Study on Driving Factors and Spatiotemporal Differentiation of Eco-Environmental Quality in Jianghuai River Basin of China

Abstract

For an in-depth analysis of the ecosystems of the Jianghuai Valley, this study utilized municipal data from 2017 to 2021. In addition, this study established an index scale evaluation system for the quality of the ecological environment in the Jianghuai Valley. This system encompasses five critical dimensions: drivers, pressures, states, impacts, and responses, in accordance with the DPSIR model. The entropy-weighted TOPSIS method combined with the gray correlation method was used to assess the ecological status of each region of the Jianghuai Valley at different time periods and the driving factors affecting the ecological quality of the Jianghuai Valley. Our study yields several key conclusions. First, it was observed that the ecological environment within the Jianghuai Valley showed a continuous upward bias in inter-annual variability. Second, there exists variation in ecological environment quality among the eleven urban areas within the Jianghuai Valley, highlighting regional disparities. Third, among the eleven urban areas in the Jianghuai Valley, Anqing has the best ecological quality, and Huainan has the worst ecological performance. Fourth, the ecological environment quality within the Jianghuai Valley demonstrates an aggregated pattern. From west to east, this pattern is delineated by distinct areas: one marked by excellent ecological environment quality, another exhibiting average ecological environment quality, followed by a zone characterized by good ecological environment quality, and finally, an area with poor ecological environment. Fifth, our analysis reveals that Q9 (indicating the percentage of excellent air days) and Q13 (denoting the annual average temperature) have a pronounced correlation with the Jianghuai Valley’s ecological quality. Conversely, Q3, which pertains to the rate of natural population growth, had the lowest relevance to the ecological quality of the Jianghuai Valley.

1. Introduction

With dense population, fertile land, abundant resources, and convenient transportation, the JAC Basin is the coverage area of the Yangtze River Economic Belt, Yangtze River Delta Integration, and Central Plains Economic Zone as well as the main gathering area of the Grand Canal Cultural Belt, which is of great importance in China’s overall socio-economic development. At the same time, the JAC Basin has important ecological functions and is an important ecological excess and buffer zone between the Yangtze River Delta and Bohai Rim regions, which is of great significance in guaranteeing national ecological security. Changes in the quality of ecological environment are mainly affected by natural processes and human activities due to strong and continuous human activities, such as lake encirclement, damming, urban expansion, and irrational development [1,2,3,4] as well as the accelerated rate of urbanization in the Jianghuai Valley, the opening of the east line of the South–North Water Diversion, and the demand for increased grain production [5]. These factors have led to a series of threats to the Jianghuai Valley, such as shrinking rivers and lakes, over-exploitation of groundwater, soil erosion, destruction of ecosystem structure, and decline in biodiversity [6,7,8,9]. Therefore, there is an urgent need to take a series of effective measures to systematically protect the Jianghuai Valley.

Fortunately, the relevant government departments have realized the problems faced by the ecological environment of the Jianghuai Valley; in order to effectively improve the ecological quality of the Jianghuai Valley according to the state-issued “14th Five-Year Plan”, the Huaihe River Basin Ecological and Environmental Supervision, and Administration Bureau, the “14th Five-Year Plan” should be strengthened to monitor the implementation of the plan, and clearly defined measures to be taken to protect the ecological system. According to the “14th Five-Year Plan” issued by the state, the Huaihe River Basin Ecological Environment Supervision Bureau has strengthened the supervision of the implementation of the “14th Five-Year Plan” and made it clear that the Huaihe River Basin will carry out water ecological environment protection in accordance with the spatial layout of “one horizontal, two verticals, three lakes, and four areas”. The Huaihe River Basin Bureau and other organizations associated with the Yangtze River and Huaihe River have carried out in-depth construction of soil and water conservation in response to the ecological problems of the Yangtze River and Huaihe River; strengthened the ecological protection of important ecological protection zones, water conservation zones, and headwater zones of the river; and implemented the water ecology protection and restoration projects of major tributaries and lakes, also implementing comprehensive management.

Other research has suggested implementation of the comprehensive management of groundwater over-exploitation areas in the Huaihe River Basin, and gradual realization of the balance of extraction and replenishment; the implementation of a number of sewage treatment, garbage disposal, urban sewage pipe network, and other projects [10]; and strengthening the comprehensive management of the Jianghuai River Basin and solidly promoting the forestry ecological construction of the Jianghuai River Basin. Through the implementation of these actions, the ecosystem quality of the Jianghuai Valley has been significantly improved, but the threats to ecological quality are still serious [11]. Therefore, it has become imperative to evaluate the changes in the ecological quality of the Jianghuai Valley and identify the relevant key influencing factors.

2. Study Design

2.1. Study Area

The research region encompasses the Jianghuai Valley (Figure 1), including both the Yangtze and Huaihe River Basins. It is worth noting that the reference to the Yangtze River Basin predominantly pertains to its lower reaches, excluding the upper and middle reaches. Likewise, the primary emphasis on the Huaihe River Valley centers around Henan, Anhui, and Jiangsu provinces, which collectively form the Yuwansu Plain. It is important to clarify that the tributaries of the Huai River flow from Shandong and Hubei and join the Huai River at the top of Dabie Mountain rather than suggesting that the tributaries of the Huai River flow into Hubei. Although the Zhugang River, a Huaihe River tributary in Hubei, has significantly higher terrain elevation compared to the flat terrain of the North China Plain Yuwansu, it is situated within the main basin of the Huaihe River. Geographically, Jianghuai Valley mainly includes the areas south of the Huaihe River and north of the Yangtze River [12].

2.2. Research Methodology

2.2.1. Regional Environmental Quality Assessment

Regional ecological environment quality assessment involves either qualitatively or quantitatively analyzing the ecological environment status of a specific area over a defined period of time. This assessment method is an important basis for ecological environment governance, regional planning, management, and resource development and utilization. Scholars have adopted different methods and tools to conduct this assessment. For example, Mo Wenbo and other scholars studied the landscape indicators affecting the water quality of the rivers entering Dongjiang Lake through redundancy and partial redundancy analysis methods. They then constructed a landscape indicator dataset, analyzed the data using bootstrapping, and finally explored the mechanism of the landscape pattern’s influence on the river’s water quality. Conversely, Cai and other scholars assessed the changes and spatial heterogeneity of ecological quality in the Yellow River Delta based on remote sensing ecological indices and geo-detection modeling, revealed the driving factors, and provided data that can be relied upon for environmental management. Additionally, other scholars also developed the remote sensing ecological index (RSEI) and the integrated ecological environment index (IEEI) to assess the ecological environment across various regions [13]. These findings highlight differences in regional ecological conditions and provide valuable insights for sustainable urban development in the future.

2.2.2. The DPSIR Model

In recent years, it is evident that researchers have employed a diverse array of methods to monitor and assess ecological conditions and changes. Notably, the pressure–state–response (PSR) model has gained extensive use in assessing ecosystem health. Furthermore, the European Environment Agency has introduced an advanced model, the DPSIR conceptual model, as an enhancement of the PSR model. The model categorizes evaluation indices for characterizing a natural regime in five components: drivers, pressures, states, impacts, and responses, with each component further subdivided into multiple indices. The DPSIR conceptual model encompasses several critical components: “Driving force” signifies potential triggers for environmental change, including factors such as regional socio-economic activities and industrial development trends [14,15]. “Pressure” refers to the immediate effects of human action on the physical setting, primarily concerning the intensity of natural resource use, energy consumption, and the level of waste emissions [16]. “State” characterizes the environmental condition under the aforementioned pressures, predominantly seen in the extent of regional ecological pollution. “Impact” elucidates the system’s effects on both human health and socio-economic structure [17,18]. The term “response” denotes the process involving countermeasures and positive policies adopted by individuals in the pursuit of sustainable development, encompassing actions aimed at enhancing resource utilization efficiency, reducing pollution, and boosting investment [19]. The DPSIR model systematically elucidates the intricate interactions between human processes and their environment, providing the ability to dissect and refine complex problems. This model, in turn, facilitates the more effective resolution of environmental and ecological security challenges. Recently, the DPSIR model has seen widespread adoption across various research domains, including land management planning, sustainable river basin management, water resource oversight, and marine environment studies [20,21,22,23,24].

Several scholars have conducted research in the field of ecological environment assessment using the DPSIR model (driving force–pressure–state–impact–response model) to construct ecological environment assessment indicators and quantitatively assess the quality of ecological environment in different regions. These studies have demonstrated the validity and diverse applications of the DPSIR model through different methods and datasets. For example, Zheng and other scholars used DPSIR to construct an ecological environment indicator system to evaluate the status of ecological environment and resource development from 2005 to 2020, which provided decision makers with dynamic information about the development of ecological environment and resources. Boori, Mukesh Sing, and other scholars constructed the ecological vulnerability index of the Republic of Tatarstan using the DPSIR model, and the results showed that the human social ecological vulnerability is exacerbated by the increase in economic activities, which further emphasizes the importance of the DPSIR model in linking economic activities with environmental impacts [25]. These studies also emphasize the comprehensive, systematic, holistic, and flexible nature of the DPSIR model, which allows it to effectively integrate multiple considerations of resources, development, environment, and human health in order to reveal the causal relationship between the environment and the economy.

2.2.3. Methodology for Assessing Environmental Quality and Drivers

The entropy-weighted TOPSIS method is a method to determine the weights of indicators by the value of evaluation indicators under objective conditions, which is characterized by strong operability and objectivity, and is able to reflect the information implied by the data, enhance the significance of the indicators’ differentiation and difference in order to avoid the analysis difficulties caused by the small difference of the selected indicators, and comprehensively reflect all kinds of information. The idea is that the greater the difference between the values of evaluation objects in a certain indicator, the more important it is, and the greater the weight is accordingly. According to the degree of variation of each indicator, the weight of each indicator can be objectively calculated to provide a basis for the comprehensive evaluation of multiple indicators. In recent years, many scholars have applied the entropy-weighted TOPSIS method in the comprehensive evaluation research of regional capacity, and Jiong Li and colleagues put forward an ecological vulnerability assessment method of scenic spots based on the entropy-weighted TOPSIS model in response to the problem of poor accuracy of traditional ecological vulnerability assessment methods. The entropy-weighted TOPSIS model is used to calculate the weight of ecological vulnerability index, determine the ecological vulnerability index, and establish the ecological vulnerability assessment model of scenic spots and then input the ecological vulnerability index into the model, which outputs the assessment results [26]. The results show that the accuracy of the method is as high as 0.98 [27]. Gray correlation analysis is a multi-factor statistical analysis method that operates through the study of the data correlation size, that is, the degree of association between the parent series and the characteristic series, and through the size of the correlation to carry out the degree of correlation between the measured data so as to assist in the decision making of a research method. The correlation analysis method requires that the sample capacity can be as small as 4, and the same applies to the data irregularity: There will be no quantitative results, and qualitative analysis results do not match the situation [28]. The application of gray correlation involves various fields of social and natural sciences, especially in the social and economic fields, which have achieved better application results. Xin Xinhe and colleagues used gray correlation analysis to analyze the systematic obstacles in the implementation of the ecological resource compensation mechanism in the public-space water source protection area and the key factors affecting the degree of ecological damage to public-space water environment resources [29].

The study of ecological environment quality drivers and their spatio-temporal differentiation in the Jianghuai Valley is categorized among the multi-attribute decision-making problems, which are often designed with complex external environments and many different attributes. The TOPSIS method is a useful and powerful method to deal with multi-attribute decision-making problems, which can well reflect the degree of similarity between the alternatives and the positive and negative ideal scenarios. The gray correlation analysis method can give a good account of the variation of factors within the project alternatives and the difference between them and the ideal solution in an information-poor environment. However, the TOPSIS method cannot well reflect the changes of factors between projects and their differences with positive and negative solutions, and the gray correlation method has some defects in the overall judgment of system solutions. Therefore, to address the above shortcomings, this paper combines the two methods of TOPSIS and gray correlation analysis to construct a new gray ideal value-approximation model to make a more scientific and reasonable preference decision for the study of ecological environment quality drivers and their spatio-temporal differentiation in the Jianghuai Valley.

2.3. Calculation of the Ecosystem Quality and Drivers Index

2.3.1. Construction of the Evaluation Index System for the Quality of the State Environment

The ecological quality of the system is closely related to the structure of the ecosystem, environmental protection policies, socio-economic factors, and more. Consequently, an assessment of EQ must incorporate these factors and follow a defined logical relationship. The DPSIR model encompasses various aspects of the economy, society, humanities, resource utilization, and environmental protection, providing a foundational framework for assessing ecological environment quality (Figure 2). Economic and social development, along with population growth, serve as driving factors (D) that spur the increased demand for social progress. This, in turn, leads to industrial and agricultural pollution, imposing pressure on the ecological environmental system (P). When the ecological environment quality continually faces this pressure over a long term, it results in a situation where the state of the ecosystem is changed (S), which in turn leads to a deterioration within the ecosystem. This deterioration has various impacts on human society (I), including issues like environmental pollution, conflicts related to land use, and a reduction in biodiversity. When these problems start affecting the ecosystem and environmental quality, government bodies and environmental protection agencies increase their investments (R) in the ecological environment system. The aim of this is to alleviate ecological quality problems, improve the ability of ecosystems to recover and maintain balance, and ultimately promote the growth of societies and populations.

In the selection of evaluation indicators, we adopted a multi-faceted, comprehensive, and holistic approach based on existing study findings. Following the scientific, holistic, and feasible principles, we constructed an evaluation index of drivers in the river and Huaihe River Basin based on the DPSIR model (

Table 1. Indicator System for Evaluation of Ecological Environment Quality in Jianghuai Valley.

Target Layers	Standardized Layer	Indicator Layer	Unit	Indicator Properties
Drivers of ecological quality in the Jianghuai Valley	Driving Force (D)	Q1 Size of population	Ten thousand people	-
		Q2 GDP per capita	Yuan (RMB)	+
		Q3 Natural population growth rate	%	-
	Pressure (P)	Q4 Annual electricity consumption of society as a whole	10,000 kWh	-
		Q5 Annual raw coal consumption by industrial enterprises	Tons	-
		Q6 Annual water consumption of industrial enterprises	10,000 cubic meters	-
		Q7 General industrial solid waste generation	10,000 tons	-
		Q8 Industrial wastewater discharge	10,000 tons	-
	Status (S)	Q9 Proportion of days with good air quality	%	+
		Q10 Cropland area	Thousands of hectares	+
		Q11 Total water resources	Billion cubic meters	+
		Q12 Forest cover	%	+
		Q13 Average annual temperature	°C	-
		Q14 Annual precipitation	mm	+
	Impact (I)	Q15 Parks per 10,000 people	Hectares	+
		Q16 Green space per 10,000 people	Hectares	+
		Q17 Annual average concentration of PM2.5	μg/m3	-
	Response (R)	Q18 Greening coverage in built-up areas	%	+
		Q19 Comprehensive utilization rate of general industrial solid waste	%	+
		Q20 Sewage treatment rate	%	+

).

2.3.2. Data Sources

This study focuses on the Jianghuai Valley, specifically encompassing several cities in different provinces: Yangzhou, Taizhou, Nantong, Yancheng, and Huai’an in Jiangsu Province; Huainan, Chuzhou, Lu’an, Hefei, and Anqing in Anhui Province; and Xinyang in Henan Province. The primary data sources include statistical yearbooks from each province and city, along with national economic and social development bulletins. These sources encompass data released by the government from 11 municipalities, such as the Statistical Yearbook of Yangzhou City, Yangzhou Municipal Bulletin of National Health and Social Progress, Taizhou Municipal Statistical Yearbook, Taizhou Municipal Bulletin of National Health and Social Progress, Taizhou Municipal Bulletin of Solid Waste Pollution Prevention and Control Information Bulletin, and Nantong Municipal Statistical Yearbook. In cases where data points are missing, linear interpolation was employed to fill these gaps.

For the nine negative indicators in the indicator system, this paper adopts the (Max − X)/(Max − Min) inverse normalization process to make the data into positive indicators. Since the units of the data of each indicator are different, (X − Min)/(Max − Min) normalization was applied to all the indicator data on this basis to solve the problem of magnitude.

2.3.3. Entropy-Weighted TOPSIS Calculation

The assessment of residents’ happiness index presents a multi-attribute decision-making challenge, necessitating the use of TOPSIS and gray association analysis for converting it into a single-objective optimization problem. TOPSIS serves as a multi-indicator evaluation method employed for decision-making analysis, enabling the identification of the optimal choice. This method is grounded in a straightforward concept: For each alternative, its closeness to the most favorable option and its degree of deviation away from the most unfavorable option are calculated. Subsequently, the relative distances to derive a comprehensive evaluation index are also calculated. Finally, based on this index, we select the optimal solution from among several candidate options. The specific decision-making process unfolds as follows:

In the initial step, we utilize the normalization method to process the indicator data, which results in the creation of the normalized decision matrix $\mathrm{V}={\left({\mathrm{v}}_{\mathrm{ij}}\right)}_{\mathrm{m}\times \mathrm{n}}$:

$$v_{ij}=\frac{x_{ij}-{}_i{}^{min}\left\{x_{ij}\right\}}{{}_i{}^{max}\left\{x_{ij}\right\}-{}_i{}^{min}\left\{x_{ij}\right\}}$$

(1)

$$v_{ij}=\frac{{}_i{}^{max}\left\{x_{ij}\right\}-x_{ij}}{{}_i{}^{max}\left\{x_{ij}\right\}-{}_i{}^{min}\left\{x_{ij}\right\}}$$

(2)

$$V=\left[\begin{array}{cc}\begin{array}{ccc}{v}_{11}& v_{12}& \cdots \\ v_{21}& v_{22}& \cdots \\ ⋮& ⋮& ⋮\end{array}& \begin{array}{c}{v}_{1n}\\ v_{2n}\\ ⋮\end{array}\\ \begin{array}{ccc}{v}_{m1}& v_{m2}& \cdots \end{array}& v_{mn}\end{array}\right]$$

(3)

The second move, we employ our method of entropy weighting to identify the weights of every indicator ${\mathrm{w}}_{\mathrm{j}}=\left({\mathrm{w}}_1,{\mathrm{w}}_2,\cdots ,{\mathrm{w}}_{\mathrm{n}}\right):$

$$p_{ij}=v_{ij}/{{\displaystyle \sum }}_{i=1}^m{v}_{ij},e_j=-1/\ln \left(m\right){{\displaystyle \sum }}_{i=1}^m·\ln p_{ij}$$

(4)

$$w_j=1-e_j/{{\displaystyle \sum }}_{j=1}^n\left(1-e_j\right)$$

(5)

In the third step, based on the indicator weights, a weighted normalized decision matrix $\mathrm{Y}$ is obtained:

$$Y={\left(y_{ij}\right)}_{m\times n}={\left(w_j{v}_{ij}\right)}_{m\times n}$$

(6)

In the fourth step, the optimal solution ${\mathrm{Z}}^{+}$ and the worst solution ${\mathrm{Z}}^{-}$ are determined:

$$Z^{+}=\left\{max{Z}_{ij}|i=1,2,3,\cdots ,\mathrm{m}\right\}=\left\{{\mathrm{Z}}_1^{+},{\mathrm{Z}}_2^{+},\cdots ,{\mathrm{Z}}_{\mathrm{m}}^{+}\right\}$$

(7)

$$Z^{-}=\left\{min{Z}_{ij}|i=1,2,3,\cdots ,\mathrm{m}\right\}=\left\{{\mathrm{Z}}_1^{-},{\mathrm{Z}}_2^{-},\cdots ,{\mathrm{Z}}_{\mathrm{m}}^{-}\right\}$$

(8)

In the fifth step, based on the TOPSIS method, the Euclidean distance ${\mathrm{D}}_{\mathrm{j}}^{+}$ is calculated with ${\mathrm{D}}_{\mathrm{j}}^{-}$:

$$D_j^{+}=\sqrt{{{\displaystyle \sum }}_{i=1}^m{\left(Z_i^{+}-Z_{ij}\right)}^2}$$

(9)

$$D_j^{-}=\sqrt{{{\displaystyle \sum }}_{i=1}^m{\left(Z_i^{-}-Z_{ij}\right)}^2}$$

(10)

Step 6: We calculate the posting schedule:

$$C_i=\frac{D_j^{-}}{D_j^{+}+D_j^{-}}$$

(11)

where $C_i\in \left[0,1\right]$, with larger values in the yearly progress index $C_i$ indicating a better ecological quality for that year. Vice versa, a lower value of $C_i$ means a worse ecological quality in that year.

Through the entropy-weighted TOPSIS method, we analyzed and calculated the annual variation of the quality of the eco-environment of eleven cities of the Jianghuai Valley from 2017 to 2021, considering both temporal and spatial dimensions. The analysis results included the interannual variation of the urban eco-environmental mass of cities within the Jianghuai Valley in 2017–2021 (

Table 2. Inter-annual changes in ecological and environmental quality of municipalities in the Jianghuai Valley, 2017–2021.

Area	2017	Sort	2018	Sort	2019	Sort	2020	Sort	2021	Sort
Yangzhou City	0.49	3	0.61	6	0.53	2	0.54	4	0.52	5
Taizhou City	0.47	6	0.65	4	0.47	7	0.43	10	0.48	9
Nantong City	0.48	4	0.46	10	0.49	4	0.45	9	0.50	7
Yancheng City	0.40	10	0.51	9	0.46	9	0.46	7	0.51	6
Huaian City	0.47	7	0.69	1	0.48	5	0.47	6	0.52	4
Huainan City	0.39	11	0.54	8	0.40	11	0.41	11	0.41	11
Chuzhou City	0.41	9	0.68	2	0.45	10	0.45	8	0.45	10
Lu’an City	0.51	1	0.67	3	0.51	3	0.58	2	0.57	2
Hefei City	0.45	8	0.38	11	0.47	6	0.49	5	0.49	8
Anqing City	0.49	2	0.58	7	0.55	1	0.59	1	0.61	1
Xinyang City	0.47	5	0.62	5	0.47	8	0.54	3	0.57	3

), the composite index representing the eco-environmental mass of these cities (

Table 3. Composite index of ecological and environmental quality of municipalities in the Jianghuai Valley, 2017–2021.

Area	Positive Ideal Solution Distance (D+)	Negative Ideal Solution Distance (D−)	Relative Proximity (Ci)	Sort
Yangzhou City	0.56	0.63	0.53	3
Taizhou City	0.63	0.53	0.46	9
Nantong City	0.67	0.57	0.46	8
Yancheng City	0.64	0.53	0.45	10
Huaian City	0.58	0.55	0.49	5
Huainan City	0.70	0.51	0.42	11
Chuzhou City	0.61	0.54	0.47	7
Lu’an City	0.56	0.71	0.56	2
Hefei City	0.61	0.56	0.48	6
Anqing City	0.53	0.70	0.57	1
Xinyang City	0.61	0.59	0.49	4

), and the interannual variation of eco-environmental mass of the entire Jianghuai Valley in the same period (

Table 4. Inter-annual changes in ecological and environmental quality in the Jianghuai Valley, 2017–2021.

Index Value	Positive Ideal Solution Distance (D+)	Negative Ideal Solution Distance (D−)	Relative Proximity (Ci)	Sort
2017	0.85	0.41	0.33	5
2018	0.71	0.51	0.42	3
2019	0.73	0.45	0.38	4
2020	0.52	0.69	0.57	2
2021	0.51	0.81	0.61	1

).

2.3.4. Gray Correlation Analysis Calculation

Grey correlation analysis is a multifactorial decision-making methodology used to analyze the interrelationships among multiple factors affecting an indicator. This method quantifies the degree of association between these different factors to determine their influence on the target indicator. To quantitatively assess how each indicator affects the eco-environmental mass in our study region and to determine the most critical factors, this paper employs a gray correlation model to calculate the correlation degree of each evaluation indicator. A higher correlation indicates a greater influence towards the quality within a study area’s eco-environment. The calculation steps are detailed below:

In the first step, the variables are dimensionless using the initialization method by dividing each number of the columns by the first number of the columns.

In the second step, we calculate the correlation coefficient using Equation (12):

$${\epsilon }_i\left(k\right)=\frac{\begin{array}{c}min\\ i\end{array}\begin{array}{c}min\\ k\end{array}\left|y\left(k\right)-x_i\left(k\right)\right|+\rho \begin{array}{c}max\\ i\end{array}\begin{array}{c}max\\ k\end{array}\left|y\left(k\right)-x_i\left(k\right)\right|}{\left|y\left(k\right)-x_i\left(k\right)\right|+\rho \begin{array}{c}max\\ i\end{array}\begin{array}{c}max\\ k\end{array}\left|y\left(k\right)-x_i\left(k\right)\right|}\left(k=1,2,\cdots ,n;i=1,2,\cdots ,m\right)$$

(12)

where y(k) represents the reference series denoting the ecological environment quality index; x_i(k) represents the comparison series, indicating the values of the indicators influencing the ecological environment quality; and ρ is the discrimination coefficient, typically ranging from 0 to 1 and often set to 0.5. Here, k ranges from 1 to 11.

In the third step, the correlation degree ${\mathsf{\theta }}_i$ is calculated as shown in Equation (13):

$${\theta }_i=\frac{1}{n}{{\displaystyle \sum }}_{k=1}^n{\epsilon }_i\left(k\right)\left(k=1,2,\cdots ,n\right)$$

(13)

The gray correlation method was employed to analyze and compute the evaluation index system of Jianghuai Valley eco-environment quality. This analysis yielded the gray correlation values for each sub-indicator within the Jianghuai Valley concerning its impact on the ecological environment quality (see

Table 5. Total gray correlation of sub-indicators in Jianghuai Valley.

Evaluation Unit	Total Gray Correlation	Sort
Q10 Cropland area	0.99	1
Q13 Average annual temperature	0.99	2
Q18 Greening coverage in built-up areas	0.99	3
Q20 Sewage treatment rate	0.99	4
Q19 Comprehensive utilization rate of general industrial solid waste	0.98	5
Q1 Size of population	0.98	6
Q12 Forest cover	0.97	7
Q6 Annual water consumption of industrial enterprises	0.96	8
Q5 Annual raw coal consumption by industrial	0.95	9
Q7 General industrial solid waste generation	0.94	10
Q8 Industrial wastewater discharge	0.93	11
Q9 Proportion of days with good air quality	0.92	12
Q4 Annual electricity consumption of society as a whole	0.92	13
Q2 GDP per capita	0.92	14
Q15 Parks per 10,000 people	0.92	15
Q16 Green space per 10,000 people	0.91	16
Q14 Annual precipitation	0.88	17
Q17 Annual average concentration of PM2.5	0.87	18
Q11 Total water resources	0.82	19
Q3 Natural population growth rate	0.5	20

). Additionally, gray correlation values were calculated for each sub-indicator in relation to different years of eco-environmental mass in the Jianghuai Valley, taking into account the changes in ecological conditions over time (

Table 6. Gray correlation of sub-indicators in the Jianghuai Valley, 2017–2021.

Indicator Layer	2017	2018	2019	2020	2021
Q1	0.92	0.82	0.87	0.96	0.90
Q2	0.86	0.76	0.81	0.95	0.88
Q3	0.54	0.70	0.62	0.65	0.71
Q4	0.84	0.73	0.78	0.95	0.85
Q5	0.82	0.73	0.77	0.93	0.84
Q6	0.84	0.71	0.74	0.93	0.82
Q7	0.82	0.68	0.72	0.93	0.83
Q8	0.80	0.68	0.74	0.94	0.82
Q9	0.98	0.90	0.96	0.99	0.97
Q10	0.86	0.75	0.78	0.95	0.86
Q11	0.84	0.77	0.74	0.94	0.85
Q12	0.91	0.85	0.86	0.98	0.92
Q13	0.97	0.90	0.97	0.99	0.97
Q14	0.94	0.90	0.91	0.99	0.97
Q15	0.81	0.71	0.75	0.94	0.83
Q16	0.86	0.77	0.82	0.96	0.89
Q17	0.95	0.91	0.92	0.98	0.96
Q18	0.97	0.89	0.95	0.99	0.97
Q19	0.95	0.90	0.96	0.99	0.98
Q20	0.96	0.91	0.95	0.99	0.97

).

3. Results

3.1. Spatial on Spatio-Temporal Variation of the Quality of the Eco-Environment in the Jianghuai Valley, 2017–2021

3.1.1. Trends in the Eco-Friendly Quality of the Jianghuai Valley, 2017–2021

By evaluating the entropy-weighted TOPSIS method-derived relative proximity value $I$, the eco-environmental quality indices of the Jianghuai Valley from 2017 to 2021 were ranked in descending order as follows: Based on the relative proximity value in the Jianghuai Valley for each year, it is evident that the highest ECQ index was observed in 2021, followed by 2020. The third highest was recorded in 2018, with the corresponding close-approach values being 0.614, 0.569, and 0.417, etc. The higher the value of corresponding proximity (${\mathrm{C}}_{\mathrm{i}}$), the better the assessment object. This signifies that the Jianghuai Valley had the best performance in these three years. Conversely, in 2019 and 2017, the eco-environmental mass index was 0.383 and 0.327, ranking fourth and fifth, respectively, reflecting the lower eco-environmental quality in the Jianghuai Valley in these two years. When analyzing the inter-annual trend chart of the Jianghuai Valley’s ECQ (Figure 3), it is evident that over time, the Jianghuai Valley’s ECQ exhibited a fluctuating pattern of improvement and then decline, followed by a steady rise, ultimately indicating an overall upward trend.

3.1.2. Trends of Eco-Environmental Quality in Urban Areas of the Jianghuai Valley, 2017–2021

The figure below illustrates that from 2017 to 2021, the ECQ varied significantly among the eleven urban areas in the Jianghuai Valley, displaying substantial disparities and geographical distinctions. Anqing stands out as having the highest overall ECQ during this period, while Huainan ranks as the lowest, reflecting a difference of 0.15 units. The average ECQ index for these eleven urban areas is 0.489, with eight of them having an ECQ index around the geographical average. This suggests that there is considerable room for improvement in these eight urban areas.

To provide a comprehensive depiction on the state of the eco-environment mass in urban areas of the 11th city in 2017–2021, we graded the indicators for comprehensive evaluation of eco-environment mass and developed a grading table of eco-environmental mass evaluation indicators.

According to the hierarchical table of comprehensive evaluation index of ecological environment quality, the eleven urban areas in the Jianghuai Valley were categorized and summarized from 2017 to 2021, which can provide a more intuitive perception of the regional ecological environment status. The details are shown in the following table (

Table 7. Classification of ECQ index levels in eleven urban areas of the Jianghuai Valley, 2017–2021.

	2017	2018	2019	2020	2021
Outstanding		Yangzhou, Taizhou, Huaian, Lua, Xinyang			Anqing
Favorable	Lu’an		Yangzhou, Lu’an, Anqing	Yangzhou, Lu’an, Anqing, Xinyang	Yangzhou, Nantong, Yancheng, Huaian, Lu’an, Hefei
General	Yangzhou, Taizhou, Nantong, Yancheng, Huaian, Chuzhou, Hefei, Anqing, Xinyang	Nantong	Taizhou, Nantong, Yancheng, Huaian, Huainan, Chuzhou, Hefei, Xinyang	Taizhou, Nantong, Yancheng, Huaian, Huainan, Chuzhou, Hefei	Taizhou, Huainan, Chuzhou, Xinyang
Mediocre	Huainan	Hefei

).

3.1.3. Temporal and Spatial Variability in Eco-Environmental Health Quality of Jianghuai Valley, 2017–2021

According to the relative proximity value derived from the entropy-weighted TOPSIS, it can be seen that the order of the relative proximity value in each year is large and small. According to the chart, the relative proximity values of the ecological environmental quality of the Jianghuai Valley from 2017 to 2021 in the order from large to small are Anqing (0.571), Lu’an (0.559), Yangzhou (0.531), Xinyang (0.489), Huai’an (0.487), Hefei (0.478), Chuzhou (0.468), Nantong (0.461), Taizhou (0.459), Yancheng (0.454), and Huainan (0.421).

The order of magnitude of the ecological quality of the Jianghuai Valley in 2017 was Lu’an (0.506), Anqing (0.486), Yangzhou (0.486), Nantong (0.476), Xinyang (0.472), Taizhou (0.471), Huai’an (0.470), Hefei (0.448), Chuzhou (0.406), Yancheng (0.404), and Huainan (0.385).

The order of magnitude of the ecological quality of the Jianghuai Valley in 2018 was Huai’an (0.685), Chuzhou (0.684), Lu’an (0.670), Taizhou (0.653), Xinyang (0.618), Yangzhou (0.606), Anqing (0.580), Huainan (0.537), Yancheng (0.508), Nantong (0.462), and Hefei (0.383).

The order of magnitude of the ecological quality of the Jianghuai Valley in 2019 was Anqing (0.553), Yangzhou (0.532), Lu’an (0.505), Nantong (0.487), Huai’an (0.480), Hefei (0.473), Taizhou (0.471), Xinyang (0.465), Yancheng (0.460), Chuzhou (0.452), and Huainan (0.403).

The order of magnitude of the ecological quality of the Jianghuai Valley in 2020 was Anqing (0.588), Lu’an (0.576), Xinyang (0.543), Yangzhou (0.540), Hefei (0.493), Huai’an (0.468), Yancheng (0.458), Chuzhou (0.453), Nantong (0.448), Taizhou (0.431), and Huainan (0.407).

In 2021, the values of ecological quality in the Jianghuai Valley were in the following order: Anqing (0.609), Lu’an (0.572), Xinyang (0.569), Huai’an (0.524), Yangzhou (0.522), Yancheng (0.512), Nantong (0.500), Hefei (0.489), Taizhou (0.478), Chuzhou (0.454), and Huainan (0.410).

Utilizing the relative proximity values derived by using the entry power method-TOPSIS, we determined an annual ranking of these values. According to the relative proximity value ranking of different regions in 2017–2021 (Figure 4), it can be seen that the region with the highest number of rankings in first place is Anqing City. Anqing City ranks first in the ranking of relative proximity values in 2019, 2020, and 2021, with specific relative proximity values of 0.553, 0.588, and 0.609, respectively. In addition, the relative proximity value ranking is more advanced is the city of Lu’an. The city of Lu’an ranked first in the relative proximity value ranking in 2017, second in 2020 and 2021, and third in 2018 and 2019. Huainan, Chuzhou, Yancheng, and Taizhou were ranked at the back of the relative proximity value ranking, and the worst performer is Huainan, which was ranked in the last place of the relative proximity value ranking in the four years of 2017, 2019, 2020, and 2021, with specific relative proximity values of 0.385, 0.403, 0.407, and 0.410, respectively.

3.1.4. Analysis of Spatial Differentiation of Ecological Environment Quality in the Jianghuai Valley, 2017–2021

To visualize these findings, we utilized the comprehensive evaluation data from the 11 cities in the Jianghuai Valley spanning 2017 to 2021. These data helped generate a map detailing the integrated assessment of the Jianghuai Valley’s eco-environmental quality over the studied period, achieved through the spatial analysis and mapping capabilities of ArcGIS (Figure 5).

Based on the space allocation arising from the comprehensive evaluation of ecological environment quality in municipal areas of Jianghuai Valley from 2017–2021, an emerging pattern of ecological quality aggregation is observed. This pattern follows a progression from west to east, with areas characterized by excellent ecological quality predominantly located in the west. This transitions to regions of general and good ecological quality and finally to areas with poor ecological quality in the east. The excellent ecological quality agglomeration area is located in the western part of the Jianghuai Valley and consists of Xinyang City, Lu’an City, and Anqing City, which are administratively subordinate to Henan and Anhui Provinces; the average ecological quality agglomeration area consists of Huainan City, Hefei City, and Chuzhou City, which are administratively subordinate to Anhui Province; the good ecological quality agglomeration area consists of Yangzhou City and Huai’an City, which are administratively subordinate to Jiangsu Province; and the poor ecological quality agglomeration area consists of Yancheng City, Taizhou City, and Nantong City.

3.2. Analysis of Ecological Quality Drivers in the Jianghuai Valley

Gray correlation analysis is an effective tool for ranking multiple factors and identifying their relative influence. In the decision-making process, prioritizing factors based on their correlation strength aids in resource allocation and strategy formulation. The gray correlation analysis, when applied to the indicators in the Jianghuai Valley, reveals relationships among factors associated with eco-environmental quality in a region. To provide a comprehensive analysis of the driving factors affecting the ecological environment quality of the Jianghuai Valley, we calculated the gray correlation for each sub-indicator across different years in this study. By comparing these correlations over time, we can analyze how the influence of various factors on ecological environment quality evolves. Furthermore, periodic calculation of gray correlations over the years allows for continuous monitoring and assessment of these driving factors, aiding in the timely detection of environmental changes and issues. This, in turn, facilitates ongoing improvements in ecological environmental protection.

3.2.1. Comprehensive Gray Correlation Analysis of Indicators in Jianghuai Valley

The top five indicators with the highest total gray correlation, in order, are Q10 cultivated land area (0.994), Q13 average annual temperature (0.991), Q18 built-up area green coverage rate (0.989), Q20 sewage treatment rate (0.989), and Q19 integrated utilization rate of general industrial solid wastes (0.980). Conversely, the five metrics with the lowest gray correlations, in order of magnitude, are Q3 natural population growth rate (0.5), Q11 total water resources (0.819), Q17 average annual concentration of PM2.5 (0.873), Q14 annual precipitation (0.882), and Q16 green space per 10,000 people (0.914). Among these indicators, Q10 (cultivated land area) holds the highest gray-scale relevance to ecosystem quality in Jianghuai Valley, suggesting its predominant influence on the region’s ecosystem quality. On the other hand, Q3 (population growth rate) has the lowest gray-scale correlation, indicating that it has the least impact on the quality of the ecosystem.

3.2.2. Gray Association between Corresponding Indicators for Each Year in the Jianghuai Valley

By observing the gray correlation of the corresponding indicators in each year, it can be seen that in 2017, the gray correlation of Q9 (proportion of good air quality days) and Q13 (annual average temperature) is the highest. Their corresponding gray correlations are 0.977 and 0.972, respectively. In 2018, the highest gray correlations are found for Q17 (PM2.5 annual average concentration) and Q20 (sewage treatment rate). Their corresponding gray correlations are 0.912 and 0.905. Among the gray correlations of the corresponding indicators in 2019, the gray correlations of Q13 (annual average temperature) and Q19 (comprehensive utilization rate of general industrial solid waste) are the highest. The corresponding gray correlations are 0.967 and 0.960, respectively. Among the gray correlations of the corresponding indicators in 2020, Q13 and Q9 have the highest gray correlations. The corresponding gray correlations are 0.989 and 0.989. Among the gray correlations of the corresponding indicators in 2021, the gray correlations of Q19 (comprehensive utilization rate of general industrial solid waste) and Q14 (annual precipitation) are the highest. Their corresponding gray correlations are 0.979 and 0.973, respectively.

In summary, when analyzing the scaled ranking of metrics within the Jianghuai Valley across the years, Q9 and Q13 consistently appear in the top five in the annual gray correlation rankings, occurring five times each. Specifically, Q9 held the first position in 2017 (0.977), the fourth position in 2018 (0.896), the third position in 2019 (0.957), the second position in 2020 (0.989), and fourth in 2021 (0.969). Meanwhile, Q13 held the first position in 2017 (0.977), the third position in 2018 (0.902), the first position in 2019 (0.967), the first position in 2020 (0.989), and the fifth position in 2021 (0.969). Over the past five years, Q18 and Q20 have consistently appeared in the gray correlation rankings, totaling four instances. Specifically, Q18, denoting green coverage of built-up areas, secured the third position in 2017 (0.965), the fifth in 2018 (0.950), the third in 2020 (0.988), and the third in 2021 (0.972). Meanwhile, Q20, representing sewage treatment rate, occupied the fourth position in 2017 (0.964), the second in 2018 (0.905), the fourth in 2019 (0.950), and the fifth in 2020 (0.987). Conversely, Q3 consistently occupied the lowest ranking in terms of relevance to ecological and environmental quality in 2017, 2019, 2020, and 2021, securing the first-place position in these years and the third-place position in 2018.

4. Deliberations

This paper adopts a novel approach, moving away from the traditional method that assesses regional ecosystem quality solely from an ecological perspective. Instead, it introduces a human-centered perspective that considers the relationship between humans and the environment. More specifically, it establishes an ecological environment quality assessment index system around this human–ecological relationship. This study focused on evaluating the ecological environmental quality of 11 urban areas in the Jianghuai Valley. Then, the evaluation results were analyzed in time and space according to the evaluation results, which showed the driving factors affecting the ecological environment quality of the Jianghuai Valley as well as the spatial and temporal differentiation changes of the ecological environment quality of the Jianghuai Valley.

The results of the study show that the ecological environment quality of the Jianghuai Valley follows a process of rising, then falling, and finally rising steadily. The overall trend is fluctuating and rising. As can be seen from the table of types of environmental trends in the eleven urban areas, Yancheng, Huainan, Hefei, Anqing, and Xinyang exhibited a consistent improvement in ecological and environmental quality between 2017 and 2021. Overall, these regions maintained a stable performance, reflecting a harmonious development across various sectors, including the economy, society, humanities, resource management, and environmental protection. Next, the eco-environmental qualities of Yangzhou, Lu’an, as well as Chuzhou areas showed a trend of increasing and then decreasing. Specifically, these areas exhibited a relatively optimistic growth rate in ecological environmental quality from 2017 to 2020, but in 2021, a downward trend was observed. Nevertheless, their overall ecological environmental quality remained slightly improved. Lastly, the Chuzhou, Huaian, Taizhou, and Nantong regions experienced fluctuating trends in ecological environmental quality from 2017 to 2021. Although these areas reached their highest ECQ in 2018, they subsequently showed a decline and continued to exhibit up and down fluctuations. Overall, there was no significant improvement in their ECQ.

In addition, according to the relative proximity value ranking of different regions in 2017–2021, it can be seen that the region with the highest number of rankings in first place is Anqing, indicating that during 2019–2021, relative to other regions in the JAC Basin, Anqing had the highest ecological environmental quality index, and the local ecological environmental quality was optimal, reaching 0.571. In addition to Anqing, Lu’an City’s ecological environmental quality index has been in the forefront in the JAC region, and its ecological environmental quality is more stable in the time dimension. On the other hand, the relative proximity values of Huainan, Chuzhou, Yancheng, and Taizhou have been ranked at the back of the list, with the worst performance being that of Huainan. Meanwhile, in the total ranking of relative proximity values in the Jianghuai Valley, Huainan is also located in the last place of the total relative proximity value rankings. The specific relative proximity value is 0.421, which indicates that Huainan has the lowest ecological environment quality compared with the whole Jianghuai Valley area.

According to the spatial distribution of the comprehensive evaluation of the ecological environment quality of the urban areas in the Jianghuai Valley from 2017 to 2021, the ecological environment quality shows an aggregation trend. From west to east, it is manifested as the aggregation area of excellent ecological environment quality, the aggregation area of average ecological environment quality, the aggregation area of good ecological environment quality, and the aggregation area of poor ecological environment. This situation may be affected by a combination of factors, including geography, population distribution, economic structure, industrial development, environmental policy, and other factors. For example, the western part of the Jianghuai Valley is relatively far away from large industrial areas and cities, which reduces the impact of industrial emissions and human activities on the ecological environment. The rapid economic development in the eastern region, however, is accompanied by high-energy-consuming and high-polluting industries, leading to an increase in the environmental load and affecting the ecological environment. At the same time, the eastern region is densely populated and has a high level of urbanization, and the gathering of the population aggravates environmental pressure and leads to the deterioration of the ecological environment. The intertwined effect of these factors has led to the difference in the geographical distribution of the ecological environment quality of the urban areas in the Jianghuai Valley, forming an aggregation performance from east to west.

In addition, the results of the study confirmed that the proportion of good air quality days in Q9 and the average annual air temperature in Q13 had a very high correlation with the ecological quality of the JAC Basin. The reason may be due to the negative impacts of air pollutants on ecosystems and biodiversity. High concentrations of air pollutants such as particulate matter and ozone can damage vegetation leaves, soil, and water bodies, which in turn affects the growth of plants, the quality of water resources, and the stability of ecosystems. And changes in average annual temperatures can affect the structure and function of ecosystems. Temperature changes may lead to changes in the distribution ranges of different species in the ecosystem, affecting the life cycle and reproduction of organisms. It can be seen that excellent air quality and suitable air temperature help maintain the health and balance of ecosystems, and they influence the survival and reproduction of organisms and regulate ecological processes and energy flow, thus maintaining the stability of ecosystems. This research result reminds human beings that they should pay attention to maintaining good air quality and temperature in the process of ecological environmental protection. Meanwhile, Q18 (greening coverage of built-up areas) and Q20 (sewage treatment rate) were highly correlated with the ecological quality of the JHB. The high correlation between greening coverage and sewage treatment rate in built-up areas and ecological environment quality is because they are directly related to the ecological elements and environmental quality in cities or built-up areas. High greening coverage and high sewage treatment rates help reduce the pollution and deterioration of the urban environment and protect the ecological balance, thus having a positive impact on the quality of the ecological environment of the Jianghuai Valley. Therefore, in improving the ecological environment and promoting eco-environmental protection, increasing the greening coverage and sewage treatment rates in built-up areas is an important measure.

Finally, this study shows that the combination of entropy-weighted TOPSIS and gray correlation method can be used to better understand the drivers of ecological and environmental quality in the Jianghuai Valley and the spatial and temporal variability of ecological and environmental quality in the Jianghuai Valley. This finding is supported by JLi J. et al. (2022), Li, H. et al. (2022), and others, who showed that the entropy-weighted TOPSIS method combined with gray correlation analysis can be a good way to analyze the degree of resource environment and identify the key influencing factors. But this method also has some limitations. First, it does not combine subjective and objective evaluation. The data used in entropy-weighted TOPSIS and gray correlation analysis come from provincial and municipal statistical yearbooks, environmental bulletins, etc., which are objective data and lack subjective evaluation. In addition, the time span of the available data is relatively small, limiting the ability to more fully characterize the changes in ecological and environmental quality in the JHB. In response to this limitation, the study expresses the intention to incorporate human subjective evaluations in future studies to provide a more comprehensive perspective for assessing the quality of the regional habitat and to ensure the objectivity and validity of the results.

5. Conclusions

In this study, entropy-weighted TOPSIS and gray correlation analysis were comprehensively utilized to assess the drivers affecting the ecological environmental quality of the Jianghuai Valley and the spatial and temporal variability changes of the ecological environmental quality of the Jianghuai Valley. The results show that, first, the interannual changes in the ecological environmental quality of the Jianghuai Valley, from largest to smallest, were in 2021, 2020, 2018, 2019, and 2017. From the perspective of time, the ecological environmental quality of the Jianghuai Valley showed a process of first rising, then falling, and finally rising steadily. As a whole, the trend fluctuates upward. Second, according to the study on the trend of the development of ecological environmental quality in the urban areas of the Jianghuai Valley from 2017 to 2021, it was found that the eleven urban areas of the Jianghuai Valley were uneven in ecological environmental quality, with a large difference between high and low, and there were geographical differences. Third, according to the relative proximity value ranking of different regions in the Jianghuai Valley from 2017–2021, it was found that during the period of 2017–2021, relative to other regions in the Jianghuai Valley, Anqing City had the highest index of ecological environmental quality, Lu’an City was at the front of the index of ecological environmental quality in the Jianghuai Valley, and the region with the lowest ecological environmental quality was Huainan City. Fourth, by analyzing the spatial differentiation state of ecological environmental quality in the Jianghuai Valley from 2017 to 2021, it was found that the ecological environmental quality in the Jianghuai Valley shows a clustering performance. From west to east, it is manifested as an excellent ecological environment quality aggregation area, general ecological environment quality aggregation area, good ecological environment quality aggregation area, and poor ecological environment aggregation area. Fifth, the analysis of the drivers of ecological environment quality in the Jianghuai Valley found that, on the whole, Q10 (cultivated land area) had the greatest influence on the ecological environment quality of the Jianghuai Valley, Q3 (population growth rate) had the least influence on the ecological environment quality, and Q9 (proportion of good air quality days) and Q13 (annual average temperature) had a very high correlation with the ecological environment quality of the Jianghuai Valley.