Coupling and Coordinated Development Analysis of Digital Economy, Economic Resilience, and Ecological Protection

Abstract

At the critical stage of China’s economic transformation, promoting integrated development among the digital economy, economic resilience, and ecological protection becomes essential to achieve high-quality national development. This study takes 30 Chinese provinces (autonomous regions/municipalities) as research subjects. A comprehensive index system evaluates coupling coordination among the digital economy, economic resilience, and ecological protection. The three-system coupled coordination model and obstacle degree model are employed. The research investigates comprehensive evaluation levels of these three systems. Evolutionary characteristics of their coupled coordination are analyzed. Influencing factors are identified through systematic examination. Key findings reveal: (1) Provincial digital economy, economic resilience, and ecological protection generally maintained good comprehensive evaluation levels between 2011 and 2021. Significant regional development imbalances persist nationwide. (2) Coupled coordination among the three systems remains moderate but shows overall growth trends. Development characteristics demonstrate notable temporal inadequacies and spatial imbalances. Provinces achieving primary and intermediate coordination levels increased from 1 to 12 during the study period. (3) Spatial autocorrelation features prominently in the three-system coordination. Cold and hot spot areas exhibit clustered distribution patterns. (4) Main obstacles constraining three-system coupling coordination show temporal and regional variations. These evolving barriers collectively form a dynamic coupling system. Policy recommendations include: Advancing coordinated development and organic integration of the three systems; enhancing cross-regional collaboration to reduce spatial and systemic disparities; and implementing multi-source driving strategies to strengthen coordinated development momentum.

1. Introduction

China’s economic development has transitioned into a new phase characterized by dual structural shifts. First, economic growth rates have declined from historically high levels to medium-high ranges. Second, the growth paradigm has transformed from extensive expansion prioritizing scale and speed to intensive development emphasizing quality and efficiency [1]. These transitions signify a critical period for restructuring economic growth drivers through kinetic energy conversion. During this process, two strategic imperatives emerge: strengthening regional economic resilience and advancing ecological environmental protection. These priorities constitute fundamental requirements for economic transformation and high-quality development under new developmental paradigms. Concurrently, the digital revolution driven by “Internet+”, big data, and blockchain technologies has spawned novel industrial patterns. The digital economy demonstrates distinctive attributes, including operational efficiency, systemic coordination, cost-effectiveness, and network connectivity. As an endogenous driver, the digital economy provides crucial support for achieving high-quality development [2]. Accelerating its growth represents a vital pathway for realizing synergistic integration across development dimensions. However, critical challenges persist in balancing three core objectives: digital economy expansion, economic resilience enhancement, and ecological conservation advancement. This tripartite coordination constitutes an emerging research frontier requiring urgent resolution. Based on this, taking the digital economy, economic resilience, and environmental protection coupling and coordinated development relationship as the research object, the theoretical and empirical research is of great practical significance for realizing China’s sustainable development. With regard to the digital economy, economic resilience, ecological protection, and the relationship between them, current academics are mainly concerned with the following five aspects.

Firstly, regarding research scope, existing studies have primarily analyzed the coupling and coordination relationships between the digital economy and regional economic resilience [3]. Additional analyses have focused on subsystems such as the digital economy, economic resilience, ecological environment, and high-quality development [4,5,6]. Secondly, in terms of indicator systems, Ding et al. measured the digital economy using metrics such as internet users per 100 people, employment proportion in computer services and software industries, per capita telecom service volume, mobile phone users per 100 people, and the digital inclusive finance index [7]. For economic resilience, Lu et al. developed an indicator system covering innovation capability, knowledge dissemination, industrial diversification, industrial correlation, professional agglomeration, industrial structure rationalization, and industrial structure advancement [8]. Yang et al. employed indicators related to policy advantages, transportation advantages, resource advantages, industrial advantages, talent advantages, and market advantages [9]. In ecological protection studies, Wang Zhang et al. created a comprehensive index combining ecological environment status and environmental response metrics [10]. Gong et al. measured ecological environment quality using energy consumption intensity, electricity consumption intensity, industrial wastewater emissions, industrial waste emissions, industrial solid waste generation, industrial pollution control investment, and domestic waste treatment rates [11]. Thirdly, regarding measurement methods, scholars predominantly apply the coupling coordination degree model. Dong et al. [12] and Luo et al. [13] use this method to analyze the coordinated development of environment, energy, and economic growth, and economy, society, and environment, respectively. Fourthly, in examining factors affecting coupling coordination, some researchers employed factor detector tests to identify drivers of ecological environment coordination in the Yellow River Delta [14]. Others applied obstacle degree models to analyze constraints hindering water resources-ecological environment-social economy coordination in the lower Yellow River [15]. Finally, optimization strategies suggest that integrating dynamic concepts into industrial production, industrial structure, and kinetic energy conversion can enhance coupled coordination between economic development and ecological protection [16]. Improvements in economic structural innovation and social security infrastructure have been shown to promote coordinated development of the digital economy and economic resilience [17].

In summary, existing studies on the digital economy, economic resilience, and ecological protection have accumulated substantial findings. These provide a foundation for comprehensively examining the coupling coordination relationships among these three domains. However, the following deficiencies still exist. First, there is the constraint of a dualistic analytical framework. Most studies focus on pairwise relationships—digital economy and economic resilience, digital economy and ecological conservation, or economic resilience and ecological conservation—without effectively integrating the synergistic mechanisms of all three systems. Particularly under the new development paradigm, the tripartite attributes of the digital economy, economic resilience, and ecological conservation have yet to form a unified theoretical framework. This gap makes it difficult for current research to reveal their dynamic interactions in China’s high-quality development process. Second, there is a lack of compatibility across heterogeneous systems. Although existing coupling coordination studies analyze the spatiotemporal characteristics of subsystems, they lack a comprehensive model that simultaneously accommodates the technological permeability of the digital economy, the structural resistance of economic resilience, and the environmental constraints of ecological conservation. This theoretical gap limits the applicability of existing models in explaining the co-evolutionary patterns of the three systems. To address these issues, this study examines provincial-level data from 30 Chinese administrative regions (2011–2021) to construct a tripartite system coupling coordination model of “digital economy–economic resilience–ecological conservation” to achieve theoretical breakthroughs in three aspects. First, it moves beyond the limitations of traditional pairwise studies by incorporating the digital economy, economic resilience, and ecological conservation into a unified analytical framework for the first time. Second, it proposes an integrated tripartite system coupling a coordination model, addressing the shortcomings of single- or dual-system perspectives. Third, it establishes an obstacle degree analysis model applicable to the three systems, offering a new approach to identifying barriers to multi-system coordinated development. This study aims to answer key questions: What are the developmental levels of the digital economy, economic resilience, and ecological conservation across Chinese provinces under different spatiotemporal conditions? What are their coupling coordination degrees? What are the major obstacle factors? The findings are expected to provide policy recommendations for local governments in accelerating digital economic growth, enhancing economic resilience, and promoting ecological conservation. These investigations aim to provide empirical evidence for optimizing digital economic strategies, strengthening economic adaptability, and improving environmental governance practices at the regional level.

2. Mechanism Analysis, Indicator Construction, and Research Methodology

2.1. Mechanism Analysis

The digital economy, economic resilience, and ecological protection form an interconnected open system characterized by synergistic linkages. These three elements demonstrate interdependent relationships and mutual reinforcement. Their interactions generate amplified synergistic effects, establishing a coordinated coupling mechanism. Its coupling mechanism is mainly manifested as follows:

2.1.1. Dual Empowerment Mechanism Driven by the Digital Economy System

Digital technologies establish a self-reinforcing cycle characterized by efficiency enhancement and structural optimization, achieved through the recombination of production factors (via digital twins and intelligent algorithms) and the transformation of innovation paradigms (manifested in platform economies and shared economic models) [18]. Specifically, the fluidity inherent in data elements disrupts the traditional law of diminishing marginal returns associated with conventional production factors. Through the dual-engine propulsion of industrial digitization (e.g., smart manufacturing and precision agriculture) and digital industrialization (e.g., cloud computing and blockchain technologies), this process catalyzes the emergence of a reinforcing feedback loop [19,20]: technological innovation → total factor productivity (TFP) growth → industrial structure sophistication → economic resilience enhancement → digital infrastructure expansion. This technological diffusion further extends to the ecological dimension, where environmental big data monitoring systems and intelligent energy management systems facilitate a paradigm shift in pollution governance—transitioning from passive remediation to proactive prevention [21]. This evolution generates a virtuous cycle: digitalized environmental monitoring → reduced ecological footprint → improved environmental carrying capacity → green technological innovation. The systemic interplay manifests as a co-evolutionary mechanism where digital transformation and ecological conservation mutually reinforce each other through continuous technological iteration and institutional adaptation [22].

2.1.2. Adaptive Adjustment Mechanism of the Economic Resilience System

Economic resilience, acting as the stabilizer of the system, constructs a multi-level feedback system through multiple buffer layers (e.g., industrial diversity and financial risk resistance reserves) and dynamic adjustment mechanisms (e.g., policy toolkits and market resilience frameworks). When the system is subjected to external shocks, the resilience mechanism initiates an adaptive cycle of “shock identification—resource reallocation—pathway innovation” [23]. In the digital economy domain, this manifests as flexible scheduling of computational resources and the capacity for reconstructing digital supply chains. In the ecological domain, it is demonstrated through the dynamic adjustment of environmental tax systems and the swift response of ecological compensation mechanisms. This adaptive capability not only enhances the development quality of the digital economy but also fosters the formation of a coordinated evolution mechanism characterized by “increased resilience thresholds → accelerated iteration of digital technologies → reduced risks in green investments → deepened ecological industrialization”.

2.1.3. Constraint—Incentive Dual Track Mechanism of Ecological Protection System

The ecological protection system forms a rigid constraint boundary through the environmental carrying threshold, and at the same time creates new development space with the help of the realization mechanism of ecological product value, constructing a dual-role framework of “constraint—incentive” [24,25]. Its mechanism of action presents a complex feature of “pressure transmission—value transformation—spatial reconstruction” [26]: On the one hand, the environmental regulation pressure based on carbon footprint monitoring forms a reverse push mechanism, promoting the in-depth transformation of the digital economy towards clean technologies (such as energy efficiency optimization of edge computing and energy conservation of distributed data centers) and circular models (such as resource sharing of cloud services and stepwise utilization of hardware equipment). On the other hand, the establishment and improvement of the ecological capital accounting system have given rise to a complete value transformation chain of “digitalization of carbon sink trading (blockchain rights confirmation) → innovation in green finance (ESG investment and financing tools) → intelligentization of ecological compensation (execution of smart contracts) → accumulation of resilient capital (securitization of natural capital)”. This dual-action mechanism transforms the ecosystem from a traditional passive protection object into a new type of active value creator. Through the spiral ascending structure of “ecological big data (biodiversity monitoring) → green new infrastructure (smart energy network) → resilience assessment system (stress test model) → sustainable digital economy (industrial ecologicalization)”, the coordinated progress of ecological protection and economic development is ultimately achieved. Collectively, digital economy development, economic resilience building, and ecological protection demonstrate interconnected synergistic mechanisms. Their tripartite interaction drives high-quality growth through coordinated reinforcement. This interdependence justifies the proposed tripartite system coupling and coordination framework (Figure 1).

2.2. Indicator Construction and Data Sources

2.2.1. Indicator Construction

The calculation of comprehensive development levels and coupling coordination degrees among the digital economy system, economic resilience system, and ecological protection system, the analysis of coupling coordination patterns, and the identification of system-obstructing factors all require the establishment of a comprehensive evaluation index system for these three systems. This index system should demonstrate diversity and inclusivity, with indicator selection adhering to principles of scientific rigor, systematic structure, and operational feasibility. For the digital economy indicators, this study references the methodology of Ding et al. [7], incorporating a digital transaction index framework. Considering data availability and scientific validity, the digital economy development index is constructed based on internet development and digitally inclusive finance. Internet infrastructure, as the foundation of the digital economy, is measured through four provincial-level indicators: internet users per 100 people and mobile phone subscribers per 100 people. The digital inclusive finance index reflects economic digitization through three dimensions: service breadth, usage depth, and digitalization level. Regarding economic resilience indicators, the research draws from Lu et al. [8] and Yang et al. [9]. The evaluation system comprises three dimensions: (1) Resistance and recovery capacity (4 indicators, including per capita GDP and urban registered unemployment rate); (2) adaptation and adjustment capacity (4 indicators, including total retail sales of consumer goods and fixed asset investment); and (3) innovation and transformation capacity (4 indicators, including urbanization rate and industrial structure upgrading). For ecological protection indicators, following Wang et al. [16], Li et al. [27], and Qu et al. [15], the system includes: (1) Ecological pressure dimension (3 indicators, including industrial wastewater discharge per unit GDP); (2) ecological status dimension (3 indicators, including urban population density); and (3) ecological governance dimension (3 indicators, including sewage treatment rate). In total, 8 guideline layers and 28 indicator layers were selected for the three systems of digital economy, economic resilience, and ecological protection. The specific evaluation indexes of coupled and coordinated development are shown in

Table 1. Evaluation index system for coordinated development of digital economy, economic resilience, and ecological protection coupling.

System Level	Guideline Layer (Weights)	Indicator Layer	Indicator Attribute	Weights
Digital economy	Internet development X1 (0.9115)	Internet users per 100 population X11	Positive	0.1162
		Cell phone subscribers per 100 population X12	Positive	0.0776
		Gross telecommunication services per capita X13	Positive	0.4134
		Information transmission, software, and information technology services as a percentage of X14	Positive	0.3042
	financial development X2 (0.0886)	Digital Financial Inclusion Index X21	Positive	0.0886
Economic resilience	Resistance and Resilience Y1 (0.1785)	GDPY11 per capita	Positive	0.0670
		Urban registered unemployment rate Y12	Negative	0.0346
		Foreign trade dependence Y13	Negative	0.0102
		Urban disposable income Y14	Positive	0.0667
	Adaption and Regulation Y2 (0.3785)	Total retail sales of consumer goods Y21	Positive	0.1035
		Total investment in fixed assets of the whole society Y22	Positive	0.0920
		General budget expenditures of local finances Y23	Positive	0.0607
		Balance of deposits in financial institutions Y24	Positive	0.1223
	Innovation and Transformation Y3 (0.4430)	Urbanization rate Y31	Positive	0.0372
		Advanced industrial structure Y32	Positive	0.1006
		Financial education expenditure Y33	Positive	0.0720
		Patent authorizations for inventions Y34	Positive	0.2332
Ecological protection	Ecological stress Z1 (0.1006)	Industrial wastewater discharge per unit of GDP Z11	Negative	0.0362
		Industrial sulfur dioxide emissions per unit of GDP Z12	Negative	0.0424
		Industrial soot emissions per unit of GDP Z13	Negative	0.0220
	Ecological status Z2 (0.4904)	Urban population density Z21	Negative	0.1819
		Parkland per capita Z22	Positive	0.2093
		Green coverage in built-up areas Z23	Positive	0.0992
	Ecological and environmental governance Z3 (0.4090)	Centralized sewage treatment rate Z31	Positive	0.0782
		Non-hazardous domestic waste disposal rate Z32	Positive	0.0662
		Comprehensive utilization rate of industrial solid waste Z33	Positive	0.2646

, and the weights of the guideline layers are obtained by summing up the weights of the corresponding indicator layers.

2.2.2. Data Sources

Considering the availability of data related to the digital economy, this paper finally selects the data of 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2011 to 2021 as the research sample. The data of relevant indicators are obtained from China Statistical Yearbook, China Environmental Statistics Yearbook, Peking University Digital Financial Inclusion Index, and statistical yearbooks of each province in previous years. For individual missing data, linear interpolation is mainly used to supplement the prediction. This method can estimate the missing data more reasonably by constructing the linear relationship between adjacent observations, thereby ensuring the integrity of the panel data and the reliability of the analysis results.

2.3. Research Methodology

2.3.1. Entropy Value Method

Commonly used indicator assignment methods include the hierarchical analysis method [28], the coefficient of variation method [29], and the CRITIC weighting method [30]. Considering the complexity of the relationship between the evaluation indicators of the ternary system, and in order to make the data better visualized and managed [31], this paper selects the entropy value method to objectively assign the sub-indicators of the ternary system of digital economy, economic resilience, and ecological protection. The specific steps are as follows:

(1) Standardization of data:

$$\text{Positive indicators:} Z_{tij}={\displaystyle \frac{X_{tij}-X_{min}}{X_{max}-X_{min}}}$$

(1)

$$\text{Negative indicators:} Z_{tij}={\displaystyle \frac{X_{max}-X_{tij}}{X_{max}-X_{min}}}$$

(2)

Among them, $Z_{tij}$ is the dimensionless index value, and $X_{min}$ and $X_{max}$ are the minimum and maximum values of each index, respectively.

(2) Determine the weights of the indicators:

$$Y_{tij}={\displaystyle \frac{Z_{tij}}{{\sum }_{t=1}^{11}{\sum }_{i=1}^{30}{Z}_{tij}}}$$

(3)

(3) Determine the information entropy of each indicator:

$$E_j=-1/\ln \left(T\times N\right){\sum }_{t=1}^{11}{\sum }_{i=1}^{30}{Y}_{tij}ln{Y}_{tij}$$

(4)

(4) Determine the weights of the indicators:

$$W_j={\displaystyle \frac{(1-E_j)}{{\sum }_{j=1}^n(1-E_j)}}$$

(5)

(5) Calculate the index of the level of development of the digital economy, economic resilience, and ecological protection for each subsystem:

$$U_{ti}={\sum }_{j=1}^n{W}_j{Z}_{tij}$$

(6)

2.3.2. Coupled Coordination Degree Model

The degree of coupling coordination is the interaction and correlation between different subsystems, and the core factors that determine the evolutionary change of the coupled system are the coupling role and the degree of coordination between each subsystem [32]. Therefore, this paper, based on the relevant literature [33], constructs a three-system coupling coordination degree model with digital economy, economic resilience, and ecological protection as subsystems. The calculation formula is as follows:

$$C=\sqrt[3]{{\displaystyle \frac{U_1{U}_2{U}_3}{{\left(\frac{U_1+U_2+U_3}{3}\right)}^3}}}={\displaystyle \frac{3\sqrt[3]{U_1{U}_2{U}_3}}{U_1+U_2+U_3}}$$

(7)

$$T={\alpha U}_1+{\beta U}_2+{\gamma U}_3$$

(8)

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

(9)

Among them, U₁, U₂, and U₃ represent the development levels of the digital economy, economic resilience, and ecological protection, respectively. C denotes the coupling degree among the three systems, and T represents their comprehensive development index. The weights α, β, and γ correspond to the three subsystems. Given the mutually compensatory relationships and equal importance among the systems, referring to the study of Dong et al. and Pan et al. [34,35], the weights are set as α = β = γ = 1/3. D represents the coupling coordination degree of the three systems, with a value range of 0 to 1. A higher D value indicates stronger coupling coordination, and more harmonious inter-system relationships. With further reference to the existing research [36], the coupling coordination degree is divided into 10 types, and the specific division is shown in

Table 2. Coupling Harmonization Level Classification.

Degree of Coupling Coordination D	Coupling Coordination Level	Degree of Coupling Coordination D	Coupling Coordination Level
0~0.09	extreme disorder	0.50~0.59	narrow coordination
0.10~0.19	severe disorder	0.60~0.69	primary coordination
0.20~0.29	moderate disorder	0.70~0.79	moderate coordination
0.30~0.39	mild disorder	0.80~0.89	good coordination
0.40~0.49	borderline disorder	0.90~1	quality coordination

.

2.3.3. Spatial Autocorrelation Model

The first theorem of geography states that everything is spatially interconnected, and the closeness of the connection between things is affected by spatial distance [37]. Based on this theory, in this paper, a binary adjacent spatial weight matrix is adopted in spatial econometric analysis to describe the spatial dependence relationship between provinces. That is, if two provinces are geographically adjacent, the value is assigned as 1; otherwise, it is 0. Meanwhile, to investigate the attenuation characteristics of the spatial effect, this paper also constructs a geographical distance attenuation weight matrix. Its weight coefficient decreases inversely with the increase in the geographical distance between provinces, so as to reflect the spatial proximity effect more comprehensively. In fact, in the process of China’s high-quality development, the development of digital economy, the enhancement of economic resilience, and the improvement of ecological protection usually have certain regional aggregation effects. The development process will be affected by the development of other provinces, and is not independent. Global autocorrelation can describe the degree of spatial aggregation of a certain variable in the study area as a whole [38]. This paper measures the correlation of the coupling and coordination degree of the digital economy, economic resilience, and ecological protection through the global Moran index $\left(Moran’s I\right)$. The specific calculation formula is as follows:

$$I={\displaystyle \frac{n{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{ij}\left(D_i-\overline{D}\right)\left(D_j-\overline{D}\right)}{{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{ij}{\left(D_j-\overline{D}\right)}^2}}$$

(10)

Among them, $D_i$ and $D_j$ denote the observed coupling coordination degree values of specific regions in the year, $w_{ij}$ is the spatial weight matrix, and n indicates the total number of regional samples.

Local spatial autocorrelation measures the clustering degree between a single spatial unit and its adjacent spatial units [39]. The calculation formula is defined as follows:

$$I_i={\displaystyle \frac{\left(D_i-\overline{D}\right)}{S^2}}{\sum }_{j=1}^n{w}_{ij}\left(D_j-\overline{D}\right)$$

(11)

2.3.4. Hot Spot Analysis

Moran’s I is unable to distinguish between “hot spot” and “cold spot” clusters [40,41]. To further analyze the spatial correlation of the coupling coordination degree among the digital economy, economic resilience, and ecological protection systems between a specific province and its neighboring regions, the $Getis–Ord G_i^{*}$ method is employed to identify high/low clusters of the three-system coupling coordination degree. This approach enables the determination of cold and hot spot areas. The calculation formula is defined as follows:

$$G_i^{*}={\displaystyle \frac{{\sum }_{j=1}^n{W}_{ij}\left(d\right)X_j}{{\sum }_{i=1}^n{X}_j}}$$

(12)

where $X_j$ is defined as the three-system coupling coordination degree, and $w_{ij}$ represents the distance-based spatial adjacency weight matrix. If the distance between two spatial units falls within a predetermined threshold d, $w_{ij}$ is assigned a value of 1. Otherwise, $w_{ij}$ is set to 0. The standardized form of the $Getis–Ord G_i^{*}$ is expressed as follows:

$$Z\left(G_i^{*}\right)={\displaystyle \frac{G_i^{*}-E\left(G_i^{*}\right)}{\sqrt{Var\left(G_i^{*}\right)}}}$$

(13)

where $E\left(G_i^{*}\right)$ and $Var\left(G_i^{*}\right)$ are the mathematical expectation and coefficient of variation, respectively. If $Z\left(G_i^{*}\right)$ is positive and significant, there is a hot spot region; if $Z\left(G_i^{*}\right)$ is negative and significant, there is a cold spot region.

2.3.5. Barrier Degree Model

After measuring the coupling level of digital economy, economic resilience, and ecological protection, it is especially important to analyze its influencing factors and clarify its obstacle factors. The barrier degree model is used to measure the degree of constraints of the barrier degree of a certain level or a specific indicator in the indicator system on the development of the overall system or a subsystem, which helps to clarify the reasons for the differences in the coupling of different regions, and puts forward differentiated recommendations to help the coupling coordination system to develop better [42]. The specific barrier degree model is calculated as follows:

$$E_{ij}=1-M_{ij}^{\prime }$$

(14)

$$f_j={\displaystyle \frac{G_j{E}_{ij}}{{\sum }_{j=1}^x{G}_j{E}_{ij}}}\times 100\mathrm{\%}$$

(15)

$$F_j=\sum f_j$$

(16)

Among them, $E_{ij}$ is the deviation degree of the indicator, $G_j$ is the contribution degree of the factor, and $f_j$ and $F_j$ represent the obstacle degree of the indicator layer and the factor layer, respectively.

3. Empirical Analysis

3.1. Analysis of the Level of Integrated Evaluations

From a holistic perspective, the comprehensive evaluation levels of the digital economy, economic resilience, and environmental protection exhibited an overall upward trend from 2011 to 2021. The evaluation index increased from 0.239 in 2011 to 0.402 in 2021, with significant regional disparities, as detailed in

Table 3. Comprehensive evaluation level of digital economy, economic resilience, and ecological protection from 2011 to 2021.

Province	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	Mean Value
Beijing	0.409	0.442	0.473	0.511	0.529	0.542	0.563	0.629	0.695	0.703	0.612	0.555
Tianjin	0.287	0.291	0.301	0.303	0.317	0.326	0.368	0.378	0.414	0.470	0.386	0.349
hebei	0.229	0.231	0.256	0.273	0.296	0.298	0.314	0.363	0.413	0.445	0.385	0.318
Shanxi	0.215	0.246	0.251	0.253	0.258	0.255	0.254	0.288	0.327	0.357	0.285	0.272
Inner Mongolia	0.258	0.264	0.294	0.322	0.316	0.311	0.333	0.368	0.394	0.423	0.347	0.330
Liaoning	0.228	0.254	0.267	0.264	0.278	0.291	0.316	0.339	0.372	0.404	0.361	0.307
Jilin	0.200	0.216	0.251	0.258	0.257	0.277	0.266	0.322	0.355	0.391	0.332	0.284
Heilongjiang	0.162	0.192	0.199	0.213	0.215	0.220	0.230	0.266	0.299	0.323	0.268	0.235
Shanghai	0.276	0.316	0.351	0.367	0.390	0.391	0.417	0.469	0.523	0.573	0.514	0.417
Jiangsu	0.356	0.381	0.413	0.425	0.459	0.462	0.496	0.554	0.599	0.648	0.597	0.490
Zhejiang	0.342	0.370	0.394	0.404	0.442	0.443	0.471	0.528	0.575	0.638	0.560	0.470
Anhui	0.252	0.270	0.293	0.311	0.337	0.343	0.377	0.424	0.461	0.485	0.444	0.363
Fujian	0.266	0.308	0.330	0.340	0.351	0.345	0.367	0.410	0.466	0.479	0.437	0.373
Jiangxi	0.204	0.218	0.234	0.240	0.253	0.233	0.268	0.316	0.369	0.391	0.360	0.281
Shandong	0.349	0.374	0.403	0.417	0.434	0.435	0.447	0.482	0.515	0.541	0.516	0.447
Henan	0.200	0.223	0.246	0.263	0.282	0.295	0.331	0.377	0.401	0.456	0.414	0.317
Hubei	0.242	0.257	0.274	0.300	0.314	0.309	0.327	0.367	0.425	0.450	0.412	0.334
Hunan	0.203	0.223	0.235	0.252	0.276	0.298	0.323	0.376	0.416	0.446	0.383	0.312
Guangdong	0.351	0.380	0.408	0.427	0.472	0.477	0.519	0.587	0.634	0.678	0.614	0.504
Guangxi	0.208	0.243	0.257	0.255	0.270	0.276	0.295	0.344	0.382	0.417	0.337	0.299
Hainan	0.219	0.253	0.273	0.271	0.291	0.296	0.297	0.329	0.386	0.440	0.368	0.311
Chongqing	0.302	0.324	0.340	0.343	0.361	0.357	0.365	0.411	0.458	0.495	0.427	0.380
Sichuan	0.204	0.220	0.238	0.252	0.299	0.296	0.322	0.370	0.406	0.450	0.389	0.313
Guizhou	0.151	0.198	0.211	0.247	0.270	0.286	0.307	0.358	0.414	0.479	0.370	0.299
Yunnan	0.171	0.188	0.228	0.228	0.238	0.255	0.274	0.318	0.369	0.421	0.333	0.275
Shaanxi	0.198	0.224	0.243	0.260	0.296	0.311	0.286	0.324	0.360	0.416	0.334	0.296
Gansu	0.118	0.140	0.180	0.197	0.210	0.233	0.264	0.310	0.354	0.397	0.310	0.247
Qinghai	0.172	0.184	0.187	0.208	0.205	0.216	0.257	0.320	0.379	0.400	0.298	0.257
Ningxia	0.235	0.253	0.294	0.310	0.297	0.298	0.321	0.377	0.402	0.445	0.340	0.325
Xinjiang	0.168	0.186	0.202	0.219	0.257	0.257	0.277	0.317	0.364	0.404	0.327	0.271
National average	0.239	0.239	0.239	0.239	0.239	0.239	0.239	0.239	0.239	0.239	0.239

. In 2021, Beijing ranked first in the comprehensive development level with a score of 0.555, followed by Guangdong with 0.504. Since the 18th National Congress of the Communist Party of China, the digital economy has emerged as a new engine driving China’s high-quality economic development. Beijing has taken a leading role in digital economy construction nationwide, with its digital economy accounting for a significant share and its core industries dominating the sector, making it a pioneer in the national digital economy development. Simultaneously, Beijing has adopted a development model that promotes green and low-carbon transformation through the digitalization of ecological industries. This has gradually enhanced Beijing’s green development level and continuously strengthened its economic resilience, leading to a significant improvement in its overall development. In contrast, Heilongjiang had the lowest comprehensive development level among the three systems in 2021, with a score of 0.235, followed by Gansu with 0.247. Provinces such as Shanxi, Jilin, Jiangxi, Guangxi, Guizhou, Yunnan, Qinghai, Ningxia, and Xinjiang also had comprehensive development levels below the regional average. These regions are primarily constrained by geographical location, resource endowments, and economic development levels, resulting in relatively lower levels of digital economy development and ecological environmental protection.

From the perspective of individual subsystems, both economic resilience and ecological protection development levels exhibited an overall upward trend. Specifically, economic resilience increased from 0.113 in 2011 to 0.305 in 2021, while ecological protection development rose from 0.543 in 2011 to 0.636 in 2021. Although the digital economy experienced a slight decline in 2021, it maintained an overall growth trend. Detailed data are illustrated in Figure 2. Among these subsystems, ecological protection development achieved the highest level, while the digital economy demonstrated the fastest growth rate. This can be attributed to significant improvements in environmental pollution control and governance under the high-quality development framework, leading to a substantial enhancement in ecological protection. Simultaneously, the iterative innovation of digital technologies and the continuous upgrading of network infrastructure have solidified the digital economy as a crucial pillar, making it a core driver of economic development. Economic resilience maintained a steady growth trajectory, likely due to its nature as an adaptive and dynamic recovery capability. Its development is underpinned by the robust foundations provided by the digital economy and ecological protection. As ecological protection efforts intensify and the digital economy advances in both scale and quality, the development level of economic resilience has also steadily improved.

From the perspective of provincial-level subsystems, significant regional disparities exist in the development levels of the digital economy. Eastern regions such as Beijing, Shanghai, Guangdong, Zhejiang, and Jiangsu exhibit higher levels of digital economy development. In contrast, western regions like Guangxi, Gansu, Yunnan, and Ningxia show relatively lower levels, although the central and western regions demonstrate faster growth rates in the digital economy, indicating strong potential for future development. In terms of economic resilience, provinces such as Guangdong, Jiangsu, Beijing, Zhejiang, and Shandong significantly outperform other regions, while Ningxia, Qinghai, Gansu, Hainan, Xinjiang, and Guizhou lag behind. Notably, Guangdong, Zhejiang, and Shandong have experienced rapid improvements in economic resilience. For instance, Guangdong’s economic resilience index increased from 0.261 in 2011 to 0.813 in 2021, representing a growth of 211.49%. This can be attributed to Guangdong’s status as the nation’s largest economy, where efforts to stimulate the vitality of various market entities, create a first-class business environment, steadily expand institutional openness, stabilize international market share, and attract foreign investment have created new growth opportunities and further enhanced development momentum. Regarding ecological protection, provinces such as Shandong, Chongqing, Jiangsu, and Zhejiang exhibit significantly higher development levels compared to others, while Gansu, Qinghai, Shaanxi, and Shanxi show relatively lower levels. In 2021, Shandong achieved the highest level of ecological protection development. This is a result of Shandong’s comprehensive implementation of the ecological protection and high-quality development strategy for the Yellow River Basin, as well as its robust promotion of green and low-carbon high-quality development pilot zones, which involve all-around, whole-process, and comprehensive efforts in ecological environmental protection.

3.2. Coupling Relationship Test

3.2.1. Stationarity Test

This study follows the standard procedure for panel data analysis, which necessitates conducting a stationarity test prior to implementing the cointegration test. For the three core variables—the Digital Economy Development Index (dige), Economic Resilience Index (res), and Ecological Protection Index (eco)—this paper employs the unit root test for serial stationarity diagnosis. The test results, as shown in

Table 4. Unit root test results.

Variable	t-Statistic	p-Value	Critical Value (1%)	Critical Value (5%)	Critical Value (10%)
dige	−2.019	0.022	−2.480	−2.380	−2.330
res	−2.384	0.014	−2.480	−2.380	−2.330
eco	−3.615	0.000	−2.480	−2.380	−2.330

, indicate that at the 5% significance level, all test statistics significantly reject the null hypothesis of the presence of a unit root. Thus, it can be confirmed that each research variable conforms to an I(0) stationary process. This foundational test effectively mitigates the risk of “spurious regression”, theoretically ensuring that the subsequent models established are reliable and valid, unaffected by non-stationary data.

3.2.2. Granger Causality Test

This study employs the Granger causality test to uncover the causal relationships among the Digital Economy Development Index (dige), the Economic Resilience Index (res), and the Ecological Protection Index (eco). The test results, as presented in

Table 5. Granger causality test results.

Null Hypothesis	F-Value	p-Value
dige does not Granger-cause res	7.159	0.000
dige does not Granger-cause eco	7.651	0.000
res does not Granger-cause dige	4.279	0.000
res does not Granger-cause eco	3.085	0.028
eco does not Granger-cause dige	5.979	0.000
eco does not Granger-cause res	6.762	0.000

, reveal the following: (1) There exists a significant unidirectional causality from digital economy to ecological protection (F = 7.159, p< 0.001), suggesting that its development may promote sustainable environmental governance through technological innovation and optimal resource allocation. (2) Digital economy also exhibits a significant causal impact on economic resilience (F = 7.651, p < 0.001), with digital technological innovations enhancing the adaptability and resilience of the economic system, thereby effectively bolstering its ability to withstand external shocks. (3) Ecological protection has a weak but significant influence on economic resilience (F = 3.085, p = 0.028), as improvements in the ecological environment indirectly strengthen economic resilience by ensuring the stability of natural resource supply. (4) Economic resilience demonstrates significant feedback effects on both digital economy (F = 5.979, p < 0.001) and ecological protection (F = 6.762, p < 0.001), indicating that enhanced stability of the economic system provides a more favorable macro-environment for digital technological innovation and ecological governance. In summary, there exists a mutual Granger causality among digital economy, economic resilience, and ecological protection, forming a dynamically interactive system of mutual promotion.

3.3. Spatial and Temporal Evolution of the Coupling Coordination Degree

From the analysis of the temporal changes in coupling coordination degree, the coupling coordination degree index among the digital economy, economic resilience, and ecological protection in China showed a steady upward trend from 2011 to 2021, indicating an overall positive development trajectory. This can be attributed to several reasons. First, as a powerful engine for high-quality economic development under the new normal, the digital economy has received significant attention. A series of policies and documents promoting big data and artificial intelligence have been introduced, driving the rapid growth of the digital economy. Second, the guiding role of national development plans has been fully utilized, emphasizing the balance between development and security and firmly maintaining the bottom line of preventing systemic risks. Third, since the 18th National Congress of the Communist Party of China, ecological civilization has been elevated to a higher strategic level, forming an integrated “Five-Sphere Integrated Plan” alongside economic, political, cultural, and social development. This has accelerated the transformation of regional development models, leading to the gradual phase-out of high-energy-consuming industries and a more rational industrial structure. The continuous optimization of these three systems has contributed to the steady rise in their coupling coordination degree.

From the analysis of the spatial evolution of the coupling coordination degree, this paper selects three time cross-sections in 2011, 2015, and 2021 to analyze the spatial pattern, as shown in

Table 6. Patterns of spatial evolution of the coupled coordination of the digital economy, economic resilience, and ecological protection.

Degree of Coupling Coordination	2011	2015	2021
Moderate disorder [0.20, 0.30)	Jiangxi, Henan, Guizhou, Yunnan, Gansu, Qinghai (6)
Mild disorder [0.30, 0.40)	Hebei, Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Hubei, Hunan, Guangxi, Hainan, Chongqing, Sichuan, Shaanxi, Ningxia, Xinjiang (15)	Qinghai (1)
Borderline disorder [0.40, 0.50)	Tianjin, Liaoning, Shanghai, Jiangsu, Fujian, Shandong (6)	Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hunan, Guangxi, Hainan, Guizhou, Yunnan, Shaanxi, Gansu, Ningxia, Xinjiang (18)	Qinghai (1)
Narrow coordination [0.50, 0.60)	Zhejiang, Guangdong (2)	Tianjin, Fujian, Shandong, Hubei, Chongqing, Sichuan (6)	Tianjin, Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Jiangxi, Hunan, Guangxi, Hainan, Guizhou, Yunnan, Shanxi, Gansu, Ningxia, Xinjiang (17)
Primary coordination [0.60, 0.70)	Beijing (1)	Shanghai, Jiangsu, Zhejiang, Guangdong (4)	Anhui, Fujian, Shandong, Henan, Hubei, Chongqing, Sichuan (7)
Moderate coordination [0.70, 0.80)		Beijing (1)	Beijing, Shanghai, Jiangsu, Zhejiang, Guangdong (5)

. In 2011, the coupling coordination degree of the three systems—digital economy, economic resilience, and ecological protection—was categorized into five types: moderate imbalance, mild imbalance, near imbalance, marginal coordination, and primary coordination. These categories included 6, 15, 6, 2, and 1 regions, respectively. Among them, provinces such as Jiangxi, Henan, Guizhou, Yunnan, Gansu, and Qinghai exhibited moderate disorder, with Gansu having the lowest coupling coordination degree. This can likely be attributed to their location in underdeveloped western regions, where insufficient digital infrastructure, a reliance on traditional industries, and relatively fragile ecological environments are prevalent. The levels of digital economy and ecological protection in these provinces were below the average of the sample provinces, particularly in Gansu, where the values were only 0.013 and 0.288, respectively. Only Beijing achieved primary coordination, primarily due to its dual advantages of location and resources, which facilitated a virtuous cycle of mutual promotion among the digital economy, economic resilience, and ecological protection, enabling it to reach a higher level of coupling coordination development ahead of other regions.

By 2015, the coupling coordination degree of the three systems had evolved into five types: mild disorder, borderline disorder, narrow coordination, primary coordination, and moderate coordination, encompassing 1, 18, 6, 4, and 1 region(s), respectively. Compared to 2011, all regions had moved away from moderate disorder, with only Qinghai remaining as a mild disorder case. Shanghai, Jiangsu, Zhejiang, and Guangdong transitioned from borderline disorder or narrow coordination to primary coordination, while Beijing advanced from primary coordination to moderate coordination. By 2021, the coupling coordination degree of the three systems was further adjusted into four types: borderline disorder, narrow coordination, primary coordination, and moderate coordination, covering 1, 17, 7, and 5 regions, respectively. Among them, Beijing achieved the highest coupling coordination degree at 0.777, followed by Guangdong at 0.750. Compared to 2015, only Qinghai remained in the borderline disorder category, while all other regions entered the coordinated development stage. The number of regions achieving moderate coordination increased from just Beijing to five regions, including Beijing, Shanghai, Jiangsu, Zhejiang, and Guangdong. This disparity is primarily due to differences in regional development conditions and stages, leading to spatial imbalances and significant variations among regions.

3.4. Spatial Correlation Analysis

Based on the above method, the global $Moran’s I$ index of the coupled coordination degree of the three systems of digital economy, economic resilience, and ecological protection in 2011–2021 was calculated, as shown in

Table 7. Global Moran’s I indices for full sample coupling harmonization.

Year	Moran’s I	Z-Value	Year	Moran’s I	Z-Value
2011	0.296 ***	2.730	2017	0.288 ***	2.667
2012	0.304 ***	2.806	2018	0.269 ***	2.506
2013	0.291 ***	2.690	2019	0.302 ***	2.775
2014	0.270 ***	2.531	2020	0.333 ***	3.012
2015	0.278 ***	2.582	2021	0.400 ***	3.539
2016	0.275 ***	2.560

Note: *** indicates significance at the levels of 1%.

. The global Moran’s I index for the period 2011–2021 was consistently positive and statistically significant (at the 1% significance level), indicating a significant spatial clustering characteristic in the coupling coordination degree. The coupled coordination degree of the three systems is not only related to its own factors, but also has a spatial dependence on neighboring areas. This suggests that the level of coordinated development among the three systems is not only related to their own factors but also exhibits spatial dependence on neighboring regions. The possible reason is that neighboring provinces have greater similarity in economic development conditions and resource environment distribution in the digital economy, economic resilience, and ecological protection, and there is a certain spatial aggregation effect.

To better reflect the spatial correlation of the coupling coordination system among the three systems, the local Moran’s I index for the coupling coordination of the three systems from 2011 to 2021 was further calculated. Moran’s I scatter plots for the years 2011, 2015, and 2021 were also constructed, as shown in Figure 3. The coupling coordination degree exhibited spatial characteristics of “high-high” clustering and “low-low” clustering, with the spatial correlation effect gradually strengthening. Provinces in the first quadrant, such as Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, and Fujian, indicated that regions with a higher coupling coordination degree were primarily concentrated in the eastern areas. These regions benefit from higher economic development levels, earlier establishment of rational industrial structures, and stronger technological innovation capabilities, which significantly promote the coordinated development of the three systems and generate a notable radiating effect on neighboring regions. In contrast, provinces in the third quadrant, including Shanxi, Liaoning, Guangxi, Guizhou, Yunnan, Gansu, Qinghai, Ningxia, and Xinjiang, suggested that central and western regions lack distinct locational advantages. Their economic development and technological management levels lag significantly behind those of the eastern regions, making it difficult to provide strong support for transformative development. Additionally, there were fewer instances of “high-low” and “low-high” clustering. The primary reason for such clustering is the mismatch between the levels of digital economy, economic resilience, and ecological protection in these provinces and their neighboring regions, resulting in significant disparities in the coupling coordination degree.

3.5. Hot Spot Analysis

To explore the spatial clustering regions of the coupling coordination degree of the three systems from the perspective of spatial aggregation characteristics, the local statistical model $Getis–Ord G_i^{*}$ was calculated, and the spatial distribution of coupling coordination degree across provinces was divided into cold and hot spots for analysis. The cold and hot spots of the coupling coordination degree among the digital economy, economic resilience, and ecological protection exhibited clustered spatial distribution patterns. Specifically, in 2011, only Shanghai was identified as a hot spot, while Qinghai, Sichuan, and Xinjiang in the western region were classified as cold spots, with the remaining provinces showing no significant clustering. This can be primarily attributed to Shanghai’s strong economic foundation, rational policy systems, and well-developed information infrastructure. In contrast, the western regions, constrained by topography and natural climate, lagged in economic development and faced relatively fragile ecological environments. By 2015, Jiangsu was added to the hot spots, while Qinghai no longer appeared as a cold spot, and Inner Mongolia became a new cold spot. A possible reason for this is that Jiangsu accelerated the improvement of information infrastructure, promoted high-quality economic development, and significantly enhanced residents’ ecological environment and informatization levels. Meanwhile, Inner Mongolia, located in a remote border area, had relatively underdeveloped infrastructure and informatization levels, with industries still in the digital transformation phase, leading to limited development outcomes and hindering coordinated development. By 2021, Anhui, Jiangxi, Zhejiang, and Fujian were added to the hot spots, while Gansu was added to the cold spots. This may be due to the concentration of various production factors in advantageous regions during the development of eastern areas. When a certain level of agglomeration was reached, industrial transfers and the construction of transportation infrastructure drove the development of surrounding regions, forming a pattern of division and collaboration. In contrast, the western regions, with their relatively low levels of digital economy and ecological protection, resulted in the development levels of the three systems falling below the average of the sample provinces, thereby forming cold spots.

In summary, both cold and hot spots exhibit contiguous spatial characteristics. Hot spots are primarily concentrated in the eastern regions, while cold spots are mainly located in the western regions. This is because the eastern regions, as pioneers of China’s reform, opening-up, and economic development, possess abundant ecological resources and a solid economic foundation. While achieving their own coordinated development, they also exert a radiating and driving effect on neighboring areas. In contrast, the western regions are constrained by less favorable geographical locations and relatively underdeveloped economic conditions, which hinder the advancement of digital applications, particularly the development of the digital economy. As a result, these areas form clusters with low levels of coordination.

3.6. Barrier Factor Analysis

This paper selected the years 2011, 2015, and 2021 as representative data intervals. Using the specified formula, the obstacle degrees and obstacle factors for the three subsystems—digital economy, economic resilience, and ecological protection—were calculated. The factors were ranked by their obstacle degrees, and the three factors with the highest frequency of occurrence were identified as the primary obstacle factors. The specific results are presented in

Table 8. Digital economy, economic resilience, and environmental protection indicator tier main barrier factors.

Provinces	Digital Economy Subsystem			Economic Resilience Subsystem			Environmental Protection Subsystem
	2011	2015	2021	2011	2015	2021	2011	2015	2021
Beijing	X13/X14/X21	X13/X14/X11	X13/X11/X12	Y34/Y24/Y21	Y34/Y22/Y21	Y34/Y22/Y21	Z22/Z33/Z31	Z22/Z33/Z21	Z33/Z22/Z21
Tianjin	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y21	Y34/Y24/Y21	Y34/Y24/Y21	Z22/Z23/Z21	Z22/Z21/Z23	Z22/Z21/Z23
Hebei	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y32	Y34/Y24/Y32	Y34/Y24/Y32	Z33/Z22/Z21	Z33/Z22/Z21	Z33/Z22/Z21
Shanxi	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y32	Y34/Y24/Y21	Y34/Y24/Y32	Z33/Z22/Z21	Z33/Z22/Z21	Z33/Z22/Z21
Inner Mongolia	X13/X14/X11	X13/X14/X11	X13/X14/X11	Y34/Y24/Y32	Y34/Y24/Y21	Y34/Y24/Y21	Z33/Z22/Z23	Z33/Z23/Z21	Z33/Z21/Z23
Liaoning	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y32	Y34/Y24/Y32	Y34/Y24/Y32	Z33/Z22/Z23	Z33/Z22/Z23	Z33/Z22/Z21
Jilin	X13/X14/X11	X13/X14/X11	X13/X14/X11	Y34/Y24/Y32	Y34/Y24/Y21	Y34/Y24/Y21	Z22/Z33/Z23	Z33/Z22/Z21	Z23/Z22/Z21
Heilongjiang	X13/X14/X11	X13/X14/X11	X13/X14/X11	Y34/Y24/Y32	Y34/Y24/Y21	Y34/Y24/Y21	Z21/Z22/Z33	Z21/Z33/Z22	Z33/Z21/Z22
Shanghai	X13/X14/X21	X13/X14/X11	X13/X14/X11	Y34/Y24/Y21	Y34/Y22/Y24	Y34/Y22/Y21	Z22/Z21/Z23	Z22/Z21/Z23	Z22/Z21/Z23
Jiangsu	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y32	Y34/Y32/Y24	Y32/Y34/Y24	Z22/Z31/Z21	Z22/Z21/Z31	Z22/Z21/Z23
Zhejiang	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y32	Y34/Y32/Y24	Y34/Y32/Y24	Z22/Z23/Z21	Z22/Z21/Z23	Z22/Z21/Z23
Anhui	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y32/Y24	Y34/Y32/Y24	Y34/Y32/Y24	Z22/Z33/Z21	Z22/Z21/Z23	Z22/Z21/Z23
Fujian	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y32	Y34/Y24/Y32	Y34/Y24/Y32	Z22/Z33/Z21	Z22/Z33/Z21	Z21/Z22/Z33
Jiangxi	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y32	Y34/Y24/Y32	Y34/Y24/Y32	Z33/Z21/Z22	Z33/Z21/Z22	Z33/Z21/Z22
Shandong	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y32	Y34/Y24/Y32	Y34/Y32/Y24	Z22/Z23/Z33	Z22/Z23/Z33	Z33/Z22/Z21
Henan	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y32	Y34/Y24/Y32	Y34/Y24/Y32	Z22/Z21/Z33	Z22/Z21/Z33	Z21/Z22/Z33
Hubei	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y32	Y34/Y24/Y32	Y34/Y24/Y32	Z22/Z33/Z23	Z22/Z33/Z21	Z33/Z22/Z21
Hunan	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y32	Y34/Y24/Y32	Y34/Y24/Y32	Z22/Z33/Z21	Z22/Z33/Z21	Z21/Z22/Z33
Guangdong	X13/X14/X11	X13/X14/X11	X13/X14/X11	Y34/Y32/Y24	Y34/Y32/Y24	Y32/Y11/Y14	Z22/Z21/Z33	Z21/Z22/Z23	Z21/Z33/Z22
Guangxi	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y32	Y34/Y24/Y32	Y34/Y24/Y21	Z33/Z22/Z31	Z22/Z33/Z23	Z33/Z22/Z21
Hainan Island	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y21	Y34/Y24/Y21	Y34/Y24/Y21	Z33/Z22/Z21	Z33/Z22/Z23	Z22/Z33/Z21
Chongqing	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y32	Y34/Y24/Y32	Y34/Y24/Y32	Z33/Z22/Z23	Z22/Z33/Z23	Z22/Z33/Z21
Sichuan	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y32	Y34/Y24/Y32	Y34/Y32/Y24	Z33/Z22/Z21	Z33/Z22/Z23	Z33/Z22/Z21
Guizhou	X13/X14/X11	X13/X14/X11	X13/X14/X11	Y34/Y24/Y21	Y34/Y24/Y21	Y34/Y24/Y21	Z22/Z33/Z21	Z33/Z22/Z23	Z33/Z22/Z21
Yunnan	X13/X14/X11	X13/X14/X11	X13/X14/X11	Y34/Y24/Y21	Y34/Y24/Y21	Y34/Y24/Y32	Z33/Z22/Z21	Z33/Z22/Z21	Z33/Z22/Z21
Shaanxi	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y32	Y34/Y24/Y32	Y34/Y24/Y32	Z21/Z33/Z22	Z22/Z33/Z21	Z33/Z21/Z22
Gansu	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y21	Y34/Y24/Y21	Y34/Y24/Y21	Z22/Z33/Z21	Z33/Z22/Z21	Z33/Z21/Z22
Qinghai	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y21	Y34/Y24/Y21	Y34/Y24/Y21	Z22/Z33/Z23	Z33/Z22/Z23	Z33/Z22/Z21
Ningxia	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y21	Y34/Y24/Y21	Y34/Y24/Y21	Z33/Z22/Z23	Z33/Z23/Z22	Z33/Z21/Z23
Xinjiang	X13/X14/X11	X13/X14/X11	X13/X14/X12	Y34/Y24/Y21	Y34/Y24/Y21	Y34/Y24/Y21	Z22/Z23/Z21	Z33/Z22/Z21	Z33/Z21/Z22

. This analysis not only reveals the main obstacle factors of each subsystem in different years, but also shows the trend of these factors changing over time.

From the perspective of the indicator layer, each subsystem consists of 90 indicators. In the digital economy subsystem, X13 (per capita total volume of telecommunications services) appeared 90 times and consistently ranked first. X14 (proportion of Information transmission, software, and information technology services) appeared 89 times, closely following. X11 (per 100 Internet users) appeared 65 times and also held an important position. The stable appearance of these three indicators over the past three years indicates that the development level of the Internet has always been a key factor restricting the development of the digital economy subsystem, and its influence has not shown a significant weakening over time. In the economic resilience subsystem, Y34 (the number of authorized invention patents) appeared 89 times in each of the three years, demonstrating the crucial importance of innovation and R&D capabilities to economic resilience. Y24 (deposit balance of financial institutions) appears 86 times, indicating the supporting role of financial capital accumulation in economic resilience. Y32 (advanced industrial structure) has appeared 53 times, demonstrating the gradual prominence of industrial structure optimization and upgrading in economic resilience. The changes of these indicators over time reflect the dynamic adjustment of the importance of the internal elements of the economic resilience subsystem. In the ecological protection subsystem, Z22 (per capita park green space area) appeared 87 times every three years, firmly ranking first. Z33 (comprehensive utilization rate of industrial solid waste) appeared 73 times, demonstrating the importance of resource recycling and utilization. Z21 (urban population density) occurred 71 times, reflecting the impact of population pressure on ecological protection. The ranking and frequency of occurrence of these indicators in different years reflect the key points and challenges that ecological protection work faces at different stages. Meanwhile, it can be seen from Table 6 that the indicators of each province have significant changes in the represented years, and the order has also been adjusted. This further indicates that the internal development of the three subsystems is not static but forms a dynamically changing coupled system. The importance of the indicators fluctuates among different years, reflecting the complexity and variability in the development process of each subsystem. As far as the guideline layer is concerned, the degree of impact barrier of the guideline layer is judged based on the frequency of occurrence of elements. The digital economy subsystem is characterized by the level of internet development X1 > the level of financial development X2; the economic resilience subsystem is characterized by the power of innovation and transformation Y3 > the power of adaptation and regulation Y2 > the power of resistance and resilience Y1; and the ecological protection subsystem is characterized by the current state of the ecological environment Z2 > ecological environmental management Z3 > ecological environmental pressure Z1. It can be seen from the results that with the rapid development of China’s economy and society, the main obstacles to high-quality development are constantly changing, and continuous attention and adjustment of response strategies are needed. In conclusion, the insufficiency of new infrastructure, the lack of significant investment in scientific research, and the insignificant improvement of the ecological environment remain the main obstacles to China’s high-quality development. However, the specific manifestations of these obstacle factors vary in different years and among different subsystems, showing a dynamic trend of change. Therefore, when formulating relevant policies, these changes need to be fully considered to achieve more precise and effective governance.

4. Discussion

4.1. Comparison with Previous Studies

This study constructed a ternary system coupling and coordination model, including digital economy, economic resilience, and ecological protection, and conducted an in-depth analysis of the data of 30 provinces in China from 2011 to 2021. Compared with previous studies, this study makes contributions in the following aspects:

Firstly, rather than merely focusing on the pairwise relationships between the digital economy and economic resilience or ecological protection, this study systematically examines the mutual interactions and coordinated development among the three. Previous studies have mainly concentrated on pairwise relationships, such as the digital economy and economic resilience [3], digital economy and ecological protection [22], and economic resilience and ecological protection [25], while this study expands this domain and offers a comprehensive perspective on the coupling and coordinated development of the ternary system.

Secondly, this study adopts a multi-model integrated analysis approach, including the coupling coordination degree model, spatial autocorrelation model, hot spot analysis model, and obstacle degree model, to conduct an in-depth exploration of the development levels, coupling coordination states, and influencing factors of digital economy, economic resilience, and ecological protection. This multi-dimensional analysis method is more comprehensive and profound than the utilization of a single model, providing a richer perspective for understanding the development mechanism of the ternary system.

Finally, this study reveals the regional differences and dynamic changes in the coupled and coordinated development of digital economy, economic resilience, and ecological protection from the perspective of spatio-temporal evolution. This spatio-temporal analysis not only discloses the coupling and coordinated states of each province at different time points but also reveals the spatial correlations and agglomeration characteristics among regions through spatial autocorrelation and hot spot analysis. Compared with previous studies that merely focused on temporal sequence changes, this provides more comprehensive spatio-temporal dynamic information.

4.2. Theoretical and Practical Implications

The theoretical significance of this study lies in enriching the theoretical system of the coordinated development of the trinity of digital economy, economic resilience, and ecological protection. By constructing a comprehensive evaluation index system, this study provides a scientific methodological basis for quantitatively analyzing the development levels of these three systems and their coupling coordination status. Meanwhile, through the analysis of the obstacle degree model, this study uncovers the main obstacle factors affecting the coordinated development of the trinity, offering significant reference for future theoretical research and policy formulation.

From a practical perspective, the results of this study have substantial guiding significance for promoting the coordinated development of the digital economy, economic resilience, and ecological protection in various provinces of China. Firstly, policymakers should fully recognize the crucial role of the digital economy in promoting high-quality economic development and ecological protection, and increase investment and support for the digital economy. Secondly, by enhancing economic resilience, each province can better cope with external shocks and uncertainties, providing a stable economic foundation for the development of the digital economy and the advancement of ecological protection. Finally, ecological protection is not only related to environmental quality but also an important guarantee for the sustainable development of the digital economy and economic resilience. Therefore, policymakers should pay attention to the protection and governance of the ecological environment to achieve a win-win situation between economic development and environmental protection.

Furthermore, the results of this study also indicate that there are significant regional disparities among provinces in terms of the development levels of the digital economy, economic resilience, and ecological protection. Therefore, policymakers should adopt differentiated policy measures based on the actual conditions of different regions to promote coordinated regional development. For instance, the eastern regions with higher development levels should further exert their leading role to drive technological innovation and industrial upgrading. Meanwhile, the central and western regions with lower development levels should increase policy support to enhance infrastructure construction and human resource levels, providing strong support for their digital economic development and ecological protection.

5. Conclusions and Policy Implications

5.1. Conclusions

This paper, based on the establishment of a comprehensive evaluation index system for the digital economy, economic resilience, and ecological protection, employs methods such as the entropy method, the coupling coordination degree model, the spatial autocorrelation model, the hotspot analysis model, and the obstacle degree model. It measures the comprehensive development levels and coupling coordination degree of the digital economy, economic resilience, and ecological protection across 30 provinces (autonomous regions, and municipalities) in China from 2011 to 2021. The paper explores the temporal trends, spatial differences, and obstacle factors of the digital economy, economic resilience, and ecological protection. The main conclusions are as follows:

First, the comprehensive development levels of economic resilience and ecological protection exhibited an overall upward trend. Although the digital economy experienced a slight decline in 2021, it maintained a general growth trend. Significant regional imbalances and systemic disparities were observed in the comprehensive development levels. Specifically, eastern regions such as Beijing, Shanghai, Jiangsu, Zhejiang, and Guangdong demonstrated higher development levels, while western regions like Gansu, Qinghai, Ningxia, and Xinjiang showed relatively lower levels. Among the three systems, ecological protection achieved the highest development level, while the digital economy exhibited the fastest growth rate. This indicates that in the process of economic development, it is necessary to balance ecological protection and enhance economic resilience, and adhere to the coordinated development path of “technology empowerment—structural optimization—ecological restoration”.

Second, in terms of the time-series change of the coupling degree of the three systems, the index of the coupling degree of coordination of digital economy, economic resilience, and ecological protection has maintained a steady upward trend from 2011 to 2021, with an overall benign development from 0.374 to 0.597. From the perspective of the overall average value, the country has experienced four types of mildly dysfunctional, on the verge of dysfunctionality, barely coordinated, and primarily coordinated, and has shown a remarkable spatial distribution characteristic of “high in the east—low in the central and western parts of China”. The spatial distribution of the coupling degree is characterized by “high in the east and low in the central and western parts of the country”. In terms of the spatial evolution of the coupling degree of the three systems, the number of provinces with moderate dysfunction, mild dysfunction, imminent dysfunction, barely coordinated, primary coordinated and intermediate coordinated has changed from 6, 15, 6, 2, 1, and 0 to 0, 0, 1, 17, 7, and 5. It shows that the imbalance in regional development still exists, and a differentiated development strategy needs to be implemented.

Third, global spatial autocorrelation analysis reveals that the coupling coordination degree of the digital economy, economic resilience, and ecological protection in China exhibits significant spatial autocorrelation and clustering characteristics. Local spatial autocorrelation analysis indicates that “high-high” and “low-low” clustering dominate. Specifically, “high-high” clustering is primarily observed in provinces such as Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, and Fujian, while “low-low” clustering is found in provinces such as Shanxi, Liaoning, Guangxi, Guizhou, Yunnan, Gansu, Qinghai, Ningxia, and Xinjiang. The cold and hot spots display contiguous and clustered spatial patterns, with hot spots concentrated in the eastern regions and cold spots mainly located in the western regions. This spatial pattern of “high in the east and low in the west” indicates that there is still room for improvement in regional coordinated development.

Fourth, the obstacle degree model analysis reveals that, from the indicator layer perspective, the main obstacle factors in the digital economy system are the total telecommunications business volume per capita, the proportion of information, software, and information technology services, and the number of internet users per 100 people. In the economic resilience system, the main obstacle factors are the number of invention patents authorized, the balance of deposits in financial institutions, and the advancement of industrial structure. In the ecological protection system, the main obstacle factors are the per capita green space area, the comprehensive utilization rate of industrial solid waste, and urban population density. From the criterion layer perspective, the obstacle degrees of each dimension, ranked from high to low, are as follows: in the digital economy subsystem, the level of internet development > the level of financial development; in the economic resilience subsystem, innovation and transformation capability > adaptation and adjustment capability > resistance and recovery capability; and in the ecological protection subsystem, the current state of the ecological environment > ecological environment governance > ecological environment pressure. This requires the government to adopt precise measures targeting specific obstacle factors when formulating policies.

5.2. Policy Implications

First, it is important to promote the coordinated development of the digital economy, economic resilience, and ecological protection, and to promote the organic integration of the three. Firstly, the digital economy empowers economic resilience and ecological protection. On the one hand, relying on digital technology improves the technical content of the total economy, optimizes the regional industrial structure, and enhances the ability of the economy to withstand external shocks. On the other hand, using intelligent means helps us to improve the ecological protection supervision and governance mechanisms, and efficiently guards the “green water and green mountains”. Secondly, economic resilience guarantees the digital economy and ecological protection. On the one hand, economic development and social progress provide a solid material foundation for the development of the digital economy, so that the digital economy becomes an important tool for high-quality economic development. On the other hand, they change the original rough development model, increase environmental regulation requirements, and lay a solid development foundation for green development. Thirdly, ecological protection for the digital economy and economic resilience are needed to create a favorable development environment. On the one hand, green development creates good conditions for the development of the digital economy, and promotes digitalization with greening. On the other hand, the construction of digital infrastructure should be strengthened, and digital scene applications should be spawned to accelerate the building of digital power.

Second, enhance cross-regional collaborative efforts to reduce development disparities among provinces and improve the coordinated development level of the digital economy, economic resilience, and ecological protection. The provinces in the eastern “high-high” agglomeration area should capitalize on their existing advantages, prioritize the advancement of new infrastructure construction such as 5G networks and the industrial internet, deepen the empowerment of the real economy by digital technologies, and reinforce regional economic resilience. Simultaneously, they should increase the intensity of collaborative governance of the ecological environment, promote the synergy of environmental governance and economic growth through green technological innovation and ecological compensation mechanisms, and exert a radiating and leading role for the surrounding provinces. The central and western regions need to focus on breaking the “low-low” agglomeration pattern, enhancing the efficiency of resource and element circulation through cross-provincial co-construction of digital infrastructure corridors and joint cultivation of digital industrial clusters. Western provinces can, in combination with their resource advantages, jointly build green energy bases integrating wind, solar, hydropower, and energy storage with adjacent regions, and explore the paths for the transformation of ecological resource values. In terms of the collaborative mechanism, it is proposed to establish a complementary and interactive model of “eastern technologies + central and western scenarios” and “western resources + national market”. Through incentive policies such as the enclave economy and tax sharing, promote the joint construction of digital economy parks and the sharing of ecological protection costs. Particularly for the “depressed areas” at the provincial borders, a cross-provincial joint development fund can be established to primarily support fundamental projects such as transportation interconnection, data sharing, and joint prevention and control of ecological pollution, and systematically enhance the coordinated development capabilities of the three regions.

Third, emphasize multi-source drivers to enhance the coordinated development momentum of the digital economy, economic resilience, and ecological protection. On the one hand, increase investment in scientific research, expand information technology R&D platforms, and empower technological innovation in traditional industries. Simultaneously, integrate the research capabilities of universities and large research institutions to accelerate deep integration with technological innovation, achieve breakthroughs in key technological fields, and strengthen support for the digital economy, economic resilience, and ecological environment governance. On the other hand, during the process of promoting the coordinated development of the three systems, pay attention to the varying development conditions across regions and adopt targeted measures tailored to specific regions and stages. For example, the eastern regions should further leverage advanced technologies, intensify the exploration and application of data elements, and play a leading and demonstrative role. The central and western regions should further exploit local characteristics and development advantages, accelerate industrial structure upgrading, increase per capita GDP, and promote the deep integration of traditional industries with new technologies to facilitate the coordinated development of the three systems.

Fourth, build a multi-dimensional system to promote high-quality development. In the digital economy field, implement a “dual-wheel drive” strategy. The government should increase investment in new digital infrastructure, focusing on the construction of 5G networks and big data centers, and simultaneously improve core indicators such as per capita telecommunications business volume and internet user penetration rate. By establishing a policy combination of “government guidance funds + enterprise R&D tax deductions”, the innovation momentum of enterprises in frontier technologies such as artificial intelligence and blockchain can be stimulated, forming a benign mechanism of coordinated development between infrastructure and technological innovation. Economic resilience construction highlights a “trinity” layout. Build a policy system of “innovation cultivation—financial support—risk prevention and control”: set up a special fund for industrial upgrading to support the creation of high-value invention patents, and establish a green channel for intellectual property pledge financing. Implement the deposit structure optimization project of financial institutions, guiding funds to tilt towards advanced manufacturing. Establish a macroeconomic pressure test model and improve the risk warning and emergency response mechanism of the industrial chain. Ecological protection promotes a “three-dimensional governance” model. Implement the urban ecological space optimization project, strengthen the full-process supervision of industrial solid waste through satellite remote sensing monitoring, and establish a dynamic balance mechanism of “green space ratio - population density”. Innovate the “ecological bank” system, connect ecological restoration projects with the carbon trading market, and at the same time, build a universal environmental protection credit system to achieve an effective combination of hard constraints of environmental supervision and soft incentives of social participation.

Future research directions: Exploring the underlying mechanisms between the digital economy, economic resilience, and ecological conservation, and how these mechanisms affect regional development disparities. This study explores how international factors influence the development of domestic digital economy, economic resilience, and ecological protection, and their interactions in the context of globalization. Developing a more refined evaluation model to measure the coupling coordination degree of the three systems and their obstacle factors more accurately will provide a scientific basis for policy-making.

5.3. Limitations and Research Directions

This study has a number of limitations. First, the sample was limited to the provincial level, but future studies could be extended to the prefecture level if the necessary data are available. Second, while this study focuses on China over the past 11 years, it would be more globally instructive if extended to a transnational context. Finally, due to the availability of data, the indicators used to measure the coupled and coordinated development of the digital economy, economic resilience, and ecological protection are not perfect. Future research could further refine these indicators.