Has the Digital Economy Affected the Status of a Country’s Energy Trade Network?

Abstract

The current global energy trade network is changing dramatically: the essence of the change is digitalization and renewable energy, and the study of the impact of the digital economy on the changing status of a country’s energy trade network is of great practical significance for each country to ensure energy security. We find that: the digital economy has a direct impact on a country’s climbing position in the fossil energy trade network and the renewable energy trade network. The high technology attributes of the digital economy lead to the rise of energy trading networks in energy-exporting countries by affecting their energy production efficiency and product competitiveness; the high energy-consuming attributes of the digital economy have an indirect effect by affecting the energy demand of energy-importing countries. The digital economy has a positive and increasing marginal nonlinear effect on the change in the status of renewable energy trade networks, and a positive but decreasing marginal nonlinear effect on the climbing status of fossil energy trade networks. This paper confirms the impact of the digital economy on a country’s changing position in the global energy trade network and provides policy insights for each country to ensure energy security.

1. Introduction and Review of the Literature

The geographical dependence of fossil energy and the basic characteristics of supporting economic operation make the spatial distribution of fossil energy endowment and fossil energy consumption different, and the cross-regional flow of fossil energy becomes inevitable [1,2]. Renewable energy sources are widely distributed, but their application depends on various types of renewable energy equipment, the manufacture of which depends on the industrial system of a country. For countries with incomplete industrial systems, they are unable to produce a large number of manufactured renewable energy products, such as optical components, electric motor parts, power generation equipment, gears, hydraulic turbines, etc., which leads to international interaction of renewable energy [3].

With the development of economic globalization, innovations in transportation, the Internet, and other technologies have made possible the long-distance, large-scale, and uninterrupted flow of energy, gradually forming a global energy trade network that regulates supply and demand [4]. The current global energy trade network is undergoing drastic changes. On the one hand, fossil energy trade networks and renewable energy trade networks coexist and are flourishing [5]. First, after the industrial revolution with conventional oil and gas energy as the main development driver, the success of the shale gas revolution in the United States in 2013 made the influence of unconventional oil and gas resources grow with each passing day, and production gradually shifted westward [6]; the rapid economic growth in China, India, and Southeast Asia has driven the global energy consumption center eastward [7]. The fossil energy trade network is being reshaped. Second, the transformation of energy structure into low-carbon has accelerated, and the renewable energy system with “green power” as the core is gradually separated from fossil energy [3]. On the other hand, the digital economy represents a technological revolution that can effectively respond to the pressure of the new pneumonia epidemic and global environmental changes, so countries continue to promote the digital upgrading process in various industries [8]. Digital energy platforms, energy internet, artificial intelligence, and other modern digital technologies are deeply integrated with the energy industry, making energy open to intelligent and flexible regulation and real-time interaction between supply and demand. The digital economy has become an important way to support countries’ energy transformation and build an energy power country [9]. In the context of dramatic changes in the global energy trade network, the position and power of different countries in it are changing rapidly, and it is possible to achieve “overtaking”. As a result, a realistic question to ponder is whether the digital economy has significantly contributed to the rise of a country’s position in the global energy trade network.

The current research on global energy trade networks mainly focuses on fossil energy. He et al. [10] pointed out that the fossil energy trade network is influenced by geographical distance, institutional differences, history and culture and political relations, and there are four major energy groups, namely, the United States, Europe–Russia, East Asia–Southeast Asia, and Australia–India–Africa, with obvious small-world characteristics and energy. The density of energy trade networks shows a wave-like upward trend [11]; and the density of energy trade networks has shown a wave-like upward trend. A large number of studies have concluded that the fossil energy trade network is undergoing dramatic changes, with the Asian region showing an overall increase in energy imports and an increased voice in the oil and gas pricing system [12]. Among them, China maintains a strong trend of global expansion in the crude oil power space, with increasing power of influence over resource endowment, resource consumption, geo-access, and other energy type countries [13]. In the study of digital and energy, most scholars believe that there is a positive “income effect” and a negative “substitution effect” on the impact of digital economy on energy consumption, and digital economy’s “substitution effect “is greater than “income effect” [14]. The “substitution effect” is greater than the “income effect”. The main reason is that digital energy is essentially the development of industry on the basis of digital technology [15]. The digital economy provides technological information products to the energy sector, logistics sector, and industrial sector to improve energy efficiency [16]. However, the expansion of digital application scenarios has led to an exponential growth of digital business [17], and the large amount of digital computing, storage, and transmission has led to an exponential growth of energy consumption. Therefore, the high energy-consuming attributes of the digital economy make its requirements for energy supply stability and adequacy increase, and the digital economy has become a key factor influencing the change of the energy trade network.

Throughout the current research, most scholars have discussed the influence of geographic distance, economic development level, and political system on fossil energy trade network, but not enough research has been done to observe global energy trade networks from the perspective of the digital economy. Under the pressure of environmental protection, the high energy-consuming properties of the digital economy make it increasingly important to study renewable energy trade networks. Compared with the existing literature, the marginal contribution of this paper is as follows: It explores the changing position of a country in the fossil energy trade network and renewable energy trade network from the perspective of digital economy, and expands the scope of the study of the global energy trade network. It also explores the indirect and nonlinear effects of the digital economy on the changing positions of energy-exporting and energy-importing countries in the global energy trade network, and provides theoretical support for a deeper understanding of the changes in the global energy trade network.

2. Mechanisms of Interaction between the Digital Economy and the Changing Status of a Country’s Global Energy Trade Network

Current production life is highly dependent on digital devices, almost all of which are powered by electricity and run 24 h a day. Data centers that centrally store and process data consume more energy and are called “non-smoking steel plants”. The digital economy has high energy consumption properties [18]. Moreover, the digital economy represents the most advanced direction of productivity development, and its development is accompanied by a large number of technological innovations, and its application is constantly improving the technology level of various industries. Therefore, the digital economy has high technology attributes [19]. The two attributes of the digital economy have an impact on the changing status of the global energy trade network in two ways.

Chanel 1: Direct transmission mechanism. The integrated application of the digital economy to energy areas is a reinvention of new ideas, new thoughts, new principles, new methods, new tools, and new means [20]. The deep integration of digital and energy can directly promote the development of renewable energy sources such as distributed photovoltaic industry, small hydropower industry, and the supply of unconventional oil and gas resources, increasing the overall strength of a country’s energy production and consumption. In addition, the penetration of digital thinking can strengthen the direct interaction between energy supply and demand, reduce the information barriers in the process of energy import and export, and directly secure energy supply [21,22]. Weaken the information advantage of countries with high intermediary centrality, stimulate the effectiveness of dialogue between the two ends of energy import and export, and thus influence the change in the status of energy trade networks.

Hypothesis 1: 

Digital economy development can directly enhance a country’s position in the global energy trade network.

Channel 2: Indirect transmission mechanism. The high-technology attributes of the digital economy improve energy-exporting countries’ efficiency of energy production and the competitiveness of energy products [23], leading to an increase in energy exports from exporting countries and accelerating the climb of their energy trade network position. The widespread use of digital technologies has increased energy productivity by 3–5% and the potential for cost improvements by 25% [24]. In addition, according to international trade theory, energy products with lower prices and higher technological content and quality have comparative advantages in international trade and help improve a country’s position in the energy trade network. In addition, the high technology attribute of digital economy can improve the quality of new energy basic data and the intelligence level of prediction modeling, so as to establish a high precision and high credibility renewable energy power prediction system, which helps the power system to adapt to the random volatility of renewable energy dispatch operation level and risk defense ability, and thus increases the production and application of renewable energy [25].

The energy-intensive attributes of the digital economy directly increase the energy demand of energy-importing countries. Every 1% increase in digital infrastructure will result in a 0.56% increase in direct energy consumption levels, and every 1% increase in Internet users will result in a 0.026% increase in per capita electricity consumption [26]. The energy consumption is expected to increase by 8–21% in 2030 [27]. In addition, the high technology attributes of the digital economy will increase indirect energy consumption [28]. First, at the current level of technology, the reduction in total energy cannot be fully offset by additional physical capital, and less energy supply will reduce the level of development of the digital economy [29]. Second, at the technological level, decoupling digital economic development from energy consumption is more difficult than decoupling technological growth from energy consumption [30]. Finally, the energy demand of new products, new models, and new businesses created by digital economy applications offset the energy saving effect brought by technological progress. Overall, digital technology applications show an inverted “U” shape relationship to energy consumption [31]. While the “substitution effect” was dominant in the early years of digital technology development, the “income effect” is now much larger than the “substitution effect”, and the development of digital technology will significantly promote the use of energy. Therefore, the development of the digital economy in importing countries will generate greater demand for energy imports and increase energy interactions between importing countries and other countries, which will lead to an increase in the bargaining power of energy importing countries and contribute to the rise of their energy trade network status.

Hypothesis 2: 

Digital economy development in energy exporting and importing countries can indirectly drive the climb in the status of global energy trade networks by increasing energy production efficiency, energy product competitiveness, and energy demand.

Channel 3: Nonlinear transmission mechanism. With the widespread use of the Internet, users such as individuals, institutions, and enterprises are generating over 200 ZB of digital resources on the C-terminus and B-terminus, and will produce over 700 ZB of data volume in 2030. The exponential increase in data resources leads to a surge in demand for processing and application of various data elements, which leads to an exponential expansion of energy demand. At the same time, the expansion of energy demand in importing countries will pull energy exporting countries to increase more energy exports based on meeting their own energy demand, so that the digital economy shows a nonlinear growth on the change of energy trade network status.

In addition, renewable energy has natural pro-digital economy characteristics compared to fossil energy. First, the widely distributed, abundant, and clean characteristics of renewable energy [32] are the preferred energy sources to support the operation of the digital economy. With the massive promotion and application of new technologies such as artificial intelligence and the Internet of Things (IOT) causing the data volume to skyrocket, the transformation of disordered data into ordered information brings challenges to energy supply and carbon emissions, and the transformation of energy from high to low carbon becomes an objective requirement for social development [33]. Second, renewable energy generation is intermittent in nature [34]. Renewable energy depends on weather and climate conditions; only through better integration of digital technology with energy production, demand, application, energy storage system, user energy habits, etc. can we achieve an adequate, sustainable, and effective supply of energy. Finally, the distributed and decentralized nature of renewable energy requires the use of multiple digital technologies applied to every link from power generation, transmission, and distribution to electricity consumption, and real-time monitoring, analysis, reporting, and optimization processing of these links, which in turn enables effective information communication between energy supply and demand. This pro-digital characteristic makes the development of digital economy more dependent on renewable energy [35], so the higher the level of digital economy development, the more obvious the climb of renewable energy trade network status, and the “marginal effect” of increasing holds. In contrast, due to the scarcity, inefficiency, and high pollution of fossil energy, the energy consumption of the digital economy also shows an exponential growth, but this growth is nonlinear with decreasing marginal effect. Based on these, hypothesis 3 is proposed.

Hypothesis 3: 

The energy consumption of the digital economy shows a nonlinear growth, and its pro-renewable energy characteristics make it have a nonlinear effect of increasing positive marginal effect on the change of renewable energy trade network status, and a nonlinear effect of decreasing positive marginal effect on the climb of fossil energy trade network status.

3. Theoretical Model

3.1. Model Construction

Theoretical reasoning suggests that the development of digital economy will have an impact on a country’s position in the global energy trade network; therefore, our basic econometric model is established based on the energy import and export balances of 181 countries divided into energy importers and energy exporters from 2011 to 2020:

$${\mathit{EN}}_{\mathit{it}}={\beta }_0+{\beta }_1{\mathit{DES}}_{\mathit{it}}+{\beta }_2{X}_{\mathit{it}}+{\upsilon }_t+{\mu }_i+{\epsilon }_{\mathit{it}}$$

(1)

The dependent variable ${\mathit{EN}}_{\mathit{it}}$ is the energy trade network centrality of the country $i$ in the year $t$, which includes fossil energy trade network centrality and renewable energy trade network centrality, representing a country’s position in the global energy trade network. The core explanatory variable ${\mathit{DES}}_{\mathit{it}}$ is the level of digital economy development of the country $i$ in the year $t$, and the magnitude and direction of the coefficient reflect its influence on the position of a country in the energy trade network. $X_{\mathit{it}}$ is the control variable. ${\upsilon }_t$ is a time fixed effect, where changes in the sample time that have a common impact on all countries will be absorbed by ${\upsilon }_t$. ${\mu }_i$ is a country fixed effect, controlling for time-varying heterogeneity due to country differences. ${\epsilon }_{\mathit{it}}$ is a random error term.

Due to the high energy-consuming nature of the digital economy, the rapid development of the digital economy in energy-importing countries leads to a significant increase in energy demand, resulting in a large amount of energy imports, which increases the bargaining power of energy-importing countries and makes their energy trade network status rise. At the same time, the development of digital economy has high technology attributes, and the development of digital economy in energy-exporting countries has led to the improvement of energy production efficiency and competitiveness of energy products, which has enhanced the quantity and quality of their energy exports and led to the rise of the energy trade network status:

$$M_{\mathit{it}}={\alpha }_0+{\alpha }_1{\mathit{DES}}_{\mathit{it}}+{\alpha }_2{X}_{\mathit{it}}+{\upsilon }_t+{\mu }_i+{ϵ}_{\mathit{it}}$$

(2)

$${\mathit{EN}}_{\mathit{it}}={\lambda }_0+{\lambda }_1{\mathit{DES}}_{\mathit{it}}+{\lambda }_2{M}_{\mathit{it}}+{\lambda }_3{X}_{\mathit{it}}+{\upsilon }_t+{\mu }_i+{ϵ}_{\mathit{it}}$$

(3)

where $M_{\mathit{it}}$ denotes the mediating variables, which are energy production efficiency, energy product competitiveness, and energy demand in year t for exporting or importing country i, respectively.

Digital economy industries are characterized by high energy-consuming properties and have nonlinear characteristics on the growth of energy demand in both energy-exporting and energy-importing countries. Does the growth of energy demand in energy-importing countries have a nonlinear effect on the change in the status of their energy trade networks? Does the growth of domestic energy demand in energy-exporting countries lead to a decrease in their energy exports, or does the significant increase in energy production due to the application of digital technologies both meet their energy demand and allow for significant exports to other countries? To investigate whether there is a nonlinear spillover of the change in the status of the global energy trade network due to the energy consumption of the digital economy, a threshold panel regression model is developed:

$$\begin{array}{cc}{\mathit{EN}}_{\mathit{it}}={\delta }_0+& {\delta }_1{\mathit{DES}}_{\mathit{it}}\cdot I(\mathit{DES}\\ & \le {\gamma }_1)+{\delta }_2{\mathit{DES}}_{\mathit{it}}\cdot I(\mathit{DES}>{\gamma }_1)+{\delta }_3{X}_{\mathit{it}}+{\upsilon }_t+{\mu }_i+{\sigma }_i+{ϵ}_{\mathit{it}}\end{array}$$

(4)

where ${\mathit{DES}}_{\mathit{it}}$ is both the core explanatory variable and the threshold variable, $\gamma$, is the threshold value to be estimated, and the other variables are defined in the same way as (1). Considering the possibility of multiple thresholds in the sample, the model is extended:

$$\begin{array}{cc}{\mathit{EN}}_{\mathit{it}}={\vartheta }_0+& {\vartheta }_1{\mathit{DES}}_{\mathit{it}}\cdot I(\mathit{DES}\\ & \le {\gamma }_1)+{\vartheta }_2{\mathit{DES}}_{\mathit{it}}\cdot I(\mathit{DES}>{\gamma }_1)+\cdot \cdot \cdot +{\vartheta }_n{\mathit{DES}}_{\mathit{it}}\cdot I(\mathit{DES}\\ & \le {\gamma }_n)+{\vartheta }_{n+1}{\mathit{DES}}_{\mathit{it}}\cdot I(\mathit{DES}>{\gamma }_n)+{\vartheta }_c{X}_{\mathit{it}}+{\upsilon }_t+{\mu }_i+{\sigma }_i+{ϵ}_{\mathit{it}}\end{array}$$

(5)

3.2. Variable Explanation and Description

Dependent variables include: fossil energy trade network centrality and renewable energy trade network centrality [36]. Intensive energy interactions between countries allow countries to obtain more direct and effective information about energy prices, energy product quality, energy production, etc., which increases the control over energy trade networks and directly expresses the power position of countries in energy trade networks. Meanwhile, the current energy interactions can be divided into two types: one in the field of fossil energy and the other in the field of renewable energy products. Thus, coal 2701, oil 2709, and natural gas 271111 and 271112 are chosen for fossil energy sources, and photovoltaic products, wind energy products, and hydro energy products are chosen for renewable energy sources with HS codes 841280, 850239, 850440, 854140, 900190, 900290, 901380; 841290, 850231 850300, 853710, 853720, 890790, 902830, 903020, 903031, 903039, 903289; 841011, 841012, 841013, 841090, 850161, 850162, 850434, 850163, 850164 850421, 850422, 850423, 850431, 850432, 850433, and data from Un Comtrade. We used Python software to transform the data into a 181 × 181 matrix by matching and summing the data by year, exporting country, and reporting country, based on $C_d\left(v_i\right)=\frac{{\sum }_{i=1, i\ne j}^n{d}_{\mathrm{ij}}}{n-1}$ ($d_{\mathrm{ij}}$ denotes a country’s energy trade links with other countries and n is 181 countries.) to calculate the degree centrality to portray a country’s position in the fossil energy trade network and the renewable energy trade network [37].

Explanatory and threshold variables include the level of development of digital economy. The digital economy is a new type of economy that reconfigures resources through modern information networks and realizes the effective matching of supply and demand in the whole process. The measurement of its development level is a complex system engineering, and a single indicator can only reflect the partial facts of the development of digital economy and cannot objectively reveal its true level. Therefore, combining the actual situation of digital economy development and the principles of data availability and reliability, the evaluation index system of the development level of digital economy is constructed by referring to the research of Zhang [38] and Qi [39], which contains four primary indicators, digital infrastructure, digital industry development, digital industry application, and digital innovation capability. The specific indicators are fixed broadband utilization rate, mobile network utilization rate, Internet user rate, computer, communication service export, ICT product export, ICT application and government service efficiency, ICT legal environment, digital patent application and science and technology journals, and the data are obtained from the World Bank WDI database and World Economic Forum WEF.

The hot card filling method was used for missing data, i.e., missing values were filled against countries with similar economic levels. The 11 indicators were standardized and categorized using SPSS26 factor analysis. Kmo = 0.784 > 0.7, Bartlett’s test of sphericity significance = 0.000, which means it rejects the original hypothesis, and the individual indicators can be assigned weights using factor analysis. The extracted loadings squared and percentage of variance of four principal components cumulatively occupied 84.439%, and the factor weights were calculated.

Mediating variables: According to the previous theoretical analysis, energy production efficiency and energy product quality are chosen as mediating variables for energy exporting countries. The quantity of energy consumption is chosen to represent energy demand for energy importing countries. The energy production efficiency of exporting countries is measured by the non-radial energy efficiency method because the energy industry minimizes labor input, fixed asset input and non-desired output exhaust emissions, and the desired output energy industry maximizes GDP [40]. Energy product quality is measured using RCA explicit comparative advantage [41].

$$\mathrm{RCA}=\frac{E_i/E_t}{W_i/W_t}.$$

(6)

Control variables: First, energy endowment is an important factor affecting a country’s position in the global energy trade network, so oil, natural gas, and coal reserves are selected as fossil energy endowment (HS) [10]. The availability of a complete industrial base is a key factor affecting the making of renewable energy products; therefore, the share of industrial gross product to GDP (Ind) is chosen to represent the degree of industrial development to represent the renewable energy endowment [42]. Secondly, Inc per capita income indicates the domestic economic development and represents the energy demand of a country’s economic development [28,43]. Finally, the difference of Democracy Index indicates the institutional distance (RU) [44], and terms of trade (TT) are the influential variables for whether energy trade is conducted between countries, which affects the position of energy exporting and importing countries in the energy trade network. The data are obtained from the UN Energy Database and the World Bank database, where the Democracy Index is obtained from the Economic report, which shows the differences in political systems between countries by classifying the degree of democracy according to the level of the composite score.

Table 1. Descriptive statistics.

	Symbol	Meaning	Mean	Standard Deviation	Minimum	Maximum
Dependent variables	FC	Fossil Energy Center Degree	16.89503	19.15683	0	137
	RC	Renewable Energy Center Degree	77.86464	47.09292	0	182
Explanatory variables	DES	Digital economy development level	16.20801	11.25363	2.260353	101.4861
Intermediate variables	Rrca	Renewable Energy Product Competitiveness	3.629837	3.292962	0	7.168821
	Frca	Fossil Energy Competitiveness	0.3823587	0.4954831	0	1.162106
	Te	Energy production efficiency	0.3310539	0.2599879	0.00082	1.865091
	LnE	Energy Demand	26.04116	2.287532	20.10151	32.23118
Control variables	Lnc	Disposable income per capita	9.338463	1.146154	6.505696	11.86101
	TT	Terms of Trade	82.59798	58.55603	0	442.62
	Ru	System Distance	5.48684	2.033938	0	9.93
	Ind	Renewable Energy Endowment	25.13687	14.25535	0	196.3263
	Lnbf	Fossil Energy Endowment	7.195835	1.016563	0.62778	11.23472

shows the descriptive statistics of the main variables.

4. Results and Discussion

4.1. Analysis of Basic Estimation Results

Since we construct panel data for 181 countries from 2011–2020, we need to select and test for mixed OLS, random effects, and fixed effects. First, the mixed OLS or variable coefficient model is tested, and the B–P test shows that the original hypothesis is rejected, and the variable intercept model should be selected. Second, the random effects or fixed effects are tested, and the Hausman test shows that the original hypothesis is rejected and the fixed effects model should be selected. Finally, the test of fixed effect or double fixed effect is conducted, and the F-test shows that the original hypothesis is rejected, and there is a time fixed effect. Therefore, the individual and time two-way fixed effects regression is finally used.

Since energy importing and exporting countries differ significantly in their dependence on energy, the difference in import and export status will lead to variability in the climbing status of the digital economy development on energy. Therefore, the sample is divided into energy importing and exporting countries and regressed separately. Columns (1) and (2) of

Table 2. Estimated results of the direct effect of digital economy development on the change in the status of energy trade networks.

	(1)		(2)
	FC	RC	FC	RC
DES	0.5448 ***	2.0502 ***	0.8761 ***	1.7531 ***
	(6.1894)	(8.2831)	(22.5665)	(19.6773)
Lnc	2.9093 ***	1.4875 ***	3.0323 ***	4.4026 ***
	(3.5051)	(19.2970)	(7.0895)	(4.5706)
TT	−0.0131	0.0724 *	0.0351 ***	0.0552 ***
	(−0.9727)	(1.6675)	(5.8782)	(3.8925)
Ru	0.1456	7.9124 ***	0.5535 **	7.1597 ***
	(0.5226)	(6.4244)	(2.3595)	(14.4518)
Ind	0.0820 *	0.5382 ***	0.1446 ***	0.3753 ***
	(1.6518)	(3.0807)	(5.4572)	(6.5549)
BF	6.0957 ***	8.0025 ***	5.6993 ***	6.2781 ***
	(14.4362)	(3.9856)	(11.6932)	(7.1881)
Fixed time	Yes	Yes	Yes	Yes
fixed Individual	Yes	Yes	Yes	Yes
Observations	401	274	401	274
R-sq	0.609	0.581	0.585	0.581

Note. *** p < 0.01, ** p < 0.05, * p < 0.1.

indicate the impact of digital economy development on the climbing status of fossil energy trade networks and renewable energy trade networks in exporting and importing countries, respectively. The results show that the level of digital economy development is significantly positive, and the level of digital economy development of importing and exporting countries has a positive contribution to the climbing country status of fossil energy trade network and renewable energy trade network, which verifies hypothesis 1.

Our study divides energy into fossil energy trade networks and renewable energy trade networks, and further divides them into exporters and importers. We regressed the direct effects, and the results perform consistently, indicating the robustness of the results in this paper. However, the climbing status of energy trade networks implies that a country has sufficient energy supply to support the high energy-consuming attributes of the digital economy and guarantee the deep development of the digital economy, so there may be a mutual causality endogeneity problem [45]. To address the endogeneity issue, this paper draws on Kim et al. [46] and Chen et al. [47] to generate instrumental variables based on the mean values of digital economy development indices of other provinces in the same year with a one-period lag and estimate the model using the instrumental variables method. The results are presented in columns (1) and (2) of

Table 3. Results of endogenous issues.

	(1)		(2)		(3)		(4)		(5)		(6)
	FC	RC	FC	RC	FC	RC	FC	RC	FC	RC	FC	RC
DES	0.5072 ***	2.8276 ***	0.8591 ***	1.7385 ***	0.4922 ***	2.6631 ***	0.8418 ***	1.7332 ***	0.9436 ***	1.7499 *	1.3307 ***	1.8811 **
	(5.2739)	(2.8543)	(20.5496)	(17.4073)	(5.1250)	(2.7213)	(20.1228)	(17.3067)	(5.8607)	(1.8126)	(3.7336)	(2.4226)
Fixed time	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
fixed Individual	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	329	230	1231	1156	329	230	1232	1157	320	274	1408	1535
R-sq	0.598	0.709	0.581	0.572	0.601	0.719	0.587	0.571	0.084	0.539	0583	0.573

Note. *** p < 0.01, ** p < 0.05, * p < 0.1.

. In addition, the country-level relevant control variables may be endogenous. For this reason, this paper lags the country-level related control variables by one period and estimates the model again using the instrumental variables method, and the results are presented in columns (3) and (4) of

Table 4. Estimated results of indirect effects of digital economy development in importing and exporting countries on changes in the status of energy trade networks.

	(1)				(2)				(3)
	TE	FC	TE	RC	Frca	FC	Rrca	RC	Lne	FC	Lne	RC
DES	0.3389 **	0.1892 *	0.4992 **	0.4421 ***	0.0477 ***	0.3839 ***	0.2943 ***	0.3655 **	0.1024 ***	0.2762 ***	0.2835 ***	0.1492 *
	(2.3642)	(1.6748)	(2.0304)	(2.6775)	(2.5878)	(4.1804)	(2.5878)	(2.2116)	(3.0098)	(5.8813)	(6.5616)	(1.8694)
Te		0.3159 ***		0.1209 ***
		(5.8221)		(5.8063)
RCA						5.2637 *		2.5724 ***
						(1.7108)		(2.8687)
Lne										0.8867 **		1.2618 ***
										(2.3699)		(2.8130)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Fixed time	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
National fixed	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	401	401	274	274	401	401	274	274	1409	1409	1536	1536
R-sq	0.102	0.669	0.276	0.664	0.0853	0.607	0.115	0.681	0.893	0.640	0.884	0.692

Note. *** p < 0.01, ** p < 0.05, * p < 0.1.

. From the regression results in Table 3, it can be seen that the means of the lagged one-period digital economic development index pass the weak instrumental variables test, while the coefficients of the key explanatory variables DES are all significantly positive at the 1% level. It can be seen that the research hypothesis still holds after excluding endogeneity.

At the same time, further referring to the study of Zhao et al. [48], the historical data of landline telephones of each country in 1990 were selected as the instrumental variables, and the interaction term between secure Internet servers and landline telephones was chosen as the instrumental variable in order to address the shortcoming that the variables do not change over time. The result shows that the Kleibergen–Paap rk LM statistic is significant at the 1% level, rejecting the original hypothesis that the instrumental variables are not identifiable. The Kleibergen–Paap rk Wald F statistic and the Cragg–Donald Wald F statistic are greater than the Stock–Yogo weak instrumental variable identification F test at the 1% significance level critical value, rejecting the original hypothesis of weak instrumental variables. Therefore, the selection of instrumental variables in our study is reasonable and reliable. The results are shown in columns (5) and (6), and we found that the DES of exporting and importing countries are significant at the 10% level, indicating that the results of our study are credible.

To analyze the indirect effects of the development of the digital economy on the climbing energy status, energy production efficiency, energy product competitiveness, and energy demand are introduced as mediating variables. The estimation results of the mediating effects are reflected in the following three aspects: (i) Column (1) shows the results of the estimation of exporters with energy production efficiency as a mediating variable. The coefficient of the digital economy on the production efficiency of fossil and renewable energy sources is significantly positive, indicating that there is a positive impact of the digital economy on the improvement of energy production efficiency. In addition, the coefficient of energy production efficiency on energy trade network status is significantly positive, indicating that the digital economy can indirectly contribute to the climbing of a country’s status in the global energy trade network through energy production efficiency. Specifically, for each unit increase in the digital economy, the fossil energy trade network and renewable energy trade network status climb directly by 0.1892 and 0.4421 units and increase energy production efficiency by 0.3159 and 0.1209 units, resulting in an indirect increase in energy trade network status of 0.1071 and 0.0634 units, with a total effect of 0.2963 and 0.5025, with indirect effects of 36% and 12% of the total utility. (ii) Column (2) shows the results of the estimation of exporters with the competitiveness of energy products as a mediating variable. The coefficients of digital economy development on fossil energy product competitiveness and renewable energy product competitiveness are 0.0477 and 0.2943, and the coefficients of energy product competitiveness on energy trade network status are 5.2637 and 2.5724. The indirect effects of digital economy influencing the climbing of global energy trade network status by increasing energy product competitiveness are 0.2511 and 0.7511, which account for 40% and 67% of the total. The indirect effects of the digital economy on the climbing status of the global energy trade network through improving the competitiveness of energy products are 0.2511 and 0.7511, accounting for 40% and 67% of the total effects. (iii) Column (3) shows the estimated results for importing countries with energy demand as a mediator. The digital economy increases fossil energy demand by 0.1024 and renewable energy demand by 0.2835 in importing countries. The increase in energy demand per unit affects the climb in the energy trading network status of importing countries by 0.8867 and 1.2618. The digital economy indirectly affects the climb in the energy trading network status of importing countries by 0.0908 and 0.3577 through the increase in energy demand, accounting for 25% and 71% of the total effect.

In fact, numerous studies have demonstrated that the development of the digital economy as a whole improves energy production, the competitiveness of energy products, and the power of energy consumption [49,50,51]. However, they do not delve further into the resulting changes in a country’s position in the global energy network. This study effectively demonstrates that the development of the digital economy will cause the climbing of the energy trade network position of exporting and importing countries by affecting energy production efficiency, energy product competitiveness, and energy consumption. Hypothesis 2 is verified.

4.2. Nonlinear Judgment

When fossil energy centrality FC and renewable energy centrality RC are used as the dependent variables, two thresholds of digital economy DES exist in the 1810 samples. Further dividing the sample into exporters and importers, 401 samples of fossil energy exporters, 1409 samples of fossil energy importers, 274 samples of renewable energy exporters, and 1536 samples of renewable energy importers have two thresholds, and the thresholds are significantly positive at the 5% level. The F-statistics are all significant at the 10% level.

The results of the threshold regressions on the panel data are shown in

Table 5. Nonlinear estimation results of the digital economy on the change in the status of energy trade networks.

	(1)		(2)
	FC	RC	FC	RC
$\mathit{DES}\left(\mathit{DES}<{\gamma }_1\right)$	0.6016 ***	1.2500 ***	0.1331 **	0.4156 ***
	(2.8711)	(3.1424)	(2.0297)	(3.0839)
$\mathit{DES}({\gamma }_1\le \mathit{DES}<{\gamma }_2)$	0.5277 ***	1.3837 ***	0.1245 **	0.4985 ***
	(3.2883)	(4.2931)	(2.2159)	(3.7440)
$\mathit{DES}(\mathit{DES}\ge {\gamma }_2)$	0.3098 ***	1.3911 ***	0.1120 **	0.5003 ***
	(2.8119)	(5.1953)	(2.3345)	(3.9627)
Control variables	Yes	Yes	Yes	Yes
Sample size	401	274	1409	1536
R-sq	0.209	0.221	0.590	0.687

Note. *** p < 0.01, ** p < 0.05.

. The coefficients of all intervals of the digital economy under the double threshold are positive and significant in the regressions for exporters (1) and importers (2), indicating that there is a significant dynamic nonlinear association between the digital economy and the climbing of the network status of energy trade. This is consistent with Zhao et al. [48] and Liu et al. [52], who argue that the development of the digital economy has led to a weakening of the boundaries of economic activities across sectors, a significant decrease in the cost of access to information, and the emergence of Metcalfe’s Law and network effects. However, this paper finds that, in the regression of fossil energy, the development of digital economy has a positive nonlinear effect on the rise of network status, with the difference that the corresponding regression coefficient is decreasing. It follows that the impact of the digital economy on the climbing status of the fossil energy trade network decreases as the digital economy develops in intensity its facilitating effect. This is due to the contradiction between the high energy-consuming properties of the digital economy and the limited properties of fossil energy. At the early stage of the development of the digital economy, the digital transformation of energy in exporting countries to improve energy production, energy product quality, and increase exports, fossil energy supply is relatively adequate. Importing countries are not limited to the number of foreign fossil energy imports, and the development of the digital economy significantly improves the climb of energy status. However, when the digital economy continues to develop, its energy consumption increases exponentially, but fossil energy is scarce and cannot be supplied indefinitely, so the spillover effect of the fossil energy trade network of the digital economy shows a significant positive and nonlinear characteristic of decreasing “marginal effect”. The opposite is true for renewable energy, which shows a significant positive and increasing nonlinear “marginal effect”. Hypothesis 3 proposed in this paper is verified.

The energy trade network position of exporting countries climbs faster than that of importing countries because energy exporting countries are fewer and more concentrated compared to importing countries, and the countries themselves have a high degree of digital economic development, rapid technological progress, and a high degree of control over energy prices, so they climb much faster than importing countries.

5. Conclusions and Recommendations

Established studies have focused more on the impact of institutional, economic, and geographic dimensions on the variation of fossil energy trade networks and less on the impact of renewable energy trade networks and the development of the digital economy. Our study uses digital economy and energy data for 181 countries worldwide from 2011–2020 to build an econometric model, and the findings are as follows: First, the digital economy has a direct impact on the climbing position of exporting and importing countries in fossil energy trade networks and renewable energy trade networks after controlling for time and individual fixed effects, and the findings remain robust to endogeneity issues. Second, due to the different effects of energy endowment and industrial development level of importing and exporting countries on fossil and renewable energy, and then based on the high energy consumption and high technology attributes of the digital economy, it is concluded that the digital economy has an indirect effect on the climbing status of the energy trade network through energy product competitiveness and energy production efficiency in exporting countries, and through energy demand in importing countries. Third, using the level of digital economy development as a threshold variable, it is found that the change in the status of fossil energy trade network presents a positive and decreasing marginal effect with a nonlinear effect, and the change in the status of a renewable energy trade network presents a positive and increasing marginal effect with a nonlinear effect, indicating that the higher the level of digital economy development, the faster a country’s status in the global energy trade network climbs. Based on these, the following policy recommendations are proposed:

(i)

Against the backdrop of the global spread of the pneumonia epidemic and the changing political and economic situation in the world, fossil energy importers and renewable energy exporters need to pay more attention to the development of the digital economy. On the one hand, the digital economy can increase the efficiency of fossil energy production and guarantee the basic energy security of each country. On the other hand, it can accelerate the development of renewable energy, establish a voice in the renewable energy trade network system, form a new international energy system, and carry out renewable energy trade network governance;

(ii)

Promote the development process of integration of digital and physical industries in each country to improve energy efficiency. The application of digital technology guides the orderly flow of energy and realizes the efficient development of the real industry. It can alleviate the problem of information asymmetry and realize efficient management and precise matching of energy in various industries, thus promoting the improvement of energy efficiency;

(iii)

Accelerate the transition from fossil energy to renewable energy and form a virtuous cycle of “digital-energy” ecosystem. Accelerating energy transformation is conducive to alleviating the problem of fossil energy depletion and the deep development of digital economy. The in-depth development of digitalization makes energy consumption more intensive, and the fossil energy-based energy structure is destructive to the environment and climate, and cannot support the huge computing power requirements, so accelerating energy transformation is a more urgent and profound inherent requirement for the development of digital economy.