Introduction

Land resources are the basic carrier for human survival and development, and the improvement of the level of land use can provide opportunities for the agglomeration of production factors, which will promote the development and utilization of the effective allocation of various types of resources, thus promoting the realization of the goal of sustainable development (Li et al., 2023; Zhang et al., 2023). The urbanization level in China has been increasing in recent years, and the role of urban land resources in the survival and development of human beings has become more and more prominent (Lu et al., 2022; Zhang and Luo, 2022). In addition to labor factors, China’s economic development is increasingly constrained by land resources, disorderly land expansion, and human activities will largely affect the rational urban land-use structure, the contradiction between sustainable development and urban land use is increasingly severe (Yuan et al., 2022; Guo et al., 2023). As an important inland river economic zone in China, the transfer of industries of the YREB in recent years has, to a certain extent, caused industrial isomorphism and waste of land resources among cities, which will restrict the coordinated development of the regional economy (Tu et al., 2023). Understanding the efficiency of urban land resource utilization is an important prerequisite for improving land issues during urban development.

Land-use status is a key factor influencing the value of ecosystem services (DeLoyde and Mabee, 2023; Yuan et al., 2024). Ecosystem services have been defined to consider the benefits that humans derive from the various service functions of ecosystems (Costanza et al., 1997; Daily and Matson, 2008). The Equivalency Factor Method is one of the methods for measuring ecosystem services value (ESV), which relies on land-use data and crop product data for its calculation. With the deepening research, a series of models such as InVEST, ARIES, and SoIVES have been developed and applied to ecosystem service valuation. Among these, the InVEST model developed jointly by Stanford University was recognized as the most important one for ecosystem service measurement because of its wide measurement range and high visualization degree (Zhai et al., 2021; Kafy et al., 2023). In recent years, the Chinese government has increased its efforts to maintain the ecological balance of the YREB and attempted to create a global ecological economic demonstration zone at the site by introducing a series of policies for the sustainable development of the watershed. Measuring the ESs of the YREB can effectively reflect the importance of the YREB in Chinese ecological protection, strengthen the social attention to the green development of the Yangtze River Basin, and promote the sustainable development of the YREB in the future. Both the Equivalency Factor Approach and the InVEST model require the use of land-use data in their calculations, so most of the current studies conclude that an important way to enhance regional ESs is to improve land use (Chen et al., 2023). However, there exists a lack of studies that validate the relationship between land use and ESs from the coupling coordination perspective, especially considering the urban development perspective. For example, what are the performance characteristics and formation mechanisms of the coordinated coupling between them? What are the interactive and coordinating mechanisms of the evolution between them in different regions? What is the spatial correlation between the evolution of them in different regions? These questions have not yet been fully explained.

On the other hand, more and more countries and regions have proved that the status of economic development and ecological protection are equally important. If there is great damage to the ecological environment, it will need to be paid more for restoration in the future, increasing intergenerational inequity. The proposal of the CCD model, through the allocation of weights to different systems, is able to effectively reflect the overall conflict or coordination between different systems (Xiao et al., 2020), and has been utilized by numerous scholars to explore the relationship between the development of different social systems (Yang et al., 2020; Zeng et al., 2023). The model can be used to quantify the relationship between economic development and ecosystems in the YREB, which is conducive to the development of more practical ecological management programs following the characteristic watersheds and provides strong support for the refinement of YREB policies (Chen et al., 2017). However, the traditional CCD model has problems such as relative concentration of coupling degree values, thus largely weakening the role of the CCD model, and is easily prone to the phenomenon of “false high coupling coordination degree” (Wang et al., 2021; Han et al., 2023).

Compared to existing research, this study’s marginal contributions are as follows: (1) From the perspective of urban ecological environment quality, the ULGUE in the YREB was objectively evaluated from the perspective of dynamic efficiency by using the Super-SBM model. (2) The combined weight method was used in measuring the weights between ULGUE and ESs, which combines the advantages of CRITIC (Criteria Importance Though Intercriteria Correlation) method and entropy weight method, and it is a more scientific and reasonable method of assigning the weights between different systems. (3) Unlike previous studies that considered the effect of land use when measuring ESs only in terms of data utilization, we further demonstrated the impact of land use on ESs from the coupling coordination perspective. In addition, the problem of volatility and credibility of the traditional CCD model is well mitigated by using the improved CCD model. (4) On the basis of measuring and classifying the CCD, the trend surface analysis, standard deviation ellipsoid analysis, and gravity model were further introduced, which explained more comprehensively and reasonably the spatial and temporal evolution mechanism of the CCD and its characteristics.

Material and methods

Study area and data

The Yangtze River, which spans the three main areas of West, Middle, and East China, is the largest river in the country and serves as the connecting link between the Silk Road Economic Belt and the 21st Century Maritime Silk Road. It is also one of China’s key strategic development regions. However, the YREB also had many ecological and environmental problems in recent years, such as soil erosion and species extinction, water pollution etc. Understanding the spatial-temporal driving mechanisms between ULGUE and ESs is beneficial to rational planning in favor of regional development and to achieving sustainable development goals in the future (Fig. 1).

Fig. 1: Location of 108 prefecture-level cities in the YREB.
Fig. 1: Location of 108 prefecture-level cities in the YREB.
Full size image

The map is based on the standard map No. GS (2020) 4619 downloaded from the website of Standard Map Service of the Ministry of Natural Resources of China, with no modification to the base map.

In addition, most of the development of human society relies on rivers (Wei et al., 2022). As a typical large river basin economic belt in the world, the YREB is facing many contradictions and problems, such as the relative shortage of arable land reserve resources, the level of land conservation and intensive use still needs to be improved, and the contradiction between the supply of and demand for land is still prominent after the high-speed development of the economy. Land resource constraints on the economic development of the YREB have become more and more prominent, so a comprehensive land-use efficiency study of the region is of great significance to the study of the sustainable development of the basin urban agglomerations in the world.

The city level within the scope of this study area is prefecture-level cities or above, and due to changes in the administrative system, Chaohu, Bijie, Tongren, and Pu’er Cities, which were annexed or newly added during the period of 2005-2020, were excluded; Meanwhile, in order to ensure the availability of the data, 108 prefectural-level cities and above in the YREB were finally obtained as the study samples. Due to the availability of land-use remote sensing data, we selected 2005, 2010, 2015, and 2020 as breakpoint years for the coupled coordination analysis. The required data come from public information, as shown in Table 1, and the statistical caliber of the data sources is basically the same, which can guarantee the reliability and accuracy of the research data. The missing data of individual index items are measured with the help of linear interpolation.

Table 1 Data sources.
Full size table

Analysis of the coupling and coordination development mechanism

Land use is the process of long-term development, management, and governance of land by human beings for the purpose of survival and development. In recent years, human demand for ecosystem supply services has been increasing with the rapid development of the global population and socio-economic development. At the same time, it has had a great impact on the land itself, which has led to a change in ecosystem service functions and values. To some extent, the ecosystems have been damaged, resulting in the reduction and degradation of ecosystem service capacity, which directly poses a serious threat to the ecological security of the region.

Ecological processes such as water cycling and energy exchange of ecosystem services are affected by different land-use types; ecological functions such as climate regulation, disturbance resistance, erosion control, and sediment retention of ecosystems are affected by land-use patterns; and the ecosystem services such as provisioning, regulating and supporting services are affected by the land-use intensity. Ecosystems continue to provide ecological services to humans, although they cannot be bought and sold in the marketplace, and land-use efficiency is a quantification of the ecological benefits of land ecosystems. Therefore, estimating the efficiency of land use and enhancing the comprehensive benefits of land ecosystems have become important prerequisites for maintaining the stability of ecosystems and ecological processes.

Previous research on land use has been carried out from a single perspective of resource allocation, and there has been a high degree of duplication in the content of the relevant studies. Few studies have quantitatively analyzed land-use efficiency from the perspective of ecological service value. On the basis of previous research, this study introduces the concept of ecosystem service in the Millennium Ecosystem Services Assessment (MA) conference, incorporates natural factors, biological factors, and economic factors into the exploration of the land-use efficiency, and cross-applies the ecological method to expand the calculation of the realistic value of the land use, so as to provide theoretical support for the coordination of the land use and ecological protection from both economics and ecology purpose (Fig. 2).

Fig. 2
Fig. 2
Full size image

Framework for analyzing the interaction mechanism between ULGUE and ESs.

Study methods

Combination weight method

Combination weight method is a comprehensive method of combining multiple single measurement models, and the advantages of different methods can be well integrated. CRITIC method is an objective assignment method proposed by Diakoulaki et al (Diakoulaki et al., 1995). It combines the advantages of the standard deviation coefficient method and so on and takes into account the comparative strength, conflict, and correlation of data among indicators. However, the single CRITIC method fails to take into account the discrete character between indicators, and the entropy weighting method can effectively make up for its deficiency. Based on this, Ma et al. integrated the two methods and constructed a combination weighting method (Ma et al., 2022). Improving the new model by combining two single-weighted models resulted in a more accurate and complete assessment. In the study, we introduced this method for the weight measurement for the ULGUE system and ES system of YREB, and the calculation steps are as follows:

Step1: Based on the calculation principle of CRITIC method, the original data and standardized operation code are imported into Stata software to generate standardized data: \({Q}_{{ij}}{\prime}\). The standard deviation σ and correlation coefficient r can be obtained through SPSS software, and the weight coefficient can be obtained: \({W}_{j}^{(1)}\). The formula is as follows:

$$W{j}^{(1)}=\frac{\sigma j\mathop{\sum}\nolimits_{j=1}^{n}(1-{r}_{ij})}{\mathop{\sum }\nolimits_{j=1}^{n}[\sigma j\mathop{\sum }\nolimits_{j=1}^{n}(1-{r}_{ij})]},(\;j=1,2,\mathrm{..}.,n)$$
(1)

where i and j are the different evaluation index; \({W}_{j}^{(1)}\) is the weight obtained by CRITIC method; and \({\sigma }_{j}\) is the standard deviation; \({r}_{{ij}}\) denotes the correlation coefficient between the ith and the jth.

Step2: Based on the entropy weight method, the weight of the algorithm is calculated as follows. \({W}_{j}^{(2)}\) is recorded as the weight of the jth algorithm obtained by the entropy weight method, and the formula calculation is as follows:

$${{P}_{i}{j}}^{\text{'}}=\frac{{X}_{ij}}{{\sum }_{i=1}^{n}{{X}_{ij}}}$$
(2)
$${ej}=-{k}\mathop{\sum}\nolimits_{i=1}^{n}{p}_{ij}\,\times\,\mathrm{ln}({{P}{ij})}$$
(3)
$${k}=\frac{1}{\mathrm{ln}(n)} \,>\, 0$$
(4)
$${{d}_{j}}=1-{{e}_{j}}$$
(5)
$${{W}_{j}}^{(2)}=\frac{{d}_{j}}{\mathop{\sum}\nolimits_{j=1}^{n}{{d}_{j}}}$$
(6)

where \({P}_{{ij}}{\prime}\) is the normalized matrix; ej is the entropy value; n is the samples’ number; dj is the information entropy redundancy; \({W}_{j}^{(2)}\) is the weight of the single indicator corresponding to the entropy weight method.

Step3: Calculate the combined evaluation value of the two weighting methods separately.

$$S1=\mathop{\sum }\limits_{i=1}^{m}\mathop{\sum }\limits_{j=1}^{n}(Qi{j}^{\text{'}}{\ast {\rm{Wj}}}^{(1)})$$
(7)
$$S2=\mathop{\sum }\limits_{i=1}^{m}\mathop{\sum }\limits_{j=1}^{n}(Pi{j}^{\text{'}}{\ast {\rm{Wj}}}^{(2)})$$
(8)

where \({Q}_{{ij}}^{{\prime} }\) is the standardized value of the single indicator corresponding to the CRITIC method, \({W}_{j}^{(1)}\) is the weight of the single indicator corresponding to the CRITIC method, and \({S}_{1}\) is the comprehensive evaluation value of CRITIC method. Where \({P}_{{ij}}^{{\prime} }\) is the standardized value of the entropy weight method. \({W}_{j}^{(2)}\) is the weight of the single indicator corresponding to the entropy weight method, and \({S}_{2}\) is the comprehensive evaluation value of the entropy weight method.

Step4: Assuming that the two empowerment methods have equal status, construct a combined empowerment method based on the CRITIC method and entropy power method so as to realize the complementary advantages between objective empowerment methods. Accordingly, the formula can be established:

$$Wj=(W{j}^{(1)}+W{j}^{(2)})/2,(j=1,\mathrm{2..}.,n)$$
(9)

where \({W}_{j}\) denotes the final weight of the jth algorithm obtained by the combination weight method.

Super-SBM model

Data Envelopment Analysis (DEA) compares the relative efficacy of divisions or units with numerous inputs and outputs using mathematical programming models. Unlike previous studies, the DEA model evaluates the level of development of the system from the perspective of efficiency and is able to reflect the causal relationship between the variables and efficiency values (Gan et al., 2023). Therefore, it is frequently applied to efficiency assessments. Although the traditional DEA model takes into account the slack problem, it does not take into account enough input and output elements, especially non-expected output elements. Tone et al. proposed the SBM-DEA model, which has effectively overcome the above shortcomings (Tone and Tsutsui, 2009). Currently, the treatment of pollution in the SBM-DEA can be grouped into two categories especially in environmental efficiency measurements: one is to consider pollution emissions as input factors, which are included in the input function together like capital or labor. The other approach is to treat pollution as an unexpected output and introduce it into the production process together with desired outputs. The SMB model takes the optimized slack variable as the objective function and also considers the difference between the input and output terms to estimate the efficiency value, which can directly deal with the problem of excess inputs and shortages of outputs in the decision unit. The Super-SBM model is based on the traditional SBM model to further consider the influence of the unexpected output indicators on efficiency. Therefore, it is a method more suitable for the current international environmental efficiency measurement (Zhang et al., 2017; Khan et al., 2021).

$${\rho}^{\ast}=\,{{\min }}\,\rho =\,\min \frac{1-\left(\frac{1}{{\rm{V}}}{\sum }_{{\rm{v}}=1}^{V}\frac{{{\rm{s}}}_{{\rm{v}}}^{{\rm{x}}}}{{{\rm{x}}}_{{\rm{v}}}^{{\rm{k}}^{\prime} }}\right)}{1+\left[\frac{1}{1+{\rm{U}}}\left({\sum }_{u=1}^{U}\frac{{s}_{u}^{y}}{{y}_{u}^{k}}+{\sum }_{i=1}^{I}\frac{{s}_{j}^{b}}{{b}_{i}^{{k}^{\prime} }}\right)\right]}$$
(10)
$$\mathop{\sum}\nolimits_{k=1}^{K}{z}_{k}^{y}{y}_{u}^{k}-{s}_{u}^{y}={y}_{u}^{{k}^{\prime} },u=1\cdots ,{U}{;}{s}_{u}^{y}\ge 0$$
(11)
$${\sum }_{k=1}^{K}\,{z}_{k}^{y}{x}_{v}^{k}-{s}_{v}^{x}={x}_{v}^{{k}^{\prime} },v=1\cdots ,V;{s}_{v}^{x}\ge 0$$
(12)
$${\sum }_{k=1}^{K}\,{z}_{k}^{y}{b}_{i}^{k}-{s}_{i}^{b}={b}_{i}^{{k}^{\prime} },i=1,2\cdots N;{s}_{i}^{b}\ge 0$$
(13)

where \({\rho }^{* }\) represents the score of the ULGUE levels, V, U, N stand for input, expected and Unexpected output variables, respectively. \(\left({S}_{u}^{y},{S}_{i}^{b},{S}_{v}^{x}\right),\left({y}_{u}^{k},{b}_{i}^{k},{x}_{v}^{k}\right)\) and \({z}_{k}^{y}\) represent input and output slack variables. The score of \({\rho }^{* }\) is greater, which means the higher ULEGU level.

Combining the previous scholars’ research and the location characteristics of the YREB, we selected 8 indicators to measure the ULGUE level of the YREB (Table 2).

Table 2 The evaluation index system of ULGUE of the YREB.
Full size table

Evaluation of ecosystem services

  1. (1)

    Habitat quality:

    $$Qhx=Hx\,*\, \left[1-\left(\frac{{D}_{hx}^{z}}{{D}_{hx}^{z}+{G}^{z}}\right)\right]$$
    (14)

    where \({Q}_{{hx}}\) indicates the habitat quality, and the value of Q ranges from 0 to 1 where larger values of Q indicates a higher level of habitat quality. x is the land-use type, \({D}_{{hx}}\) is the threat level in the raster of h; z represents the scaling parameters of x and \({H}_{x}\) is the habitat suitability; And G means the half-saturation constant.

  2. (2)

    Soil retention:

    $$Ea=G\ast M\ast LS\ast C\ast P$$
    (15)
    $$Ep=G\ast M\ast LS$$
    (16)
    $$SR=Ep-Ea$$
    (17)

    where SR is the soil retention (t/hm2), Ea is the potential annual average soil loss, and Ep is the actual annual average soil loss; LS is the dimensionless, representing slope length and slope steepness factor; There are two important factors which will make a great influence for the SR, which is G-the rainfall erosivity factor and other is M-the soil erodibility factor; C and P mean cover-management factor and support practices factor, respectively.

  3. (3)

    Water yield:

    $${{Y}_{x}}=\left(1-\frac{{AET}_{x}}{{P}_{x}}\right)\ast {{P}_{x}}$$
    (18)

    where \({Y}_{x}\) is the total water yield (mm) calculated by the annual water yield module at the InVEST model, x means the pixel, \({{AET}}_{x}\) is the actual annual evapotranspiration, and \({P}_{x}\) is the annual average precipitation.

  4. (4)

    Carbon storage:

$$NP{P}_{(x,t)}=APA{R}_{(x,t)}\ast {\varepsilon }_{(x,t)}$$
(19)
$$CS=1.63NPP$$
(20)

where \({CS}\) means carbon storage (Mg of C); \({{NPP}}_{\left(x,t\right)}\) is the net primary productivity at grid x in month t; \(\scriptstyle{{APAR}}_{(x,t)}\) is the absorbed photosynthesis active radiation at pixel x in month t, and \({\varepsilon }_{\left(x,t\right)}\) is the actual light use efficiency of pixel x in month t.

Improved coupling coordination degree model

In this study, the CCD model was revised and used to explore the interaction relationship between ULGUE and ESs. The model can explain the interdependencies, associations, and relationships between the different systems (Liu et al., 2021). However, it is not reasonable to use traditional coupling models with low validity, where the measured coupling values (C) are centrally distributed at the 1 end of the scale, and thus the calculated coupling coordination values are mainly dependent on the coordination degree (T) values. Futhermore, the development degree depends on the system itself, while the role of the system coordination level is weakened, leading to the difficulty in realizing the value and significance of the CCD. On the other hand, the C is not an average distribution function between [0, 1] in the traditional CCD model, and the validity problem originates from the model itself. This study adopted the modified coupling degree model of Wang et al. The model overcomes to a certain extent the reliability problems of the traditional coupling coordination degree model in terms of the subjectivity of the indicator construction, the volatility of the coupling results and the non-comparability, and disperses the coupling degree values as much as possible to be distributed in [0, 1], increasing the differentiation of the coupling degree values, so as to have a higher degree of efficiency in the field of social sciences (Wang et al., 2021; Han et al., 2023).

$$C=\sqrt{\left[1-\frac{\mathop{\sum }\nolimits_{i=j,j=1}^{n}\sqrt{{({{U}_{i}}-{{U}_{j}})}^{2}}}{\mathop{\sum }\nolimits_{m=1}^{n-1}m}\right]\ast \left(\mathop{\prod }\limits_{i=1}^{n}\frac{{U}_{i}}{\max {{U}_{i}}}\right)^{\frac{1}{n-1}}}$$
(21)
$$T=\mathop{\sum }\limits_{i=1}^{n}{{\alpha}_{i}}\times {{U}_{i}},\mathop{\sum}\limits_{i=1}^{n}{{\alpha}_{i}}=1$$
(22)
$$CCD=\sqrt{C\times T}$$
(23)

where \(C\) indicates the coupling degree between ULGUE and ES; \(T\) is the coordination index, and \({CCD}\) is the coupling coordination degree between ULGUE and ESs; \({U}_{i}\) and \({U}_{j}\) represent the comprehensive evaluation weights of the ULGUE and ESs, respectively; α is the total system weight between ULGUE and ESs. Referring to previous research, α is given the same weight, that is, \({\alpha }_{1}\) = \({\alpha }_{2}\) = 0.5 (Li et al., 2021; Yang et al., 2022). Based on existing studies, we divided the CCD into five degrees (Fig. 3).

Fig. 3
Fig. 3
Full size image

The division of CCD level.

Trend surface analysis

Trend surface analysis is able to accurately demonstrate the spatial distribution patterns of geographic elements in space from a local orientation perspective (Feng et al., 2023). Trend surface analysis is a smooth mathematical surface, and it can focus on reflecting the trend of spatial data in a wide range of changes, it is the ideal tool to reveal the spatial change law of continuously distributed phenomena on the surface area, but also often used in practice to describe the main method of spatial trend. The spatial divergence of CCD between ULGUE and ESs was analyzed in this study, and it was expressed as:

$${{Z}_{i}}({{x}_{i}},{{y}_{i}})={{T}_{i}}({{x}_{i}},{{y}_{i}})+{{\xi}_{i}}$$
(24)

where \({Z}_{i({x}_{i},{y}_{i})}\) is the actual observation data; \({T}_{i({x}_{i},{y}_{i})}\) is the trend function, \(\xi i\) is the random error.

Standard deviation ellipse

The standard deviation ellipse method is one of the classic methods for analyzing the directional characteristics of spatial distribution and can be used to quantitatively explain the overall characteristics of the spatial distribution from a global and spatial point of view. (Lefever, 1926; Zhou et al., 2019). Therefore, it is widely used in ecological and economic areas (Du et al., 2019; Xia et al., 2020). Therefore, the study further used the standard deviation ellipse by using the ArcGIS10.8 software to reflect the spatial evolution trajectory of the CCD over time.

$$\tan \theta =\frac{(\mathop{\sum }\nolimits_{i=1}^{n}{\overline{{X}_{i}}}^{2}-\mathop{\sum }\nolimits_{i=1}^{n}{\overline{{Y}_{i}}}^{2})+\sqrt{[{(\mathop{\sum }\nolimits_{i=1}^{n}{\overline{{X}_{i}}}^{2}-\mathop{\sum }\nolimits_{i=1}^{n}{\overline{{Y}_{i}}}^{2})}^{2}]+4{(\mathop{\sum }\nolimits_{i=1}^{n}{X}_{i}-{Y}_{i})}^{2}\left]\right.}}{2\mathop{\sum }\nolimits_{i=1}^{n}{X}_{i}-{Y}_{i}}$$
(25)
$${\sigma}_{x}=\frac{\mathop{\sum}\nolimits_{i=1}^{n}(\overline{{X}_{i}}\cos \theta -\overline{{Y}_{i}}\,\sin \theta)^{2}}{n}$$
(26)
$${\sigma}_{y}=\frac{\mathop{\sum}\nolimits_{i=1}^{n}(\overline{{X}_{i}}\sin \theta -\overline{{Y}_{i}}\,\cos \theta)^{2}}{n}$$
(27)

where \(({\overline{X}},{\overline{Y}})\) represents the weighted average center; \(\overline{{X}_{i}}\), \(\overline{{Y}_{i}}\) respectively represent the coordinate deviation from the location of each research object to the average center; tanθ represents rotation angle; And \({\sigma }_{x}\) and \({\sigma }_{y}\) represent the standard deviation along the x-axis and y-axis, respectively.

Gravity model

The gravity model in economic geography is a spatial interaction model that can be used to solve different problems by changing the definition of parameters and parts (Ma et al., 2022). Therefore, we tried to use the Newtonian Gravity Model to analyze the CCD space interaction potential between ULGUE and ESs. The formula is as follows:

$${{R}_{ij}}=k\frac{{M}_{i}{M}_{j}}{{d}_{ij}^{2}}$$
(28)
$${{R}_{i}}=\mathop{\sum }\limits_{j=1}^{n}{{R}_{ij}}$$
(29)

where \(d\) is the distance index; \(k\) means the gravitational constant; \(i\) and \(j\) are two different regions. Rij is the CCD’s spatial connectivity strength between different regions; \({R}_{i}\) represents the sum of the spatial connection between city \(i\) and other cities; \({M}_{i}\) and \({M}_{j}\) represent the CCD’s value in the regions, respectively.

The main research method and framework of this study are shown in Fig. 4.

Fig. 4
Fig. 4
Full size image

Workflow chart for analyzing CCD levels between ULGUE and ESs in YREB.

Results

The spatiotemporal changes of ULGUE and ESs

The evolution of ULGUE

Figure 5 shows the distribution of ULGUE in YREB from 2005 to 2020 calculated using MaxDEA5.2 software, and we further quantify the ULGUE score at different levels.

Fig. 5
Fig. 5
Full size image

The whole trend in the ULGUE levels in the YREB from 2005 to 2020.

From the overall evolution trend, it can be seen that the ULGUE level of YREB as a whole shows an overall fluctuating trend of change that first decreases, then increases, and finally smooths out gradually. According to the research needs, we extracted the score status of ULGUE in 2005, 2010, 2015, and 2020, and further carried out the spatial evolution analysis and the percentage of score intervals (Fig. 6). As a whole, these cities showed obvious regional differences in 2005, with Chongqing (1.82) having the highest score, and an obvious ring-shaped distribution area of high-value zones was formed in Jiangsu Province and Zhejiang Province. In 2010 and 2015, it is obvious that there were more high-value areas concentrated in the downstream area, and in 2015, the middle and downstream reaches formed an obvious ULGUE high-value patchy distribution area. In 2020, affected by the global epidemic and other special events, the overall level of YREB’s ULGUE remains relatively stable, but the regional spillover effect of ULGUE is weakened, and the high-value areas are still concentrated in parts of Jiangsu and Yunnan, of which Lijiang City has the highest ULGUE, with a score of 1.24. Yancheng City and Shanghai City are in second and third place, with scores of 1.21 and 1.18, respectively. On the other hand, according to the statistics of the ULGUE’s score, it can be seen that the interval with the largest change is (0.8–1.00), and the largest number located in the interval is 51 in 2015, which accounted for 47.22%. The number located in the interval (1.31–2.00) is zero in the later part of the study year, and most of them are in the range of (0.71–0.80) and (0.81–1.00), indicating that the regional variability of the overall ULGUE is gradually decreasing, and the level of development between regions is becoming more and more coordinated and stable.

Fig. 6: The spatial and temporal distribution of ULGUE in the YREB.
Fig. 6: The spatial and temporal distribution of ULGUE in the YREB.
Full size image

The sub-figures a–d denote the study years in 2005, 2010, 2015, and 2020, respectively.

The measuring of the ESs

Based on the regional characteristics of YREB and the research needs, we selected the four ecosystem of habitat quality, carbon storage, water yield and soil retention in the InVEST model for ESs measurement. Figure 7 shows the change amount of spatial distribution in YREB under the four ecosystems in 2005, 2010, 2015, and 2020.

Fig. 7: The spatial and temporal distribution of ESs in the YREB.
Fig. 7: The spatial and temporal distribution of ESs in the YREB.
Full size image

a denotes the ESs trend change from the year of 2005 to 2010; b denotes the ESs trend change from the year of 2010 to 2015; c denotes the ESs trend change from the year of 2015 to 2020.

Habitat quality has the most obvious decreasing trend of the four systems, with most of the prefectures showing a decreasing trend in the latter part of the study year. Among them, the decreasing trend in the lower and middle regions is relatively concentrated and obvious. The total amount of carbon storage of the YREB also shows a decreasing trend. In 2015, CS showed significant spatial heterogeneity, with the most obvious decline in concentration areas forming within several prefecture-level cities in Jiangsu Province. The water yield system is mainly influenced by meteorological factors and land-use types, so the high-value aggregation area is formed in the eastern coastal areas, such as Wenzhou, Zhoushan, and Lishui City. However, WY in coastal areas showed a significant downward trend in the later years of the study because of the global temperature changes and more typhoons in recent years. Unlike other systems, soil retention is strongly influenced by vegetation cover and topographic relief, and changes in the factors are more stable in the study years, so the spatial variation of the soil retention system is stable in the study years, and SR maintained an increased level in most areas.

Overall, the ESs in the YREB showed a decreasing trend in the years 2005, 2010, 2015, and 2020, with spatial changes in habitat quality, carbon storage, and water yield but soil retention being more stable. The main low-value areas are mainly concentrated around the large cities, with the rapid development of urbanization in recent years, these cities have frequent transfer of land-use types, and a large amount of arable land and forests have been converted into building land to meet the needs of urban development.

The CCD between ULGUE and ESs

For YREB, the spatial evolution trend of the CCD of ULGUE and ESs is shown in Fig. 8. The average CCD of YREB in 2005, 2010, 2015, and 2020 are 0.38, 0.40, 0.43, and 0.45, respectively, which are at the level of basically balanced, and the level shows a gradual increasing on the whole. This is closely related to the ecological protection policies implemented by YREB in recent years. On the other hand, the regional spillover effect of CCD is obvious, and this character is most obvious in the middle and downstream reaches in 2015 and 2020. In 2005, the regions with a high CCD were Hangzhou (0.53) and Chongqing (0.52), which were the only two cities that achieved moderately balanced degrees in the year. In 2015 and 2020, the number of cities in moderately balanced degrees increased from 9 to 14, respectively, which indicates that the coordination between economic development and ecological protection in YREB has increased significantly. However, the low-level area of CCD is mainly concentrated in Jiaxing, Shanghai, and Xuzhou City, where the economic development level is higher, the population is denser, and the urban ecological land area is limited, so the overall CCD level is lower. We further analyzed the highest and lowest value zones in these four years and found that the difference between them in 2005 was 0.30, the difference in 2010 was 0.31, the difference in 2015 was 0.43, and the difference in 2020 reached 0.44. The difference in the CCD between the YREB regions has gradually increased, and the trade-offs between economic development and ecological protection between the regions have shown greater differences.

Fig. 8: The spatial and temporal distribution of CCD in the YREB.
Fig. 8: The spatial and temporal distribution of CCD in the YREB.
Full size image

The sub-figures a–d denote the study years in 2005, 2010, 2015, and 2020, respectively.

On the other hand, we compare the results of the classical CCD model with the improved CCD model used in this study (Fig. 9). It can be clearly found that the level of CCD under the improved CCD model was lower than that of the classical CCD model, which is partly due to the calculated coupling degree is relatively concentrated in the classical CCD model, which greatly reduces the effectiveness of using the coupling degree. Additionally, the distribution of CCD in the upper, middle, and lower reaches is relatively more concentrated in the improved CCD model. To a certain extent, this is more consistent with the current development of YREB, that is, the upper, middle and lower reaches of YREB show different advantages, and the development status of the same reach is more similar.

Fig. 9: The changing trend of the coupling coordination degree in YREB in the year 2000/2010/2015/2020.
Fig. 9: The changing trend of the coupling coordination degree in YREB in the year 2000/2010/2015/2020.
Full size image

The sub-figure a represents the results of the traditional CCD model; b represents the results of the improved CCD model.

Standard deviation ellipse

By using ArcGIS 10.8 software, we analyzed the overall dynamic spatial evolution trends of the CCD in 2005, 2010, 2015, and 2020 using standard deviation ellipses. The results are shown in Fig. 10. It can be developed that the center position of the CCD is mainly concentrated within Jingzhou city, and the center position is constantly shifted to the southwestern direction, which indicates that the overall CCD in the southwestern region shows a better level, which suggests that CCD is slightly stronger in the southwest than in the northeast for aggregation. Meanwhile, the analysis of the θ reveals that the rotation angles of the CCDs of YREB are mainly concentrated in the range of 72°–73°, and the overall change of the rotation angle in the selected years is not significant and shows an increasing trend. According to the calculation results of the flatness (α), it can be seen that the flatness increased from 62.63% in 2005 to 63.06% in 2020, which indicates that the gap of CCD is gradually increasing between different regions and shows an imbalance character (Table 3).

Fig. 10
Fig. 10
Full size image

The spatial changes of the standard deviation ellipse and average center of CCD in the YREB.

Table 3 The results of the standard deviation ellipse of CCD in YREB.
Full size table

Trend surface analysis results

In Fig. 11, the z-axis represents the attribute values of the CCD in YREB, and the x-axis and y-axis represent the east and north directions, respectively. The blue curve is the fitted line of spatial variation of CCD in the north-south direction, and the green curve is the fitted line of spatial variation of CCD in the east-west direction.

Fig. 11: The trend surface analysis of CCD in the YREB.
Fig. 11: The trend surface analysis of CCD in the YREB.
Full size image

The sub-figures a–d denote the study years in 2005, 2010, 2015, and 2020, respectively.

From the characteristics of the curve change of the CCD, an inverted “U” shape trend is maintained in the north-south direction from 2005 to 2020, and the northern end of the blue trend line shows a decreasing trend, while the southern end shows a decreasing and then increasing trend, and the distribution character of “high in the south and low in the north” is maintained in the period, indicating that CCD is always better in the south than north. In the east-west direction, the inverted “U” shape trend is also maintained, but the slope and curvature are reduced compared with the north-south direction. Except for 2005, the distribution trend of high in the west and low in the east was consistently maintained in the rest of the years, suggesting that the CCD interacted more favorably in the west than in the east in another year. In addition, the slopes of the fitted curves during the study period are always greater in the north-south direction than the east-west direction, indicating that the north-south direction is the main direction of spatial differentiation of CCD from 2005 to 2020. In addition, the gap between the north and south ends gradually intensified, and the gap between the east and west ends gradually moderated, indicating that the gap CCD gradually intensified in the north-south direction and gradually moderated in the east-west direction, which is consistent with the conclusions drawn from the basic analyses of CCD in the previous section.

Gravity model analysis

In order to further indicate the spatial association paths of CCD and its linkage strengths, the study utilizes ArcGIS 10.8 to map the bidirectional spatial association networks of CCD in 2005, 2010, 2015, and 2020 given that the node linkages of the spatial network did not change substantially during the study period, as shown in Fig. 12. In addition, due to a large number of research units, in order to achieve a better graphical effect, we refer to the previous studies to hide the linkage lines whose linkage strength is less than the mean value, and utilize the natural breakpoint method to classify the linkage strength into five categories, which are represented by I–V from weak to strong, respectively, in which City A to B is the direction of emitting linkage lines, and City B to A is the direction of receiving linkage lines.

Fig. 12: The structure of the coupled coordination space network in the YREB.
Fig. 12: The structure of the coupled coordination space network in the YREB.
Full size image

The sub-figures a, c, e, and g denote the direction of emitting linkage lines from city A to B in the years 2005, 2010, 2015, and 2020, respectively; The sub-figure b, d, f, and h denote the direction of emitting linkage lines from city B to A in the year of 2005, 2010, 2015, and 2020 respectively.

Overall, YREB’s CCD has obvious spatial correlation network characteristics, the whole spatial correlation network is a connected whole because there is no isolated point, so the overall connection is strong. Also, there are differences in the direction of the correlation relationship between nodes, and the correlation level and number have been enhanced. Specifically, although there is no independent region in the network, it is dominated by I-level strength in terms of association strength, which accounts for about 66% of the whole network, and V-level strength of the city consists of a few cities, which are mainly interconnected relationships included in the cities of Zigong, Neijiang, Ziyang, Maanshan, Nanjing, Zhenjiang, Wuxi City, and so on. In terms of the association direction, the number and density of high-level association lines of city A to city B are slightly higher than that of city B to city A, indicating that the radiating power of the city is superior to its attractiveness, and this feature is mainly embodied in the association network of III, IV and V levels. In terms of temporal changes, the number of connections fluctuates and increases, and the intensity of high-level connections also expands slightly, and the spatial connection network tends to be closer and more balanced from 2005 to 2020.

Discussion

Changes in ULGUE and ESs

In terms of the overall evolutionary trend, the ULGUE in YREB shows a fluctuating upward movement ultimately remaining stable. In addition, in 2019-2020, the impact of special events such as the global epidemic will be fatal to the economic development of the world. Under such a development environment, the ULGUE of YREB maintains a relatively stable level, which is evident that the risk-resistant capacity of the current development model of YREB is strong, which also reflects the effectiveness of a series of ecological and economic development model regulation implemented by China in YREB in recent years. In addition, at the level of different watersheds, ULGUE in the downstream area shows higher levels, which is inconsistent with the findings of Jin et al (Jin et al., 2018). On the one hand, it is because the scholars’ evaluation system does not incorporate unexpected indicators, and our ULGUE’s evaluation indicator system is more in line with current development needs. On the other hand, their ULGUE’s score in the YREB covers the period 2005-2014, which cannot consider the influence of special events such as epidemics. We found that the ULGUE of YREB shows obvious spatial spillover effects, and the spatial correlation is characterized by “small agglomeration and large distribution”, which is in line with the findings of the study by Zhang et al. (Zhang and Luo, 2022; Zhong et al., 2022). With the increasing importance of the concept of green development in China, the implementation of a series of environmental protection plans, such as the Outline of the YREB Development Plan and the YREB Ecological Environmental Protection Plan, has alleviated the irrationality of YREB land use to a certain extent from an anthropogenic point of view.

Based on the regional conditions of YREB, we selected four sub-ecosystems, namely habitat quality, carbon storage, water yield, and soil retention, to measure ESV in YREB. As a key factor affecting ESs, frequent land type conversion can have a great negative impact on regional ESs (Wang et al., 2022; Yang et al., 2022). In recent years, with the increasing level of urbanization in YREB, more and more people are flocking to large cities, resulting in an imbalance of land-use supply and demand. A large amount of land in the cities was converted into building land, and the original ecological landscape was artificially destroyed, which led to a decrease in the level of ESV so that the northeastern part of the YREB, such as the cities in Jiangsu and Anhui Provinces appeared more low-values areas of ESs. On the other hand, water yield is the most obvious change within the study year, because water yield is mainly affected by climatic factors, the high-value zones are mainly clustered within the eastern coastal area. Moreover, the frequent occurrence of typhoon weather is also one of the important reasons for the shift of the center of high-value areas. With the influence of global warming and urbanization in recent years, changes in temperature, precipitation, and other climatic factors can have a negative impact on ES, and the shift in land-use type caused by population migration to large cities is also negative for the enhancement of ESs. Overall, the overall ESs of YREB showed a slightly decreasing trend in the study years, which is consistent with the TU et al. (Tu et al., 2023). Although YREB has implemented a series of ecological protection policies in recent years, the implementation of these policies shows an obvious lag for the reason that the effect of ecological policy implementation is a long-term process, and the current ecological protection efforts in YREB are lagging behind the extent of ecosystem damage caused by human production activities.

Changes in CCD

Changes in spatiotemporal relationships between ULGUE and ESs

The Kuznets curve is often used to describe the relationship between economic growth and environmental protection. In 2005, the reform and opening up led to rapid economic growth in the YREB, but most of the cities in the CCD were only at the moderately unbalanced level. With the increasing economic strength, the basin’s emphasis on the ecological environment gradually increased, and the CCD level began to move to a basically balanced level. After years of effort, YREB’s ecological protection and environmental remediation is gradually moving into the middle and back end of the environmental Kuznets curve, the overall level of CCD in YREB is increasing in the study year which indicates that the overall level of eco-economic balance in YREB is increasing.

According to the results of the gravity model analysis, the spatial correlation strength of CCD among YREB cities has been increasing, which also confirms the close connection between the development of YREB cities. This is directly related to China’s control of the development of YREB in recent years, and the Chinese government prefers to develop YREB into a basin economy with global influence. According to the results of the spatial evolution analysis, the center of the CCD gradually shifts to the southwest region, which is mainly because most of the southwest regions are located in the upstream zone of the Yangtze River Basin, and in order to build a strong ecological barrier for the whole basin, the rough economic development mode has been strictly prohibited by the regional government, so the upstream region of the YREB shows a higher degree of coordination. However, the spatial heterogeneity among the CCD regions in the YREB is gradually increasing, and inter-regional differences in industrial structure and development patterns make the CCD show obvious differences among different regions, especially in the north-south direction of the YREB, where the spatial level variability of the CCD shows the most obvious.

Comparation between traditional CCD model and improved CCD model

Figure 7 indicates the results of the traditional CCD model compared with the improved CCD model. It is clear to see that the traditional CCD model has higher values than the improved CCD model. And the distribution of traditional CCD model values shows stronger volatility. Wang et al., who proposed the improved CCD model, suggested that this is because the traditional coupling C-value is not an evenly distributed function between [0, 1], this makes the calculated C-value more prone to extreme values. In the case of the traditional CCD model, this is reflected in the fact that the C-value is more likely to be falsely high. On the one hand, for the reason of the C-value is concentrated at the high end of the distribution, the calculated CCD value is mainly dependent on the T-value, which makes the role of the system coordination level weakened, resulting in the CCD value difficult to fully realize the value and significance of the coupling coordination degree measurement itself. On the other hand, T should be calculated using arithmetic weighting rather than geometric weighting when calculating the T-value for the reason that geometric weighting reduces the range of the T-value, and the comprehensive evaluation index should reflect the complementary relationship between the subsystems (Wang et al., 2021). On this basis, some scholars have also verified the applicability of the model in various aspects, and it can be assumed that the improved CCD model is closer to the development status of the study area than the traditional CCD model, especially in studying the coupling and coordinated relationship between regional economy and ecology (Li and Wang, 2024; Luo and Yang, 2024).

This study took YREB’s ULGUE and ESs system as an example for validation and found that the improved CCD model solved the problems of volatility and credibility of the CCD results to a certain extent, and confirmed that spatial and temporal variations of the research object can significantly affect the credibility of the coupled coordination degree model furtherly. However, both the traditional and improved CCD models still have the problems of the subjectivity of indicator construction, volatility of coupling results, and non-comparable reliability. Based on the validation of the model in studying the coupled coordination relationship between ecological and economic systems in the region, how to further address these shortcomings of the model is a feasible direction for future coupled coordination modeling research.

Policy implications

  1. (1)

    Establishing a mechanism for regional synergistic development and promoting the integration of regional development in the YREB. There existed a spatial imbalance of ULGUE in the YREB. Therefore, in order to promote the improvement of ULGUE in the YREB, it is necessary to fully investigate and deeply analyze the reasons for the spatial imbalance of ULGUE so as to achieve synergistic development of each region. On the one hand, Shanghai, Jiangsu, Zhejiang, and other provinces demonstrated stronger in economic development and scientific and technological innovation. As the growth poles of ULGUE, they should give full play to their leading role, actively share the fruits of industrial transformation, and upgrade the outstanding experience of enhancing the efficiency of resource utilization. On the other hand, the YREB should take advantage of the major opportunity of regional integration to break down administrative barriers between regions. It is necessary to promote the construction of a cross-regional ecological protection mechanism, gradually reduce the gap between the green development levels of the cities in the YREB region, give full play to the comparative advantages of the prefecture-level cities to complement their shortcomings and introduce advanced, efficient, and clean industries to promote the green transformation of industrial development, so as to improve the green development level of the YREB region as a whole.

  2. (2)

    Implement the sustainable development concept and build a new model of green sustainable development. The enhancement of ESs can play a good role in promoting the ULGUE among cities, so it is necessary to promote the innovation of the green development model. Therefore, it is necessary to promote the innovation of green development mode and continuously upgrade the level of green development. In the development process of urbanization, it is necessary to take into account equity and efficiency, coordinate the relationship between land use and ecological environment and promote integrated regional ecological and environmental protection development.

  3. (3)

    Implement a high level of opening up to the outside world and raise the threshold for the introduction of foreign investment. As an important development base in China, the YREB should realize the strategy of opening up to the outside world at a high level, and actively carry out linkages with foreign investors to achieve efficient docking. In addition, the introduction of foreign investment should not just cater to the development demand and take over polluting enterprises but should focus on the screening of polluting enterprises and raise the threshold of their preparation. Cities in the upstream area should take advantage of local resource endowment as the basis to screen the industries to be undertaken, and fully consider the coordination between the industries undertaken to prevent the environmental pollution caused by blind acceptance and transfer, and at the same time gather the industries to be undertaken to cultivate the industrial clusters.

Conclusion

Conclusion

The results of the study show that: (1) the ULGUE of YREB shows an overall upward trend in time change, which shows that YREB is more risk-resistant and reflects the effectiveness of a series of watershed development policies implemented by China; (2) during the study period, the ESs of YREB also shows a downward trend, and its low-value area is concentrated in the northeastern part of YREB where the ecological protection is lagging behind the impacts of human activities; (3) in terms of the coupling coordination between ULGUE and ESs, most cities in YREB have reached the basic equilibrium level, but the differences in the industrial structure and development mode among different cities lead to obvious spatial heterogeneity. To some extent, our study verifies that the improved CCD model has certain rationality and feasibility.

Study limitations

This study mainly verified the impact of land use on ESs from the perspective of coupling coordination. In other words, improving the efficiency of green land use will mitigate the decline of ESs to a certain extent. The YREB, as a representative river development belt in the world, will benefit the ecological protection of the watershed in the future through rational planning of land-use structure and pattern. However, this study still has some limitations in the research methodology and study area, which are mainly reflected in the following aspects:

  1. (1)

    The current academic evaluation of ULGUE has not formed a unified system, and we have selected 8 indicators from economic and ecological aspects for evaluation. Future scholars can further explore the ULGUE evaluation system by combining the study area and national development strategy.

  2. (2)

    For the evaluation of ecosystem services, the ESs measured by the InVEST model lacks rigorous tools for validating the results. Therefore, we focus more on the analysis and study of the overall trend of ESs, which does not affect the analysis of CCD trends.

  3. (3)

    In the analysis of the spatial evolution of CCD, we have analyzed it from the perspective of overall trend, local orientation evolution and spatial correlation, and future studies can utilize more diversified perspectives for further analysis.

  4. (4)

    Whether it is the traditional or the modified coupling coordination degree model, there is still the problem of confidence in the subjectivity of indicator construction. Researchers generally refer to the indicator system used in previous papers and construct different element layers and indicator systems based on the research object and geographical characteristics. Therefore, the subjectivity of indicator construction further affects the reliability of the final coupling results. In addition, the model is still not simplified enough, especially when deduced to the multivariate system, the computational difficulty increases exponentially. How to develop a more generalized model or how to improve the reliability and stability of the model in the future is also an issue that deserves to continue to be explored in the future.

  5. (5)

    The article studies the coupling relationship between land green use efficiency and ecosystem services in urban clusters in YREB, and the findings of the study are instructive for the economic development of river belts in the world. In the future, we plan to study the characteristics of the coupling between different types of land-use policies and ecosystem services, with a view to enriching the relevant research and providing a certain basis for the formulation of land policies.