.160	0.131	0.449	2
2022	0.158	0.137	0.463	1

Table 6. Variation in the standard deviation ellipse parameter of the HQD index in the YRB.

Year	2010	2014	2018	2022
Angle of rotation θ/°	80.074	79.317	78.648	78.436
The standard deviation along the x-axis/km	933.882	920.565	912.477	904.683
The standard deviation along the y-axis/km	512.074	517.050	520.972	521.048

Table 7. Calculation results of the factor detector of HQD.

Year		x1	x2	x3	x4	x5	x6	x7
2010	q	0.86	0.97	0.81	0.57	0.73	0.53	0.58
2010	p	0.00	0.00	0.00	0.00	0.00	0.00	0.00
2014	q	0.840	0.957	0.862	0.857	0.864	0.412	0.812
2014	p	0.00	0.00	0.00	0.00	0.00	0.00	0.00
2018	q	0.863	0.965	0.829	0.845	0.894	0.432	0.830
2018	p	0.00	0.00	0.00	0.00	0.00	0.00	0.00
2022	q	0.905	0.861	0.908	0.853	0.878	0.277	0.831
2022	p	0.00	0.00	0.00	0.00	0.00	0.00	0.00

Table 8. The variance inflation factors of HQD drivers.

Variable	x1	x2	x3	x4	x5	x7
VIF	1.776	7.363	2.916	5.790	1.905	2.660
Tolerance	0.563	0.136	0.343	0.173	0.525	0.376

Table 9. Regression parameters of GTWR model.

	Maximum Value	Minimum Value	Average Value	Standard Deviation
x1	0.892	−0.843	0.044	0.234
x2	0.890	−0.673	0.068	0.184
x3	0.849	−0.238	0.227	0.215
x4	0.978	−1.330	−0.028	0.343
x5	0.428	−0.256	0.064	0.096
x7	0.661	−0.274	0.215	0.129

Table 10. The parameters of GTWR.

Serial Number	Parameter	Value
1	Bandwidth	2.5538
2	Residual Squares	0.0006
3	Sigma	0.0023
4	AICc	−1092
5	R²	0.9935
6	Adjusted R²	0.9931
7	Spatio-temporal Distance Ratio	1.7476

Table 11. Sensitivity analysis of entropy weight TOPSIS model.

Subsystems	Indexes	Sensitivity Coefficient	Relative Contribution
Drivers	Resident income	0.162	0.055
	Per capita GDP	0.101	0.035
	Consumption rate	0.104	0.036
	Urban–rural income gap	−0.062	0.021
Pressures	Per capita electricity consumption	−0.093	0.032
	Wastewater discharge per unit of GDP	−0.051	0.017
	Per capita water consumption	−0.089	0.030
	Population density	−0.206	0.070
State	Green space coverage in built-up areas	0.103	0.035
	Proportion of tertiary industry	0.056	0.019
	Per capita highway mileage	0.374	0.128
	Public transport	0.155	0.053
Impact	Per capita fixed asset investment	0.142	0.049
	Economic opening structure	0.361	0.123
	Labor productivity	0.135	0.046
	Productivity of capital	0.122	0.042
Response	Innovation input	0.094	0.032
	Innovation output	0.129	0.044
	Urban university ratio	0.300	0.102
	Open environment	0.088	0.030

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Share and Cite

MDPI and ACS Style

Zhang, M.; Qi, S. The Spatio-Temporal Evolution and Driving Factors of High-Quality Development in the Yellow River Basin during the Period of 2010–2022. Sustainability 2023, 15, 13512. https://doi.org/10.3390/su151813512

AMA Style

Zhang M, Qi S. The Spatio-Temporal Evolution and Driving Factors of High-Quality Development in the Yellow River Basin during the Period of 2010–2022. Sustainability. 2023; 15(18):13512. https://doi.org/10.3390/su151813512

Chicago/Turabian Style

Zhang, Mengna, and Shanzhong Qi. 2023. "The Spatio-Temporal Evolution and Driving Factors of High-Quality Development in the Yellow River Basin during the Period of 2010–2022" Sustainability 15, no. 18: 13512. https://doi.org/10.3390/su151813512

APA Style

Zhang, M., & Qi, S. (2023). The Spatio-Temporal Evolution and Driving Factors of High-Quality Development in the Yellow River Basin during the Period of 2010–2022. Sustainability, 15(18), 13512. https://doi.org/10.3390/su151813512

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu