Open AccessArticle

The Development of Economic–Social–Ecological Complex Systems in the Yellow River Basin, China

Yuyang Li

Haiguang Hao

^1,*

Lihui Sun

Mengxiao Liu

and

Ding Wang

Chinese Academy of Environmental Sciences, Beijing 100012, China

China National Environmental Monitoring Centre, Beijing 100012, China

Author to whom correspondence should be addressed.

Sustainability 2025, 17(2), 511; https://doi.org/10.3390/su17020511

Submission received: 11 November 2024 / Revised: 6 January 2025 / Accepted: 7 January 2025 / Published: 10 January 2025

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Abstract

The economic, social and ecological elements in the region constitute a complex ecosystem. The development trend, internal coordination and interactive effects of the economic–social–ecological (ESE) system have consistently constituted pivotal scientific propositions in the context of the social development process. The Yellow River Basin holds strategic importance, acting as both an ecological barrier and a center for economic development within China. Based on these considerations, this study focuses on the Yellow River Basin and innovatively establishes a theoretical framework and measurement model for the development of the ESE system. Quantitative methods, including the coupled coordination model and augmented regression tree model, are employed to evaluate the development, coordination, spatial patterns and influencing factors of the ESE system in the study area. The findings reveal that the economic and social subsystems are rapidly developing. Over the study period, the focus of ESE system development shifted eastward. Furthermore, there were noticeable disparities in the factors influencing coordinated ESE system development across the upper, middle and lower reaches of the Yellow River Basin. Thus, sustainable development policies for the region must be tailored to local conditions. This study also offers insights into the potential development paths for the Yellow River Basin and other river basins in China, contributing practical value to the promotion of sustainable development and the construction of an ESE system that reflects the unique characteristics of the Yellow River Basin.

Keywords:

eco-economic system; Coupling Coordination Degree Model; spatiotemporal differentiation characteristics; boosted regression tree model; Yellow River Basin

1. Introduction

Economic development and ecological security tension antagonism is a major dilemma common to all countries in the world today; at present, human beings are faced with environmental pollution, population explosion, food shortage, resource destruction, energy tension and other problems seriously affecting the social and economic development, scientific and correct understanding and handling of economic and social development and ecological environment relationships, which the world’s countries urgently need to solve [1,2,3]. Some studies have shown that, in less developed regions, the root cause of poverty lies in the lack of coupling and coordination mechanisms between social, economic and environmental elements, which is not conducive to the transformation of ecological advantages into regional development advantages [4]. The economic–social–ecological (ESE) system is the product of human society to protect the environment and save resources; it is a modern economic system that integrates and coordinates ecological benefits, economic effects and social benefits [5]. Compared to the previous concept of development centered on economic construction, the ESE system puts ecological issues on the same important position as economic construction; it is not a simple superimposition of concepts but puts more emphasis on economic development based on ecological and environmental protection. It is a brand new development mode of integration and development, and it is an important means to promote the sustainability of human society. The development of the ESE system is conducive to changing the ideological concept of the opposition between the environment and economic development, avoiding the negative economic model of growth without development and building a new paradigm of civilization centered on ecological civilization. In terms of content, research by foreign scholars on the development of the ESE system has predominantly focused on the relationships between the economy and ecology or between the economy and other factors such as energy, population and the environment. These studies have primarily aimed to analyze the coordinated development between these systems by constructing models and further exploring their internal mechanisms. In contrast, Chinese scholars tend to place less emphasis on the social system, often integrating it within the economic system. There is a relative scarcity of studies that treat the social system as an independent subsystem and compare it to the ecological and economic systems in a comprehensive manner.

With the gradual deepening of human society’s understanding of sustainable development, the concept of coordinated ecological, economic and social development has become a new model for modern economic development. The economic, social and ecological elements in the region form the ESE system. Compared to resource-based development approaches such as circular economy, green economy and low-carbon economy, the ESE system-based development approach places more emphasis on the organic coordination of ecosystems, social systems and economic systems [6]. Since Costanza proposed that ecological economics is the product of the interaction between the ecosystem and the economic system, scholars have conducted quantitative analysis from the perspective of the interaction between subsystems to explore the causal relationship between the ecological environment and economic development [7]. For instance, Martínez employed the coupled coordination degree model to assess the coupled and coordinated relationship among the economy, society and ecology in coastal areas [8]. Xu et al. conceptualized economic development and ecological environmental protection as a unified system and established an evaluation index system to assess its performance [9]. Yan et al. conducted a quantitative study of Australia’s environmental dynamics by constructing a model to analyze the coupled and coordinated development of energy, economy and ecology, measuring a comprehensive evaluation value [10]. Gao et al. integrated the development characteristics of the ecological, economic and social subsystems to construct an evaluation index system for the Qilian Mountain National Park [11], while Liang et al. focused on the Yangtze River Delta urban agglomeration, analyzing the coupling and coordination between finance and environment, along with its spatiotemporal evolution [12]. A review of the relevant literature reveals that the predominant methodological approach involves first establishing an evaluation index system for the coordinated development of regional economy, society and ecology. This is typically followed by the optimization of evaluation indicators using principal component analysis (PCA) [13]. The determination of index weights is commonly carried out using system analysis, analytic hierarchy process (AHP) or entropy methods. Subsequently, regional development is measured through models such as the gray theory [14], fuzzy hierarchical evaluation and other similar approaches. The synergistic relationship between two subsystems is typically quantified using a range of analytical techniques, including the coefficient of variation method, structural equation model, simultaneous equation model, gray relational analysis method and Gini coefficient method [15,16]. The most commonly employed measures of the synergistic relationship between multiple systems encompass the composite coupling coordination model, input–output method, data envelopment analysis and coupling coordination model [17,18,19,20,21].

As the world’s most populous developing country and an emerging nation in the field of ecological civilization construction, China’s experience in the process of ESE system development is worthy of emulation. China’s ecological civilization construction, especially the ecological protection and high-quality development strategy of the Yellow River Basin, embodies the concept of the coordinated development of economy, society and ecosystem. The Yellow River Basin represents a significant ecological barrier and economic zone in China, occupying a pivotal role in the country’s economic and social development, as well as its ecological security. The principal urban agglomerations in the Yellow River Basin are situated in the central and western regions of China, where economic development is comparatively underdeveloped. The economic structure is characterized by a high proportion of secondary industry, with traditional resource-based industries and manufacturing industries representing the dominant sectors [22]. Concurrently, the conventional extensive economic growth trajectory has resulted in considerable environmental degradation. In 2019, General Secretary Xi Jinping delivered an important speech at the Symposium on Ecological Protection and High-Quality Development of the Yellow River Basin, highlighting the ecological protection and high-quality development of the Yellow River Basin as an important national strategy based on a deep understanding of the major contradiction in China’s society in the new era and the outstanding problems of the Yellow River Basin. The Chinese government’s efforts not only contribute to the sustainable development of the Yellow River Basin region but also provide valuable experience for the construction of global ESE. Further strengthening the research on ESE in the Yellow River Basin will not only help promote the construction of regional ecological civilization but also provide theoretical and practical guidance for other developing regions. Despite the significant efforts and explorations made by the Chinese government and people to promote ESE development in the Yellow River Basin, most of the current research on ESE development in China has been concentrated on the developed eastern coastal areas. Studies specifically addressing the coordinated development of the ESE system in the context of the Yellow River Basin remain scarce. Furthermore, existing research on the Yellow River Basin tends to focus on large-scale studies at the provincial or regional level. These studies typically emphasize the promotion strategies for coordinated development [23,24] and the measurement of the coordination level within the basin [25,26], with relatively fewer investigations into the driving forces behind these developments. Given the substantial regional differences within the Yellow River Basin, even within the same province, large-scale studies often fail to address local issues in sufficient detail. Therefore, to inform more locally tailored policies, smaller-scale studies focusing on the development status and driving forces at a more granular level are essential. In addition, counties are the basic unit of economic and social organization in China. Promoting environmental protection and high-quality economic development in the counties will enable a more regionally specific formulation of coordinated regional economic, social and environmental development [27]. Therefore, to more systematically analyze the characteristics of ESE development and the driving factors in large watersheds like the Yellow River Basin at a smaller scale, this study empirically examines the overall spatial and temporal evolutionary trends of the ESE system and its internal coordination mechanisms. By investigating the interconnections and feedbacks among the ecological, economic and social dimensions of counties across the entire Yellow River Basin, the study aims to explore the development model of ESE in the region. Ultimately, the goal is to propose pathways and strategies for promoting the high-quality development of the Yellow River Basin’s ecological and economic systems.

2. Materials and Methods

2.1. Overview of the Study Area

The Yellow River Basin is situated in the north-central region of China, spanning an area between 96° and 119° east longitude and 32° and 42° north latitude. It extends in an east–west direction for a distance of 1900 km and in a north–south direction for a distance of 1100 km. The total area of the watershed is 750,000 km² (Figure 1). The Yellow River has its source in the northern foothills of the Bayan Kara Mountains and flows through nine provinces, namely Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan and Shandong, before entering the Bohai Sea. Its total length is 5464 km. The basin exhibits significant variation in natural conditions, including geomorphology, climate and precipitation, which are reflected in its hydrological characteristics. These can be broadly divided into three sections: the upper, middle and lower reaches.

The upper basin covers 386,000 square kilometers, representing 51% of the total area of the Yellow River. It is characterized by high water volume, low sand concentration, substantial hydropower potential and abundant ecological resources. However, it remains sparsely populated and economically underdeveloped. The middle reaches cover 344,000 square kilometers, accounting for 46% of the total area. This region is characterized by lower water levels, higher sand concentrations and greater ecological pressure. The downstream basin spans 22,000 square kilometers, representing 3% of the total area. Situated close to the Bohai Sea, this region has experienced more rapid economic development and plays a critical role in flood control.

The study area is defined as the county-level administrative units through which the main stream of the Yellow River and its primary tributaries flow, without considering provincial or municipal boundaries. The selection of this area is based on the natural resources and ecological–economic characteristics of the Yellow River Basin [28], with particular emphasis on the impact of the river’s main stream and tributaries on regional development. The study area was selected according to the 2019 administrative division standards (Map Review No. GS (2019) No. 1822). In cases where counties have been merged or split, data are allocated proportionally based on the relevant area to ensure consistency and analytical accuracy.

2.2. Data Sources

The socio-economic data required for the study were sourced from a variety of official sources, including the statistical yearbooks and development bulletins of provinces, cities and counties within the study area, the work reports of prefectural-level cities and the WIND database. The WIND Database (available at https://www.wind.com.cn/, accessed on 4 April 2024) is provided by Wind Information Co., Ltd. and is a widely used platform for financial, economic and social data analysis. It covers macroeconomic data, industry statistics and financial market information (such as stock and bond markets) for both China and global markets. The database is recognized for its high quality and authority in academic research. In order to circumvent the incoherence that would otherwise be caused by the absence of data, the interpolation method and gray correlation prediction method were employed to process some of the missing data. The fundamental geographic data (including water bodies and administrative boundaries) necessary for the spatial visualization analysis were obtained from the Geospatial Data Cloud (https://www.gscloud.cn/sources/, accessed on 15 March 2024) and the Resource and Environmental Science and Data Centre (http://www.resdc.cn/, accessed on 15 March 2024). In this study, data analysis and map visualization representation were conducted using ArcGIS 10.8, GeoDa 1.20.0.36, R4.4.0 and Stata14.

2.3. Construction of Indicator System

A comprehensive ESE system evaluation is the foundation upon which ESE system planning and sustainable development strategies are formulated. In the process of evaluating various economic indicators, the use of a reasonable, scientific and objective indicator screening method is of significant importance. In order to construct a comprehensive indicator system that can truly reflect the status of the ESE system in the counties of the Yellow River Basin, this paper constructs a comprehensive indicator system for the ESE composite system based on the principles of comprehensiveness, regional variability, representativeness and accessibility in the selection of indicators and draws on the results of the existing research [29,30], combined with the social, economic and ecological conditions of the Yellow River Basin (Table 1).

(1): Economic Subsystem. The indicators selected for the economic coordination subsystem focus on three key aspects: economic scale, economic structure and economic vitality, which comprehensively reflect the level of economic development in the Yellow River Basin. The economic scale is assessed through indicators such as GDP and economic density, which reflect the overall economic size and development potential of the region. The economic structure is analyzed by examining the proportion of value-added in the primary, secondary and tertiary industries, which reveals the optimization and development of the industrial structure. Economic vitality is assessed through indicators such as GDP growth rate, growth rate of the tertiary industry and urbanization rate, which reflect the driving force and future potential of economic development.
(2): Social Subsystem. The selection of indicators for the social subsystem emphasizes population status, social potential and quality of life. Population status is reflected through indicators such as population density and the natural population growth rate, which capture the distribution and trends in population change. Social potential is measured by indicators such as years of education per capita, the share of internal R&D expenditure and the unemployment rate, which collectively reflect the region’s labor force productivity, investment in science and technology and the effectiveness of the labor market. Quality of life is assessed through indicators such as per capita disposable income, the number of public library books per capita and the availability of medical resources, which comprehensively reflect improvements in living standards, social welfare and access to healthcare.
(3): Ecological Subsystem. The indicators for the ecological subsystem focus on evaluating the quality of the ecological environment, including habitat quality, vegetation cover and water resource abundance. Habitat quality is assessed through the biodiversity index, which reflects the richness of species in different ecosystem types, providing a measure of ecosystem health. Vegetation cover is evaluated through the share of each land use type in the study area, indicating the degree of land greening and plant cover. Water resource abundance is assessed by the water production capacity and the water resource richness index, which reflect the sustainability of water resources in the region. Additionally, energy consumption is evaluated through the energy consumption per unit of GDP, which measures the ecological cost of economic growth in the region.

2.4. Methods

2.4.1. Entropy Weight TOPSIS Method

Currently, there is no unified and standardized indicator system in place for a comprehensive assessment of the ESE system. In accordance with the principles governing the construction of an indicator system, this study draws upon the Technical Specification for the Assessment of the State of the Ecological Environment (HJ192-2015) [31], a Chinese national standard, and the fundamental geographical and socio-economic characteristics of the Yellow River. The study employs a modified entropy method and a judgement matrix to obtain the weights of 39 secondary indicators, including regional GDP, urbanization rate, habitat quality index and population density, which are selected as references for the basin [32].

(1) Selection of indicators: There are

m

years,

n

districts and counties and

h

indicators.

X_{α i j}

is the value of the

j

th indicator for district

i

in year

a

(2) Dimensionless Processing

P o s i t i v e I n d i c a t o r s : {X^{'}}_{α i j} = \frac{X_{α i j} - m i n X_{α i j}}{m a x X_{α i j} - m i n X_{α i j}}

(1)

{N e g a t i v e I n d i c a t o r s : X}^{'}_{α i j} = \frac{m a x X_{α i j} - X_{α i j}}{m a x X_{α i j} - m i n X_{α i j}}

(2)

(3) The weight of the

i

th sample value under the

j

th indicator should be calculated.

y_{a i j} = {X^{'}}_{α i j} / \sum_{a} \sum_{i} {X^{'}}_{α i j}

(3)

(4) The entropy value of the

j

th indicator is calculated in accordance with the following procedure:

e_{j} = - k \sum_{e} \sum_{i} y_{a i j} l n (y_{a i j})

(4)

k

> 0,

k = \ln (m n)

(5) The objective is to calculate the information entropy redundancy.

g_{i} = 1 - e_{j}

(5)

(6) The weight of each indicator is calculated in accordance with the following methodology.

w_{j} = g_{j} / \sum_{i} g_{j}

(6)

2.4.2. Coupling Coordination Degree Model

The Coupling Coordination Degree Model (CCDM) is one of the methods used to measure the degree of coupling and coordination between two systems. When measuring the degree of coupling coordination, the lower the degree of coupling coordination, the more the development of the system tends towards disorder, while the higher the degree of coupling coordination, the more the development of the system tends towards order. It can be seen that changes in coupling coordination can reflect how the synergistic effects of each subsystem or element develop over time and space. In the development process of the ‘ecological–economic–social’ complex system, the three subsystems interact and influence each other to form a coupled complex system. In this paper, the coupling coordination model is used to measure the coupling coordination between the ecological, economic and social subsystems and to assess whether they are interdependent or mutually constraining. The formula for calculating the degree of coupling is as follows:

C_{n} = n {\frac{U_{1}, U_{2} \dots U_{m}}{[\prod (U_{1} + U_{2})]}}^{\frac{1}{n}}

(7)

Since this article deals with three subsystems, it is assumed that the comprehensive evaluation indices of the three subsystems are

U_{1}

U_{2}

and

U_{3}

, respectively, and the calculation model for their coupling degree is as follows:

C = {[\frac{U_{1} \times U_{2} \times U_{3}}{{(U_{1} + U_{2} + U_{3})}^{3}}]}^{1 / 3}

(8)

As the coupling degree merely indicates the strength of the interaction between systems, it is evident that there is a correlation between the systems in question. However, it is unable to represent the internal connections within the coupled system. Consequently, the concept of Coupling Coordination Degree (CCD) has been introduced, which is capable of reflecting both the internal coordination relationship and the development trend of the coupled system. The comprehensive development index is presented below.

T = α U_{1} + β U_{2} + γ U_{3}

(9)

In this context, T represents the comprehensive development index, while α, β and γ denote the weights of the three subsystems, which are all assumed to be 1/3 in this instance. The CCD is calculated using the following formula:

C C D = \sqrt{C \times T}

(10)

Based on the above definitions and the strength of inter-system relationships expressed in terms of coupling and coordination, and considering that the data from the CCD in the Yellow River Basin are mainly distributed between 0.15 and 0.35, this uneven distribution suggests that equal grading (e.g., 0–0.1, 0.1–0.2, 0.2–0.3, etc.) may not capture the subtle variations between regions. Identical grading could place regions with similar coordination levels into the same category, potentially overlooking significant regional differences. In contrast, ungraded distributions can accommodate these subtle variations, allowing for more accurate and nuanced analyses. Based on previous studies and the actual conditions in the Yellow River Basin [33], the unequal coupling coordination is classified into nine grades, reflecting the characteristics exhibited by the system (Table 2).

2.4.3. Boosted Regression Tree Model

Boosted regression trees (BRTs) represent a self-learning method and an advanced form of regression based on the Classification and Regression Trees (CART) algorithm, which combines two algorithms: regression trees and boosting [34]. The calculation process can be broadly divided into two parts. Firstly, the degree of influence of the independent variables on the dependent variable is randomly extracted from the training data set for analysis. Secondly, the results are cross-validated multiple times. The learning rate of the enhanced regression tree model determines the contribution of each tree to the growth of the model, while the complexity of the tree determines the number of non-leaf nodes in each tree and whether the model can fit the interaction effect. In contrast to traditional statistical techniques, the operation of this method is not contingent upon the existence of linear relationships. Even in cases where two features exhibit a high degree of overlap, the model is capable of automatically splitting and combining them in a reasonable manner, thereby obviating the need to test for collinearity. Furthermore, the enhanced regression tree model is capable of handling complex non-linear relationships and relationships between missing data. The cross-validation processing mode significantly enhances the stability and accuracy of the results [35].

The boosted regression tree method is used in this paper. The BRT equation package written by Elith in the R language (3.5.0) is used to conduct a boosted regression tree analysis with the factor of the driving factor as the independent variable and its effect on the degree of coordination of ESE system development as the dependent variable [36]. The degree of coupling and coordination of the ESE system is a continuous variable, so a Gaussian distribution was used. The shrinkage was set to 0.01, with 50% of the data used for regression tree analysis and 50% for training each time. The depth was set to 5, and a total of 10 cross-validations were performed.

3. Results

3.1. Analysis of ESE System Scores

3.1.1. Analysis of Economic, Social and Ecological Subsystems

To better understand the temporal characteristics of the integrated ESE system in the Yellow River Basin over time from 2000 to 2020, Figure 2 shows a violin plot of the ESE system scores and the scores of the three subsystems, ecological–economic–social. The shape changes observed in Figure 2a suggest that the overall ESE system score of the Yellow River from 2000 to 2020 exhibits a ‘single-peak’ distribution. Additionally, the median shows a general upward trend over time, with the peak of the kernel density curve shifting upward. This suggests that the overall ESE system score in the Yellow River Basin improved gradually from 2000 to 2020. After 2015, the distribution shifted to an elongated ‘pointed cone’ shape, becoming broader. This suggests an increase in the dispersion of the data, reflecting an expanding gap in regional ESE system development. As shown in Figure 2b, the economic subsystem score also shows a gradual upward trend, with a significant increase in 2020. Concurrently, the data for the economic subsystem in 2015 and 2020 exhibit a relatively broad distribution, indicating an intensification of economic development imbalances across the Yellow River Basin regions. As illustrated in Figure 2c, the score of the ecological subsystem also exhibited an upward trend but with a more limited range of variation. This still indicates an improvement in the ecological environment of the Yellow River Basin. Conversely, the overall data distribution is relatively narrow, indicating that the scores of the ecological subsystem are relatively concentrated and that the differences in the ecological environment among various regions are relatively minor. As illustrated in Figure 2d, the score of the social subsystem also demonstrated an upward trajectory over time, particularly in 2015 and 2020, when the score distribution exhibited a notable widening, reflecting the growing disparities in social development across different regions. In general, the Yellow River Basin has witnessed a degree of advancement across all indicator scores between 2000 and 2020. In particular, the scores of the economic and social subsystems have increased significantly over time, indicating that the overall development of the Yellow River Basin, especially the socio-economic situation, has improved greatly during the study period. Concurrently, the distribution shapes of the scores of all indicators in 2020 have become elongated, most notably in the economic and social subsystems, reflecting an increasing degree of regional development imbalance during this period.

3.1.2. Analysis of Changes in the Comprehensive ESE System Scores

From a spatial distribution perspective (Figure 3), it can be observed that the ESE system development levels of all counties and districts in the Yellow River Basin have exhibited a notable improvement between 2000 and 2020. Areas with comparatively lower ESE system development levels are concentrated in the southern part of the middle Yellow River, primarily in Ningxia and Gansu, with pockets in Shaanxi and Northern Shanxi. This creates a zone of low ESE system development stretching from east to west in the Yellow River Basin. The area of low-level eco-economic development is situated in the central Yellow River Basin, which is divided into four distinct areas: east, west, north and central. The eastern, western and northern regions have all experienced notable advancement in terms of coordinated economic, social and ecological development, resulting in a distinctive distribution of development levels, with the central area exhibiting a ‘high on three sides and low in the middle’ pattern. It is important to note that the ESE system’s development layout within the Yellow River Basin is dynamic. Over time, the scores of the composite system have undergone changes in different ways in different regions, exhibiting spatial and temporal variations, as discussed below.

In 2000, areas of high-level ESE system development in the Yellow River Basin were concentrated in Qinghai Province and Northern Inner Mongolia. In contrast, the areas undergoing low-level development were located in Western Gansu, Northern Shaanxi and Eastern Inner Mongolia. During this period, the highest comprehensive ESE system score was in Dawukou District, Shizuishan City, with a value of 0.1849. Located in the northernmost part of the Ningxia Hui Autonomous Region, it is close to Wuhai City in Inner Mongolia. At this time, the district with the lowest ESE system score was Anning District in Lanzhou City, with a value of 0.0292. It is located in the central part of Gansu Province. By 2005, the overall ESE system level of the Yellow River Basin had improved to a certain extent, particularly in the eastern region (Central Shandong, Northern Henan and Southern Shanxi and Shaanxi Provinces). At this juncture, the district with the highest ESE system score was Lixia District in Jinan City, with a value of 0.2659. Located in Central Shandong, it showed one of the fastest growth rates in the comprehensive ESE system score. At this juncture, the district with the lowest comprehensive ESE system score was Gaolan County in Lanzhou City, with a value of 0.0304. It is also located in the central region of Gansu Province. By 2010, the development of the ESE system level in the Yellow River Basin had become more balanced. While the eastern and northern regions saw significant ESE system development, the central region also showed notable improvement. During this period, Kundulun District in Baotou City was the district with the highest overall ESE system score, with a score of 0.1960. This was a decrease of 26.29% compared to Lixia District in 2005. At this point, Gaolan County in Lanzhou City still had the lowest overall score, at 0.0374, a 23.03% increase from 2005. By 2015, the overall ESE system development level of the Yellow River Basin had improved significantly, particularly in the eastern Henan and Shandong regions, as well as the Northern Inner Mongolia and Shaanxi regions. During this period, Lixia District in Jinan City had the highest ESE system score at 0.4315, a 200% increase from Kundulun District in 2010. At this juncture, the district with the lowest comprehensive ESE system score was Jianzha County in Huangnan Tibetan Autonomous Prefecture, with a score of 0.0456. By 2020, the overall level of ESE system development in the Yellow River Basin had once again improved, with the majority of districts and counties in the entire basin achieving a comprehensive ESE system score of over 0.1. The pace of ecological economic development in the western region accelerated during this five-year period. During this period, Lixia District of Jinan City retained its position as the district with the highest ESE system comprehensive score, with a value of 0.4975, representing a 15.3% increase compared to 2015. This is an illustrative example of the consistent and favorable advancement of the ESE system in the eastern region. At this juncture, the district with the lowest comprehensive score for ESE system is Dongxiang Autonomous County in Linxia Hui Autonomous Prefecture, with a value of 0.0537. This represented a 17.76% increase from the score of Jianzha County in 2015. Western Gansu’s surrounding areas still have significant potential for improvement in ESE system performance.

An analysis of the temporal and spatial differences in the ESE system development of the Yellow River Basin over the five-year period from 2000 to 2020 reveals that the comprehensive development level of the ESE system in the counties of the entire Yellow River Basin has improved during the study period. The region exhibiting the most pronounced increase in development level is the lower reaches of the Yellow River Basin, followed by the middle reaches. In contrast, the development level of the ESE system in the upper reaches has increased at a relatively slower pace, with the majority of the observed growth occurring after 2015. The development hierarchy shifted from downstream > upstream > midstream in 2000 to downstream > midstream > upstream 2020. There is considerable scope for enhancement of the comprehensive ESE system level of the midstream region, particularly in the central and western areas of Gansu Province. Additionally, the spatial distribution patterns of high- and low-level ESE regions over the years suggest potential spatial correlation in county-level development, warranting further investigation

3.1.3. Analysis of the Developmental Center of Gravity Shift in the ESE System

This paper employs the standard deviation ellipse (SDE) methodology to examine the shifts in the center of gravity of ESE system development across the counties of the Yellow River Basin. It elucidates the spatial transformation characteristics of the ESE system development level in the Yellow River Basin from 2000 to 2020. The results are illustrated in Figure 4. (1) From 2000 to 2020, the center of gravity of the ESE system development shifted from 109.81° E, 35.38° N to 110.47° E, 35.41° N. It was predominantly situated in the south-eastern region of the geometric center of the Yellow River Basin. (2) Based on the direction of movement of the center of gravity of the ellipse and the migration trajectory, it can be observed that, between 2000 and 2005, the center of gravity of the level of ESE system development in the Yellow River basin gradually shifted towards the northeast. Between 2005 and 2015, the center of gravity of the level of ESE system development in the study area exhibited a northward deviation of approximately 20° from the original trajectory, continuing to move eastward in an easterly northerly direction. From 2015 to 2020, the movement direction shifted 100° clockwise, gradually heading southwest. (3) Regarding the distance, the center of gravity of ESE system development from 2000 to 2020 showed a general eastward shift along the east–west axis. This was followed by a northward shift (2000–2015) and a subsequent southward shift (2015–2020) along the north–south axis. The distance shifted northward was slightly greater than the distance shifted southward, while the distance shifted eastward was markedly greater than the distance shifted northward.

Table 3 illustrates the typical values and distribution of the standard deviation ellipses pertaining to the levels of ESE system development observed in the counties of the Yellow River Basin. (1) During the period 2000–2020, the length of the X-axis of the standard deviation ellipse of the level of ESE system development was greater than that of the Y-axis, indicating a distribution pattern in a ‘southwest–northeast’ direction. (2) The shape index of the standard deviation ellipse initially declined and then increased. From 2000 to 2015, the shape index decreased, with the ellipse approaching a perfect circle. From 2015 to 2020, the shape index of the standard deviation ellipse demonstrated an upward trajectory. However, the overall flattening of the standard deviation ellipse throughout the research period suggests that the overall level of ESE system development in the Yellow River Basin exhibits a clustering tendency. (3) During the study period, the direction angle of the standard deviation ellipse demonstrated a fluctuating increasing trend, increasing from 82.7648° in 2000 to 84.362° in 2020, representing a clockwise rotation of the ellipse by 1.5972°. The county’s ESE system development in the Yellow River Basin underwent a gradual transformation in its direction of growth, shifting from a ‘southwest–northeast’ trajectory to a ‘due east–due north’ one. (4) The length of the major axis of the standard deviation ellipse indicates that, during the study period, the length of the X-axis decreased from 622.5143 km in 2000 to 580.7756 km in 2020. Additionally, the length of the Y-axis fluctuated from 314.1205 km to 309.2143 km in a ‘rising–falling–rising–falling’ trend, suggesting that the distribution of counties and districts with a high level of ESE system development in the Yellow River Basin has become increasingly concentrated over time.

The diagram analysis shows that, from 2000 to 2020, the focus of ESE system development in the Yellow River Basin has remained in the southeastern area near its geometric center. The distribution pattern of ESE system development shifted from a ‘southwest–northeast’ direction to an ‘east–west’ direction. This shift indicates a steady increase in sustainable development in the southeastern Yellow River Basin. From 2000 to 2015, there was a notable shift in the center of gravity of ESE system development towards the north-east, accompanied by a change in the shape of the ellipse towards a more circular form with a reduced perimeter and area. This suggests that the ESE system development of the eastern and northern regions of the Yellow River Basin accelerated during this period, accompanied by an increase in spatial agglomeration. From 2015 to 2020, there was a slight shift to the southwest, accompanied by an increase in the shape index of the standard deviation ellipse and a slight increase in the perimeter area. This suggests that the ESE system development of the southwestern Yellow River region accelerated slightly during this period and that regional differences in the comprehensive development level of the ESE system in the east–west direction decreased.

3.2. Analysis of the Coupling Coordination Degree (CCD)

3.2.1. Spatiotemporal Analysis of the CCD

The degree of coupling and coordination between the ESE system is indicative of the equilibrium, resilience and potential for growth of the regional economic–ecological–social complex system. The degree of coupling and coordination of the ESE system in each county within the Yellow River Basin was calculated from 2000 to 2020 based on a comprehensive evaluation index system and coupling coordination model of the ESE system of the Yellow River Basin. ArcGIS 10.8 software was employed for the visual processing of the data in accordance with the coupling coordination standard table (Table 2), resulting in the generation of a spatial differentiation map of the CCD of counties in the Yellow River Basin from 2000 to 2020 (Figure 5). As illustrated in Figure 5, the ESE system coupling coordination levels of the counties in the Yellow River Basin from 2000 to 2020 can be classified into six categories: well coordinated, basically coordinated, on the verge of imbalance, mildly imbalanced, moderately imbalanced and severely imbalanced. The specific situation is as follows:

In the year 2000, the highest value of coupling coordination observed in the Yellow River basin was recorded in Dawukou District, Shizuishan City, with a value of 0.3851 and a type of basically coordinated. Lixia District (Jinan City) and Jinfeng District (Yinchuan City) followed with CCDs of 0.3772 and 0.3476, classified as ‘basically coordinated’ and ‘on the verge of imbalance’, respectively. The lowest CCD was observed in Hainan District (Wuhai City) at 0.1632, classified as ‘serious imbalance’. Gaolan County and Anning District (Lanzhou City) followed, with CCDs of 0.1655 and 0.1681, respectively, both classified as ‘serious imbalance’. In 2005, Lixia District (Jinan City) recorded the highest CCD at 0.4726. This CCD was classified as ‘basically coordinated’. Kundulun District and Qingshan District (Baotou City) followed, with CCDs of 0.4503 and 0.4000, respectively. These CCDs were classified as ‘basically coordinated’. The lowest CCD was recorded in Anning District (Lanzhou City) at 0.1712. Gaolan County (Lanzhou City) and Hainan District (Wuhai City) followed, with CCDs of 0.1716 and 0.1805, respectively. Both districts remained in the ‘severely unbalanced’ category. In 2010, Lixia District (Jinan City) again recorded the highest CCD at 0.438. This was followed by Kundulun District (Baotou City) and Huimin District (Hohhot City), with CCDs of 0.4262 and 0.4000, respectively, and both were classified as ‘basically coordinated’. The lowest CCD remained in Anning District (Lanzhou City) at 0.1932, classified as ‘severely unbalanced’, followed by Gaolan County and Honggu District (both in Lanzhou City), with values of 0.1946 and 0.2039, respectively. Gaolan County was classified as ‘severely unbalanced’, while Honggu District was ‘moderately unbalanced’. In 2015, Lixia District (Jinan City) recorded the highest CCD at 0.6022, classified as ‘basically coordinated’. Kundulun District (Baotou City) and Huimin District (Hohhot City) followed, with CCDs of 0.4738 and 0.4000. The lowest CCD was recorded in Gaolan County (Lanzhou City) at 0.2118, classified as ‘medium imbalance’, followed by Honggu District and Anning District (both in Lanzhou City), with values of 0.2175 and 0.222, respectively, both classified as ‘moderately imbalanced’. In 2020, Lixia District (Jinan City) recorded the highest CCD at 0.6577, classified as ‘good coordination’. This was followed by Huaiyin District (Jinan City) and Linxia City, with CCDs of 0.5049 and 0.4908, classified as ‘basically coordinated’. The lowest CCD was observed in Dongxiang Autonomous Prefecture at 0.2423, classified as ‘medium dysfunction’, followed by Jinzhai County and Jishishan Bao’anzu Dongxiangzu Salar Autonomous County, with values of 0.2437 and 0.2449, respectively, both classified as ‘medium dysfunction’.

The overall CCD of the ESE systems across counties in the Yellow River Basin showed a gradual upward trend throughout the study period. Between 2000 and 2020, the CCD increased significantly, from 0.1632 to 0.3851, then to 0.2423 and, finally, to 0.6577. The minimum value increased by 0.0791 and the maximum by 0.2726, both compared to the 2000 levels. The main CCD level shifted from moderate to severe imbalance in 2000 to a state of borderline imbalance and near-coordination by 2020. Throughout the study period, counties with the highest CCD were consistently located in Shandong Province and Inner Mongolia, while those with the lowest levels were primarily in Gansu Province. This indicates that the CCD distribution in counties within the Yellow River Basin may show significant spatial correlation and clustering.

3.2.2. Hot Spot and Cold Spot Analysis of CCD

To further investigate the spatial characteristics of counties’ Coupling Coordination Degree (CCD) in the Yellow River Basin, data from five selected years (2000, 2005, 2010, 2015 and 2020) were analyzed for clustering patterns, classifying 434 counties into seven hot spot and cold spot types based on CCD clustering and significance tests, as shown in Figure 6 and Table 4.

As indicated in Table 4, the number of high hot spots peaked in 2005 at 77 but subsequently declined to 62 by 2020. This may reflect an initial period of active economic and social development, with rising ecological pressures eventually limiting further increases in coordination. The high cold spot count remained consistently elevated, suggesting that CCD improvements in some underdeveloped areas have been challenging and may require additional policy support and economic investment. Moreover, the number of cold spots trended upward from 2000 to 2010, indicating that some regions in the basin lagged in economic, social and ecological development during this period, likely due to ecological constraints and resource/environmental pressures. Sub-hot spots remained relatively stable, implying that some areas have untapped development potential.

Based on the spatial distribution in Figure 6, the number of high cold spots in the upstream area has increased over time, while high hot spots have decreased. This suggests that ecological protection policies and resource limitations may be impacting the ESE system’s coupling coordination upstream, resulting in lagging development. In contrast, the middle and lower reaches have maintained relatively stable high hot spot counts, with a peak in 2005 followed by a gradual decline. This indicates that early-stage rapid socio-economic development in these areas may have now stabilized, likely due to resource and environmental carrying capacity limits. High hot spot areas initially appeared in clusters in Eastern Shandong Province, Northeastern Inner Mongolia and Southwestern Qinghai Province from 2000. Over time, these clusters shifted eastward and had mostly disappeared from Qinghai by 2015. In 2000, hot spot areas were primarily in Qinghai and Inner Mongolia but migrated southeast over time, centralizing in Southern Qinghai and Southern Inner Mongolia by 2010 and then mainly in Inner Mongolia by 2020. Meanwhile, hot spots in Shandong in 2010 gradually declined, concentrating mainly in Eastern Inner Mongolia and Central Henan by 2020. Sub-hot spots appeared in Qinghai, Inner Mongolia and Shandong, with fluctuating shifts eastward from 2000. High cold spots increased from 120 in 2000 to 126 in 2020, clustering in the Central Yellow River Basin (Gansu, Ningxia, Northern Shaanxi and Northern Shanxi), and showed a westward shift. Cold spot regions, mostly in Central Shaanxi and Southern Inner Mongolia, decreased slightly from 35 in 2000 to 31 in 2020. Sub-hot spot regions, sparsely located in Eastern Qinghai, Central Shaanxi and Western Shanxi, also declined from 25 to 15 over this period.

In summary, CCD hot spots in the Yellow River Basin’s counties are primarily clustered in the eastern, western and northern regions, while cold spots are centrally concentrated. There is clear spatial aggregation in the basin’s ecological–economic coordination, with notable variations between the upper, middle and lower reaches.

3.3. Analysis of Influencing Factors for CCDs

3.3.1. Selection of Influencing Factors for the CCDs of the ESE System

The coordinated development of the economy, society and ecology in the Yellow River Basin is influenced by a variety of factors. As shown by the indicator weights in Table 1, the factors with the greatest influence on the ESE system in the Yellow River Basin are science and technology education, water resources and economic structure. To avoid the logical issue of ‘evaluating the results using the same indicators that constitute them,’ the indicators in Table 1 are not repeated. Given the differences in topography, geomorphology and cultural background across the upper, middle and lower reaches of the Yellow River Basin, and drawing on previous studies [37,38,39,40], this paper selects four factors related to the coupling of natural and human activities: natural environment, industrial structure, science and education and administrative location, as shown in Table 5.

Natural Environment: Environmental pollution and geographic factors play crucial roles in initial economic and social development. Air quality, represented by PM₂.₅ levels, affects residents’ health and reflects pollution levels, while proximity to water bodies highlights resource accessibility and ecological protection. As a vital resource in the Yellow River Basin, water resource health is fundamental to the basin’s ecosystem.

Industrial Structure: Economic development levels and industry structure vary significantly across regions. The proportions of secondary and tertiary industries illustrate trends in economic activity and industrial upgrading. Regions with higher shares of these industries tend to have more advanced economies, while those dominated by primary industries are more agricultural. Differences in industry structure strongly influence economic development, impacting both ecological and social systems.

Administrative Location: The Yellow River Basin spans multiple provinces, leading to diverse administrative advantages among counties. Distance to provincial capitals reflects a region’s access to policy support, resources and transportation infrastructure. Counties closer to provincial capitals often benefit from increased policy and resource support, fostering social and economic development.

Science and Education: Factors like patent applications and the proportion of college students are vital for the coordinated development of the ESE system. Scientific and technological innovation optimizes spatial development, supports energy-saving industries and advances ecosystem protection. Transitioning industries from high-pollution models to green, low-carbon frameworks drives sustainable practices, reduces pollution and promotes industrial upgrading. This study posits that science, technology and education facilitate sustainable economic and ecological synergy, aligning with the strategic needs of the Yellow River Basin.

Given the regional differences in natural geography and socio-economic factors across the basin, a boosted regression tree (BRT) model was applied to analyze the coupled coordination degree and seven influencing factors for the upper, middle and lower reaches. The mean squared errors (MSEs) for these regions are 0.0012, 0.0008 and 0.001, respectively, each meeting the 5% deviation range criterion (threshold MSE = 0.0025), indicating a robust model fit.

3.3.2. Analysis of Influencing Factors on the CCDs of the ESE System

The results of the relative importance of the impact factors are shown in Figure 7 and analyzed below. This analysis reveals the varying degrees of influence that each factor has on the coordinated development of the ecological, economic and social systems across the upper, middle and lower reaches of the Yellow River Basin. Understanding these relationships allows for more targeted policy recommendations aimed at improving the integration and sustainability of the ESE systems in different regions.

In the upper reaches of the Yellow River Basin, the relative importance of each factor from 2000 to 2020 is ranked as follows: S (21.77%), RTS (20.95%), Pat (16.52%), Stu (15.36%), DGC (11.53%), PM2.5 (9.56%) and DGW (4.28%). This ranking highlights the key drivers of changes in the CCDs in this region. Topographic factors (S) are the most significant, reflecting the unique geographical conditions of Qinghai and Tibet. The region’s uneven terrain presents challenges to stable socio-economic development, while its natural beauty drives tourism. In addition to topography, the industrial structure (RTS) and science and technology education (Pat and Stu) also play important roles in ESE system coordination. However, factors such as administrative location, air quality and certain natural location factors have a relatively limited influence on coordination in the upper reaches.
In the middle reaches of the Yellow River Basin, the relative importance of each factor from 2000 to 2020 is ranked as follows: Pat (40.82%) > Stu (20.57%) > PM2.5 (16.04%) > S (7.89%) > RTS (7.02%) > DGW (4.55%) > DGC (3.12%). This ranking reveals the key drivers of changes in CCDs in this region. Science and technology (Pat) and education (Stu) have the most significant influence on advancing ESE system coupling coordination. Environmental and topographical factors (S and PM2.5), as well as industrial structure elements (RTS), also play notable roles. The middle reaches are rich in coal mining resources, where large-scale development has caused various ecological and environmental challenges. As a result, improving resource utilization and advancing the tertiary industry, supported by scientific and technological progress, are crucial for achieving coordinated ESE system development in this area.
In the lower reaches of the Yellow River Basin, the relative importance of each factor from 2000 to 2020 is ranked as follows: DGC (28.97%) > PM2.5 (22.75%) > Pat (16.00%) > Stu (10.99%) > RTS (7.59%) > S (7.14%) > DGW (6.56%). This ranking helps explain the factors driving changes in CCD in the lower reaches. In this economically advanced region, key drivers of eco-economic coordination include administrative location (DGC) and environmental quality (PM2.5). The lower Yellow River Basin hosts many large provincial capitals, which contribute to the region’s economic growth through transportation and economic cooperation. However, the concentration of economic activities and industries around these urban centers exacerbates local air pollution, which often spreads to outlying counties and districts lacking the resources to mitigate it effectively. This creates imbalances in ESE system development. While administrative location and environmental factors are central to ESE system coordination, science and technology (Pat) and education (Stu) also play vital roles. Technological advancements improve economic efficiency and reduce pollution, while education fosters environmental awareness and develops skilled labor, supporting higher levels of ESE system coordination.

Based on the marginal effects of individual factors on the CCD (Coupling Coordination Degree) illustrated in Figure 8, a detailed analysis of each factor reveals distinct patterns. For natural environmental factors, the maximum DGW (distance to groundwater) value in downstream areas is significantly lower than in upstream and midstream areas, indicating higher water accessibility downstream. In the upstream region, the marginal effect of DGW follows an initial increase, then decreases and, eventually, stabilizes with distance. This pattern reflects a trade-off between ecological protection and economic development, where areas closer to water bodies face stricter ecological protection and greater environmental pressures, resulting in a negative marginal effect. As the distance from water sources increases, these pressures lessen, allowing moderate development to positively influence coupling coordination. In the middle and lower reaches, the CCD remains relatively stable with DGW changes, suggesting that distance from water has minimal effect on the coordination levels in these regions. The slope factor (S) also plays a significant role in the upper reaches, where the high-altitude, mountainous topography constrains economic development but enhances ecological protection. Moreover, the PM2.5 concentration is typically higher in the lower reaches, and as it rises, its effect on ESE (ecological, social and economic) system coordination follows a ‘U’-shaped pattern. This analysis highlights the region-specific dynamics between environmental factors and CCDs across the Yellow River Basin, emphasizing the importance of tailored strategies for effective ESE system management.

The marginal effect of the industrial structure on the CCD of the ESE system in the Yellow River Basin exhibits regional variation. In the upper and lower reaches, the CCD shows an upward fluctuation with an increasing share of the tertiary industry. However, this effect is less pronounced in the middle reaches. Unlike the upstream area, which emphasizes eco-tourism, and the downstream area, where high-tech industries are emerging, the middle reaches possess abundant energy resources like coal and oil. Here, resource-based industries dominate the economy, and although the tertiary industry is growing, the continued focus on energy extraction and heavy industry limits its impact on ESE system coordination in the short term.

In terms of administrative location, there is a notable difference in influence between the lower reaches of the Yellow River Basin and other regions. Proximity within 25 km of a large provincial capital city has a significant impact on enhancing the coordination of ESE system factors in the lower reaches. This proximity advantage suggests that counties and districts closer to provincial capitals in the lower reaches benefit from access to resources such as capital, technology, talent and favorable policies, all of which contribute to integrated ESE system development. In the middle and upper reaches, however, the impact of provincial capitals is more limited. Here, the influence of capital cities on local development depends on factors such as administrative planning, transportation infrastructure and policy implementation. Counties in these areas exhibit less reliance on provincial capitals, leading to a weaker influence of administrative proximity on the ESE system’s coupled coordination degree.

Regarding science and technology education, the downstream region has the highest number of college students per 10,000 people (Stu), which can be attributed to its advanced economic development. Similarly, due to policy support, the upstream region also shows a significantly high Stu value during the study period, surpassing that of the middle reaches. In the downstream region, although the growth rate of the ESE system was initially slow, it accelerated after 2015 (Section 3.1.1), and the focus of ecological–economic development shifted westward (Section 3.1.3). This shift highlights the growing positive impact of increasing college student numbers in the upstream area on ESE system coordination, indicating that educational resources have a cumulative effect on development. The change in the number of patents (Pat) reveals that, as technological advancement progresses in the upper reaches of the Yellow River Basin, the marginal effect of its coupling with the ESE system fluctuates. This suggests that, at present, technological development is not yet a stable or sustained driving force in this region. In the middle reaches, the marginal effect curve of Pat is smoother but still shows significant fluctuations, indicating that, while traditional industries dominate, technological progress has a substantial impact on the coordination of the ESE system. However, this impact is not yet direct enough, partly due to the unequal application of technological advancements driven by policies and market demands. In the downstream region, the Pat curve shows a distinct pattern of initial growth followed by a decline and eventual stabilization. This pattern reflects the strong initial impact of technological progress on ESE system integration, but as technological saturation and transformation constraints arise, this influence diminishes, leading to stabilization during the mature stage.

An examination of the marginal effect curve of the CCD of the upper, middle and lower reaches of the Yellow River Basin on the seven influencing factors (Figure 8) reveals that the degree of response of the influencing factors to changes in the CCD varies across different regions. When the same influencing factor index is considered, the marginal effect observed in downstream areas is greater than that seen in midstream areas, and the marginal effect in upstream areas is greater than that observed in downstream areas. In other words, when the same policy interventions are applied, the degree of change (sensitivity) in the ESE system coupling coordination of downstream areas is greater than that of midstream areas, which, in turn, is greater than that of upstream areas. This phenomenon can be attributed to three principal factors.

The first factor is the disparity in the stage of economic development. The downstream region is the most economically developed area in the Yellow River Basin, exhibiting a high degree of industrialization and urbanization, as well as a relatively complete economic structure. This also indicates that the ecological environment in the downstream region is experiencing significant stress, and there is considerable potential for enhancing the integration of the ESE system. Furthermore, the high level of industrialization in the downstream region gives rise to a more intricate interplay between the ecological environment and the economic system. The optimization of one system frequently results in a notable enhancement of the overall coupling. Consequently, the policy measures (industrial structure adjustment, environmental protection, etc.) resulting from administrative location exert a more pronounced influence. The middle reaches of the Yellow River Basin represent a nexus of industrial and agricultural activity within the wider basin. The level of economic development is intermediate between that of the upper and lower reaches, and the impact of policy interventions on the ecological environment and the economy is also moderate. Given that the pressure on resources and the environment in the middle reaches is less than in the lower reaches, economic development is relatively rapid. Consequently, the marginal effect of policy interventions is relatively high, although not as significant as in the lower reaches. The upper reaches of the Yellow River Basin are characterized by a preponderance of areas designated for ecological functions, with a concomitant low level of industrialization and urbanization. Despite the relatively favorable ecological environment, the level of economic development is low. Consequently, the marginal effect of influencing factors on the intervention is small, and the effect of policies or environmental improvement measures on improving the coupling and coordination is limited.

The second factor is the discrepancy in the environmental carrying capacity and development potential. Despite the downstream region experiencing greater pressure on resources and the environment due to its developed economy, it nevertheless possesses greater potential for improving the ecological environment or restructuring the economy. The high marginal effect indicates that there is considerable scope for optimizing the coupling of the ESE system. Despite the favorable economic conditions in the midstream region, the transformation of the industrial structure and the pressure on the resource environment are significant, resulting in a relatively limited impact of intervention. The upstream region is endowed with abundant resources and exhibits minimal environmental pressure. However, due to its relatively underdeveloped economic structure, comprising a narrow industrial base and constrained economic growth potential, the impact of intervention measures is more pronounced in environmental protection than in economic advancement. Consequently, the extent of coordination between the ESE system has remained relatively unchanged, and the marginal effect is comparatively low in the upstream region.

Finally, with regards to the factors of scientific and technological innovation and industrial restructuring, the impact of these factors on each region will also result in differences in marginal effects. The downstream region exhibits a greater number of patents and a larger population of college students, as well as stronger capabilities in scientific and technological innovation. Consequently, the marginal effect brought about by scientific and technological innovation is more significant. The driving force of scientific and technological and industrial optimization has enabled the ESE system coupling degree of the downstream region to be rapidly improved, as evidenced by the higher marginal effect. The industrial structure of the midstream region is undergoing adjustment, yet the marginal effect of the influencing factors exhibits a slight lag at a relatively high level of economic development. This indicates that, despite the midstream region demonstrating a certain degree of innovation capacity and industrial foundation, its marginal effect in environmental governance and coupling and coordination is relatively weak. The upstream region displays limited capabilities with regards to technological innovation and industrial upgrading. In particular, the impact of innovation-driven economic development in this region is less pronounced than in the midstream and downstream regions, resulting in a comparatively lower marginal effect.

By combining the relative importance of the impact factors (Figure 7), it is possible to provide a further summary of the characteristics of the ESE system coupling and coordination in different regions of the Yellow River Basin. (1) The marginal effect and relative importance of the factors in the upper reaches of the Yellow River Basin are particularly pronounced in the domains of the environment and science and education. The ecological environment in the upper reaches is relatively fragile, and improvements to the environment make a significant contribution to coupling and coordination. However, the lack of innovative and educational resources results in significant fluctuations in the marginal effects of the number of college students (Stu) and the number of patent grants (Pat), preventing the formation of a stable and sustainable driving force. (2) The middle reaches of the Yellow River Basin represent a transitional area, exhibiting a more balanced marginal effect and relative importance. This is driven not only by natural environmental factors but also by science and technology and industrial structure. Among the factors considered, the number of patent authorizations (Pat) in the middle reaches of the river has the greatest relative importance for the coordination of ESE system coupling. The advancement of science and technology has emerged as a pivotal driver for the advancement of the ESE system in the middle reaches of the river. The region has facilitated the green transformation and sustainable development of the regional economy primarily through the implementation of innovative technologies. (3) The lower reaches of the Yellow River Basin are characterized by a high level of economic development and urbanization. The marginal effect and relative importance of coordinated development of the ESE system are concentrated in the following factors: administrative location, environmental factors and scientific and educational level. In particular, the proximity to the provincial capital (DGC) and favorable environmental conditions exert a considerable influence in the lower reaches. Concurrently, scientific and technological innovation (Pat) also assumes a role, indicating that the advancement of the ESE system in the lower reaches is contingent upon the resource dividends yielded by locational advantages and technological advancement. The accumulation of environmental pollutants during the process of urbanization represents a significant factor influencing the coordinated development of the ESE system.

4. Discussion

This study unveils the spatiotemporal evolution characteristics of the economic, social and ecological subsystems within the Yellow River Basin and assesses their integrated ESE system. By conducting a quantitative analysis, it examines the coupling coordination dynamics among these subsystems and identifies the key factors influencing this coordination.

The findings indicate a steady increase in ESE system development across the basin from 2000 to 2020, attributed to the Chinese government’s focus on high-quality development and ecological restoration projects, such as returning farmland to grassland and forests [41]. However, the level of ESE development and CCD varies significantly across counties and districts, influenced by factors such as natural geography, administrative location, transport networks, resource endowment and local strategic policies. In the middle reaches, coordination improvement is relatively slow due to an inadequate ecological foundation and a concentration of resource-intensive industries. The upstream areas are gradually progressing with national policy support, although overall coordination lags due to slower economic and social development. In contrast, downstream areas have achieved a higher level of economic, social and ecological coordination through resource optimization and environmental protection policies. The development center of gravity of the ecological–social–economic (ESE) system in the Yellow River Basin exhibits a trend of gradual eastward shift, accompanied by the increasing concentration of high levels of development in this direction. In the Yellow River Basin, innovation, resource-environmental costs and public service inputs are key drivers of ESE system coordination. For upstream regions rich in natural resources and ecological value, the focus should be on establishing watershed ecological compensation mechanisms, enhancing Western Development and Rural Revitalization Strategies, maximizing regional strengths and improving residents’ income and well-being to address development lags [42,43]. Most middle reaches, located on the Loess Plateau, face environmental challenges from dense coal resources and intensive, long-term development [44]. Here, strategies should deepen farmland restoration and employ the Yellow River Basin’s ecological protection and high-quality development initiatives to balance energy development and ecological preservation in Shanxi, Shaanxi and Inner Mongolia. The lower reaches, with relatively higher ESE coordination, still face issues like water scarcity, environmental pollution, soil erosion and competition in agriculture and industry [45]. Thus, downstream efforts should focus on enhancing ecological compensation methods, combining ecological and economic compensation mechanisms for arable land preservation and addressing food security and farmers’ income.

Although the results of this study suggest that the Yellow River Basin cannot be regarded as a single, unified ecosystem, its ecological functions exhibit significant regional differences. The degree of coordinated development of the ESE system, the driving factors and even the interrelationships among the three subsystems vary across different regions. However, the interconnections between regions within the basin should not be overlooked. The ecological functions of these regions are interconnected, and it would be simplistic and unwise to treat the basin’s issues as isolated regional problems based solely on their differences. For example, the steep topography and resource scarcity in the upstream areas limit development, while the erosion caused by loose, mountainous geology leads to the transport of large amounts of sediment downstream. This, in turn, accelerates ecological degradation in the middle reaches, intensifying erosion and contributing to siltation in the downstream areas. Additionally, water pollution in the downstream regions is exacerbated by pollutants carried from the energy industry zones in the midstream. Therefore, despite the downstream areas exhibiting higher socio-economic development and ESE coordination, these issues cannot be addressed in isolation. From this perspective, the challenges and advantages faced by the upper, middle and lower reaches of the Yellow River Basin are interdependent. This highlights the importance of studying the basin as a large-scale watershed. A comprehensive understanding of its ecological, economic and social linkages can provide a theoretical foundation for coordinated development, resource management, environmental protection and policy formulation across the entire watershed. This approach will support the sustainable development of the Yellow River Basin and contribute to the broader goal of ecological civilization.

Initially, regions with abundant natural resources leveraged these assets for economic and social growth, but over time, with the depletion of non-renewable resources, it became clear that sustainable development requires a balanced human–land relationship. Thus, the ESE coordinated development concept emerged [46]. In summary, this study highlights the coordinated development status of various subsystems in the Yellow River Basin and analyzes the role mechanisms of their influencing factors, offering significant practical and policy implications. Additionally, the study’s innovative approach in selecting indicators and employing data methodology provides a valuable reference for future research on the Yellow River Basin. Future research can further expand this perspective by incorporating higher-resolution micro-regional data and including additional indicators, such as aquatic organisms and desertification, to deepen the understanding of the basin’s development dynamics.

However, there are still some issues that require further exploration, particularly in the construction of the indicator system. As shown in Table 1, some indicators, such as GDP Growth Rate, Urbanization Rate, Unit GDP Energy Consumption, Natural Population Growth Rate and Registered Unemployment Rate, have relatively low weights. Although these indicators are given lower weights, they are essential for ensuring the comprehensive coverage of the ESE system. For example, GDP Growth Rate, while not always heavily weighted, reflects long-term economic trends and is crucial for analyzing economic sustainability and quality. Similarly, Urbanization Rate affects resource allocation, labor markets and social welfare. Unit GDP Energy Consumption is critical for assessing the balance between economic growth and ecological impacts, especially in resource-based regions like the Yellow River Basin. Other indicators serve similar purposes; despite their low weighting, they remain important for assessing regional development. However, an excessive number of indicators can hinder interpretability and efficiency, so future work will focus on prioritizing internal indicators with greater interpretive power.

Additionally, because this analysis is based on basin-wide indicators, it has not fully considered the characteristics of local areas, such as the impact of mineral resources in the middle reaches. Due to data limitations, cultural factors, policy elements and aquatic organisms were not included in the analysis. Future research can address these gaps by incorporating high-resolution micro-regional data, regional characteristic indicators, policy factors and cultural considerations for more in-depth local studies. Furthermore, water quality and aquatic organisms are critical factors in large basin studies, and these will be integrated into future research to deepen our understanding of the dynamics and mechanisms of regional coordinated development. In conclusion, while the study reveals critical insights into the Yellow River Basin’s ESE system, further research should focus on refining the indicator system and incorporating localized data, policy factors and environmental metrics for a more holistic analysis.

5. Conclusions

Since the beginning of the 21st century, the ESE system in the Yellow River Basin has made notable progress, with significant improvements in social and economic conditions. However, regional disparities in economic and social development have become increasingly apparent. The primary driving factors behind these imbalances include geographical variations and the process of industrialization. Given the diverse ecological carrying capacities across the basin, development strategies must be customized to address the specific conditions of each region. In the middle and lower reaches of the basin, where economic development is accelerating, greater emphasis should be placed on ecological protection alongside the promotion of mineral resource development and industrialization. In contrast, the upstream areas should focus on enhancing ecological protection and green development policies to foster the coordinated development of ecology and economy. Nevertheless, it remains essential to strike a balance between resource utilization and ecological protection, guided by a unified, comprehensive development strategy for the entire basin. Additionally, promoting interregional cooperation and policy alignment is vital.

The findings of this study lay the foundation for future long-term, large-scale research on ESE coordination. Subsequent studies should, based on simplifying indicators while enhancing their explanatory power, further investigate the long-term impacts of sociocultural factors, policy changes and water-related environmental changes on regional ESE coordination. With the support of big data and remote sensing technologies, more accurate analyses and predictions can be made to guide the sustainable development of the Yellow River Basin.

Author Contributions

Conceptualization, Y.L.; Methodology, Y.L.; Validation, Y.L.; Formal analysis, Y.L.; Investigation, Y.L.; Resources, D.W.; Data curation, Y.L.; Writing—original draft, Y.L.; Writing—review and editing, Y.L., H.H., L.S. and M.L.; Visualization, Y.L.; Supervision, H.H. and L.S.; Project administration, H.H. and L.S.; Funding acquisition, H.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Fundamental Research Funds for the Central Public-interest Scientific Institution, grant number 2024YSKY-58, and the National Natural Science Foundation of China, grant number 42361144881.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

Li, H.; Wen, Z.; Wan, Y.; Hu, J. How Does the Horizontal Watershed Ecological Compensation Mechanism Affect Regional Economy? A County-Level Empirical Study on the Xin’an River Basin, China. Ecol. Indic. 2024, 166, 112506. [Google Scholar] [CrossRef]
Makhdum, M.S.A.; Usman, M.; Kousar, R.; Cifuentes-Faura, J.; Radulescu, M.; Balsalobre-Lorente, D. How Do Institutional Quality, Natural Resources, Renewable Energy, and Financial Development Reduce Ecological Footprint without Hindering Economic Growth Trajectory? Evidence from China. Sustainability 2022, 14, 13910. [Google Scholar] [CrossRef]
Ulucak, Z.Ş.; İlkay, S.Ç.; Özcan, B.; Gedikli, A. Financial Globalization and Environmental Degradation Nexus: Evidence from Emerging Economies. Resour. Policy 2020, 67, 101698. [Google Scholar] [CrossRef]
Guo, Y.; Liu, Y. Sustainable Poverty Alleviation and Green Development in China’s Underdeveloped Areas. J. Geogr. Sci. 2022, 32, 23–43. [Google Scholar] [CrossRef]
Shmelev, S.E. Ecological Economics. Ecol. Econ. 2012, 79, 71–79. [Google Scholar]
Hou, H.; Guan, Y. ESE System: The Foundation and Support of Ecological Civilization Construction in China. Ecol. Econ. 2020, 36, 13–24. [Google Scholar]
Costanza, R. What Is Ecological Economics? Ecol. Econ. 1989, 1, 1–7. [Google Scholar] [CrossRef]
Martínez, M.L.; Intralawan, A.; Vázquez, G.; Pérez-Maqueo, O.; Sutton, P.; Landgrave, R. The Coasts of Our World: Ecological, Economic and Social Importance. Ecol. Econ. 2007, 63, 254–272. [Google Scholar] [CrossRef]
Xu, M.; Chen, C.; Deng, X. Systematic Analysis of the Coordination Degree of China’s Economy-Ecological Environment System and Its Influencing Factors. Environ. Sci. Pollut. Res. 2019, 26, 29722–29735. [Google Scholar] [CrossRef]
Yan, X.; Chen, M.; Chen, M.Y. Coupling and Coordination Development of Australian Energy, Economy, and Ecological Environment Systems from 2007 to 2016. Sustainability 2019, 11, 6568. [Google Scholar] [CrossRef]
Gao, Y.; Feng, Q.; Li, Z. An Assessment on the Coordinated Development of Ecology, Economy, and Society in the Gansu Section of Qilian Mountain National Park. Ecol. Sci. 2022, 41, 1197–1204. [Google Scholar]
Liang, H.; Ren, B. Coupling Coordination and Spatiotemporal Evolution of Finance and Environment in the Yangtze River Delta Urban Agglomeration. Stat. Decis. 2022, 6, 159–163. [Google Scholar]
Zhong, X.; Liu, Y. Coupled Coordination Degree of Tourism-Economy-Ecological System in Guangdong Province. Trop. Geogr. 2012, 32, 568–574. [Google Scholar]
Ma, F.; Liu, Q. Spatial Correlation Pattern and Influencing Factors of “Water Footprint Intensity of Grain Crops” in Heilongjiang Province. Ecol. Econ. 2021, 37, 121–127+144. [Google Scholar]
Zhang, L.; Bai, Y.; Tian, L.; Ji, M.; Pan, S. Research on the Coordination Degree of Ecological Protection and High-Quality Development of Urban Agglomerations Along the Yellow River. People’s Yellow River 2022, 04, 15–19. [Google Scholar]
Zhang, L.; Tang, H. Research on the Measurement of Urban ESE System Coordinated Development Level in the Yangtze River Delta. Ecol. Econ. 2023, 39, 103–110. [Google Scholar]
Zhao, X.; Chen, Z.; Huo, X. Research on the Input–Output Efficiency Based on Co-evolution: A Case of Shaanxi Agriculture-Related Vocational Colleges. Stat. Inf. Forum 2018, 33, 123–128. [Google Scholar]
Cao, N.; Cui, X.; Wen, S. Exploring Differences and Evolution of Coordination Level of the Industrial Structure, Economy and Ecological Environment Complex System in Beijing–Tianjin–Hebei Urban Agglomeration. Sustainability 2024, 16, 11. [Google Scholar] [CrossRef]
Yang, Y.; Bao, W.; Liu, Y. Coupling Coordination Analysis of Rural Production–Living–Ecological Space in the Beijing-Tianjin-Hebei Region. Ecol. Indic. 2020, 117, 106512. [Google Scholar] [CrossRef]
Zhang, D.; Yang, S.; Wang, Z.; Yang, C.; Chen, Y. Assessment of Ecological Environment Impact in Highway Construction Activities with Improved Group AHP–FCE Approach in China. Environ. Monit. Assess. 2020, 192, 451. [Google Scholar] [CrossRef]
Yang, A.; Zhao, J.; Lin, Y.; Chen, G. Coupling and Coordination Relationship of Economic–Social–Natural Composite Ecosystem in Central Yunnan Urban Agglomeration. Sustainability 2024, 16, 7. [Google Scholar] [CrossRef]
Xu, Y.; Wang, C. Ecological Protection and High-Quality Development in the Yellow River Basin: Framework, Path, and Countermeasure. Bull. Chin. Acad. Sci. 2020, 35, 875–883. [Google Scholar]
Liu, H.; Ren, D. The Realistic Dilemma of Ecological Protection and Economic Coordinated Development in the Yellow River Basin and the Coping Strategies. Ecol. Econ. 2021, 37, 148–153. [Google Scholar]
Sun, J.; Yu, D. The Basic Logic, Promotion Mechanism, and Implementation Path of Coordinated Development of Infrastructure in the Yellow River Basin. Econ. Manag. Rev. 2024, 40, 18–31. [Google Scholar] [CrossRef]
Li, M.; Li, Q.; Wang, C.; Ding, X. Spatiotemporal Evolution of Human-Land Coordination for High-Quality Development in the Yellow River Basin and Its Influencing Factors. Arid Land Res. Environ. 2022, 36, 89438–89448. [Google Scholar]
Zhang, L.; Wang, Y.; Xia, Y.; Liu, T.; Bai, Y. Research on the Coordinated Development of Resources, Environment, and Economy in the Yellow River Basin. People’s Yellow River 2022, 44, 11–15. [Google Scholar]
Zhang, Y.; Lu, R.; Tang, B. Study on the Vulnerability of Ecological–Economic System and Obstacles in the Poor Areas of Northern Guangdong: Taking 8 Counties of Shaoguan City as an Example. Ecol. Econ. 2021, 37, 213–220. [Google Scholar]
Li, M.; Cai, S.; Zhang, H. Factor Endowments and the Economic Spatial Dissimilarity in the Yellow River Valley. Econ. Geogr. 2011, 31, 14. [Google Scholar]
Xiong, C.; Zhang, Y.; Wang, W. An Evaluation Scheme Driven by Science and Technological Innovation—A Study on the Coupling and Coordination of the Agricultural Science and Technology Innovation-Economy-Ecology Complex System in the Yangtze River Basin of China. Agriculture 2024, 14, 1844. [Google Scholar] [CrossRef]
Wang, Z.; Zhang, M.; Cui, Z.; Wei, Y.; Bai, Y.; Qu, K. Coastal Ecological Security Assessment in Laizhou Bay, China: From the Perspective of Demographic-Social-Economic-Natural Complex Ecosystem. Environ. Sci. Pollut. Res. Int. 2024, 31, 39232–39247. [Google Scholar] [CrossRef]
HJ192-2015; Technical Specification for the Assessment of the State of the Ecological Environment. Ministry of Ecology and Environment, People’s Republic of China: Beijing, China, 2015.
Yang, L.; Sun, Z. The Development of Western New-Type Urbanization Level Evaluation Based on Entropy Method. Econ. Probl. 2015, 3, 115–119. [Google Scholar] [CrossRef]
Dou, R.; Zhao, Y.; Li, K.; Jiao, B. Pattern Differentiation and Influence Factors of Coupling and Coordination of High-Quality Development Efficiency in the Yellow River Basin. J. Beijing Norm. Univ. 2024, 60, 69–79. [Google Scholar]
Müller, D.; Leitão, P.J.; Sikor, T. Comparing the Determinants of Cropland Abandonment in Albania and Romania Using Boosted Regression Trees. Agric. Syst. 2013, 117, 66–77. [Google Scholar] [CrossRef]
Gao, W.; Nan, Y.; Guo, H.; Bai, X.; Zhu, D. Boosted Regression Trees in Extracting Wetlands Data in Xiaoxing’an Mountains. Northeast. For. Univ. J. 2023, 51, 137–142. [Google Scholar] [CrossRef]
Elith, J.; Leathwick, J.R.; Hastie, T. A Working Guide to Boosted Regression Trees. J. Anim. Ecol. 2008, 77, 802–813. [Google Scholar] [CrossRef]
Liu, H.; He, C. Mechanisms and Tests for Green Finance to Promote Urban Economic High-Quality Development—Empirical Evidence from 272 Prefecture-Level Cities in China. Invest. Res. 2021, 40, 37–52. [Google Scholar]
Han, X.; Liu, X.M.; Liu, W.T.; Yu, W.L. Spatio-Temporal Characteristics of the Coupling Coordinating Relationship Between Green Finance and Economic High-Quality Development in the Yellow River Basin and Its Driving Factors. Econ. Geogr. 2023, 43, 121–130. [Google Scholar] [CrossRef]
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. [Google Scholar] [CrossRef]
Wu, Y.X.; Chen, B.Y.; Zhang, L. Coupling and Coordination Situation and Driving Factors of Socio-Economic and Eco-Environment in the Yellow River Basin. Soil Water Conserv. Bull. 2021, 41, 240–249. [Google Scholar] [CrossRef]
Huang, C.; Ma, J.; Wei, D.; Zhang, L.; Zhang, Y.; Du, Y. History, Experience, and Enlightenment of CPC’s Protection and High-Quality Development of the Yellow River Basin. China Popul. Resour. Environ. 2022, 32, 1–9. [Google Scholar]
Jin, B. Carrying Forward the Past of the Western Development—An Unprecedented Move of China’s Economic Development. Reg. Econ. Rev. 2020, 5, 16–22. [Google Scholar] [CrossRef]
Wang, X.; Zhou, W.; Ren, D. Coupling and Coordinated Development of Ecological Environment, Economic Development, and Urbanization in the Upper Reaches of the Yellow River Basin: A Case Study of the Gansu Section. J. Northwest Norm. Univ. 2024, 60, 64–73. [Google Scholar] [CrossRef]
Zhao, H.; Li, Z.; Wang, H.; Liu, Y.; Li, M. Simulation and Resilience Regulation of Water-Energy-Food-Carbon Nexus in the Middle Reaches of the Yellow River. J. North China Univ. Water Resour. Electr. Power 2024, 45, 9–19. [Google Scholar] [CrossRef]
Yang, Y.; Wang, Y.; Xu, Q. Driving Factors and Decoupling Effect of Water Resources Utilization in the Yellow River Basin. Arid Land Geography, 2024; in press. Available online: https://link.cnki.net/urlid/65.1103.X.20240612.1751.002 (accessed on 12 June 2024).
Ma, S.; Wang, R. Social-Economic-Natural Complex Ecosystem. Acta Ecol. Sin. 1984, 4, 1–9. [Google Scholar]

Figure 1. Location map of the study area.

Figure 2. Comprehensive ESE scores in the Yellow River Basin from 2000 to 2020 (a). Economic subsystem scores in the Yellow River Basin from 2000 to 2020 (b). Ecological subsystem scores in the Yellow River Basin from 2000 to 2020 (c), and social subsystem scores in the Yellow River Basin from 2000 to 2020 (d). The deepening color represents the increase in year from 2000 to 2020.

Figure 3. Spatiotemporal variation of county–level ESE system development in the Yellow River Basin, 2000–2020.

Figure 4. Migration trajectories of ESE system centers of counties in the Yellow River Basin and standard deviation ellipses.

Figure 5. Spatiotemporal analysis of ESE system CCDs in the Yellow River Basin (2000–2020).

Figure 6. Trend map of cold and hot spot distribution of CCDs in the Yellow River Basin (2000–2020).

Figure 7. Relative importance of individual influencing factors on CCDs in the upper, middle and lower reaches of the Yellow River Basin.

Figure 8. Marginal effects of individual influencing factors on the CCDs in the upper, middle and lower reaches of the Yellow River Basin.

Table 1. Evaluation index system for the county-level ESE composite system in the Yellow River Basin.

System	Primary Index	Secondary Index	Unit	Attribute	Weight
Economic Subsystem	Economic Strength	Regional GDP	100 million RMB	Positive	0.124
		Economic Density	100 million RMB/km²	Positive	0.190
		Fixed Asset Investment	100 million RMB	Positive	0.142
		Fiscal Revenue	100 million RMB	Positive	0.163
		GDP Growth Rate	%	Positive	5.43 × 10⁻⁴
	Economic Vitality	Growth Rate of the Tertiary Industry	%	Positive	2.67 × 10⁻⁴
		Urbanization Rate	%	Positive	6.06 × 10⁻⁴
		Growth Rate of Fiscal Revenue	%	Positive	7.37 × 10⁻⁴
		Financial Institutional Loan Balance per Unit Area	10,000 RMB	Positive	0.289
	Economic Structure	Proportion of Added Value of the Primary Industry	%	Positive	0.054
		Proportion of Added Value of the Secondary Industry	%	Positive	0.018
		Proportion of Added Value of the Tertiary Industry	%	Positive	0.017
Ecological Subsystem	Resource and Environmental Cost	Unit GDP Energy Consumption	Tons/10,000 RMB	Negative	2.48 × 10⁻⁴
		CO₂ Emissions per Unit GDP	Tons/10,000 RMB	Negative	1.71 × 10⁻⁴
		SO₂ Emissions per Unit GDP	Tons/10,000 RMB	Negative	1.70 × 10⁻⁴
		Smoke Emissions per Unit GDP	Tons/10,000 RMB	Negative	3.09 × 10⁻⁴
	Environmental Quality Index	Arable Land Area	ha	Positive	0.281
		Green Land Area	ha
		Wetland Area	ha
		Construction Land Area	ha
		Other Land Use Area	ha
	Water Resource Abundance	Total Water Output	/	Positive	0.372
	Water Resource Abundance	Per Capita Water Output	/	Positive	0.300
	Vegetation Condition	NDVI Index	/	Positive	0.025
	Vegetation Condition	Vegetation Coverage	%	Positive	0.020
Social Subsystem	Population Status	Population Density	People/km²	Negative	8.18 × 10⁻⁴
		Natural Population Growth Rate	%	Negative	0.005
		Average Years of Education per Person	Years	Positive	0.042
	Social Potential	Proportion of R&D Internal Expenditure to GDP	%	Positive	0.344
		Registered Unemployment Rate	%	Negative	5.49 × 10⁻⁴
		Number of Public Library Books per Capita	Books/Person	Positive	0.291
	Living Quality	Per Capita Disposable Income of Residents	RMB	Positive	0.094
		Proportion of Population Covered by Basic Pension Insurance	%	Positive	0.095
		Number of Hospital Beds per 10,000 People	Beds/10,000 People	Positive	0.127

RMB = Chinese yuan. Weights smaller than 10⁻³ are presented in scientific notation for clarity.

Table 2. Classification of the CCD levels.

CCD Value Range	Coordination Level	Grade	Description
0.000 ≤ CCD < 0.150	Extreme Imbalance	(I)	Severe imbalance between systems
0.150 ≤ CCD < 0.200	Severe Imbalance	(II)	Significant imbalance between systems
0.200 ≤ CCD < 0.250	Moderate Imbalance	(III)	Moderate imbalance between systems
0.250 ≤ CCD < 0.300	Mild Imbalance	(IV)	Mild imbalance between systems
0.300 ≤ CCD < 0.350	Marginal Imbalance	(V)	Slight imbalance between systems
0.350 ≤ CCD < 0.650	Basic Coordination	(VI)	Basic coordination between systems
0.650 ≤ CCD < 0.800	Good Coordination	(VII)	High degree of system coordination
0.800 ≤ CCD ≤ 1.000	Excellent Coordination	(VIII)	Near-perfect coordination between systems

Table 3. Standard deviation ellipse parameters of ESE system changes in the Yellow River Basin (2000–2020).

Year	Centroid Coordinates (°)		Perimeter (km)	X-Axis (km)	Y-Axis (km)	Azimuth (°)	Shape Index
Year	X-Coord.	Y-Coord.	Perimeter (km)	X-Axis (km)	Y-Axis (km)	Azimuth (°)	Shape Index
2000	392119.5481	3917948.9211	3022.7255	622.5143	314.1205	82.7648	0.4954
2005	408293.7169	3926826.9951	3005.6813	610.7035	323.9350	82.1024	0.4696
2010	424560.8765	3927155.1351	2920.3619	590.3956	318.8167	82.3851	0.4600
2015	453131.2944	3930867.7651	2860.4907	572.1032	320.6484	83.5044	0.4395
2020	452291.1223	3920234.7543	2861.3641	580.7756	309.2143	84.3620	0.4676

Table 4. Changes in the number of cold and hot spots of CCDs.

Type of Spot	2000	2005	2010	2015	2020
High Hot Spot	63	77	69	64	62
Hot Spot	8	2	8	3	7
Secondary Hot Spot	6	5	3	5	9
Secondary Cold Spot	25	23	27	12	15
Cold Spot	35	59	50	23	31
High Cold Spot	120	119	119	132	126

Table 5. Explanation of the influencing factors.

Influencing Dimension	Explanation of Influencing Factor	Unit	Symbol
Natural Environment	Distance from county center to secondary water body	km	DGW
	Average slope of the county	°	S
	Annual average concentration of PM_2.5 in the county	μg/m³	PM
Industrial Structure	Ratio of tertiary industry value added to secondary industry value added	%	RTS
Administrative Location	Distance from county center to provincial capital	km	DGC
Science and Education	Number of college students per 10,000 people	people	Stu
Science and Education	Number of invention patents granted in the year	units	Pat

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Li, Y.; Hao, H.; Sun, L.; Liu, M.; Wang, D. The Development of Economic–Social–Ecological Complex Systems in the Yellow River Basin, China. Sustainability 2025, 17, 511. https://doi.org/10.3390/su17020511

AMA Style

Li Y, Hao H, Sun L, Liu M, Wang D. The Development of Economic–Social–Ecological Complex Systems in the Yellow River Basin, China. Sustainability. 2025; 17(2):511. https://doi.org/10.3390/su17020511

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Li, Yuyang, Haiguang Hao, Lihui Sun, Mengxiao Liu, and Ding Wang. 2025. "The Development of Economic–Social–Ecological Complex Systems in the Yellow River Basin, China" Sustainability 17, no. 2: 511. https://doi.org/10.3390/su17020511

APA Style

Li, Y., Hao, H., Sun, L., Liu, M., & Wang, D. (2025). The Development of Economic–Social–Ecological Complex Systems in the Yellow River Basin, China. Sustainability, 17(2), 511. https://doi.org/10.3390/su17020511

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