Digital Economy and Environmental Sustainability: Analysis of Cross-Country Coordination

Abstract

This paper calculates the level of digital economy development and the degree of economic and ecological integration in 100 countries from 2009 to 2022 using the entropy weight method and equal weight method. It empirically tests the impact, influencing mechanisms, and spatial effects of the digital economy on the integration of economy and ecology. Furthermore, it analyzes the coordination degree of the digital economy and economic–ecological integration across different continents using coupling coordination analysis. The research results are as follows: First, the digital economy has promoted the integrated development of economy and ecology in various countries, with a more significant effect in high-income and upper-middle-income countries. Countries with higher levels of digital economy development show a more pronounced promoting effect. Second, the digital economy can reduce energy consumption, promote the efficient utilization of clean energy, lower carbon emissions, and thus facilitate the integrated development of economy and ecology through industrial structure optimization and driving technological innovation. Third, a country’s digital economy has a positive spillover effect on the economic and ecological integration development of its neighboring countries. The coupling coordination analysis reveals that Europe has the highest coupling coordination degree of the digital economy and economic–ecological integration, with significant spatial autocorrelation. This is followed by the Americas, Asia, and Africa. Therefore, countries should actively promote digital economy development, strengthen digital economic cooperation, jointly address international environmental and climate issues, and promote the integration of economy and ecology.

1. Introduction

With the rapid development of the global economy, climate change and environmental issues have become increasingly prominent. The accelerated processes of industrialization and urbanization have made carbon emissions and energy consumption critical constraints on sustainable economic development. Against the backdrop of global climate change, the integration of economy and ecology has become a global focus. This integration aims to achieve a win–win economic development model that minimizes the consumption of natural resources and environmental pollution while maximizing economic output, striking a balance between economic and social development and ecological conservation. Many countries have formulated relevant strategies and goals to achieve the coordinated development of economic growth and ecological protection through measures such as reducing carbon emissions, improving energy efficiency, and promoting the development of clean energy. The coordinated development of economy and ecology has thus become a focal point of the international community and a crucial direction for economic policymaking and development strategies worldwide.

Meanwhile, digital development, as a new engine of economic growth, is profoundly reshaping the global economic structure and development paradigm. Accelerating digital development has become a strategic choice for nations to build new competitive advantages in the digital era. Digital development encompasses three interconnected and mutually reinforcing dimensions: the digital economy, digital society, and digital government. The digital economy serves as a driver of high-quality economic growth, the digital society enhances public services to meet people’s aspirations for a better life, and the digital government accelerates the transformation of government functions, modernizing governance systems and capabilities. At the core of digital development is the digital economy, characterized by information technology and digitalization. The digital economy encompasses fields such as e-commerce, digital payments, big data, artificial intelligence, and the Internet of Things. Its highly informational, intelligent, and efficient nature has profoundly impacted countries worldwide, emerging as a new driving force for global economic growth. An increasing number of nations have incorporated the digital economy into their development strategies, significantly increasing investment and cooperation in this area. According to the *Global Digital Economy White Paper (2023)*, digital economy development has continued to accelerate in major countries worldwide. In 2022, the combined digital economy of five major economies—the United States, China, Germany, Japan, and South Korea—reached USD 31 trillion, accounting for 58% of their GDP, an increase of approximately 11 percentage points compared to 2016. The digital economy grew by 7.6% year-over-year, outpacing GDP growth by 5.4 percentage points. By transcending international and spatial constraints, the digital economy connects various economic entities through advanced network technologies. The transnational flow of data and the widespread dissemination of digital technologies have injected new vitality into global innovation and development, driving a new wave of globalization.

The digital transformation has injected new vitality into the real economy. Traditional industries have been upgraded and transformed by adopting advanced digital technologies, further expanding their production and market boundaries. The digital economy has accelerated the transformation of traditional industries from scale and speed-oriented to quality and efficiency-oriented [1]. The digital economy also provides innovative models for market transformation, especially under new conditions of digitization, intelligence, and networking. The competitive interaction between market entities is more three-dimensional and efficient, further promoting the optimization and upgrading of economic structure [2]. In addition, the development of the digital economy has effectively reduced carbon emissions and maximized resource utilization by fostering the innovation and application of low-carbon technologies, improving resource efficiency, and advancing low-carbon industries. It has provided opportunities for emerging industries such as cloud computing, big data, and artificial intelligence, facilitating the integration of economy and ecology. Furthermore, digital economic cooperation offers countries new methods and ideas for jointly addressing climate change and environmental challenges.

In summary, this paper aims to explore in depth the impact and mechanism of the digital economy on the integration of economic and ecological development, analyze how this process generates different effects globally, and examine the potential of the digital economy in promoting sustainable economic development and ecological protection. Specifically, the paper addresses the following aspects:

First, it reveals the impact of the digital economy on the integration of economic and ecological development. Through empirical research, the paper analyzes how the digital economy, while driving economic growth, also promotes environmental improvement, and explores its specific performance and effectiveness in different countries and regions.

Second, it examines the mechanism of interaction between the digital economy and ecological development. The paper explores how the digital economy, through mechanisms such as technological innovation and industrial transformation, achieves the dual goals of economic growth and ecological sustainability.

Third, it investigates the spatial spillover effects of the digital economy at the national level. The paper analyzes the spillover effects of the digital economy on the integration of economic and ecological development in various countries, explores the interactive relationships between digital economies in different nations and regions, studies the cross-border transmission paths and impacts, and assesses whether the digital economy can create similar green development models globally, as well as the presence of differentiated effects between regions.

Finally, the paper proposes policy recommendations for promoting the integration of economic and ecological development through the digital economy. Based on the empirical research findings, it offers concrete suggestions for formulating digital economy development policies for countries around the world, particularly on how to optimize digital economy policy frameworks and promote the deep integration of digital and green economies. Additionally, policy recommendations will be made on how to foster international cooperation, establish multinational digital economy standards and policies, strengthen technology transfer, and promote global digital economy collaboration to achieve the shared goal of coordinated global economic and ecological development.

Therefore, the main work of this article is as follows:

1. By combining the contexts of digitalization and low-carbon development, this study examines the relationship between digital economy development and the integration of economy and ecology. This approach goes beyond a narrow focus on the digital economy’s impact on green development to provide a more comprehensive analysis of its influence on the coordinated development of green growth and economic expansion.

2. The study uses cross-country panel data to offer new perspectives on international cooperation concerning environmental and economic issues.

3. By conducting a coupling coordination analysis and using spatial autocorrelation tests, the paper compares the levels of coupling coordination between digital economy development and economic–ecological integration across continents, offering insights for regions with lower levels of coordinated development.

2. Literature Review and Theoretical Analysis

2.1. Literature Review

Research on measuring the development level of the digital economy has primarily focused on constructing multidimensional comprehensive evaluation indicator systems. These dimensions often include digital infrastructure, digital innovation, digital industrial structure, and digital trade, with entropy weight methods commonly used to calculate results [3,4,5]. Based on these indices, many scholars have further explored the impact of the digital economy on various aspects of economic and social life. For example, the digital economy has been shown to promote high-quality economic development and contribute positively to shared prosperity, with technological innovation and industrial structure optimization serving as mediating factors [6,7,8]. In terms of international trade, the digital economy has significant positive effects on both imports and exports, enhancing China’s competitiveness in global trade and increasing domestic value added in intermediate goods exports by lowering trade costs [9,10,11]. Furthermore, the digital economy significantly improves industrial structures, optimizes intelligent supply chain management for enterprises, and strengthens customer relationship management, injecting new vitality into enterprise development, improving employment conditions, and increasing labor income [12,13,14].

In recent years, the integration of economic and ecological systems has received widespread attention. Domestic scholars have studied the coupling and coordination development between economic and ecological environments in regions such as Anhui, Qinghai pastoral areas, Hunan Province, Gansu Province, and Chongqing City. These studies often employ entropy weight methods to measure coordination levels and evaluate their evolution trends using coupling and coordination models [15,16,17,18,19]. Research on the coordination between economic development and ecological protection has deepened over time. International studies emphasize the compatibility of shared economic models with circular economies, achieving balances between multi-centered development and habitat availability through multi-objective optimization. However, some studies have found that informal economies, formal economies, governance, financial development, and urbanization positively influence ecological footprints, leading to environmental degradation [20,21,22]. Domestic research reveals that while technological innovation, economic growth, and environmental coordination have generally improved, there are distinct phase characteristics and regional disparities. For instance, logistics development exhibits a “U-shaped” relationship with economic–ecological coordination, showing varied impacts across regions. Environmental regulations, overall, promote coordination, but their effects vary regionally. The coupling coordination between the digital economy, ecological protection, and urban–rural integration has been on the rise. However, the overall levels and coordination of the digital economy, industrial development, and ecological environments exhibit regional differences. The digital economy enhances regional economic coordination by improving technological innovation and ecological quality. Economic growth pressures exhibit an “inverse U-shaped” impact on coordination, with technological innovation and industrial structure upgrades serving as key internal mechanisms [23,24,25,26,27,28,29].

Most of the research on the impact of the digital economy on the integration of economic and ecological development focuses on the impact of the digital economy on ecological sustainability. Studies by foreign scholars began earlier. Studies have shown that digital development allows digital elements to gradually replace tangible factors, reducing energy consumption. The use of digital technologies also makes production, distribution, and consumption processes greener, saving resources and reducing pollution to some extent [30,31]. In contrast, the research on the impact of the digital economy on the integrated development of economy and ecology in China is relatively late, and it mostly focuses on the impact of the digital economy on ecological protection and low-carbon development. There are more studies from the perspectives of interprovincial and urban areas. Studies show that the digital economy reduces carbon intensity, promoting regional low-carbon transitions. Green technology innovation, industrial structure upgrades, improved energy efficiency, and optimized energy consumption structures serve as significant mediating factors in this process [32,33,34,35]. The digital economy also contributes to the improvement of green total factor productivity (GTFP), with this effect being stronger in the eastern regions and areas with higher levels of digital economy development. It achieves this by enhancing green technologies, which have a positive impact on GTFP. Similar research shows that digital transformation improves the green total factor productivity of the circulation industry through technological innovation and human resource allocation. Some studies focusing on urban clusters have found that the digital economy of the Yellow River Basin urban cluster can significantly improve green total factor productivity, with positive spatial spillover effects [36,37,38]. The digital economy also has a significant driving effect on the development of low-carbon industries, which operate through energy and resource flows. Some people also analyze from the perspective of green and low-carbon industry transformation, studying how the digital economy can empower high-quality development of industries [39,40]. Moreover, the digital economy has improved resource allocation and energy efficiency in China, driven industrial transformation and upgrading, and optimized the ecological environment, thereby facilitating the transition to an economic–ecological integration model [41,42].

Through the literature review, it was found that although progress has been made in the fields of digital economy and green development, there are still gaps to be filled. Existing research primarily focuses on the relationship between the digital economy and carbon emissions, low-carbon transformation, and green productivity, with relatively little direct attention given to the connection between the digital economy and the integration of economic and ecological development. The digital economy not only drives ecological sustainability but also promotes economic growth, helping to achieve a win–win situation between ecological protection and economic development. Furthermore, most current studies are concentrated at the local level, with fewer studies addressing the national level. However, with the intensification of global warming, transnational digital economy cooperation is becoming increasingly important, offering new avenues for environmental cooperation. At the same time, the coordination of the digital economy and economic–ecological integration will contribute to global economic growth. Therefore, researching the national-level integration of the digital economy and economic ecology, and learning from the experiences of well-developed countries, is crucial for nations striving to achieve high-quality economic development.

2.2. Theoretical Analysis

The digital economy facilitates the integration of economic and ecological systems by driving industrial transformation. On the one hand, the digital economy uses intelligent technologies and big data analytics to optimize production processes, enabling enterprises to utilize energy more efficiently, thus achieving the transformation and upgrading of traditional industries. Simultaneously, the application of new technologies fosters the emergence of new industry forms, making digital industries a key pillar in promoting industrial structure transformation and upgrading [43].

On the other hand, industrial transformation has spurred the rapid growth of clean energy industries. With the application of digital technologies, the production efficiency and utilization rates of renewable energy have significantly improved, thereby reducing reliance on traditional high-carbon energy sources. This shift propels the industrial structure dominated by energy-intensive heavy industries toward a more technologically advanced and environmentally friendly direction, laying a foundation for green development and improved environmental quality [44]. Additionally, industrial transformation encourages enterprises to adopt more energy-efficient and environmentally friendly production processes and equipment, leading them toward circular economic models. This effectively reduces the consumption of natural resources and environmental degradation, thereby improving energy efficiency and achieving a sustainable transformation of production methods.

Hypothesis 1 (H1). 

The digital economy positively impacts economic–ecological integration.

The digital economy enhances the level of economic–ecological integration by promoting technological innovation. On one hand, the digital economy provides enterprises with abundant data resources. Through data analysis and mining, enterprises gain deeper insights into market trends, consumer demands, and potential directions for product improvements, which facilitate better product innovation and market positioning. At the same time, the rise of the digital economy transcends spatial and traditional industrial constraints, significantly accelerating the dissemination of information and knowledge. This trend enables more convenient collaboration between regions and industries, creating broader opportunities for cross-sectoral technological diffusion and talent mobility.

On the other hand, supported by advanced information technologies such as artificial intelligence (AI) and 5G, industrial enterprises are actively exploring digital technological innovations to improve production processes and optimize production models. This progress gradually leads to the transformation of high-energy-consuming products [45]. Additionally, technological innovations enable the efficient and clean utilization of fossil energy sources like coal. For example, integrating Carbon Capture, Utilization, and Storage (CCUS) technology into coal-fired power plants holds the potential for achieving zero carbon emissions, promoting the clean transformation of the coal industry chain, and advancing low-carbon industrial development.

Hypothesis 2 (H2). 

The digital economy promotes economic–ecological integration through industrial transformation and technological innovation.

With the deepening of globalization, the relationships among nations have grown increasingly interdependent. As an emerging industry, the digital economy plays a critical role in international cooperation. First, the rise of the digital economy overcomes geographic and temporal constraints, facilitating the cross-border flow of technology, capital, and talent. This leads to the international optimization of resource allocation. The application of digital technologies allows neighboring countries to more deeply influence regional industrial structures and energy utilization patterns, creating favorable conditions for promoting the integration of economic and ecological systems. This digitalization trend fosters cross-national cooperation and exchange, accelerating the dissemination and adoption of green technologies and injecting new momentum into global environmental protection and sustainable development initiatives.

Second, digital economic cooperation promotes the sharing of experience and technology while driving the global spread of digital technologies. Within the framework of digital economic cooperation, countries can engage in more profound collaborations on carbon governance, positively impacting economic–ecological integration. Cross-border carbon governance practices receive support from digital economic advancements, providing a strong backing for global green transitions. This, in turn, promotes joint efforts within the global community toward achieving sustainable development goals [46].

Hypothesis 3 (H3). 

The digital economy exerts spatial spillover effects on economic–ecological integration.

3. Methods and Data

3.1. Explained Variable: Degree of Economic–Ecological Integration (DT)

The integrated development of economy and ecology refers to the process where, during economic development, full consideration is given to the protection and improvement of the ecological environment, promoting the coordinated development of economic activities and ecosystems, and achieving a balance between economic growth and environmental sustainability. This concept emphasizes incorporating ecological elements into the entire process of economic development, seeking positive interactions and win–win outcomes between the two, with the aim of not only avoiding harm to the ecological environment while pursuing economic growth, but also actively promoting the protection and restoration of the environment. Considering that this paper studies the integrated development of both economy and ecology, it is necessary to take into account both economic and ecological aspects. Therefore, this article draws on the relevant literature and conducts research from three dimensions: socio-economic, resource utilization, and energy consumption. Corresponding secondary indicators are selected, and an evaluation index system is constructed to measure the degree of economic and ecological integration development in 100 countries from 2009 to 2022 [47]. The list of 100 countries will be attached as Appendix A at the end of the main text. The evaluation index system for economic–ecological integration is shown in Table 1.

In this paper, we assigned attributes to each indicator based on its impact on the integration of economic and ecological development. If an indicator positively impacts economic–ecological integration, its attribute is positive; otherwise, it is negative.

The integration of economic and ecological development focuses on achieving a win–win situation between economic growth and environmental protection, with equal importance given to both. Therefore, this study adopts the equal weighting method to measure the degree of economic–ecological integration. The equal weight method assigns the same weight to each indicator. This method assumes that all indicators have equal importance in the evaluation process, thus allocating the same weight to each indicator. It assumes that each indicator contributes equally to the result, overlooking potential differences and varying levels of influence among the indicators. Specifically, the secondary indicators are first standardized. Subsequently, the index is assigned the same weight, synthesized step by step from the secondary indicators upward: from secondary indicators to primary indicators and from primary indicators to the overall degree of economic–ecological integration. Arithmetic averaging is applied at each aggregation level [48].

The formula for standardizing positive indicators is as follows:

(1)$\mathrm{X}=100\times {\displaystyle \frac{\mathrm{X}-{\mathrm{X}}_{\mathrm{m}\mathrm{i}\mathrm{n}}}{{\mathrm{X}}_{\mathrm{m}\mathrm{a}\mathrm{x}}-{\mathrm{X}}_{\mathrm{m}\mathrm{i}\mathrm{n}}}},$

The formula for standardizing negative indicators is as follows:

(2)$\mathrm{X}=100\times {\displaystyle \frac{{\mathrm{X}}_{\mathrm{m}\mathrm{a}\mathrm{x}}-\mathrm{X}}{{\mathrm{X}}_{\mathrm{m}\mathrm{a}\mathrm{x}}-{\mathrm{X}}_{\mathrm{m}\mathrm{i}\mathrm{n}}}},$

The synthesis of secondary indicators into primary indicators is as follows:

(3)${\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{o}\mathrm{r}}_1={\displaystyle \frac{\sum {\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{o}\mathrm{r}}_2}{{\mathrm{N}}_2}},$

The first level indicator synthesizes the degree of integrated development of economy and ecology as follows:

(4)$\mathrm{D}\mathrm{T}={\displaystyle \frac{\sum {\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{o}\mathrm{r}}_1}{{\mathrm{N}}_1}},$

Among them, i represents the country, j represents the secondary indicators, indicator₂ represents each secondary indicator, N₂ represents the number of secondary indicators, indicator₁ represents the synthesized primary indicators, and N₁ represents the number of primary indicators. The economic and ecological integration development level of each country, denoted as DT_it, is calculated using the data of the aforementioned 10 secondary indicators through the equal weight method. The data are sourced from databases such as the World Bank and CEInet Statistics Database.

3.2. Core Explanatory Variable: Digital Economy Development Level (DE)

The concept of the digital economy is quite broad, encompassing any economic form that directly or indirectly utilizes data to guide the allocation of resources and drive productivity growth. At the technological level, it includes emerging technologies such as big data, cloud computing, the Internet of Things, blockchain, artificial intelligence, and 5G communication. At the application level, typical examples include “new retail” and “new manufacturing”. This article draws on the relevant literature and systematically sorts out and summarizes the connotation and main characteristics of the digital economy, considering the average level of development of the digital economy globally and the availability of data. It divides the digital economy into four sectors: digital talents, digital innovation, digital infrastructure, and digital trade status. It serves as the measurement core for the development of the digital economy and establishes an evaluation index system by selecting corresponding indicators. Based on this, the development level of the digital economy in 100 countries from 2009 to 2022 is calculated [43]. The evaluation index system for the digital economy is shown in Table 2.

The positive and negative values of the indicator attributes are consistent with the reasons mentioned above. We assigned attributes to each indicator based on its impact on the digital economy. If an indicator positively impacts the digital economy, its attribute is positive; otherwise, it is negative.

The entropy method is an objective weighting approach inspired by the concept of information entropy. It calculates the information entropy of indicators and determines their weights based on the extent of relative variation in each indicator’s impact on the overall system. Specifically, it assigns weights according to the differences in the values of each indicator, thereby deriving the corresponding weight for each indicator. The entropy method assumes that the weights of indicators are determined by their “information entropy”. The greater the information entropy, the larger the amount of information the indicator carries, and vice versa. In other words, the higher the entropy, the greater the diversity and variability of the indicator, thus assigning it a higher weight. The entropy method typically assumes that indicators are relatively independent of each other, with no direct interaction effects. It also relies on the variability of data, assuming that the data themselves have a certain level of stability and credibility. By applying the entropy method to calculate the digital economy, it can objectively reflect the differences and significance of various indicators. The results are accurate and unaffected by subjective factors, making the calculation structure objective and reliable. Therefore, this paper uses the entropy method for measurement, and the calculation process is as follows: The standardization of positive indicators is as follows:

(5)${\mathrm{X}}_{\mathrm{i}\mathrm{j}}^1=100\times {\displaystyle \frac{{\mathrm{X}}_{\mathrm{i}\mathrm{j}}-\mathrm{m}\mathrm{i}\mathrm{n}\left({\mathrm{X}}_{\mathrm{i}\mathrm{j}}\right)}{\mathrm{m}\mathrm{a}\mathrm{x}\left({\mathrm{X}}_{\mathrm{i}\mathrm{j}}\right)-\mathrm{m}\mathrm{i}\mathrm{n}\left({\mathrm{X}}_{\mathrm{i}\mathrm{j}}\right)}},$

The standardization of negative indicators is as follows:

(6)${\mathrm{X}}_{\mathrm{i}\mathrm{j}}^1=100\times {\displaystyle \frac{\mathrm{m}\mathrm{a}\mathrm{x}\left({\mathrm{X}}_{\mathrm{i}\mathrm{j}}\right)-{\mathrm{X}}_{\mathrm{i}\mathrm{j}}}{\mathrm{m}\mathrm{a}\mathrm{x}\left({\mathrm{X}}_{\mathrm{i}\mathrm{j}}\right)-\mathrm{m}\mathrm{i}\mathrm{n}\left({\mathrm{X}}_{\mathrm{i}\mathrm{j}}\right)}},$

After standardization, a value of 0 appears. Considering the subsequent logarithmic processing of the data, it is therefore translated as follows:

(7)${\mathrm{X}}_{\mathrm{i}\mathrm{j}}^2={\mathrm{X}}_{\mathrm{i}\mathrm{j}}^1+0.0001,$

The weight P_ij of the jth indicator in the i-th country is as follows:

(8)${\mathrm{P}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{X}}_{\mathrm{i}\mathrm{j}}^2}{{\sum }_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{X}}_{\mathrm{i}\mathrm{j}}^2}},$

The K value and the entropy value E_j of the j-th indicator are as follows:

(9)$\mathrm{K}={\displaystyle \frac{1}{\ln \left(\mathrm{m}\mathrm{n}\right)}},$

(10)${\mathrm{E}}_{\mathrm{j}}=-\mathrm{K}\times {\sum }_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{P}}_{\mathrm{i}\mathrm{j}}\times \ln \left({\mathrm{P}}_{\mathrm{i}\mathrm{j}}\right),$

The coefficient of variation D_j of the jth indicator is as follows:

(11)${\mathrm{D}}_{\mathrm{j}}=1-{\mathrm{E}}_{\mathrm{j}},$

The weight W_j of the jth indicator is as follows:

(12)${\mathrm{W}}_{\mathrm{j}}={\displaystyle \frac{{\mathrm{D}}_{\mathrm{j}}}{{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{D}}_{\mathrm{j}}}},$

The comprehensive evaluation value Z_i of the i-th country is as follows:

(13)${\mathrm{Z}}_{\mathrm{i}}={\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{j}}\times {\mathrm{X}}_{\mathrm{i}\mathrm{j}}^1,$

Among them, i represents the country, j represents the third level indicator, m represents the total number of countries, and n represents the total number of third level indicators. The digital economy development level of each country, denoted as DEit, is calculated using the data of the above 16 tertiary indicators through the entropy weight method, which objectively assigns weights to each indicator [43]. The data are sourced from databases such as the World Bank, the International Telecommunication Union (ITU), and UNCTAD.

3.3. Control Variables

3.3.1. Economic Development Level (gdp)

Traditional economic growth models often rely on high energy consumption and high-emission industries, which pose challenges to the integration of economic and ecological development. However, as global attention to climate change continues to rise, economic growth drives many countries and regions to increase investment and research in low-carbon technologies and clean energy, thereby promoting the integration of economic and ecological development.

3.3.2. Carbon Emission Intensity (co2)

Carbon emissions not only exacerbate global climate change but also cause significant damage to the ecological environment. Evidently, carbon emissions have a negative impact on the integration of economic and ecological development.

3.3.3. Urban Population Growth Rate (urb)

Regions with rapid urban population growth may more easily achieve economies of scale in renewable energy production and equitable distribution, thereby advancing the development of new energy technologies and low-carbon products. However, larger urban populations may lead to higher consumption levels and carbon footprints, causing excessive resource consumption and increased environmental pressure, which in turn exacerbate carbon emissions and ecological degradation.

3.3.4. Degree of Openness (ex)

On the one hand, openness to the outside world can lead to technology transfer and green investments, attracting advanced foreign low-carbon technologies and management practices to promote the development of domestic low-carbon industries. On the other hand, some countries may lower their carbon emissions by transferring high-carbon industries abroad, which could lead to increased carbon emissions in other countries, hindering their economic and ecological integration development.

3.3.5. Foreign Direct Investment (fdi)

Foreign direct investment can facilitate the dissemination and application of low-carbon technologies, enhance the environmental and low-carbon levels of local industries, and promote the integration of economic and ecological development. However, FDI may also bring about problems such as over-exploitation of resources, environmental degradation, and increased carbon emissions.

3.3.6. Industrial Added Value (ind)

Energy consumption and fossil fuel combustion in industrial production processes generate significant greenhouse gas emissions, such as carbon dioxide, which negatively affect low-carbon green performance development. This makes it challenging for industries to achieve green transformation and hinders the integration of economic and ecological development.

3.4. Limitations of Methods and Data

This study collected raw data from websites and excluded countries with excessive missing values. For the remaining data with missing values, interpolation methods were used to fill in the gaps, and calculations were then performed according to the aforementioned methods. However, it is important to note that both the selection of indicators and the choice of methods have certain limitations.

Regarding the limitations of indicators, the relevant data for the control variables are sourced from the World Bank. This paper is based on the research foundation of the digital economy and the integration of economic and ecological development, combining the existing literature findings to select a corresponding indicator system. However, as the connotation and extension of the digital economy are still in a continuous process of dynamic evolution, the selected indicators may not fully represent the most authoritative standards currently recognized by academia. Additionally, adjustments or changes in the indicator system may have some impact on the results of the econometric analysis in practical applications, leading to a certain level of uncertainty in the research conclusions. Future research needs to continuously monitor the developments in both the theoretical and practical aspects of the digital economy, optimizing indicator selection to enhance the scientific rigor and reliability of the research.

Regarding the limitations of the method, the entropy weight method is suitable for situations where there are significant differences in data and each indicator is relatively independent. However, its limitation lies in the inability to consider expert experience and sensitivity to extreme values. The advantage of the equal weight method lies in its simplicity and intuitiveness, but its limitation lies in ignoring the differences in importance of each indicator in actual decision-making, as it assumes that all indicators have the same impact on the results. In practical applications, when choosing an appropriate weighting method, it should be comprehensively considered based on the characteristics of the specific problem, the nature of the data, and the decision objectives.

4. Results

4.1. Econometric Model Specification

To verify whether the digital economy has a positive driving effect on the integration of economic and ecological development, this paper constructs a panel model as follows:

(14)${\mathrm{D}\mathrm{T}}_{\mathrm{i}\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{D}\mathrm{E}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\alpha }}_2{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\phi }}_{\mathrm{i}}+{\mathsf{\mu }}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{t}},$

where i represents the country, t represents the year, DT_it denotes the level of economic and ecological integration development of country i in year t, DE_it indicates the level of digital economy development of country i in year t, which is the core explanatory variable of this paper, α₀ represents the intercept term, α₁ is the estimated parameter of the core explanatory variable, the digital economy, X_it represents other control variables, φ_i denotes individual fixed effects, μ_t represents time fixed effects, and ε_t is the random disturbance term of the model.

From the perspective of data characteristics, the core advantage of the fixed effects model lies in its ability to control for unobserved heterogeneity at the individual or time level. There may be unobservable factors in the data that are related to the explanatory variables, and these factors remain relatively stable across different individuals or time periods. The fixed effects model effectively controls for these factors, making the estimation results more accurate. In terms of estimation results, the fixed effects model can provide more consistent parameter estimates when dealing with specific types of data. In the presence of fixed effects, the estimation from the random effects model may be biased, whereas the fixed effects model ensures the consistency of parameter estimates in large samples. Therefore, the estimates derived from the fixed effects model are statistically more reliable and better reflect the true relationships between variables. From a theoretical perspective, the research question in this paper is more suitable for explanation using the fixed effects model. This paper emphasizes the importance of specific country and time effects in the analysis, and the fixed effects model aligns better with the theoretical framework. Therefore, this paper will use the fixed effects model for regression analysis.

4.2. Baseline Regression Results

To ensure the rigor of the empirical model, this paper successively adds control variables when performing regression analysis using the data. The basic model is estimated through a panel fixed effects model and clustered robust standard errors. In order to comprehensively and quantitatively analyze the impact of digital economy development on economic and ecological integration development, this paper employs Stata 18 software for baseline regression analysis. The regression results of the panel data model are shown in Table 3.

From the baseline regression results, the core explanatory variable—the digital economy—has a significantly positive impact on the integration of economic and ecological development. In Table 3, the results in column (7) show that, holding other variables constant, for every one-unit increase in the level of digital economy development, the degree of economic and ecological integration improves by 0.1171 units at a 1% significance level. This demonstrates that the digital economy has a significant positive driving effect on the integration of economic and ecological development, meaning that the development of the digital economy can promote the integration of economy and ecology.

4.3. Robustness Tests

4.3.1. Replacing the Core Explanatory Variable

To verify the robustness of the regression results, this paper first conducts robustness tests by replacing the core explanatory variable. The entropy weight method-based digital economy measure is replaced by digital economy development levels obtained through the principal component analysis method in SPSS Statistics 26.0, and by the TIMG index calculated by Wang Zhe et al. [48]. The results are shown in Table 4. The tests reveal that the digital economy still has a significant and positive impact on the integration of economic and ecological development. The coefficients remain positive and consistent with the baseline regression results, thereby validating the robustness of the previous findings.

4.3.2. OLS Regression

Ordinary Least Squares (OLS) regression was conducted. After incorporating all variables, it was found that the impact of the digital economy on the integration of economic and ecological development is significantly positive at the 1% level. This result is consistent with the baseline regression findings and is presented in column (2) of Table 5.

4.3.3. Changing the Sample Size

Considering that the “digital economy” first became a topic of discussion at the G20 Summit in 2016 and has since received widespread attention, this paper excludes the sample data from 2009 to 2015 for robustness testing. The results, shown in column (4) of Table 5, indicate that the impact of the digital economy on the integration of economic and ecological development remains significantly positive and consistent with the baseline regression findings.

4.3.4. Lagged Variables

Considering that the digital economy may not immediately impact green and low-carbon development but instead exhibits a certain degree of lag, this paper examines the effects of the digital economy on the integration of economic and ecological development by lagging the digital economy by one and two periods. The results, shown in Table 6, indicate that even after lagging by one and two periods, the digital economy still exerts a positive influence on the integration of economic and ecological development. This suggests that the promotion of green and low-carbon development by the digital economy has a certain lag effect and represents a long-term impact process. The impact of the digital economy on the integration of economic and ecological development remains significant at the 1% level, further validating the robustness of the previous estimation results.

4.4. Endogeneity Analysis

Selecting appropriate instrumental variables for the core explanatory variable is the main approach to addressing the issue of endogeneity. This study draws on the method of Huang Qunhui et al. (2019) [49], using the number of fixed telephone subscribers in each country in 1984 as an instrumental variable for the digital economy.

On the one hand, the internet is an extension of traditional communication technologies, and the level of telecommunications infrastructure and usage habits in a region’s history can influence the adoption of internet technology in subsequent stages. On the other hand, as the frequency of use of traditional telecommunications tools such as fixed telephones declines, their impact on economic development also diminishes, thus satisfying the exclusion restriction.

However, the original data of the selected instrumental variable are in cross-sectional form, making them unsuitable for direct application in panel data econometric analysis. Therefore, a time-varying variable is introduced to construct a panel instrumental variable. Specifically, the interaction term between the lagged one-period digital economy and the number of fixed telephone subscribers in 1984 for each country is constructed as the instrumental variable for the digital economy [50].

The measurement results are shown in Table 7: On the one hand, after addressing the endogeneity issue, the impact of the digital economy on the integrated development of the economy and ecology remains significantly positive, consistent with the results of the baseline regression. On the other hand, the Kleibergen–Paap rk LM statistic test demonstrates the validity of the selected instrumental variable, and the Kleibergen–Paap rk Wald F statistic test confirms that there is no weak instrument problem.

4.5. Heterogeneity Analysis

The previous analysis has demonstrated that the digital economy exerts a positive impact on the integrated development of the economy and ecology. However, due to significant differences in the development levels and stages across countries, it is necessary to further investigate whether this effect varies based on income levels. Following the World Bank classification, the 100 selected countries are grouped into four categories based on per capita income: high-income countries, upper-middle-income countries, lower-middle-income countries, and low-income countries. The impact of the digital economy on the integration of economic and ecological development at different income levels is tested, and the results are shown in Table 8.

The regression results indicate that the digital economy has a significantly positive impact on the integration of economic and ecological development in high-income and upper-middle-income countries. However, the impact is insignificant in lower-middle-income and low-income countries. This may be because higher-income countries already have relatively well-established digital economy systems and higher levels of economic development. In these countries, the digital economy not only further promotes high-quality economic development but also facilitates industrial transformation toward green and low-carbon development, achieving the integration of economic and environmental benefits. In contrast, in lower-income countries, the digital economy is relatively underdeveloped. During the process of promoting digitalization, these countries tend to focus primarily on its impact on economic growth, without adequately addressing its broader implications. Consequently, the digital economy exhibits an insignificant impact on the integration of economic and ecological development.

Additionally, this paper calculates the average development level of the digital economy and divides it into two dimensions: high and low. Using the low-dimension digital economy development level as a baseline, interaction terms are further constructed to conduct a heterogeneity analysis. The results are presented in column (5) of Table 8. The findings suggest that the higher the level of digital economy development, the stronger its positive effect on the integration of economic and ecological development.

The primary reasons for this are as follows: A higher level of digital economy development enhances resource allocation efficiency and reduces resource waste and environmental costs. It promotes the application of clean energy and green technologies, thereby reducing carbon emissions. It facilitates the transformation and upgrading of industrial structures, fostering the emergence of low-resource-consumption and high-value-added industries. It creates an environment conducive to technological innovation, driving innovations in green technology and ecological protection. By transcending geographical constraints, it reduces regional development disparities, thereby alleviating the pressure of unbalanced development on the ecological environment.

4.6. Mechanism Analysis

4.6.1. Industrial Transformation Mechanism

The industrial structure is measured using the added value of the service sector (ser), which reflects the regional development model. Compared to manufacturing and heavy industry, the service sector typically has lower carbon emissions. This is because services primarily involve the provision of services and information rather than extensive material production and processing, resulting in lower carbon emissions and a positive impact on the integration of economic and ecological development.

Columns (1) and (2) in Table 9 show that the digital economy has a significantly positive impact on industrial transformation, and industrial transformation, in turn, exerts a positive influence on the integration of economic and ecological development. Therefore, the digital economy can promote the integration of economic and ecological development by driving industrial transformation.

4.6.2. Technological Innovation Mechanism

The export of high-tech products is often accompanied by an enhanced innovation capacity. Countries ranking high in high-tech exports also tend to feature prominently on the lists of the most innovative nations globally. Therefore, this paper uses high-tech exports (htech) as a measure of a country’s innovation capacity.

Industrial enterprises utilize digital technology to innovate and improve production processes, optimize production models, enhance production efficiency, and reduce actual energy consumption. Additionally, technological innovation can drive the clean transformation of high-pollution, high-energy-consuming industrial chains, thereby fostering low-carbon industrial development.

Columns (3) and (4) in Table 9 indicate that the digital economy has a significantly positive impact on technological innovation, and technological innovation has a positive influence on the integration of economic and ecological development. As a result, the digital economy can promote the integration of economic and ecological development by fostering technological innovation.

4.7. Expanded Analysis

This paper divides the digital economy into four components: digital talent, digital innovation, digital infrastructure, and the current state of digital trade, and examines their effects on the degree of economic and ecological integration. The regression results indicate that digital innovation and digital trade have a significantly positive impact on the integration of economic and ecological development, whereas digital talent and digital infrastructure show no significant effect.

4.7.1. Digital Innovation

Digital innovation drives the development and application of green technologies and ecological products, enhancing resource utilization efficiency and reducing environmental pollution, thereby promoting the deep integration of the economy and ecology.

4.7.2. Digital Trade

Digital trade facilitates the flow and sharing of green products and technologies across regions, providing a broader market for low-carbon products, environmental technologies, and sustainable development models. The cross-regional optimization of resource allocation helps reduce resource consumption and environmental impact during production and distribution processes, fostering eco-friendly economic activities.

4.7.3. Digital Talent

The insignificant impact of digital talent may be attributed to its indirect role in the integration of the economy and ecology. While digital talent serves as the foundation for the development of the digital economy, its effects are primarily realized through technological innovation or policy implementation. Therefore, its direct influence on the integration of economic and ecological development may be relatively weak in the short term.

4.7.4. Digital Infrastructure

Although the construction of digital infrastructure (e.g., 5G networks, data centers) is a critical support for the development of the digital economy, the initial construction phase may involve significant energy consumption and carbon emissions, which diminishes its short-term positive effects on the ecological environment. Additionally, the benefits of infrastructure development are typically realized over the long term, requiring a prolonged period for ecological benefits to manifest.

This paper further divides the degree of economic and ecological integration into three dimensions: social economy, resource utilization, and energy consumption, and conducts a regression analysis in conjunction with the level of digital economy development. The results indicate that the digital economy has a significantly positive impact on social economy and resource utilization, but no significant effect on energy consumption.

4.7.5. Positive Impact on Social Economy and Resource Utilization

The digital economy promotes social and economic development and enhances resource utilization efficiency by optimizing resource allocation and improving production efficiency. For example, digital management, intelligent supply chains, and the application of the sharing economy effectively reduce resource waste and over-exploitation. Digital technologies enhance transparency and foster technological innovation, providing direct momentum for social-economic growth and resource-saving development.

4.7.6. Insignificant Impact on Energy Consumption

On the one hand, the development of the digital economy relies on high-energy-consuming digital infrastructure, such as data centers and network equipment, which may offset its potential contributions to energy optimization in the short term. On the other hand, the promotion of clean energy and low-carbon technologies requires time, and the impact of the digital economy on energy structure adjustment may exhibit a certain lag. Additionally, disparities in digital economy development levels across regions may lead to uneven energy consumption reduction effects, making it difficult to observe a comprehensive impact.

The above measurement results are shown in Table 10.

4.8. Spatial Autocorrelation Test

Given the flow of factors and trade exchanges between countries, these interactions may influence each other’s development. Therefore, this paper employs a spatial econometric model to further analyze the spatial effects of the digital economy on economic and ecological integration. Before estimating the spatial effects, a spatial autocorrelation test is conducted on the digital economy and economic–ecological integration. Using the global Moran’s index, this study calculates the spatial correlation of the digital economy and economic–ecological integration across 100 selected countries from 2009 to 2022 under three different spatial weight matrices:

W1: Contiguity weight matrix;

W2: Nearest neighbor-based contiguity weight matrix;

W3: First-order inverse distance matrix.

The results show that the Moran’s index values for both the digital economy and economic–ecological integration are significantly positive during 2009–2022, indicating significant spatial autocorrelation. In other words, countries with higher levels of digital economy development and economic–ecological integration tend to have neighboring countries with similarly higher levels of digital economy development and economic–ecological integration. This confirms the appropriateness of using a spatial econometric model for further analysis.

4.9. Spatial Econometric Model Specification

To select the appropriate spatial econometric model, the following steps are undertaken: Under all three weight matrices, the Spatial Error Model (SEM) passes both the LM test and the robust LM test, whereas the Spatial Autoregressive Model (SAR) only passes the LM test but fails the robust LM test. To further confirm the choice of the model, the LR test is conducted. The results are significant at the 1% level, indicating that the Spatial Durbin Model (SDM) cannot be simplified to either the SAR or SEM. The results of the Hausman test suggest that a fixed-effects model should be used under all three weight matrices. Subsequently, spatial regressions of the digital economy and economic–ecological integration are conducted under three types of fixed effects: time-fixed effects, individual-fixed effects, and two-way fixed effects. The results indicate that the overall goodness-of-fit is highest under the time-fixed effects. Therefore, this paper ultimately adopts a time-fixed-effects Spatial Durbin Model (SDM) for estimation. The specified model is as follows:

SDM: DT_it = α + ρW·DT_it + βDE_it + θW·DE_it + ηW·X_it + μ_t + ε_it,(15)

where DT_it represents the level of economic and ecological integration of country i in year t, DE_it represents the level of digital economy development of country i in year t, ρ is the spatial autoregressive coefficient, θ is the spatial spillover coefficient, W is the spatial weight matrix, μ_t denotes the time-fixed effects, and ε_it is the error term.

4.10. Results of the Spatial Panel Econometric Model

Table 11 presents the estimation results of the spatial econometric model. First, the core explanatory variable, digital economy, has a significantly positive impact on economic and ecological integration at the 1% significance level. This indicates that the digital economy plays a positive role in promoting the integration of economic and ecological development. From the perspective of spatial interaction effects, the coefficient of the spatial interaction term for the digital economy is also significantly positive at the 1% level. This suggests that the impact of the digital economy on economic and ecological integration is also spatially significant. In other words, the advancement of the digital economy helps improve the levels of economic and ecological integration in neighboring countries or regions.

4.11. Coupling Coordination Analysis

The digital economy has promoted the integration of economic and ecological development, while the integration of economic and ecological development, in turn, has boosted the development of the digital economy. Both play an increasingly important role in driving the transformation of growth dynamics and advancing high-quality development, exerting a profound influence on promoting regional coordinated development. The results presented earlier have shown that the digital economy can positively impact the integration of economic and ecological development by driving industrial transformation and fostering technological innovation. Meanwhile, the integration of economic and ecological development balances ecological benefits and economic growth, which further supports the development of the digital economy. The two systems are mutually coordinated and mutually reinforcing.

Therefore, this paper employs a coupling coordination model to conduct an in-depth analysis of the synergy between the digital economy and the integration of economic and ecological development. Furthermore, it uses the Moran index to explore the spatial effects of the coupling coordination degree between the two systems.

The coupling coordination degree of the two systems is calculated as follows:

(16)$\mathrm{T}=\mathsf{\alpha }{\mathrm{U}}_1+\mathsf{\beta }{\mathrm{U}}_2,$

(17)$\mathrm{C}={\left[{\displaystyle \frac{{\mathrm{U}}_1\times {\mathrm{U}}_2}{{\prod }_{\mathrm{i}\ne \mathrm{j}}\left({\mathrm{U}}_1+{\mathrm{U}}_2\right)}}\right]}^{1/2},$

(18)$\mathrm{D}=\sqrt{\mathrm{C}\times \mathrm{T}},$

where T represents the comprehensive coordination index, U₁ is the comprehensive index of the digital economy level, U₂ is the comprehensive index of economic and ecological integration development, and α and β denote the weights of the digital economy and economic–ecological integration development, respectively. Since both are equally important, α = β = 0.5, C represents the coupling degree, and D represents the coupling coordination degree, where i, j = 1, 2 [51].

Based on this calculation, the coupling coordination degrees of the four continents—Asia, Europe, the Americas, and Africa—were obtained, as shown in Figure 1. From the perspective of temporal evolution, the coupling coordination degree across all continents exhibits a steady upward trend. However, the growth in coupling coordination degrees shows significant regional differences. Among them, Europe has the highest coupling coordination degree, followed by the Americas, with Asia ranking third. In recent years, Asia has shown a trend of surpassing the Americas, while Africa remains the lowest.

Europe’s higher level of economic development, mature digital technologies, and competitive digital infrastructure have significantly enhanced the efficiency of traditional industries’ digital transformation, reduced energy consumption, and promoted the integration of economic and ecological development. The experience of Europe is valuable for other countries to learn from. Europe has long provided strong policy support for ecological protection and economic development, such as reducing carbon emissions, advocating renewable energy, and encouraging green technological innovation. Such policy support has created a favorable environment for the coordination of digital economy development and economic–ecological balance. Europe also demonstrates a high level of innovation capability in both digital and green technologies. For example, smart energy management systems and clean energy technologies are typical cases of the integration of the digital economy with economic–ecological development, making it easier to achieve coordinated development. In addition, European residents and businesses exhibit a strong awareness and demand for environmental protection and sustainable development. Consumer demand for green products and services has, to some extent, further driven the integration of the digital economy with economic and ecological development.

This paper employs the Moran’s index (Moran’s I) under the adjacency weight matrix to examine whether the coupling coordination degree between the digital economy and economic–ecological integration development in the four continents exhibits geographical spatial correlation.

As shown in Table 12, from 2009 to 2022, the Moran’s index for the coupling coordination degree of Europe’s digital economy and economic–ecological integration development was consistently positive and passed the significance test. This indicates that the coupling coordination degree in Europe exhibits significant spatial correlation. The reason lies in Europe’s advanced economy and relatively mature integration of the digital economy with economic and ecological development. This coordination generates spillover effects that benefit neighboring regions.

In contrast, the Moran’s index for the Americas was not significant, indicating that the coupling coordination degree in this region lacks spatial correlation. In other words, the coupling coordination between the digital economy and economic–ecological integration in a given area does not influence neighboring regions. This may be due to the inclusion of both North and South American countries under the “Americas” category, where development levels vary greatly. As a result, the spatial correlation of the coupling coordination degree appears insignificant.

In Asia, the coupling coordination degree became significant starting in 2014. This may be attributed to Asia’s increasing emphasis on the digital economy in recent years. Additionally, as China, the world’s second-largest economy, enters a mature stage of digital economic development, it has exerted positive impacts on other regions in Asia.

For Africa, the Moran’s index was not significant. This could be attributed to factors such as Africa’s underdeveloped economy and lagging digital technologies.

5. Discussion

This study still has certain limitations.

First, these limitations are reflected in the definition of the connotation and extension of the digital economy. Since the digital economy is still evolving, the current indicator system is somewhat superficial and lacks comprehensive coverage and in-depth analysis. The digital economy indicators used currently do not sufficiently highlight their timeliness and variability, making it difficult to accurately reflect the rapidly changing characteristics of the digital economy. With advancements in technology and changes in the market environment, the definition and scope of the digital economy continue to evolve. In future research, the connotation of the digital economy may undergo further adjustments, which will directly impact the reliability and accuracy of the research results. Therefore, future studies must delve deeper into the connotation and extension of the digital economy and continuously update and refine the indicator system, so that research results become more comprehensive and accurate, thereby enhancing their academic value and policy guidance significance.

Second, when conducting the heterogeneity analysis, this study primarily categorizes countries based on income levels and the extent of digital economy development for comparison and research. While this classification method helps reveal the relationship between digital economy development and economic development across countries, it may overlook the unique characteristics and differences of some countries in specific areas. Therefore, future research should further deepen the heterogeneity analysis by employing more detailed classification criteria to uncover deeper influencing factors. For example, countries can be classified based on whether they are resource-rich, as resource-based countries may face different challenges and opportunities in digital economy development. Additionally, more refined classifications based on a country’s pillar industries, geographical location, innovation capabilities, and other factors would also help reveal the different development paths and effects of the digital economy in various economic structures and cultural contexts.

Finally, the impact of the digital economy may exhibit a duality. On the one hand, the digital economy, by promoting the application of digital processes and intelligent technologies, helps reduce energy consumption and carbon emissions, especially within traditional industries. Against the backdrop of rapid advancements in digital technologies, digital transformation has become an inevitable path for enterprises. Through the accumulation and application of data assets, as well as the deep application of digital technologies in production processes and supply chains, enterprises have driven quality transformations and efficiency improvements in traditional industries [52]. On the other hand, with the rapid development of the digital economy, numerous challenges have also emerged, such as the widening digital divide, ongoing pressures related to data security and privacy protection, and the potential monopoly risks associated with the platform economy [53]. Therefore, the dual impact of the digital economy serves as a reminder that while digital transformation helps drive sustainable economic and social development, it also brings new energy challenges. How to balance digital progress with energy consumption has become an important issue in future research and policy formulation.

6. Conclusions and Policy Recommendations

Based on panel data from 100 countries between 2009 and 2022, this study constructs an evaluation index system for the integration of digital economy and economic–ecological development. It measures the level of digital economy development and the degree of economic–ecological integration across these countries. This study empirically examines the impact of the digital economy on economic–ecological integration, analyzes the mechanisms and spatial effects, and evaluates the coupling coordination degree between the two. The main conclusions are as follows:

First, the benchmark regression results show that the digital economy promotes the integrated development of economy and ecology across countries. After conducting robustness and endogeneity tests, the conclusion remains consistent, further validating the positive role of the digital economy in advancing the coordinated development of economy and ecology. Additionally, the results of the heterogeneity analysis reveal significant differences in the role of the digital economy in promoting the integration of economy and ecology among countries with different income levels. The digital economy has a significant role in promoting the integration of economy and ecology in high-income and upper-middle-income countries, which typically have relatively advanced digital infrastructure, higher technological innovation capabilities, and existing policy frameworks and business practices in the green economy sector. The development of the digital economy in these countries can more effectively promote innovation in green technologies and industry upgrades, thus fostering a deeper integration of economic growth and ecological protection. However, for lower-middle-income and low-income countries, the impact of the digital economy on the integration of economy and ecology is not significant. This phenomenon may be attributed to the relatively weak infrastructure for digital economy development in these countries, as well as the slower pace of digital transformation, which limits the full potential of the digital economy in driving green development. These countries also face greater fiscal pressure and resource shortages, lacking sufficient financial support for green technology innovation and digital transformation. Moreover, there may be significant conflicts between environmental protection and economic development, with the introduction of the digital economy requiring more institutional guarantees and policy support to realize its full potential. Countries with a higher level of development in the digital economy show a more significant promotion of the integration of economy and ecology. Through strengthening digital infrastructure, driving green innovation, and optimizing resource allocation, these countries have made notable progress in achieving coordinated development between the economy and ecology.

Second, the results of the mechanism analysis indicate that the digital economy plays an important role in reducing energy consumption, improving the efficient use of clean energy, and reducing carbon emissions through industrial structure optimization and technological innovation, thereby promoting the integration of economy and ecology. The digital economy has facilitated the growth of emerging low-carbon industries, driving innovative changes in production models, supply chain management, and industrial structures, and creating new digital business models. This has effectively reduced resource and energy consumption, further decreased carbon emissions, and promoted the development of emerging economies. The digital economy has also improved the efficiency and quality of low-carbon technology research and development, accelerated breakthroughs and applications of low-carbon environmental protection technologies, and optimized technological innovation and scientific decision-making in energy, environmental, and climate-related carbon emissions fields using digital technologies [54]. Further analysis shows that digital innovation and digital trade have directly contributed to the coordinated development of the economy and ecology. Digital innovation can achieve a win–win situation for economic growth and ecological protection by improving production efficiency, reducing costs, and optimizing resource allocation. Digital trade, through promoting international trade in low-carbon products, has fostered the formation of global green supply chains, thereby advancing the coordinated development of the global economy and ecology. However, the ecological effects of digital talent and digital infrastructure depend more on indirect pathways or long-term accumulation, meaning that in the short term, these factors do not significantly contribute to the integration of the economy and ecology. The digital economy has a more direct impact on socio-economic and resource utilization, as the application of digital technologies enhances production efficiency, optimizes resource allocation, and drives industrial upgrading, enabling industries to use existing resources more efficiently. Through digital transformation, businesses can not only enhance their own economic benefits but also promote the efficient operation of the socio-economy by allocating resources more intelligently and positioning markets more precisely. Additionally, digital technologies provide support for the development of emerging industries, such as artificial intelligence, big data, and the Internet of Things. These technologies have not only driven the rise of high-tech industries but also accelerated the digital transformation of traditional industries. They have fundamentally changed the production, distribution, and consumption of products and services, thus enhancing the growth potential of the overall economy. However, the improvement in energy consumption due to the digital economy is more of a long-term latent effect. The digital economy, by promoting the innovation and application of green technologies, provides long-term technological support for reducing energy consumption. In the long run, this will effectively reduce energy waste and carbon emissions. Although the spread and application of these technologies may have limited immediate impact, as the technologies mature and markets gradually expand, they will have a profound impact on energy consumption.

Third, the results of the spatial effect analysis indicate that the digital economy not only promotes the integration of economy and ecology within its own country but also generates positive spillover effects to neighboring countries. These spillover effects improve the green performance of neighboring countries and help advance the integration of economy and ecology in these countries. The rapid development of the digital economy, through technological innovation, data sharing, and multinational cooperation in green industries, has provided neighboring countries with valuable successful experiences and practical models, thereby indirectly promoting the transformation of these countries’ green economies and their ecological sustainability. The analysis of the coupling coordination degree between regional digital economies and the integration of economy and ecology further reveals performance differences across regions. The results show that Europe exhibits the highest coupling coordination degree between the digital economy and the integration of economy and ecology, with significant spatial autocorrelation, indicating that Europe has developed a relatively complete regional coordination mechanism and policy system for promoting the integration of the digital economy and green ecology. The Americas, Asia, and Africa follow in the coupling coordination ranking. However, while the Americas and Africa have made some progress in the integration of the digital economy and ecology, the coupling coordination degree in these regions does not show significant spatial autocorrelation. This suggests that these regions have not yet formed a strong regional network or cooperation model for the coordinated development of the digital economy and ecology, leading to an uneven development process. As for Asia, although the coupling coordination degree between the digital economy and the integration of economy and ecology remained relatively stable at first, it has significantly improved since 2014. This increase is due to multiple factors, including the accelerated development of digital infrastructure in Asian countries, proactive government policies promoting green economic transitions, and the strengthening of cross-national and regional cooperation in sustainable development. As a result, Asia is gradually demonstrating stronger coordination and autocorrelation in the integration of the digital economy and ecology, becoming an important participant in the global green transition.

Based on the research conclusions, this paper proposes the following policy recommendations:

6.1. Strengthen the Development of the Digital Economy

Increase R&D Investment in Digital Technology: Governments should actively encourage innovation and technological breakthroughs to promote the widespread development and deep application of the digital economy.

Enhance the Cultivation and Recruitment of Digital Talent: Efforts should be made to expand related academic programs, provide more training opportunities, and actively attract international talent to meet the urgent demand for multi-level and multi-disciplinary expertise required by the digital economy.

Accelerate Digital Transformation Across Industries: Businesses and sectors should be encouraged to adopt advanced digital technologies to improve production efficiency, optimize management processes, and facilitate the comprehensive application of the digital economy on a broader scale.

6.2. Establish Mechanisms for International Cooperation to Foster Collaboration and Sharing in the Digital Economy

Promote Collaboration Among Digitally Advanced Countries: Countries with well-developed digital economies should strengthen technological collaboration to drive innovation and technology sharing. Joint research and collaborative innovation can enhance the level and scope of digital technologies, injecting fresh momentum into the global digital economy. In particular, digital technologies can provide innovative solutions and opportunities for collaboration in addressing climate change and advancing carbon market cooperation.

Support Less Digitally Developed Nations: Developed countries can assist less advanced nations in building digital infrastructure and fostering the development of their digital economies. This support can include financial assistance, technical aid, and talent training. Additionally, strengthening the exchange and collaboration of digital talent can help these nations cultivate and attract skilled professionals, narrow the digital divide, and achieve sustainable digital economic development.

Promote Policy Coordination and Sharing of Best Practices: Countries should enhance policy alignment and cooperation in the digital economy sector by sharing experiences and resources, ultimately fostering the internationalization and globalization of the digital economy.

6.3. Coordinate and Integrate Policies for Digital Economy and Economic–Ecological Integration

Avoid Policy Conflicts and Establish Unified Frameworks: Governments should actively avoid conflicts between policies and strive to create an integrated policy framework that effectively leverages the digital economy’s positive impact on economic–ecological integration.

Learn from European Best Practices: European countries have demonstrated significant success in integrating the digital economy and low-carbon transitions. Their policies on renewable energy, energy efficiency, and carbon markets can provide valuable lessons for other countries. By studying these examples, nations can better design innovative policies that promote synergy between the digital economy and economic–ecological integration.

Develop Tailored Policies to Maximize Synergistic Effects: By drawing on international best practices, countries can formulate more targeted policies that ensure alignment between the digital economy and economic–ecological integration. This will help maximize the linkage effects of these two domains, ultimately achieving a win–win scenario for sustainable economic development.

Author Contributions

Conceptualization, L.G.; methodology, L.G.; software, H.W.; validation, H.W.; formal analysis, L.G.; data curation, H.W.; writing—original draft, H.W.; writing—review and editing, H.W.; supervision, L.G. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article, and further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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.

Figure and Tables

Figure 1. Coupling coordination degrees across continents.

Table 1

Evaluation index system for economic–ecological integration.

Primary Indicators	Secondary Indicators	Specific Indicators	Indicator Attributes	Data Sources
Social Economy	Economic Development Status	GDP per capita	Positive	World Bank
	Urban Development Level	Urbanization rate	Positive	World Bank
	Industrial Structure	Share of service sector value-added in GDP	Positive	World Bank
Resource Utilization	Forest Resource Abundance	Forest coverage rate	Positive	World Bank
	Water Resource Abundance	Per capita renewable inland freshwater resources	Positive	World Bank
	Food Resource Abundance	Agricultural land as a percentage of total land	Positive	World Bank
Energy Consumption	Energy Supply and Demand Level	Energy self-sufficiency rate	Positive	World Bank
	Energy Consumption Structure	Share of renewable energy in total energy consumption	Positive	CEInet Statistics Database
	Energy Production Structure	Share of clean energy in total energy production	Positive	World Bank
	Energy Intensity	Energy consumption per unit of GDP	Negative	World Bank

Table 2

Evaluation index system for the digital economy.

Primary Indicators	Secondary Indicators	Tertiary Indicators	Indicator Attribute	Data Sources
Digital Talent	Digital Talent	Proportion of education expenditure in GDP	Positive	World Bank
		Proportion of R&D expenditure in GDP	Positive	World Bank
Digital Innovation	Digital Innovation Input	Number of patent applications	Positive	World Bank
	Digital Innovation Output	Number of published articles in scientific journals	Positive	World Bank
Digital Infrastructure	Digital Equipment	Number of fixed telephone users per 100 people	Positive	World Bank
		Number of mobile phone users per 100 people	Positive	World Bank
		Number of fixed broadband users per 100 people	Positive	World Bank
		Number of mobile broadband users per 100 people	Positive	ITU
	Network Equipment Quality	International internet bandwidth per capita	Positive	ITU
	Network Security	Number of secure internet servers	Positive	World Bank
	Internet Penetration Rate	Internet penetration rate	Positive	World Bank
Digital Trade Performance	Digital Services Trade	Value of digitally deliverable services trade	Positive	UNCTAD
		Proportion of digitally deliverable services trade in total services trade	Positive	UNCTAD
	Digital Goods Trade	Value of ICT goods trade	Positive	UNCTAD
		Proportion of ICT goods trade in total services trade	Positive	UNCTAD
		International market share of ICT goods trade	Positive	UNCTAD

Table 3

The benchmark regression results of the impact of digital economy on the integrated development of economy and ecology.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Variables	DT	DT	DT	DT	DT	DT	DT
de	0.1462 ***	0.1437 ***	0.1226 ***	0.1174 ***	0.1174 ***	0.1173 ***	0.1171 ***
	(4.95)	(4.77)	(4.21)	(4.24)	(4.25)	(4.25)	(4.31)
gdp		0.0032	0.0232 **	0.0253 **	0.0253 **	0.0253 **	0.0264 ***
		(0.30)	(2.01)	(2.40)	(2.41)	(2.41)	(2.65)
co2			−0.0169 ***	−0.0174 ***	−0.0174 ***	−0.0174 ***	−0.0174 ***
			(−3.73)	(−3.90)	(−3.89)	(−3.88)	(−4.01)
urb				−0.0013 *	−0.0014 *	−0.0014 *	−0.0013 **
				(−1.95)	(−1.96)	(−1.97)	(−2.00)
ex					0.00002	7.86 × 10−6	8.36 × 10−6
					(0.12)	(0.06)	(0.07)
fdi						−0.00002	−0.00002
						(−0.62)	(−0.61)
ind							−0.0005
							(−0.10)
Constant	0.3423 ***	0.2608	−0.0653	−0.1108	−0.1123	−0.1120	−0.1261
	(242.11)	(0.95)	(−0.24)	(−0.45)	(−0.45)	(−0.45)	(−0.59)
Observations	1400	1400	1400	1400	1400	1400	1400
R-squared	0.260	0.260	0.337	0.348	0.348	0.348	0.348

Notes: Numbers in parentheses are t-statistics; *** (p < 0.01), ** (p < 0.05), * (p < 0.1).

Table 4

Robustness test by replacing the core explanatory variable.

	(1)	(2)	(3)	(4)
Variables	DT	DT	DT	DT
Principal Component Analysis of Digital Economy	0.0422 *	0.0332 *
	(1.89)	(1.70)
TIMG			0.0006 *	0.0006 **
			(1.93)	(2.11)
gdp		0.0299 ***		0.0467 ***
		(2.84)		(3.07)
co2		−0.0186 ***		−0.0273 ***
		(−4.31)		(−6.18)
urb		−0.0013 *		−0.0017
		(−1.89)		(−1.45)
ex		5.28 × 10−6		−0.0001
		(0.04)		(−0.84)
fdi		−0.00001		−0.00001
		(−0.50)		(−0.65)
ind		−0.0004		−0.0051
		(−0.08)		(−0.98)
Constant	0.3345 ***	−0.2108	0.3339 ***	−0.4384
	(45.39)	(−0.95)	(22.07)	(−1.37)
Observations	1400	1400	765	765
R-squared	0.226	0.327	0.169	0.298

Notes: Numbers in parentheses are t-statistics; *** (p < 0.01), ** (p < 0.05), * (p < 0.1).

Table 5

Robustness tests with OLS and changing sample size.

	(1)	(2)	(3)	(4)
Variables	DT	DT	DT	DT
DE	0.3869 ***	0.1242 ***	0.1092 ***	0.1018 ***
	(8.92)	(4.84)	(2.98)	(3.00)
gdp		0.0437 ***		0.0077
		(10.18)		(0.48)
co2		−0.0522 ***		−0.0054
		(−23.26)		(−1.22)
urb		−0.0119 ***		−0.0009
		(−13.06)		(−1.44)
ex		0.0001		−0.00003
		(1.18)		(−0.25)
fdi		−0.0002 **		−0.00002
		(−2.14)		(−0.76)
ind		0.0099 **		0.0019
		(2.16)		(0.21)
Constant	0.3330 ***	−0.4366 ***	0.3493 ***	0.1671
	(123.95)	(−12.77)	(163.78)	(0.43)
Observations	1400	1400	700	700
R-squared	0.124	0.532	0.167	0.192

Notes: Numbers in parentheses are t-statistics; *** (p < 0.01), ** (p < 0.05).

Table 6

Robustness test of lagged core explanatory variables.

	(1)	(2)	(3)	(4)
Variables	DT	DT	DT	DT
L.DE	0.1661 ***	0.1379 ***
	(5.27)	(4.90)
L2.DE			0.1869 ***	0.1598 ***
			(5.51)	(5.36)
gdp		0.0257 **		0.0253 **
		(2.50)		(2.39)
co2		−0.0167 ***		−0.0153 ***
		(−3.93)		(−3.67)
urb		−0.0014 *		−0.0015 **
		(−1.89)		(−2.03)
ex		0.00001		−2.30 × 10−6
		(0.10)		(−0.02)
fdi		−0.00002		−0.00001
		(−0.60)		(−0.56)
ind		−0.0004		−0.0002
		(−0.08)		(−0.03)
Constant	0.3417 ***	−0.1202	0.3420 ***	−0.1310
	(241.07)	(−0.54)	(237.63)	(−0.56)
Observations	1300	1300	1200	1200
R-squared	0.255	0.341	0.244	0.322

Notes: Numbers in parentheses are t-statistics; *** (p < 0.01), ** (p < 0.05), * (p < 0.1).

Table 7

Endogenous analysis of the impact of digital economy on the integrated development of economy and ecology.

	(1)	(2)	(3)	(4)
Variables	DE	DT	DT	DT
fixed telephone × L.DE	0.0123 ***
	(5.55)
de		0.1376 ***	0.1391 ***	0.1394 ***
		(5.08)	(5.17)	(5.22)
gdp	0.0809 *	0.0224 ***	0.0241 ***	0.0249 ***
	(1.71)	(3.77)	(4.21)	(4.29)
co2	−0.0069	−0.0167 ***	−0.0171 ***	−0.0171 ***
	(−1.22)	(−6.42)	(−6.58)	(−6.69)
urb	−0.0017 *		−0.0013 ***	−0.0013 ***
	(−1.92)		(−3.48)	(−3.51)
ex	−0.0001		0.00001	7.67 × 10−6
	(−0.35)		(0.26)	(0.14)
fdi	−0.0000			−0.00001
	(−0.13)			(−0.84)
ind	−0.0089			−0.0004
	(−1.19)			(−0.15)
Constant	−1.7185 *	−0.0261	−0.0611	−0.0698
	(−1.73)	(−0.21)	(−0.51)	(−0.65)
Observations	1400	1400	1400	1400
R-squared	0.504	0.982	0.983	0.983
Kleibergen–Paap rk LM statistic		9.028
		[0.0027]
Kleibergen–Paap rk Wald F statistic		41.711
		{16.38}

Notes: Values in parentheses are t-statistics; *** (p < 0.01) * (p < 0.1); values in square brackets represent the p-values for the under-identification test; values in curly brackets represent the critical values for the weak instrument test at the 10% significance level.

Table 8

Heterogeneity analysis of the impact of digital economy on the integrated development of economy and ecology.

	(1)	(2)	(3)	(4)	(5)
Variables	High-Income Countries	Upper-Middle-Income Countries	Lower-Middle-Income Countries	Low-Income Countries	DT
de	0.0826 **	0.1604 ***	0.0509	1.4429	0.0502 **
	(2.33)	(6.36)	(0.61)	(2.07)	(2.04)
de × high					0.0652 ***
					(3.27)
gdp	0.0533 ***	0.0372 **	−0.0264	0.0552 *	0.0271 ***
	(3.62)	(2.08)	(−1.43)	(2.45)	(5.63)
co2	−0.0183 ***	−0.0075	−0.0139	−0.0121 ***	−0.0169 ***
	(−2.82)	(−1.53)	(−1.67)	(−6.23)	(−12.09)
urb	−0.0007	−0.0035	−0.0008	0.0039	−0.0013 ***
	(−1.26)	(−1.51)	(−0.46)	(0.79)	(−4.38)
ex	−0.0000	−0.0001	0.0000	0.0003	−6.57 × 10−6
	(−0.07)	(−0.26)	(0.09)	(2.27)	(−0.16)
fdi	−0.0000	0.0001	−0.0000	0.0004	−0.00001
	(−0.26)	(0.15)	(−0.39)	(1.85)	(−0.78)
ind	−0.0154 *	−0.0154 **	0.0112	0.0081	−0.0005
	(−1.96)	(−2.36)	(1.56)	(1.27)	(−0.29)
Constant	−0.4283	−0.1424	0.8365 **	−1.1618 *	−0.1491
	(−1.34)	(−0.32)	(2.42)	(−2.76)	(−1.60)
Observations	602	420	322	56	1400
R-squared	0.481	0.439	0.227	0.847	0.983

Notes: Numbers in parentheses are t-statistics; *** (p < 0.01), ** (p < 0.05), * (p < 0.1).

Table 9

Mechanism verification of the impact of digital economy on the integrated development of economy and ecology.

	(1)	(2)	(3)	(4)
Variables	Ser	DT	Htech	DT
de	0.7813 **		6.5219 *
	(2.42)		(1.76)
ser		0.0281 ***
		(5.48)
htech				0.0092 ***
				(7.78)
gdp	0.2975	0.0245 **	0.2244	0.0282 ***
	(1.16)	(2.11)	(0.90)	(2.75)
co2	−0.0538	−0.0172 ***	0.0646	−0.0188 ***
	(−1.01)	(−4.68)	(1.34)	(−4.33)
urb	−0.0220 **	−0.0009	−0.0016	−0.0014 **
	(−2.36)	(−1.61)	(−0.27)	(−2.14)
ex	−0.0064 ***	0.0002 *	−0.0001	0.00001
	(−4.13)	(1.71)	(−0.05)	(0.09)
fdi	−0.0003 *	−6.44 × 10−6	0.0002	−0.00002
	(−1.92)	(−0.29)	(1.62)	(−0.69)
ind	0.5412 ***	−0.0163 ***	−0.0368	−0.0006
	(3.35)	(−2.98)	(−0.76)	(−0.11)
Constant	5.0416	−0.3994 *	−5.6108	−0.1532
	(1.35)	(−1.70)	(−1.07)	(−0.70)
Observations	1400	1400	1400	1400
R-squared	0.759	0.398	0.424	0.338

Notes: Numbers in parentheses are t-statistics; *** (p < 0.01), ** (p < 0.05), * (p < 0.1).

Table 10

Expansion analysis of the impact of digital economy on the integrated development of economy and ecology.

	(1)	(2)	(3)	(4)
Variables	DT	DT1	DT2	DT3
de		0.1989 ***	0.0312 **	0.1213
		(5.13)	(2.11)	(1.65)
talent	−0.1618
	(−0.33)
innovate	0.1578 ***
	(5.05)
infrastructure	0.0149
	(0.33)
trade	0.2485 ***
	(3.01)
gdp	0.0233 **	0.0343	0.0355 ***	0.0094
	(2.29)	(1.29)	(2.88)	(0.39)
co2	−0.0166 ***	−0.0080	−0.0048	−0.0395 ***
	(−3.77)	(−1.62)	(−1.25)	(−4.19)
urb	−0.0013 **	−0.0025 ***	−0.0006	−0.0009
	(−2.08)	(−2.92)	(−1.31)	(−0.55)
ex	−0.00002	−0.0004 ***	0.0002	0.0003
	(−0.14)	(−2.82)	(1.58)	(0.92)
fdi	−0.00001	−0.00004	−0.00001	7.58 × 10−6
	(−0.50)	(−0.78)	(−0.90)	(0.21)
ind	−0.0006	−0.0046	−0.0058 *	0.0088
	(−0.12)	(−0.32)	(−1.96)	(0.82)
Constant	−0.0511	−0.2588	−0.4176	0.2979
	(−0.23)	(−0.63)	(−1.48)	(0.57)
Observations	1400	1400	1400	1400
R-squared	0.361	0.472	0.149	0.174

Notes: Numbers in parentheses are t-statistics; *** (p < 0.01), ** (p < 0.05), * (p < 0.1).

Table 11

The spatial regression results of the impact of digital economy on the integrated development of economy and ecology.

	W1		W2
Variables	Main	Wx	Main	Wx
DE	0.1995 ***	0.6880 ***	0.1572 ***	0.6620 ***
	(7.46)	(4.62)	(5.72)	(2.60)
gdp	0.0337 ***	0.0464 ***	0.0313 ***	−0.0191
	(9.74)	(2.58)	(9.15)	(−1.09)
co2	−0.0455 ***	−0.0255 ***	−0.0456 ***	−0.0296 ***
	(−24.55)	(−3.18)	(−24.67)	(−3.68)
urb	−0.0088 ***	−0.0047 *	−0.0074 ***	−0.0117 ***
	(−10.83)	(−1.71)	(−8.75)	(−4.47)
ex	0.00003	−0.0009 ***	−0.0001 ***	−0.0010 ***
	(0.67)	(−5.04)	(−3.01)	(−6.11)
fdi	−0.0002 ***	−0.0011 ***	−0.0001 **	−0.0008 ***
	(−3.46)	(−2.61)	(−2.50)	(−2.75)
ind	0.0115 ***	−0.0159	0.0139 ***	0.0657 ***
	(3.45)	(−0.85)	(4.26)	(3.73)
rho	0.1177		0.0187
	(1.45)		(0.21)
sigma2_e	0.0016 ***		0.0015 ***
	(26.42)		(26.45)
Observations	1400	1400	1400	1400
R-squared	0.522	0.522	0.559	0.559
	W3
Variables	Main		Wx
DE	0.1354 ***		1.1602 ***
	(4.96)		(4.65)
gdp	0.0311 ***		−0.0031
	(8.69)		(−0.11)
co2	−0.0449 ***		−0.0509 ***
	(−23.91)		(−4.17)
urb	−0.0087 ***		−0.0102 **
	(−9.96)		(−2.44)
ex	8.40 × 10−6		−0.0010 ***
	(0.20)		(−4.52)
fdi	−0.0002 ***		−0.0017 ***
	(−2.98)		(−3.31)
ind	0.0139 ***		0.0446
	(4.10)		(1.56)
rho	0.2803 **
	(2.47)
sigma2_e	0.0016 ***
	(26.42)
Observations	1400		1400
R-squared	0.397		0.397

Notes: Numbers in parentheses are z-statistics; *** (p < 0.01), ** (p < 0.05), * (p < 0.1).

Table 12

Moran’s index test for coupling coordination degree among states.

	Asia		Europe		Americas		Africa
Year	I	p-Value	I	p-Value	I	p-Value	I	p-Value
2009	−0.005	0.239	0.195	0.000	0.025	0.094	−0.066	0.443
2010	0.009	0.158	0.193	0.000	0.022	0.107	−0.054	0.385
2011	0.010	0.153	0.189	0.000	0.011	0.142	−0.004	0.181
2012	0.013	0.136	0.186	0.000	0.004	0.171	−0.041	0.322
2013	0.004	0.184	0.189	0.000	0.009	0.150	0.013	0.130
2014	0.022	0.098	0.191	0.000	−0.001	0.192	0.010	0.139
2015	0.026	0.081	0.185	0.000	−0.008	0.221	0.015	0.124
2016	0.033	0.061	0.189	0.000	−0.005	0.205	0.012	0.131
2017	0.040	0.043	0.186	0.000	−0.004	0.199	−0.012	0.207
2018	0.041	0.040	0.185	0.000	−0.004	0.192	−0.014	0.216
2019	0.039	0.045	0.178	0.000	0.000	0.173	−0.006	0.190
2020	0.045	0.033	0.172	0.000	−0.001	0.170	−0.029	0.275
2021	0.049	0.026	0.169	0.000	0.003	0.154	−0.037	0.310
2022	0.033	0.058	0.164	0.000	0.000	0.168	−0.042	0.331

Appendix A

1. List of 100 countries:

Asian countries: United Arab Emirates (ARE), Armenia (ARM), Azerbaijan (AZE), China (CHN), Georgia (GEO), Indonesia (IDN), India (IND), Iran (Islamic Republic of) (IRN), Iraq (IRQ), Israel (ISR), Japan (JPN), Kazakhstan (KAZ), Kyrgyzstan (KGZ), Cambodia (KHM), Republic of Korea (KOR), Kuwait (KWT), Sri Lanka (LKA), Mongolia (MNG), Malaysia (MYS), Nepal (NPL), Oman (OMN), Pakistan (PAK), Philippines (PHL), Singapore (SGP), Thailand (THA), Tajikistan (TJK), Uzbekistan (UZB), Vietnam (VNM)

European countries: Albania (ALB), Austria (AUT) Belgium (BEL), Bulgaria (BGR), Belarus (BLR), Cyprus (CYP), Czech Republic (CZE), Germany (DEU), Denmark (DNK), Spain (ESP), Estonia (EST), Finland (FIN), France (FRA), Britain (GBR), Greece (GRC), Croatia (HRV), Hungary (HUN), Ireland (IRL), Iceland (ISL), Italy (ITA), Lithuania (LTU), Luxembourg (LUX), Latvia (LVA), Moldova (MDA), Malta (MLT), Netherlands (NLD), Norway (NOR), Poland (POL), Portugal (PRT), Romania (ROU), Russian Federation (RUS), Serbia (SRB), Slovak Republic (SVK), Slovenia (SVN), Sweden (SWE), Türkiye (TUR) Ukraine (UKR)

American countries: Argentina (ARG), Bolivia (BOL), Brazil (BRA), Canada (CAN), Chile (CHL), Colombia (COL), Costa Rica (CRI), Ecuador (ECU), Guatemala (GTM), Honduras (HND), Jamaica (JAM), Mexico (MEX), Nicaragua (NIC), Panama (PAN), Peru (PER), Paraguay (PRY), El Salvador (SLV), Uruguay (URS), United States (USA)

African countries: Botswana (BWA), Congo (DRC) (COD), Algeria (DZA), the Arab Republic of Egypt (EGY), Ethiopia (ETH), Ghana (GHA), Kenya (KEN), Morocco (MAR), Mozambique (MOZ), Mauritius (MUS), Namibia (NAM), Sudan (SDN), Tunisia (TUN), Zambia (ZMB)

Oceania countries: Australia (AUS), New Zealand (NZL)

• 2.

  Data Source

World Bank: https://data.worldbank.org.cn/ (accessed on 1 November 2023)

ITU: https://datahub.itu.int/data/?e=CHN&i=11632&u=per+100+people (accessed on 5 November 2023)

UNCTAD: https://unctadstat.unctad.org/datacentre/ (accessed on 5 November 2023)

CEInet Statistics Database: https://ceidata.cei.cn/new/ (accessed on 11 November 2023).

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