Construction of an Indicator System

Constructing an indicator system is crucial for assessing the coupling coordination between the quality of economic development and urban ecological resilience. The degree of coupling coordination between the two factors was established based on the measurement of both factors. In the context of the quality of economic development, based on the new development concept [28] and in accordance with existing research [29], the quality of economic development comprises three dimensions in this study: scale of economic development, optimization of economic structure, and sustainability of economic development. This enables the establishment of an indicator system for assessing economic development quality. In the context of urban ecological resilience systems, this study adopts a resilience perspective [30], and based on the principles of data availability and scientific rigor, it references the relevant research [31, 32]. The pressure-state-response model is introduced to construct an indicator system for urban ecological resilience (Table 1).

Entropy TOPSIS

In the literature, the methods for assessing indicator systems are mainly divided into subjective and objective weighting methods. The subjective weighting methods involve assigning specific numerical values or weights to indicators through expert evaluation or surveys, displaying a high degree of subjectivity [33]. The objective weighting methods include principal component analysis, correlation function, and entropy, which contribute to enhancing the objectivity and scientific rigor of indicator assessment [34]. Although the entropy method can overcome the shortcomings of the subjective weighting method, it can bias the indicator weights due to the significant variability in the values of a particular indicator. Therefore, the TOPSIS method is introduced for correction. The TOPSIS method assesses the relative distance between each measurement indicator and its optimal (or worst) solution, thus providing a basis for judgment. This approach aims to obtain a comprehensive evaluation index for corrected economic development quality and urban ecological resilience. It helps reduce interference from reference sequences on samples, thereby yielding accurate and reasonable evaluation results [35]. The specific steps are outlined below.

First, the original data matrix (X) is constructed.

In the Equation, x_ij represents the original value of indicator j for city i, and m and n denote the total number of cities and indicators, respectively.

Next, the possible dimensional impact on the data is eliminated by standardizing the data to obtain standardized results (Z_ij).

Positive indicator:

Negative indicator:

Here, x_ijmin and x_ijmax are the minimum and maximum values of indicator j for city i, respectively.

The third step involves calculating the indicator weights (W_j) using the entropy method. e ^j = −k ∑[(Y^ij / ∑Y^ij ) ⋅ ln(Y^ij / ∑Y^ij )], k = ln1m

Here, e_j is the information entropy, and Y_ij is the value of Z_ij after data translation to avoid zero values in entropy calculations.

The fourth step involves constructing the weighted matrix (R).

The fifth step establishes the best (t_i⁺) and worst solutions (t_i^–)

In the sixth step, the Euclidean distance of each scenario is calculated for the ideal and anti-ideal solutions (T_i⁺), (T_i^–).

Finally, the comprehensive evaluation index (U_ij) is calculated.

In the Equation, U_ij represents the comprehensive evaluation index, ranging from 0 to 1. A higher value indicates a higher level of economic development quality and urban ecological resilience, whereas a lower value implies a lower level of development.

Coupling Coordination Degree

A coupling coordination degree is employed to assess the level of coordinated development between different regions, identify regions with imbalanced development, and promote coordination and integrated development between regions [36]. This model is introduced to reflect the overall level of coordinated development between the two systems, and the calculation Equation is as follows [37]:

In the Equation, U₁ and U₂ are indicators of the quality of economic development and urban ecological resilience, respectively; T represents the comprehensive evaluation index of the two systems; D denotes the coupling coordination degree of the two systems; C represents the coupling degree of the two systems; and α and β are undetermined coefficients. Based on relevant research findings and practical results of this study, both factors are found to have an equal effect; hence, α and β are both assigned a value of 0.5. According to a previous study [38], the coupling coordination degree is classified into ten types with three levels based on magnitude (Table 2).

Kernel Density Estimation

Kernel density estimation is a nonparametric statistical method proposed by Rosenblatt [39]. It is employed to estimate the probability density function of continuous random variables. This method uses sample data to estimate the probability density function without relying on specific assumptions. This study introduces this method and draws the kernel density curve of the coupling coordination degree between economic development quality and urban ecological resilience in the Yangtze River Economic Belt. Then, the distribution dynamics and evolutionary characteristics of the coupling coordination level in this region are analyzed. The calculation Equation is as follows [40]:

Here, G_h(x) represents the kernel density estimate; n is the number of cities in the study area; h is the bandwidth; x is the mean of the observed values; x_i represents independently and identically distributed observed values; and K is the kernel function. The Gaussian kernel function is selected in this study.

Spatial Autocorrelation Models

Spatial autocorrelation models can measure the spatial structure and clustering degree of attribute data for spatial units, which are divided into global and local autocorrelations. Global autocorrelation indicators reflect the degree of correlation of attribute values between regions and their spatial neighbors, probing the overall spatial patterns in the study area. Local autocorrelation indicators reveal the degree of correlation of attribute values between each spatial unit and its neighbors. The calculation Equation is as follows [41]:

Global Moran’s index:

Local Moran’s index:

Here, n is the number of cities in the study area; X_i and X_j are the attribute values of regions i and j, respectively; and C_ij is the spatial weight matrix in the study area, representing the degree of influence between spatial units i and j. The range of I is between [−1, +1], and values greater than 0 indicate a positive spatial correlation between units. The closer I is to 1, the stronger the positive correlation between units. A value of 0 indicates no spatial correlation between units, whereas values less than 0 indicate a negative spatial correlation between units. The closer I is to −1, the stronger the negative correlation between units.

Dagum Gini Coefficient and Its Decomposition

The Dagum Gini coefficient method proposed by Dagum [42] can effectively measure regional disparities. Compared with traditional Gini coefficients, coefficients of variation, and Theil indices, the Dagum method provides a more accurate reflection of differences within and between regions. Additionally, it can precisely measure the contribution of regional disparities to overall differential changes [43]. The overall Gini coefficient is decomposed into three parts: the contribution of intraregional differences (G_w), the net contribution of interregional differences (G_nb), and the contribution of overdensity (G_t). This study adopts this method to calculate the spatial differences in the coupling coordination levels between the upstream, midstream, and downstream regions of the Yangtze River. The calculation Equation is as follows [44]:

Here, G represents the overall Gini coefficient; k is the number of divided regions; n is the number of cities in the study area; n_j and n_h represent the number of cities in regions j and h, respectively; δ is the average coupling coordination degree of economic development quality and urban ecological resilience in the Yangtze River Economic Belt; j and h denote the defined regions; i and r denote cities within the defined regions; and y_ji and y_hr represent the coupling coordination levels of city i in region j and city r in region h, respectively.

Here, G_jj represents the Gini coefficient within a region; G_jh represents the Gini coefficient between regions; δ_j and δ_h are the average levels of coupling coordination in regions j and h, respectively; and |y_ji-y_hr| denotes the absolute difference in coupling coordination levels between city i in region j and city r in region h.

Here, G_w represents the contribution of within-region differences; G_nb represents the net value contribution of between-region differences; G_t represents the contribution of supervariable density; p_j denotes the proportion of the number of cities in region j to the total number of cities; s_j represents the proportion of the degree of coupling coordination in region j to the total degree of coupling coordination of all cities in the sample; and D_jh denotes the relative influence of the coupling coordination level between regions j and h.

Here, d_jh represents the total impact of the coupling coordination level between regions j and h; p_jh denotes the supervariation first moment between regions j and h; and F is the cumulative density function of the degree of regional coupling coordination.

Temporal Evolution Characteristics

According to the results of the coupling coordination model (Fig. 2), the coupling coordination degree of the Yangtze River Economic Belt shows an upward trend from 2011 (0.439) to 2021 (0.561). This trend indirectly reflects the close connection between the quality of regional economic development and urban ecological resilience. Improved economic development quality enhances urban ecological resilience, and the level of urban ecological resilience is a reflection of the quality of economic development; the higher the urban ecological resilience, the higher the economic development. However, due to the extensive area covered by this study and the comprehensive influence of natural endowment and policies, significant differences are observed in the coupling coordination degrees of the upper, middle, and lower regions of the Yangtze River Economic Belt. In descending order, the coupling coordination degree is lowest upstream, significantly lower than the overall level, and highest downstream, significantly higher than the overall level; further, the middle region’s coupling coordination degree is almost consistent with the overall level. This indicates spatial heterogeneity in the coupling coordination degree of economic development quality and urban ecological resilience in the Yangtze River Economic Belt.

Fig. 2.

Time-varying coupling coordination degree.

Dynamic Evolution Characteristics

To further understand the dynamic evolution of the coupled and coordinated relationship between economic development quality and urban ecological resilience, the kernel density estimation method was employed to generate kernel density plots for the coupling coordination levels. As depicted in Fig. 3, the dynamic evolution of the coupling coordination level in the Yangtze River Economic Belt from 2011 to 2021 exhibits two characteristics. In terms of spatial distribution, the center of the kernel density curve shows a rightward trend, indicating that the coupling coordination between economic development quality and urban ecological resilience has gradually strengthened with the development of the socioeconomy and increased attention to the ecological environment. In terms of the distribution pattern, the shape of the main peak changes from flat and wide to sharp and narrow, and the peak value gradually increases. The transition from double peaks to a single peak indicates a tendency toward diminishing absolute differences in the coupling coordination level of the Yangtze River Economic Belt, displaying dynamic convergence characteristics. Overall, the coordinated role between the economic development quality and urban ecological resilience in the Yangtze River Economic Belt continues to strengthen. The development disparities between various cities are narrowing, indicating that the comprehensive promotion of high-quality development in China highlights its ecologically oriented core position and gradually enhances the coordination between economic development and ecological sustainability.

Spatial Evolution Characteristics

As depicted in Fig. 4, spatial differences are observed in the coupling coordination types of economic development quality and urban ecological resilience in the Yangtze River Economic Belt at the urban scale from 2011 to 2021, as described below:

First, in 2011, most cities in the Yangtze River Economic Belt were in a state of imbalanced decline. Of them, one city (0.91%), Chenzhou, located in the southern part of the middle reaches, experienced severe imbalance. There were 40 (36.36%) cities with moderate and mild imbalance, mainly distributed in the southwestern part of the upper reaches. Further, 32 (29.09%) cities were on the verge of imbalance, concentrated in the western and northern parts of the middle reaches. Cities with reluctantly coordinated and basic coordination types totaled 37 (33.64%), forming clusters in the lower reaches with sporadic distribution in the northeastern part of the upper reaches and the southeastern part of the middle reaches.

Second, in 2014, transition and coordinated development types became dominant in the transformation of urban coupling coordination. Of them, 32.73% of the cities were classified under the transition type, mainly concentrated in the northern part of the middle and upper reaches. Five cities, namely, Suqian, Jinhua, Taizhou, Zhangjiajie, and Guiyang, rose to the category of barely coordinated development, accounting for 26.36% of this type of city. Shanghai and Chongqing transitioned to intermediate coordination cities. The proportion of cities in the disordered decline type was 25.45%, a decrease of 10.91% compared to 2011; these cities were mainly concentrated in the western part of the upper and middle reaches.

Third, from 2014 to 2018, the State Council issued policies such as the “Guiding Opinions on Promoting the Development of the Yangtze River Economic Belt by Relying on the Golden Waterway” and the Outline, outlining the development pattern of the Yangtze River Economic Belt as one with “one axis, two wings, three poles, and multiple points.” The quality of economic development and urban ecological resilience has improved to varying degrees. The coupling coordination levels between the two have continuously increased, gradually entering a stage of coordinated development. In 2018, the proportion of mildly imbalanced cities was only 9.10%, scattered mainly in the upstream southwest. Cities on the verge of imbalance accounted for 34.55% of the total, whereas barely coordinated and primarily coordinated cities accounted for 52.73%. Shanghai, Suzhou, and Chengdu were classified as intermediate coordinated cities, and Chongqing was considered a well-coordinated city.

Fig. 3.

Kernel density estimation of the coupling coordination degree.

Finally, from 2018 to 2021, with the establishment of a more effective mechanism for regional coordinated development in the Yangtze River Economic Belt, the degree of coupling coordination between economic development quality and urban ecological resilience continued to improve. In 2021, cities in the Yangtze River Economic Belt primarily exhibited a coordinated development pattern. Of them, 70.91% of cities were barely coordinated and primarily coordinated, predominantly distributed in the middle and lower reaches. Cities in the upper reaches, such as Zhaotong and Lincang, belonged to the mildly imbalanced category. Cities on the verge of imbalance accounted for 24.54% of the total, mainly distributed in the southwestern part of the upper reaches and the northern part of the middle reaches. Shanghai, Wuhan, and Chengdu belonged to the intermediate coordination category. Overall, in terms of the economic development quality and coupling coordination of urban ecological resilience in the Yangtze River Economic Belt, the predominant shift was from the imbalanced and declining type to the coordinated development type. The hierarchical spatial pattern of “lower reaches>middle reaches>upper reaches” did not undergo any significant change.

Spatial Agglomeration Characteristics

To determine the spatial agglomeration characteristics of the coupling coordination level between economic development quality and urban ecological resilience in the Yangtze River Economic Belt, the global Moran’s I value of the coupling coordination degree in the study period was calculated using spatial autocorrelation methods. Table 3 indicates that the global Moran’s I values corresponding to the coupling coordination levels in the study area for each year are all greater than 5.975 and pass the significance test at the 0.001 level. This indicates significant spatial agglomeration in the coupling coordination levels of economic development quality and urban ecological resilience in the Yangtze River Economic Belt. Thus, cities with higher coupling coordination levels tend to be relatively concentrated, and those with lower coupling coordination levels also exhibit a relative concentration. From 2011 to 2021, the global Moran’s I value of the coupling coordination degree in the study area decreased from 0.493 to 0.446. This indicates a certain degree of spatial agglomeration in the coupling coordination between cities in the Yangtze River Economic Belt. However, this agglomeration tendency is weakening, implying that cities with high and low coupling coordination tend to be spatially adjacent.

Fig. 4.

Spatial evolution of the coupling coordination degree.

Using the local spatial autocorrelation model, we computed the local Moran’s I values for the coupling coordination level between the quality of economic development and urban ecological resilience and categorized them into four spatial agglomeration types: high–high, high–low, low–high, and low–low. As depicted in Fig. 5, discernible spatial variations are found in the agglomeration patterns of the coupling coordination level. High–high agglomeration areas are mainly concentrated in the downstream Yangtze River Delta region, gradually forming a high–high agglomeration spatial pattern centered on Shanghai in 2021. The cities in this region rely on resource endowments to establish a rational industrial structure in order to drive social and economic green transformation. This enhances urban ecological resilience and promotes high-quality economic development. Consequently, a virtuous interaction emerges between high-quality economic development and urban ecological resilience.

Moreover, these cities stimulate the development of surrounding cities, showing certain spatial spillover effects. Chongqing, Kunming, Chengdu, and other new first-tier cities belong to a high–low agglomeration cluster. These cities exhibit prominent features with regard to their economy and green development, and the coupling coordination level between the two aspects is relatively high. However, their radiating effects on surrounding cities are insufficient. Thus, these cities form a high–low developmental disparity with surrounding cities. In the low–high agglomeration category, only Huainan in 2018 and Shangrao in 2021 fit this agglomeration type. Huainan, which is located in the hinterland of the Yangtze River Delta, and Shangrao, which is adjacent to Zhejiang, exhibit a relatively high level of coordinated development with surrounding cities. However, no significant spillover effect is observed in these regions; to some extent, their development is constrained by the surrounding cities, resulting in a low–high spatial pattern. Low–low agglomeration areas are primarily located in relatively remote regions such as Bijie, Zunyi, and Zhaotong. These regions exhibit significant disparities in economic development quality and urban ecological resilience compared with more developed downstream regions. The low technological level of industrial development in these areas makes it challenging to attract innovative talent, thereby hindering robust support for green development [45]. Moreover, the relatively slow economic development influenced by various factors underscores the need to enhance regional coordinated development. Therefore, it is crucial to identify ways to strengthen the coordinated development of different types of cities, ensure efficient circulation of factors, and facilitate the appropriate allocation of resources. Maximizing the region’s advantages is essential for simultaneously improving its quality of economic development and level of urban ecological resilience.

Fig. 5.

Spatial agglomeration of coupling coordination levels.

Spatial Difference Characteristics

The Dagum Gini coefficient decomposition is employed to measure the spatial difference characteristics of the coupling coordination between economic development quality and urban ecological resilience.

Intraregional Differences

As depicted in Fig. 6, the overall Gini coefficient of the coupling coordination between economic development quality and urban ecological resilience in the Yangtze River Economic Belt from 2011 to 2021 experienced a decreasing trend, dropping from 0.147 in 2011 to 0.081 in 2021, indicating a gradual reduction in the overall regional imbalance. This reduction is attributable to the implementation of the development strategy of the Yangtze River Economic Belt, which follows the principles of regional coordinated development, promoting the coordination and high-quality development of the upper, middle, and lower reaches of the region. Considering the evolution of differences in the three major regions, the greatest disparities are observed in the upstream region, followed by the midstream region; the downstream region exhibits the smallest differences. This trend indicates that the imbalance in the coupling coordination level is most severe upstream and the spatial imbalance is weakest downstream. This could be attributable to the presence of certain “polarization phenomena” in the upstream region. Representative provincial capitals such as Chongqing, Kunming, and Chengdu exert a siphon effect on surrounding cities, resulting in relatively large differences in coupling coordination levels in the upstream region. In contrast, the downstream region experiences a strong driving force from urban centers, resulting in higher levels of overall economic development and a more favorable ecological environment and contributing to smaller internal disparities in the region. In terms of temporal evolution, the coefficient of variation in the three major regions has experienced varying degrees of decline, indicating that internal coupling coordinated development in the upper, middle, and lower reaches of the Yangtze River has achieved certain results.

Fig. 6.

Overall and regional disparities in the level of coupling coordination.

Regional Disparities

As depicted in Fig. 7, the largest difference exists between the upstream and downstream regions, with a mean value of 0.172. The difference between the upstream and midstream regions has a mean value of 0.129, whereas that between the midstream and downstream regions is the smallest and has a mean value of 0.099. This trend implies that regional differences in the quality of economic development and coupled coordination level of urban ecological resilience in the Yangtze River Economic Belt are mainly attributable to the disparity between the upstream and downstream regions. The significantly small difference between the midstream and downstream regions indicates a relatively close coupled coordination level between these regions, while a certain disparity remains between the midstream and upstream regions. From the perspective of temporal evolution, the overall differences between the three major regions display a decreasing trend. Moreover, the development disparities between different regions exhibit similar characteristics over time. The Gini coefficients between downstream and upstream regions, as well as between midstream and upstream regions, show a slight decline in 2012, followed by a brief increase. However, from 2013 to 2021, they demonstrate a consistent annual decline. Therefore, it is essential to accurately formulate and implement specific measures based on the developmental advantages of different regions to enhance the level of coordinated development of the three regions.

Sources and Contributions of Regional Disparities

As presented in Table 4 and Fig. 8, the contribution rate of interregional differences is the highest, with a mean value of 58.214%. The mean contribution rate of intraregional differences is 24.837%, whereas that of supervariable density is the lowest, with a mean value of 16.949%. This indicates that the spatial differences in the coupling coordination level of the Yangtze River Economic Belt are primarily attributable to interregional differences, followed by intraregional differences and supervariable density. From the perspective of temporal evolution, the contribution rate of interregional differences first increases and then decreases, whereas that of intraregional differences remains relatively stable at approximately 24%. Conversely, the contribution rate of supervariable density exhibits a more complex fluctuation pattern, displaying a W-shaped trend. Overall, regional disparities remain the primary factor influencing the differences in coupling coordination levels in the Yangtze River Economic Belt, and the issue of overlapping samples has a relatively minor impact. In the future, to achieve high-quality coordinated development in the Yangtze River Economic Belt, greater attention should be paid to interregional differences.

Fig. 7.

Regional disparities in the coupling coordination level.

Fig. 8.

Contribution rate of the coupling coordination level in regional disparities.