From Imbalance to Synergy: The Coupling Coordination of Digital Inclusive Finance and Urban Ecological Resilience in the Yangtze River Economic Belt

Abstract

In the context of rapid urbanization and digitalization, scientifically assessing the spatio-temporal interaction between digital inclusive finance (DIF) and urban ecological resilience (UER) is crucial for promoting the coordinated development of the regional ecology and economy. This study investigates the spatiotemporal evolution of the coupled coordination degree (CCD), the decoupling phenomenon, and its hindering factors in the Yangtze River Economic Belt (YREB) by utilizing the kernel density analysis, standard deviation ellipse, decoupling model, and obstacle degree analysis. Through systematic analyses, this paper aims to elucidate the development disparities among regions within the YREB, identify problematic areas, and propose targeted improvement measures. The results show that (1) The CCD between DIF and UER in the YREB has increased annually from 2011 to 2020. However, there are persistent imbalances, with an overall low level of coordination and uneven spatial development, and a trend of “higher coordination in the east and lower coordination in the west”. (2) The overall CCD of the YREB has reached at least the primary coordination level, with the coupling enhancement speed ranked as “downstream > midstream > upstream”, and regional differences decreasing. (3) The decoupling analysis reveals a predominant decoupling trend between DIF and UER, indicating that the digitization of financial services has not concurrently increased ecological pressures. (4) The obstacle degree analysis identifies resilience and digitalization as major barriers hindering CCD. This study provides a scientific basis and analytical framework for understanding the current spatiotemporal interaction between DIF and UER in the YREB, offering an important reference for formulating more effective policies.

1. Introduction

In the context of global climate change and ecological challenges, developing urban ecological resilience has become a major focus worldwide. In recent years, rapid urban expansion and accelerated economic development have led to severe issues within ecosystems, including resource overconsumption, environmental degradation, and biodiversity loss [1]. These issues not only influence urban development but also significantly impact the quality of life for their residents [2]. Therefore, enhancing the resilience of cities to address and adapt to various environmental pressures has become a critical task that urgently requires attention. Strengthening urban ecological resilience not only enhances the environmental carrying capacity of cities but also fosters sustainable economic development and ensures social stability. When experiencing climate change and ecological pressures, enhancing the adaptability and resilience of cities through systematic strategies has become a key focus in both academic and practical fields.

Urban ecological resilience (UER) is a concept in sustainable urban development. It evaluates how effectively urban ecosystems can endure and rebound from various uncertainties. Holling introduced the concept of resilience into the field of ecology [3]. Research in this field not only spans various geographical regions and perspectives but also employs diverse methodologies, encompassing landscape ecology, hazard studies, geography, and urban planning, resulting in numerous landmark findings. Current research has shifted from analyzing single ecosystems to more complex social–ecological system (SES) studies [4]. Furthermore, the interplay between URE, urban development, social well-being, and economic activities has become a main research focus [5,6]. URE is crucial for sustainable urban development. In exploring effective coping strategies and solutions, financial innovation, particularly digital inclusive finance (DIF), significantly contributes to advancing global financial inclusion, economic growth, and social development [7]. By integrating traditional financial services with digital technologies, DIF transcends the spatial limitations of traditional physical outlets, improving both the reach and effectiveness of financial services. Additionally, in the green industries, financing costs are reduced [8,9,10,11,12], thereby effectively promoting urban green development. Additionally, in the context of DIF, inclusivity enables a wide range of social groups to be reached, particularly in remote and impoverished regions [13]. Finally, DIF directs funds towards more sustainable projects and enterprises [14,15]. It significantly contributes to the synergy between green economic development and environmental protection.

Treating the relationship between DIF and UER as a simple one-way or purely positive influence may oversimplify the complex interactions between these two systems. Firstly, many researchers have examined the impact of DIF on UER. On the one hand, DIF can positively affect UER by promoting financial inclusion [9], increasing employment [16,17], enhancing quality of life and security [17,18,19,20], supporting micro and small enterprises [11], and promoting gender equality [8]. By providing financial support and fostering technological innovation, DIF can improve energy efficiency and consumption [14,15,21,22]. However, some argue that the effects on mitigating environmental pressure are relatively limited [23]. On the other hand, at an early stage, DIF may undermine UER due to market arbitrage and increased energy consumption. Additionally, the development of DIF may contribute to unplanned urbanization and urban sprawl, impairing ecological land use and exacerbating the urban heat island effect. Therefore, it is clear that academics hold diverse views on the impact of DIF on UER. Secondly, UER also positively supports DIF. Enhancing UER improves the allocation of funds, thereby promoting DIF by supporting the financial needs of green innovation programs. Improved UER implies better risk management, with enhanced market planning and disaster response mechanisms reducing potential financial losses due to natural disasters [24]. However, some studies indicate conflicts between prioritizing environmental protection and the development of DIF [25]. Finally, DIF and UER form a dynamic two-way complex relationship that promotes financial inclusion and sustainability while potentially hindering each other. Therefore, a profound relationship of mutual constraint and promotion exists between the enhancement of UER and the development of DIF.

Our review of the literature reveals that previous studies predominantly focused on the unidirectional effects between the two systems, with a particular emphasis on their positive impacts. Although recent research has started to explore the potential negative consequences of DIF on ecosystems and the reciprocal influence of ecological resilience on DIF’s development, these discussions remain limited in several key areas. First, existing studies lack a unified analytical framework to address the complexities across different regions and temporal-spatial contexts, which is essential to fully capture the intricate, bidirectional interactions between DIF and UER. For instance, DIF has the potential to enhance financial inclusion and contribute to UER, but its associated resource consumption and environmental pressures have not been adequately considered. In particular, the rapid development of DIF may, under certain circumstances, result in unsustainable patterns of resource consumption and unregulated urban expansion, thereby intensifying ecological stress and undermining long-term environmental sustainability. Simultaneously, an overemphasis on ecological protection, although it enhances UER, may impose constraints on financial inclusion and economic growth. As a result, previous studies have not fully explored the two-way interaction between DIF and UER when examining the complexity of their coordinated development, especially in terms of how their relationship varies across different spatial and temporal scales, and regional heterogeneity. Second, the existing research lacks a dynamic coupling–decoupling perspective on the interaction between these two systems, failing to fully capture how DIF and urban ecosystems coordinate or decouple across different regional and temporal contexts. Previous studies have predominantly focused on the stages of coordinated development between the two systems, with limited attention to periods of uncoordinated development. This research aims to address that gap by integrating the varying developmental stages of both systems into a unified analytical framework, exploring their interaction mechanisms from a multi-dimensional coupling–decoupling perspective. Such an approach not only enhances our understanding of the overall coordinated development trends but also provides a more robust, evidence-based foundation for policy formulation. Addressing these issues will contribute to a deeper understanding of how coordinated and uncoordinated phases unfold and offer more targeted recommendations for future policy design. The flowchart (Figure 1) illustrates the overall framework of the research. By addressing the research gaps identified in previous studies, this paper aims to contribute to the existing literature by conducting a detailed analysis of data from 108 cities in the YREB from 2011 to 2020. The study seeks to elucidate the CCD between the two systems and to systematically examine the factors influencing these interactions. Specifically, the study will investigate the following research questions: First, how do the spatiotemporal characteristics of DIF and UER evolve, and what patterns of synergy or conflict do they exhibit across different regions? To address this, the study employs the CCD model, kernel density analysis, and standard deviation ellipse to quantitatively assess the dynamic interaction between the two systems in a spatiotemporal framework. These models facilitate the analysis of regional agglomeration and diffusion trends, as well as their evolution over various time periods. Second, what mechanisms underpin the coupling or decoupling of DIF and UER across various regions, and under what conditions does decoupling occur? By incorporating a decoupling model, this study examines the bidirectional interaction mechanisms between DIF and UER, identifying the conditions under which decoupling or coupling manifests at distinct stages of development. The analysis further explores the socio-economic and environmental factors influencing these phenomena. Third, what are the main obstacles to the coordinated development of DIF and UER, and how do these obstacles vary across different regions and temporal contexts within the YREB? To answer this, the study applies an obstacle degree model to identify and quantify the main factors that impede the CCD of DIF and UER. Additionally, the model allows the exploration of regional and temporal variations in these obstacles and their dynamic changes over time. By integrating these analytical approaches, the study reveals the interactive dynamics of DIF and UER in regional development, providing a scientific foundation for the formulation of differentiated policy strategies.

The potential contributions are as follows: Firstly, this study deeply analyzes the dynamic evolution of the characteristics of CCD based on accurate measurements of DIF and UER in the YREB, thereby more comprehensively revealing the complex interaction mechanisms between the two systems. Our analysis not only uncovers the coordinated development trajectory of these two factors within the YREB but also provides targeted optimization recommendations, such as establishing innovation funds to integrate digitalization with ecological goals, strengthening environmental oversight of financial services, and creating digital network platforms. Secondly, we apply the decoupling model to assess changes in the coupling relationship over time, further deepening the understanding of interactions between the two systems and of how DIF and UER interact across different stages of development. This analysis highlights the need for policy measures that synchronize ecological and financial growth, such as launching government-backed green innovation funds and supporting regional carbon trading platforms. Thirdly, we use the obstacle degree model to identify the specific factors. In addition, we analyze the influences of these factors across different regions and time periods, and propose targeted improvement measures. Based on these findings, we propose several optimization strategies. These include expanding green spaces and establishing ecological corridors in urban planning. Additionally, measures such as promoting the development of green innovation funds, enhancing environmental oversight of financial services, and advancing the construction of regional carbon trading platforms should be implemented to further support sustainable development.

The remainder of the paper is organized as follows: In the second section, we explore the coupling and coordination mechanisms of DIF and UER. Subsequently, in the third section, we provide details about the study area, data sources, and the selection of the indicator system and methods. Next, we present the empirical analysis. Following this, the fifth part offers an in-depth discussion of the empirical results. Then, the paper concludes with targeted measures, along with a discussion of limitations and future prospects.

2. Theoretical Framework

2.1. The Impact of UER on DIF

(1) Incentivizing financial innovation. Firstly, an increase in UER can stimulate financial institutions to develop new digital financial products and services [26,27]. Secondly, achieving UER requires the support of innovative technologies, such as blockchain, which can enhance the transparency and security of financial transactions [28,29,30]. Additionally, these technologies can improve service efficiency and reduce costs, leading to new business models and service transformations [31,32,33]. The improved UER not only promotes financial innovation but also drives digital financial products to better serve ecological needs through technological optimization, particularly in the development and application of green financial products. (2) Driving the financing requirements of sustainable development projects. Firstly, increased UER encourages people to pay more attention to the green infrastructure, which not only improves community engagement but also enhances social cohesion [34]. Secondly, with the increasing emphasis on UER, DIF can provide financial support for green infrastructure projects [35,36,37,38]. By offering specialized loan and investment products, financial institutions can encourage and accelerate the implementation of these projects. Digital finance acts as a core driver of financial support for the implementation of sustainable projects. Consequently, UER becomes a catalyst for the development of the green infrastructure. (3) Promoting environmentally friendly financial decisions. Firstly, UER enhances the financial system’s ability to respond to environmental risks, prompting financial institutions to give greater consideration to environmental factors in their decision-making processes. This encourages financial institutions to prioritize environmentally friendly projects in their investment and lending decisions [30]. Institutions increasingly favor environmentally friendly projects in investment and lending decisions, fostering a synergy between financial and ecological systems. Secondly, UER further motivates governments and regulators to consider environmental factors in a more comprehensive manner when formulating financial policies; this is achieved through increased public awareness of sustainable development. The improvement of policy support and regulatory frameworks subsequently provides a solid institutional guarantee for the implementation of environmental financial decisions. (4) Optimizing financial system risk management and emergency response mechanisms. Firstly, the ability of the financial system to effectively respond to natural disasters and other emergencies is enhanced [39,40,41], thereby reducing financial risks. Secondly, improving UER helps enhance emergency response mechanisms, enhancing the financial market’s robustness and security [41], which consequently establishes a more robust foundation for the development of DIF. Then, ensuring that financial innovation remains sustainable is important for addressing environmental challenges (Figure 2).

2.2. The Impact of DIF on UER

(1) Promoting green technological innovation. DIF strengthens support for green technological innovation by improving capital allocation efficiency, thereby optimizing industrial structures and encouraging the clustering of production service activities [42,43,44]. This indirectly enhances UER. In this process, DIF, as an innovative incentive mechanism, lowers the threshold for enterprises to finance green technologies, thus stimulating green innovation and promoting green consumption and economic expansion [44,45]. By facilitating access to finance for green projects, DIF fosters the concentration of green industries. (2) Strengthening environmental risk management capabilities. Firstly, DIF utilizes big data technology to enable governments and businesses to identify and assess environmental risks more accurately. By leveraging artificial intelligence, DIF not only improves data processing efficiency but also enhances the accuracy of forecasting and planning [46,47], thus providing solid data support for the planning and implementation of ecological protection measures. Secondly, DIF optimizes the overall credit environment by improving data collection and analysis capabilities [43,48]. These innovations not only bolster the implementation of ecological protection measures but also promote real-time environmental monitoring and risk assessments, thereby indirectly strengthening the ecological resilience of cities through improved environmental risk management. (3) Enhancing community vitality and adaptability. DIF enables more individuals and enterprises to access financial resources, facilitating access for economically disadvantaged populations and small to medium-sized businesses [30,49,50]. This financial inclusion not only increases the overall economic resilience of society but also enhances the economic vitality and adaptive capacity of communities. By fostering community-level economic vitality, DIF contributes to the stability and sustainable development of urban ecosystems. (4) Promoting the development of green products. By leveraging green loans, sustainable bonds, and eco-friendly impact investments, DIF offers crucial financial support for green projects and sustainable development [30,51,52]. These projects, which include green buildings, renewable energy, and sustainable transportation systems, contribute to improve environmental quality. Consequently, the innovation of green financial products within DIF strengthens the financial system’s support for ecological projects, further enhancing the UER (Figure 2).

3. Methodology

3.1. Study Area

The YREB spans eastern, central, and western China and encompasses nine provinces and two municipalities (Figure 3), approximately between 97° and 123° E and 21° and 35° N. It links the Yangtze River Delta urban agglomeration, the city clusters in the midstream region of the Yangtze River, and the Chengdu–Chongqing urban agglomeration, forming a region with diversified industries such as services, high-tech, agriculture, and manufacturing. Over the past few years, the YREB has been transforming into a high-tech and service-oriented industrial structure that plays a central role in enhancing industrial competitiveness and promoting sustainable social and ecological development. However, as the economy expands with the development of digital transformation, this process is accompanied by heightened environmental pressures and increased resource consumption. Therefore, analyzing the coordinated development between DIF and UER is meaningful. Based on its natural geographic location, administrative division, and economic and social development level, the YREB is segmented into upstream, midstream, and downstream regions, and subsequent regional-scale studies will be conducted based on this division.

3.2. Data Sources

This study excludes Bijie and Tongren, and examines 108 cities in the YREB over the period from 2011 to 2020. The primary data sources include the China Statistical Yearbook, China Urban Statistical Yearbook, and Statistical Yearbooks of Prefectural-Level Cities, encompassing indicators related to urban economic performance, environmental conditions, and social development. The DIF index is compiled by the Digital Finance Research Center of Peking University and Ant Financial Group [53]. Specifically, this study utilizes data from the Peking University Digital Inclusive Finance Index, which comprehensively measures the level of digital financial development in each city across dimensions such as digital payments, digital credit, and digital wealth management. The index is recognized for its timeliness and extensive data coverage. To address missing data, gaps are filled using linear interpolation. Subsequently, the entropy weight method is utilized to determine the weight of each index associated with DIF and UER, ensuring a more objective and scientifically sound allocation of weights.

3.3. Evaluation Frameworks

3.3.1. Evaluation Framework for DIF

DIF is based on digitization and aims to improve the accessibility, availability, affordability, and quality of financial services through the proliferation of fintech innovations [53]. The core objective of DIF is to ensure the universality, effectiveness, and modernization of financial services. Therefore, the evaluation framework for DIF is structured around three main aspects: the breadth of coverage, the intensity of usage, and the degree of digital integration [11,15,54,55] (Table 1). This indicator system is based on financial inclusion and financial development theories, which comprehensively reflect the inclusiveness of financial services in the context of the digital economy. The coverage breadth involves the geographic distribution of financial service outlets, the diversity of financial product types, and the exposure rate of various groups of people. This dimension allows the analysis of the regional distribution and accessibility of financial services, with a particular focus on service availability in economically underdeveloped areas. It assesses whether financial services are truly inclusive, particularly in regions with significant economic disparities. The usage depth refers to financial products and services, including not only the number of accounts but also and more importantly, the continued use of financial services, the degree of activity, and the frequency of transactions. This indicator captures the real value and appeal of financial services to consumers, revealing the deeper role that digital finance plays in fostering economic development. Lastly, the degree of digital integration is reflected in the convenience, low cost and flexibility of products, indicating that more effective digital financial inclusion promotes the popularization and deepening of financial services. The DIF index reflects the abilities of enterprises, investors, consumers, and other market participants to access convenient financial services in the digital economy.

3.3.2. Evaluation Framework for UER

Based on a previous study, the evaluation framework for UER is constructed from three dimensions: resistance, resilience, and adaptability [56] (Table 2). This system is grounded in both ecological resilience theory and sustainable urban development theory, providing a comprehensive evaluation of a city’s ability to resist, recover, and adapt to environmental changes. Resistance indicators measure a city’s ability to withstand environmental pressures, natural disasters, or emergencies, reflecting its resource-carrying capacity and responsiveness to short-term shocks. Indicators such as water consumption per GDP and particulate air pollution are used to assess resource consumption and environmental pressure, which are particularly relevant for policy-making related to climate change and pollution control. Resilience refers to a city’s ability to restore normal operations following an environmental shock. Indicators like the drainage pipe density and solid waste utilization rates reflect urban infrastructure conditions and a city’s capacity for self-repair in response to crises. Adaptability gauges a city’s long-term ability to adjust to environmental changes. Indicators such as public transport availability and green innovation achievements are employed to evaluate the city’s progress in advancing green development and sustainable transportation.

The three dimensions of resistance, recovery, and adaptability offer a comprehensive framework for assessing a city’s ability to cope with environmental shocks across varying time scales. In the short term, resistance determines a city’s capacity to endure shocks; in the medium term, resilience reflects its ability to repair itself; and in the long term, adaptability measures the city’s flexibility in adjusting to evolving environments. These dimensions are interdependent and mutually reinforcing, reflecting the core principle of ecological resilience theory that urban resilience is crucial not only for addressing immediate crises but also for ensuring long-term ecological sustainability.

3.4. Methods

Analyzing the spatial distribution patterns and dynamic evolution of CCD between DIF and UER over time is essential for identifying regions that display high levels of coupling and coordination, as well as those experiencing developmental imbalances. This analysis is crucial for informing regional policy adjustments and optimizing resource allocation. In this study, kernel density analysis was employed to reveal the spatiotemporal distribution characteristics of CCD between DIF and UER. This method effectively identifies regions within the YREB with high concentrations of DIF and UER, clarifying the distribution patterns of financial resources and ecological resilience across different spatial units. Kernel density analysis forms the foundation for identifying spatial disparities in coupling and coordination, providing a crucial basis for a subsequent spatial trend analysis. Then, the standard deviation ellipse was used as a complementary method to further examine the spatiotemporal characteristics of coupling and coordination, shedding light on the spatial expansion direction and overall trends in the relationship between DIF and UER. Through a quantitative analysis, this method captures the directional spatial distribution of DIF and UER in the YREB, aiding in the identification of developmental biases across regions. It is especially effective for analyzing spatial imbalances across regions. However, relying exclusively on a spatiotemporal analysis may not fully capture the complex dynamic interactions between DIF and UER. To achieve a more comprehensive understanding of the spatiotemporal evolution of these two systems, a decoupling analysis is necessary to capture the interaction patterns. The decoupling analysis not only compensates for the limitations of the spatiotemporal analysis but also uncovers the dynamic mechanisms underpinning system coupling and their temporal characteristics. Finally, the obstacle degree model further identifies the key barriers or bottlenecks in the coupling and coordination process. By quantifying these barriers, the core factors limiting the coordinated development of the two systems can be precisely identified, enabling policymakers to make targeted policy adjustments and optimize resource allocation.

3.4.1. Coupled Coordination Degree

To explore the interactions between UER and DIF in the YREB, this study applied a CCD model based on the existing literature [57,58,59] that was designed to measure the synergy between UER and DIF. The formula is shown below.

(1)$C=2\sqrt{{\displaystyle \frac{U_1\times U_2}{{\left(U_1+U_2\right)}^2}}}$

(2)$T=\alpha U_1+\beta U_2$

(3)$D=\sqrt{C\times T}$

C is the degree of coupling, U₁ and U₂ represent the DIF index and the UER index, and T is the comprehensive coordination index. α and β are coefficients to be determined. Based on the comprehensive results from the theoretical analysis, policy considerations, and sensitivity analysis, we conclude that DIF and UER hold equal importance in this study. Consequently, α = β = 0.5. The higher the value of D, the more coordinated and balanced the coupling between the two systems. This paper divides them into six levels [60] (Table 3).

3.4.2. Kernel Density Estimation

Kernel density estimation is based on the distribution characteristics of the data, and it is a non-parametric estimation technique. It overcomes the subjectivity of the parametric estimation, exhibiting weak model dependence and strong robustness. This method is used to systematically depict the distribution, morphology, and polarization characteristics. The specific formulas are as follows [61]:

(4)$f(c)={\displaystyle \frac{1}{N_{\rho }}}{\displaystyle \sum _{i=1}^{N}K(\frac{C_i-\overline{c}}{\rho })}$

(5)$K(c)={\displaystyle \frac{1}{2\pi }}\exp (-{\displaystyle \frac{c^2}{2}})$

where K (.) represents the kernel function, N denotes the number of observations, c is the mean value, and C_i indicates the independent and identically distributed observations. The bandwidth ρ determines the smoothness and accuracy of the density function curve.

3.4.3. Standard Deviation Ellipse

Standard deviation ellipse is primarily utilized to reveal the spatial distribution patterns and directional characteristics of the study area [62]. In this study, we use four basic parameters: the center of gravity coordinates, azimuthal angle, and the lengths of the major and minor axes [63]. The formulas are as follows:

(6)$\overline{X_w}=\begin{array}{c}{\displaystyle \sum _{i=1}^n{w}_i}{x}_i\\ {\displaystyle \sum _{i=1}^n{w}_i}\end{array}\overline{Y_w}=\begin{array}{c}{\displaystyle \sum _{i=1}^n{w}_i}{y}_i\\ {\displaystyle \sum _{i=1}^n{w}_i}\end{array}$

(7)$tan\theta ={\displaystyle \frac{\left({\displaystyle \sum _{i=1}^n{w}_i^2}{x}_i^2-{\displaystyle \sum _{i=1}^n{w}_i^2}{y}_i^2\right)+\sqrt{{\left({\displaystyle \sum _{i=1}^n{w}_i^2}{x}_i^2-{\displaystyle \sum _{i=1}^n{w}_j^2}{y}_i^2\right)}^2+4{\displaystyle \sum _{i=1}^n{w}_i^2}{x}_i^2{y}_i^2}}{2{\displaystyle \sum _{i=1}^n{w}_i^2}{x}_i^2{y}_i^2}}$

(8)$\begin{array}{l}{\sigma }_x=\sqrt{{\displaystyle \frac{{\displaystyle \sum _{i=1}^n{\left(w_i\overline{x_i}cos\theta -w_i\overline{y_i}sin\theta \right)}^2}}{{\displaystyle \sum _{i=1}^n{w}_i^2}}}}\\ {\sigma }_y=\sqrt{{\displaystyle \frac{{\displaystyle \sum _{i=1}^n{\left(w_i\overline{x_i}sin\theta -w_i\overline{y_i}cos\theta \right)}^2}}{{\displaystyle \sum _{i=1}^n{w}_i^2}}}}\end{array}$

where X_i and Y_i denote the spatial location of the first research object, n equals 108, W_i denotes the weight, $\overline{X_w}$ and $\overline{Y_w}$ are the coordinates of the center of gravity, θ denotes the azimuthal angle, $\overline{x_i}$ and $\overline{y_i}$ denote the deviation of the study object from the mean center of gravity, σ_x is the long axis, and σ_y is the short axis.

3.4.4. Decoupling Model

The decoupling model is a tool that explores the relative growth rates of multiple variables to assess their relative development [64]. This model employs the concept of “elasticity” to dynamically reflect the relationship between two variables. The specific formula is defined as follows:

(9)$DI={\displaystyle \frac{\Delta UER/UE{R}_t}{\Delta DIF/DI{F}_t}}$

DI denotes the decoupling index, ΔDIF represents the DIF growth rate, ΔUER represents the UER growth rate, and UER_t and DIF_t represent the UER and DIF in period t, respectively. By considering the positive and negative values and the magnitude of the decoupling index, the decoupling status of DIF and UER can be determined. To analyze the decoupling status of DIF and UER, Tapio uses 0, 0.8, and 1.2 as critical values, classifying e into three categories: negative decoupling, decoupling, and coupling. Eight decoupling types are defined [65] (Table 4).

3.4.5. Obstacle Degree Model

The obstacle degrees are classified into six levels (Table 5), with higher degrees indicating a greater negative impact on the coupling coordination. The calculation formula is as follows [60]:

(10)$Q_{ij}=I_{ij}\times w_j/{\displaystyle \sum _{j=1}^m{I}_{ij}}\times w_j$

I_ij = 1 − X_ij denotes the deviation of the indicator, representing the gap between the actual value and the optimal value; w_j is the corresponding weight, and m is the number of indicators for j.

4. Results

4.1. Dynamic Temporal Analysis of CCD

4.1.1. Temporal Characteristics of the CCD

Based on the CCD model, this paper homogenized the CCD of the sample cities in each region. Accordingly, the mean value evolution trend of the CCD was plotted, along with a heat map illustrating the CCD of the 108 cities (Figure 4).

The CCD showed an increasing trend in the YREB from 2011 to 2020, revealing significant progress in promoting the synergistic development of these areas. Specifically, the overall CCD rose from 0.3481 to 0.5972 during the examination period, and the average annual growth rate was 6.18%. This indicates that the CCD has transformed from a state of intermediate incoordination to primary coordination over the past decade, and it is expected to continue rising steadily. Under this trend, the synergy between enhancing UER and promoting DIF has increasingly strengthened, thereby generating composite benefits for the coordinated growth of the regional economy. This upward trend can be attributed, in part, to a series of policy changes and economic transformations that have played a crucial role in driving regional development. For example, the implementation of the Yangtze River Economic Belt Development Strategy has significantly promoted green development, industrial upgrading, and ecological protection. This strategy emphasizes balancing economic growth with environmental sustainability, leading to better synergy between UER and DIF. Additionally, the national push for high-quality growth and industrial transformation has encouraged regions to reduce their reliance on traditional industries and shift towards high-tech and service-oriented sectors, further enhancing the coupling coordination. Particularly in the downstream region, the CCD grew from 0.3752 to 0.6387, reflecting an average annual growth rate of 6.09%. Furthermore, the coupling coordination level surpassed the upstream and midstream regions, with the downstream region improving from an uncoordinated to coordinated state over the past decade. Analyzing the reasons reveals that policy support and the economic transformation in Shanghai, Jiangsu, and Zhejiang as coastal economic development zones have played a pivotal role in promoting high-quality growth. These regions have actively adjusted their economic structures, shifting from traditional manufacturing industries to high-tech industries and modern service sectors, thus enhancing the CCD. In contrast, while the CCD of the upstream and midstream region has also increased, the overall level remains lower than that of the downstream region. The CCD of the upstream region increased from 0.3293 to 0.5650, and that of the midstream region increased from 0.3333 to 0.5777, with average annual growth rates of 6.18% and 6.30%. This discrepancy reflects the differential progress and effectiveness in enhancing the synergy between UER and DIF across different regions. The upstream and midstream regions, although making strides in improving coordination, have faced challenges such as insufficient resource allocation, an economic structure in need of further optimization, and delayed industrial upgrading. Nevertheless, these regions have shown steady improvement, driven by gradual adjustments in industrial structure and increased policy focus on environmental sustainability.

4.1.2. Evolution of CCD Types

Based on the classification of the CCD types (Table 3), the divisions of the cities are visualized, and the evolution of the CCD from the perspective of cities is depicted (Figure 5a–d). From 2011 to 2020, the CCD exhibited an increasing trend, transitioning from a state of intermediate incoordination to primary coordination. During the 2011–2014 period, intermediate incoordination predominated, while in the 2014–2020 period, primary coordination became dominant. This trend was consistent with the temporal characterization and showed a gradient upward trend in the CCD from the upstream region to the midstream region and then to the downstream region. Additionally, intermediate coordination and superior coordination areas sporadically appeared in some parts of the YREB, but these areas did not significantly drive surrounding regions. This trend reflected profound changes in urban development and regional coordination in the YREB. The policy initiatives and the implementation of regional integration strategies facilitated the coordinated development of cities to some extent. Especially after 2014, with the in-depth promotion of the China’s Belt and Road Initiative (BRI) and the YREB Development Strategy, significant progress has been made in infrastructure construction, industrial upgrading, and environmental protection within the region. These factors have accelerated the mutual coordination and support among cities, contributing to the formation and expansion of the primary coordination status. However, the uneven spatial distribution of CCD also highlights some issues. Despite the positive development trend in the YREB, regions with intermediate coordination and superior coordination were relatively sporadic and not widely distributed, indicating persistent development imbalances within the region. This high degree of local coordination has not effectively stimulated neighboring regions, reflecting potential deficiencies in industrial layout, resource allocation, and policy support. Addressing these issues requires more refined regional policies and strategic planning.

Based on the classification of the CCD types (Table 3), various types of cities were visualized, and the evolution of the spatial pattern of the CCD was plotted (Figure 5e,f). Notably, the variations in different types of CCD exhibited significant heterogeneity. On the one hand, the proportion of cities with intermediate incoordination sharply decreased from 2011 (83.33%) to 2014 (0.00%), with no cities exhibiting intermediate incoordination in 2014. On the other hand, slight incoordination showed a rising and then declining trend, increasing from a low level in 2011 (15.74%) to a peak in 2013 (71.30%), and then continuously declining in subsequent observations, ultimately leading to a state of harmonization for most samples in the YREB by 2020. In contrast, the proportions of primary coordination and intermediate coordination showed steady growth amidst fluctuations. Primary coordination rose from a very low level (0.93%) in 2011 to a significantly high level (65.74%) in 2020, indicating a trend of dominance. Intermediate coordination increased from a low level (0.93%) in 2012 to a relatively high level (31.48%) in 2020. Although the proportion of cities with superior coordination remained small, it also showed a gradual growth trend. Another noteworthy phenomenon is that in 2015, 52.78% of cities entered the stage of coordination, surpassing the proportion of cities that remained in the incoordination stage. Thus, the year 2015 can be regarded as a significant turning point, marking an important milestone in the enhancement of UER and DIF in the YREB.

4.1.3. Dynamic Evolution

We conducted an analysis of the evolution and distinctive features of the CCD for DIF and UER in the YREB over time. However, this analysis did not fully reveal the dynamic evolution of the characteristics of the CCD. Therefore, we further applied the kernel density estimation to comprehensively portray the dynamic evolutionary features of the CCD. Additionally, we analyzed development from multiple perspectives such as the distribution location, morphology, ductility, and polarization characteristics to reveal the subtle differences in its dynamic evolution (Figure 6).

The distribution curves both overall and in each region, showed a rightward shifting trend of varying magnitudes. This suggests that the trend of synergistic development of UER and DIF across all regions of the YREB further strengthened from 2011 to 2020, which aligns with the time-series characterization in the previous section. Throughout the examination period, the distance of the rightward shift of the overall, upstream, and midstream regions was close to and less than that of the downstream region, suggesting that the downstream region experienced the fastest increase in CCD during this period. This may be because the development of digital infrastructure was significantly slower in the midstream and upstream region due to weaker factor endowments and resource allocation, while the downstream region benefited from a faster rate of economic development [2].

The main peak of the overall distribution curve showed a gradient upward trend, while the width further narrowed. This reflects that the CCD of the YREB as a whole was transitioning from decentralized to centralized, with inter-regional differences further narrowing and the overall level continuously improving. When specifically analyzing each region of the YREB, the upstream and midstream regions showed a similar upward trend in the distribution curve. Both regions experienced an accelerated rise of the main peak in 2017, and the width further narrowed. In contrast, in the downstream region, although the height of the main peak continued to rise, the width of the main peak was also widening, indicating that regional differences were expanding.

The distribution curves of the overall and in each region showed different degrees of rightward skewness. This phenomenon suggested that some cities within the upstream, midstream, and downstream regions were clearly ahead in terms of coupling coordination. Notably, the distribution of the downstream regions was more extended than that of the upstream and midstream regions, showing an increasing trend over the years. This indicated that cities in the downstream regions with higher levels of coupling coordination would continue to strengthen their development momentum, further widening the gap with other cities in the downstream regions. It is understandable that the downstream regions were leading in both ecological governance and digital financial development, especially cities such as Shanghai, Hangzhou, Hefei, and Suzhou, thus showing the most significant distributional stretch.

From the perspective of polarization characteristics, the overall and various regions did not exhibit significant polarization, showing the distribution characteristics of “a single main peak and low side peaks” with no significant trend of multilevel differentiation. This indicated that the synergistic development of UER and DIF in the cities of the YREB was generally balanced, without serious imbalances between environmental governance and digital financial development. It should be noted that although the downstream region had not formed a clear polarization feature during the examination period, it already showed the embryonic characteristics of polarization. Therefore, the government can strengthen policy guidance to promote balanced development in terms of UER and DIF development in the downstream regions.

4.2. Spatial Distribution and Pattern Analysis of CCD

4.2.1. Spatial Pattern of the CCD

To accurately characterize the spatial pattern of the CCD of UER and DIF in the YREB, this study applied the Natural Breaks Method to classify the CCD into a low level, medium–low level, medium–high level, and high level. Then, we used ArcGIS to visualize the evolution of the spatial pattern in the years 2011, 2014, 2017, and 2020 (Figure 7). The CCD in all years showed significant spatial differentiation characteristics. Firstly, high-level CCD values appeared sporadically in the upstream, midstream, and downstream areas. For example, high-level cities in the upstream region were primarily concentrated in resource-rich provincial capitals and municipalities, such as Chengdu and Chongqing, which had strong foundations in ecological governance and digital financial development. Similarly, high-level cities in the midstream region were mainly located in provincial capitals such as Wuhan, Changsha, and Nanchang, benefiting from geographical advantages and regional economic development, with higher levels of ecological resilience and digital financial services. High-level cities in the downstream region were primarily located along the Yangtze River estuary and coastal areas, such as Shanghai, Nanjing, and Hangzhou. These cities had high levels of ecological resilience and digital inclusive financial services due to their advantageous geographical locations and economic development, making them important node cities of the YREB. Secondly, the low level of CCD was mainly concentrated in the upstream and midstream region. These areas usually lacked resources and experienced slow economic development. Then, in the upstream region, some remote mountainous or distant areas may have faced issues such as environmental degradation and lagging economic development, resulting in lower levels of ecological resilience and digital financial services. Low-level cities in the midstream region may have been constrained by factors such as outdated industrial structures and difficulties in economic structural transformation, relatively lacking a base for digital financial services and incentives to improve their ecological environment.

4.2.2. Standard Deviation Ellipse Analysis

In terms of the distribution orientation, significant heterogeneity was observed in the overall distribution and its regions in the YREB. Specifically, the study further employed standard deviation ellipse analysis (Figure 8). The overall area exhibited a northeast–southwest axial distribution, with an elliptical azimuth angle stable at around 70° (Table 6) that showed only minor changes between 2011 and 2020 (a slight decrease from 72.22° to 71.93°). This indicated that the spatial distribution direction of CCD remained largely stable during this period. The upstream regions also followed a northeast–southwest axial distribution, with the ellipse azimuth stabilizing around 35° (35.05° in 2011 and 34.66° in 2020). In contrast, the midstream and downstream regions showed a northwest–southeast axial distribution. The azimuth of the ellipse in the midstream regions changed substantially, from 125.06° in 2011 to 142.82° in 2020, reflecting a notable shift in the primary distribution direction of CCD in the midstream region. This result indicated that the spatial distribution of CCD exhibits distinct regional characteristics and directional tendencies. Specifically, these northeast–southwest and northwest–southeast axial distribution patterns revealed a significant geographic differentiation. From the perspective of the distribution shape, the overall eccentricity remained the highest at around 0.63 (0.6373 in 2011 and 0.6338 in 2020), indicating that the spatial CCD showed significant linear characteristics. These linear characteristics, combined with the orientation analysis, suggest that the development of CCD was distributed along the northeast–southwest direction. The eccentricity in the midstream regions was the smallest, at about 0.1, indicating that the distribution of the CCD in midstream cities was more uniform, with smaller spatial differences. From the perspective of the centroid, the overall CCD centroid was located in the eastern urban area of Jingzhou and showed a migration trend towards the northwest (from 113° 14′ E, 29° 57′ N in 2011 to 113° 13′ E, 29° 58′ N in 2020). The midstream and downstream regions showed the same trend, whereas the centroid in the upstream regions migrated northeast, indicating that the focus of economic development, ecological protection, and the application of DIF was gradually moving towards the northwest. However, the upstream region showed a different migration pattern, with a slight shift northeastward (from 104° 15′ E, 28° 30′ N in 2011 to 104° 18′ E, 28° 30′ N in 2020), suggesting that the spatial center of gravity in this region differed significantly from other areas and was likely influenced by the region’s unique ecological and economic development model.

4.3. Decoupling Analysis

4.3.1. Explanation of Decoupling Types

The decoupling types (Table 4) are explained below. RD occurs when both UER and DIF decrease, but the deceleration in UER is greater than that of DIF. In the context of this study, recessive decoupling suggests that while both ecological resilience and financial inclusion are deteriorating, the ecological aspect is declining more rapidly. This points to a scenario where policies should prioritize ecological restoration even as financial conditions worsen. In SD, UER decreases while DIF increases. This means that despite financial growth, urban ecological resilience is weakening, indicating a disconnect between financial development and environmental sustainability. This type of decoupling highlights the need for integrating ecological considerations into financial policy planning. In WD, both UER and DIF increase, but the growth rate of UER is lower than that of DIF. This indicates a positive trend where ecological resilience improves but at a slower pace compared to financial inclusion. Policymakers can focus on accelerating ecological efforts to match the pace of financial inclusion growth. In the EC scenario, the growth rates of UER and DIF are approximately equivalent. This suggests a balanced and harmonious development between financial inclusion and ecological resilience, and policies should aim to maintain this equilibrium. In RC, both UER and DIF decrease, but their deceleration rates are similar. This type indicates a synchronized decline, where financial and ecological resilience both need targeted interventions to prevent further deterioration. In END, both UER and DIF increase, but UER grows at a significantly higher rate than DIF. This suggests a scenario where ecological resilience outpaces financial inclusion, possibly due to strong environmental policies. In this case, policies may need to support financial inclusion to keep pace with ecological advancements. In SND, UER increases while DIF decreases. This type of decoupling indicates that ecological resilience is improving, despite a decline in financial inclusion, highlighting an imbalance that requires policies to strengthen financial inclusion without compromising ecological gains. In WND, both UER and DIF decrease, but UER’s deceleration is less than that of DIF. This suggests a scenario where both areas are in decline, but ecological resilience is slightly more stable than financial inclusion. In this case, policies should address both declining factors, with a focus on mitigating the financial downturn’s impact on ecological efforts.

4.3.2. Decoupling Analysis at the Overall Level

This study measured the decoupling status of the YREB across different time periods and plotted the time-series characteristics (Figure 9). The examination revealed that the region was primarily characterized by decoupling, with weak decoupling dominating the early and middle periods. The development of the digital financial industry was still accompanied by an increase in ecological pressure. However, the rate of increase in ecological pressure was slower than the rate of growth of the digital financial industry. Over time, the number of strongly decoupled cities gradually increased, implying that more cities achieved an effective separation between economic growth and environmental pressure, meaning that economic growth no longer came at the expense of the environment. This situation, where economic growth is accompanied by improved or stable environmental quality, places equal emphasis on environmental protection and economic growth. It is worth noting that the number of cities with negative decoupling showed an accelerating trend in the later stages. This trend is detrimental to sustainable development and may lead to environmental degradation, affecting the long-term healthy socio-economic development. The emergence of this trend may be related to the increased environmental pressure caused by the vigorous development of industrial policies in some parts of the region, suggesting a need to concentrate on environmental protection and the construction of ecological civilization while promoting industrialization and urbanization.

4.3.3. Decoupling Analysis at the City Level

The decoupling types (Table 4) are illustrated through spatial visualization (Figure 10). We showed the decoupling statuses of the DIF and UER in the YREB for each city from 2011 to 2020. From the perspective of cities, the initial phase of this study showed that the cities with weak decoupling were in a cluster pattern, while a small number of cities with strong decoupling was observed throughout the YREB. In the middle stage, the number of cities with strong decoupling decreased in the upstream region, while it increased significantly in the other regions. The upstream regions may have faced increased environmental constraints on resource exploitation, prompting an adjustment in their economic growth patterns that led to a decreased number of cities with strong decoupling. In contrast, the midstream and downstream regions may have succeeded in achieving a balance between economic growth and environmental protection by advancing the optimization of their industrial structure from traditional manufacturing to services and high-tech industries, enhancing environmental governance, and adopting more green and low-carbon strategies in urban planning and infrastructure development. At a later stage, a large number of cities with expansive negative decoupling appeared in all regions of the YREB, but this state was not stable and showed significant fluctuations. This phenomenon suggests that the YREB experienced a period of positive environmental improvement and ecological construction in the later stages, where UER grew faster than DIF. The reasons may include that environmental policies, green development strategies, and ecological restoration projects started to gain benefits, providing a solid foundation for sustainable urban development.

4.4. Analysis of Obstacle Factors

To analyze the spatial distribution characteristics of obstacle factors and provide a more intuitive understanding at the city level in the YREB, this study refined the obstacle degrees into six levels based on the following classification criteria: extremely low (0–9.99%), low (10.00–19.99%), medium–low (20.00–29.99%), medium–high (30.00–39.99%), high (40.00–49.99%), and extremely high (≥50.00%) (Table 5). These classifications are based on the calculated results from the obstacle degree model and are tailored to the specific conditions of the study area. Additionally, this study also references relevant research on the classification of obstacle degrees [60] to ensure the scientific validity and applicability of the classification. Subsequently, maps depicting the distributions of UER and DIF in 2011, 2014, 2017, and 2020 were created. These maps provide a comprehensive perspective for identifying and comparing differences in obstacle degrees among various cities, thereby offering a solid foundation for targeted policy formulation and intervention measures.

4.4.1. Obstacle Factors at the Overall Level

The decoupling analysis revealed the dynamic relationship between the development of DIF and UER, highlighting the decoupling patterns between UER and DIF across different cities and their development stages. This study further explored the obstacle factors affecting the CCD, focusing on the different dimensions of UER (Figure 11a) and DIF (Figure 11b).

In terms of the dimensions of UER (Table 2), resilience was the most significant factor constraining the development of the CCD in the YREB, with its hindrance degree stable at around 55%, showing a slightly decreasing trend in fluctuations. This high degree of hindrance indicated that cities in the YREB had a weak ability to recover from disturbances, posing a large challenge to the CCD. The obstruction degree of adaptability remained at around 35% and tended to stabilize. Adaptability relates to the ability of a city to adjust its structure and function to remain operational in the face of change. This result might reflect that cities in the YREB were relatively better at adapting to environmental and socio-economic changes, but there remained a considerable potential for further enhancement. In contrast, resistance was the smallest, at only about 10%, and did not impose a significant constraint on the development of CCD.

In terms of the dimensions of DIF (Table 1), the digitization level presented the highest impediment and was consistently higher than the other two constraints during the review period, remaining at around 40% and showing a tendency to increase over time. The high degree of hindrance indicated that while the use of digital technology was widespread in finance, significant challenges persisted in promoting inclusive financial services, such as insufficient technological infrastructure and low digital literacy. The breadth of coverage and depth of usage exhibited comparable levels of impediment, each remaining around 30%, with a slight downward trend over the past decade. The comparable and slightly decreasing degrees of obstacles in these two dimensions might reflect that with the development and popularization of fintech, more people had begun to use digital financial services, while the geographical coverage of these services was gradually expanding.

4.4.2. Obstacle Factors at the City Level

From the perspective of UER (Figure 12), resistance with a low level of impediments predominated from 2011 to 2017, accounting for over 90% of cities. Only a few upstream cities fell into the extremely low obstruction level. However, after 2017, the number of cities classified as an extremely low level increased dramatically, quickly replacing those in the low obstruction level and becoming dominant. In terms of adaptability, cities with a medium–high level were the most common, and only a few cities in the medium–low level, such as Kunming and Hefei, were sporadically distributed in the upstream and downstream regions. After 2017, a few cities in the eastern coastal areas began to exhibit high and extremely high levels of adaptability impediments. In contrast, the degree of resilience impediments remained high and stable throughout the examined period, with cities in the extremely high obstruction level maintaining dominance.

When analyzing the different dimensions of DIF (Figure 13), the obstruction level in terms of coverage breadth exhibited significant volatility from 2011 to 2017. Cities with a medium–high obstruction level predominated, while a few cities with a low obstruction level formed block distributions located in the midstream and downstream regions. After 2017, cities with a medium–low obstruction level gradually replaced those with medium–high obstruction levels as the dominant type. In terms of usage depth, cities with medium–low and medium–high obstruction levels were nearly equal over the past decade, showing a tendency to alternate in dominance. Regarding the digitization dimension, cities with a medium–high obstruction level were prevalent in the early years, with a few cities exhibiting high obstruction level in the midstream and downstream regions. However, over time, the overall level of obstruction increased rapidly, leading to nearly all cities in the midstream and downstream regions having high or extremely high obstruction levels, with only a few cities in the upstream maintaining a medium–high obstruction level.

5. Discussion

The digital finance industry has developed rapidly, driven by technological innovation, policy support, and market demand, significantly contributing to regional economic prosperity in the YREB. Digital technologies have also shown substantial potential in supporting environmental and climate projects, enhancing cities’ adaptive capacity to cope with climate change [66]. However, rapid urbanization and digitization have exerted considerable pressure on ecosystems, resulting in escalating environmental pollution and resource over consumption issues. These developments present a potential threat to the region’s sustainable development, necessitating comprehensive measures to reconcile economic growth with ecological protection. Through the use of digital technology in environmental management, improvements in policy frameworks, and the promotion of green finance, it is possible to achieve economic growth while safeguarding ecosystem health and sustainability. Some researchers have discussed the unidirectional relationship between DIF and UER [7,15]. However, this relationship is not unidirectional and simple, but complex and bidirectional. Consequently, this study adopts a synergistic development perspective, examining the coupling coordination and decoupling characteristics of DIF and UER. Our goal is to elucidate the non-linear relationship between the two systems and identify the obstacles affecting this relationship.

5.1. Comparison of Related Studies

We conducted a comprehensive analysis of the CCD between DIF and UER in the YREB from 2011 to 2020 by constructing and applying multiple models. This analysis was segmented across various regions and dimensions, yielding results that are partly consistent with existing studies while also revealing new insights. On one hand, the overall CCD has improved, transitioning from intermediate incoordination to primary coordination; especially, the intermediate coordination and superior coordination has been steadily increasing. This significant enhancement in the CCD in DIF and UER can be attributed to the in-depth promotion of the China’s Belt and Road Initiative and the YREB Development Strategy, which have promoted infrastructure construction and industrial upgrading within the region. Additionally, the development of modern service sectors and high-tech industries has reduced the reliance on traditional manufacturing, thereby further balancing the environmental protection and economic development to ultimately increase the CCD.

On the other hand, the spatial distribution and its changes have exhibited significant geographical characteristics and directional tendencies. Notably, the spatial center of gravity of the CCD shifted from the southeast to the northwest during this period. This trend indicates that the emerging advantages in the central and western parts of China have gradually become key forces driving the overall CCD of the YREB. The reason for this change might be related to these regions’ rapid development, which not only have a strong economic foundation but also benefit from policy support and scientific and technological innovation, providing strong guarantees for their regional development. These factors enable cities to more effectively invest in green projects and implement environmental protection measures, thereby enhancing the CCD of UER and DIF. Additionally, some researchers’ studies support this view, demonstrating that the ecological effects of DIF are more significant in the eastern regions [11,18].

In the spatiotemporal analysis of the decoupling model, we found that in the short term, the negative decoupling state was unsustainable. This mainly stemmed from the lag effect between rapid economic expansion and environmental protection measures. Some studies have shown that DIF primarily focuses on economic benefits in its early stages, while the implementation of environmental protection technologies and green financial policies lags behind, leading to increased environmental pressures [54]. This situation is particularly evident in resource-intensive industries and highly polluted regions, reflecting a lack of strength and continuity in the implementation of environmental policies, especially in areas experiencing accelerated industrialization and urbanization. However, in the long run, the expansion of DIF has not imposed an excessive load on the ecosystem. The overall decoupling relationship has manifested in a benign state of development. This positive decoupling phenomenon may be related, on one hand, to the increased adoption of fintech in the region. Digital financial services have balanced economic growth and environmental protection by promoting economic efficiency while having a smaller impact on the ecological environment. A previous study supports this idea [18]. On the other hand, urban planning and policy adjustments have been effective at alleviating environmental pressures to some extent. Studies have shown that reliance on traditional high-pollution and high-energy-consumption industries has decreased through the introduction of green industrial parks and the implementation of eco-compensation policies [67,68]. Additionally, in 2018, the Chinese government began implementing the policy of “Greater Protection, Not Greater Development” in the YREB, further optimizing the resource structure. These policy measures and industrial restructuring in the region confirm that a better balance between digital financial inclusion and ecological sustainability can be achieved. However, there are differing views suggesting that although the government has started to focus on environmental protection, the implementation and regulation of policies are not yet strong enough. Consequently, environmental protection measures cannot timely and effectively respond to the environmental pressures brought about by economic growth [66]. These arguments suggest that policymakers need to ensure the simultaneous and enhanced implementation of environmental protection measures while promoting economic digitalization. Therefore, the formulation of green growth policies, raising environmental standards, and encouraging investment in R&D—especially in technological innovations in energy efficiency, renewable energy, and environmental technologies—are key drivers of decoupling. In summary, the short-term negative decoupling and the lagged effects of policy implementation remain significant concerns.

In this study, we analyzed the constraint levels of each indicator within the evaluation of UER and DIF using the obstacle degree model. On one hand, within the UER system, resilience is identified as the most critical factor limiting the development of the CCD between UER and DIF in the YREB from 2011 to 2020. This is primarily due to the YREB experiencing several severe floods and droughts during the study period, coupled with inadequacies in post-disaster urban system recovery efforts. The lack of urban resilience significantly impacted ecological resilience [69], thereby impeding the synergistic development of the system. Factors such as insufficient financial resources, a fragile infrastructure, and inadequate governance structures are interlinked, posing severe challenges to urban resilience. On the other hand, within the DIF system, digitization has consistently been the main obstacle [11]. This is likely due to the lagging digital infrastructure in many second-tier and third-tier cities and rural areas in the YREB, restricting the promotion and application of digital services and hindering DIF development. Additionally, the energy consumption associated with digital development adversely affects ecologically sensitive areas to some extent [70,71]. The Environmental Kuznets Curve indicates that during the early stages of digital development, regional disparities and imperfect inter-regional digital collaboration networks result in high economic and environmental costs, generating significant carbon emissions and resource consumption [65]. The economic and environmental benefits can only be realized at a certain level of digitization. Overall, the accessibility and inclusiveness issues stemming from the lagging digital infrastructure in some regions make digitization levels a major impediment to the coordinated development of the system.

5.2. Optimization of CCD

Firstly, the results of the decoupling analysis indicate that the time lag between economic expansion and the implementation of ecological policies has become a significant obstacle to the coordinated development of the system. Therefore, the government should play a more assertive leadership role in integrating digitalization with ecological sustainability. On one hand, a government-backed green innovation fund should be established to prioritize projects that simultaneously promote the spread of digital finance and enhance environmental sustainability, such as smart energy-saving solutions and low-carbon transportation. This fund should encourage the deep integration of financial technology and green technology, especially in the highly polluted areas of the YREB, prioritizing projects that can effectively reduce carbon emissions and resource consumption. On the other hand, environmental oversight of digital financial services should be strengthened to ensure compliance with ecological protection standards. Fintech initiatives, particularly those employing blockchain and big data technologies to improve environmental data transparency and resource management efficiency, should be actively promoted. Additionally, the establishment of a pollution emissions trading market and a carbon pricing mechanism should be pursued, along with the development of a regional carbon trading platform to facilitate the green transformation of the YREB. Differentiated policies should be formulated: upstream regions should focus on protecting water sources and promoting eco-tourism and organic agriculture, while midstream and downstream regions should emphasize industrial upgrading and the application of environmentally friendly technologies.

Secondly, urban resilience is a critical factor in the system’s coordinated development. In urban planning, green spaces and ecological corridors should be expanded to enhance the city’s ecological absorptive capacity. Priority should be given to the construction of green infrastructure in ecologically sensitive areas to mitigate the economic and social impacts of extreme weather events by strengthening UER. Simultaneously, the urban infrastructure could be reinforced, drainage systems optimized, and effective flood warning and prevention mechanisms established, while smart city technologies should be leveraged to enhance the ability to respond to natural disasters. A dedicated disaster response fund can be established to facilitate rapid resource mobilization during emergencies, ensuring that cities maintain adequate resilience when confronting critical challenges.

Thirdly, in certain regions of the YREB, the underdeveloped digital infrastructure has become a significant barrier to the system’s coordinated development. Thus, in this region, it is crucial to strengthen digital infrastructure. A potential solution is to leverage public-private partnerships, where the government leads digital infrastructure projects, financial institutions participate in funding, and private enterprises manage construction, thereby reducing the opportunity costs associated with digital infrastructure development. Additionally, creating more opportunities in the digital economy will help cultivate digital talent and ensure the sustainable development of the region’s digital ecosystem

Lastly, the country’s gradient economic development and the disparities in economic foundations have led to a distribution pattern of “high in the east and low in the west” in terms of CCD. To address this, a digital network platform has been implemented to enhance the efficiency of capital flows and facilitate inter-regional cooperation in industry and technology. The ultimate objective is to leverage high-CCD regions to drive the development of lower-CCD areas and narrow the regional gap.

5.3. Limitations and Future Prospects

Although this study has made significant progress in studying the CCD of DIF and UER in the YREB, it has several limitations. Firstly, this study relies on city-level panel data. With advancements in data collection technology and data quality, future studies could use more detailed county-level data to delve deeper into the CCD of smaller-scale areas, thereby providing more specific decision support for policies. Secondly, although this study focuses on one of the most representative economic zones, its conclusions and recommendations are of some reference value for other characteristic economic regions in China. However, when applying this research model to other countries or regions globally, considering differences in economic development, financial systems, and urban construction is crucial. Future research should examine the variability of coupling dynamics between DIF and UER through various phases of national growth and explore customized development strategies tailored to each country’s specific conditions. Thirdly, in the spatiotemporal analysis of the CCD in the YREB, we employed a kernel density analysis and standard deviation ellipse. However, these methods may exhibit limitations in sensitivity to extreme values and anomalies. Specifically, these analyses aim to reveal overall spatial distribution trends and expansion directions. While these methods tend to smooth data, they may obscure important localized fluctuations or anomalies. For instance, in rapidly changing urban regions, extreme economic growth or environmental stress points may be overlooked due to the smoothing effect, potentially leading to inaccuracies in assessing the actual coupling and coordination dynamics. This limitation could result in policymakers failing to identify critical ‘hot spots’ in specific regions in a timely manner, thus hindering targeted interventions. Future research could use methods such as a hotspot analysis or local spatial statistics, which are more adept at capturing local variations. Additionally, the obstacle degree model quantifies the influence of various factors on CCD, aiding policymakers in targeting and optimizing the most critical bottlenecks. However, the issue of weight assignment persists in its application. For instance, in specific geographical contexts, ecological and environmental factors may hold greater practical significance than economic considerations. However, due to limitations in the allocation of weights, the impact of ecological resilience on the CCD could be systematically underestimated. The selection of weights for different indicators may significantly impact the identification and prioritization of obstacle factors. In future research, the application of the geographically weighted regression method could be considered, allowing for dynamic adjustments to weights, thereby more accurately reflecting intra-regional differences.

6. Conclusions

This paper explored the CCD of DIF and UER in 108 cities within the YREB from 2011 to 2020. We employed a combination of models, including the CCD model, kernel density analysis, standard deviation ellipse, decoupling model, and obstacle degree analysis, to explore the spatiotemporal evolution, decoupling trends, and obstruction factors. The main conclusions are as follows: Firstly, the CCD between DIF and UER increased annually from 2011 to 2020. However, there are still imbalances and inadequacies in CCD development, which are reflected in the overall level of coupling coordination within the region, uneven spatial development, and a higher coordination in the east and lower in the west. Secondly, in terms of spatial distribution, by 2020, the overall CCD had reached at least the primary coupling coordination level, with the coupling speed following the order of “downstream > midstream > upstream”. Regional differences decreased, showing no signs of polarization. The center of gravity clearly shifted from the southeast to the northwest during this period. Thirdly, the decoupling analysis indicated that the YREB exhibited a decoupling between DIF and UER. This suggests that the burden on the ecosystem has not increased in tandem with the digitization and popularization of financial services. Fourthly, the obstacle degree analysis revealed that the resilience and digitalization significantly obstructed the CCD in most cities. These analyses provide a comprehensive understanding of the complex dynamics in the synergistic development of the CCD of DIF and UER in the YREB, serving as a foundation for devising specific strategies.

Author Contributions

X.C. (Xi Chen): conceptualization, data curation, investigation, funding acquisition, visualization, writing—original draft, writing—review and editing, methodology. X.H.: conceptualization, data curation, investigation, software, visualization, writing—original draft, methodology. T.Y.: writing—review and editing. Y.Z.: data curation, software, visualization. X.C. (Xufeng Cui): funding acquisition, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The datasets used and/or analyzed during the current study available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

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

Figures and Tables

Figure 1. Framework of the research.

Figure 2. Interactive coupling coordination mechanism between DIF and UER.

Figure 3. Map of the Yangtze River Economic Belt study area.

Figure 4. Temporal characteristics of the CCD. (a–d) CCD heat map of the overall, upstream, midstream, and downstream regions.

Figure 5. Variations in the number of CCD types. (a–d) CCD types in 2011, 2014, 2017 and 2020; (e) Coupling coordination type transfer chord diagram; (f) Coupling coordination type change column chart.

Figure 6. Dynamic evolution of the characteristics of the CCD. (a–d) The dynamic evolution of the CCD in the overall, upstream, midstream, and downstream regions.

Figure 7. Spatial pattern of CCD. (a–d) The evolution of the spatial pattern in the years 2011, 2014, 2017, and 2020.

Figure 8. Evolution of spatial patterns in the CCD.

Figure 9. Variation in decoupling types.

Figure 10. Decoupling relationships and the corresponding spatial heterogeneity.

Figure 11. Obstacle factors at the system layer. (a) Dimensions of UER; (b) Dimensions of DIF.

Figure 12. Obstacle factors in the UER system at the city level.

Figure 13. Obstacle factors in the DIF system at the city level.

Table 1

Indicators of digital inclusive finance.

Index	Dimension Layer	Sub-Level	Attributes	References
DIF	Coverage breadth	Account coverage ratio	Positive	[15,54]
	Usage depth	Payment index	Positive
		Money fund index	Positive
		Credit index	Positive	[11,54]
		Insurance index	Positive
		Investment index	Positive
		Creditworthiness index	Positive
	Digitization level	Convenience index	Positive
		Mobility index	Positive	[15,55]
		Creditization index	Positive
		Affordability index	Positive

Table 2

Indicators of urban ecological resilience.

Index	Dimension Layer	Sub-Level	Attributes	References
UER	Resistance	Carbon emission intensity	Reverse	[36,56]
		Energy consumption intensity	Reverse
		Air particulate pollution	Reverse
		Electricity consumption per GDP	Reverse
		Industrial wastewater discharge per GDP	Reverse
		Water consumption per GDP	Reverse
	Adaptability	Drainage pipe density in built-up areas	Positive	[40,56]
		Volume of municipal waste collected	Positive
		Solid waste utilization	Positive
		Road cleaning rate	Positive
		Urban wastewater treatment rate	Positive
		Non-hazardous waste disposal rate	Positive
	Resilience	Greenery coverage rate in built-up areas	Positive	[38,39]
		Green space ratio in built-up areas	Positive
		Public transit availability	Positive
		Per capita park and green space area	Positive
		Healthcare conditions	Positive
		Green innovation achievements	Positive

Table 3

Classification of CCD.

Coupling Coordination Degree	Classification Results
Superior coordination	0.8 < D ≤ 1.0
Intermediate coordination	0.6 < D ≤ 0.8
Primary coordination	0.5 < D ≤ 0.6
Slight incoordination	0.4 < D ≤ 0.5
Intermediate incoordination	0.2 < D ≤ 0.4
Extreme incoordination	0 < D ≤ 0.2

Table 4

Decoupling indexes and decoupling types.

Decoupling Types		ΔUER	ΔDIF	DI	Implication
Decoupling (D)	Recessive decoupling (RD)	−	−	[1.2, +∞)	The deceleration in UER is greater than that in DIF.
	Strong decoupling (SD)	−	+	(−∞, 0)	UER decreases, while DIF increases.
	Weak decoupling (WD)	+	+	[0, 0.8)	The growth rate of UER is less than that of DIF.
Coupling (C)	Expansive coupling (EC)	+	+	[0.8, 1.2)	The growth rates of UER and DIF are approximately equivalent.
	Recessive coupling (RC)	−	−	[0.8, 1.2)	The deceleration rates of UER and DIF are approximately consistent.
Negative decoupling (ND)	Expansive negative decoupling (END)	+	+	[1.2, +∞)	The growth rate of UER exceeds that of DIF.
	Strong negative decoupling (SND)	+	−	(−∞, 0)	UER increases, while DIF decreases.
	Weak negative decoupling (WND)	−	−	[0, 0.8)	The deceleration in UER is less than that in DIF.

Table 5

Obstacle degree type.

Obstacle Degree	Classification Results
Extremely low	0–9.99%
Low	10.00%–19.99%
Medium–low	20.00%–29.99%
Medium–high	30.00%–39.99%
High	40.00%–49.99%
Extremely high	>50.00%

Table 6

Standard deviation ellipse parameters of CCD from 2011 to 2020.

Region	Year	Longitude (E)	Latitude (N)	Semi-Major Axis	Semi-Minor Axis	Rotation	Eccentricity
Overall	2011	113°14′	29°57′	875,100.92	317,384.64	72.22	0.6373
	2020	113°13′	29°58′	868,909.31	318,166.13	71.93	0.6338
Upstream	2011	104°15′	28°30′	210,176.12	464,134.16	35.05	0.5472
	2020	104°18′	28°30′	210,351.77	460,286.61	34.66	0.5429
Midstream	2011	113°38′	28°52′	297,956.31	256,218.22	125.06	0.1400
	2020	113°38′	28°52′	259,817.43	283,501.19	142.82	0.0835
Downstream	2011	104°15′	28°30′	175,316.44	289,927.24	140.35	0.3953
	2020	104°18′	28°30′	175,699.75	289,414.39	140.20	0.3929

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