Digital Economy Development and Green Economic Efficiency: Evidence from Province-Level Empirical Data in China

Abstract

The digital economy development promotes green transformation in China’s economy. Based on provincial-level data from 2011–2019, an ArcGIS spatial analysis is used to explore the spatial and temporal evolution patterns of the digital economy development and green economic efficiency. The digital economy development’s impact on the green economic efficiency is tested through fixed effect, mediation effect, and spatial Durbin models. The digital economy development and green economic efficiency increased during the study period. Spatial patterns of high-level areas spread to form “clusters” with surrounding areas. The digital economy development’s catalytic effect on the green economic efficiency holds after robustness and endogeneity tests. A heterogeneity analysis shows that the digital economy development promotes the green economic efficiency in the eastern and central regions; the impact on the western region is non-significant. Compared with industry digitalization, digital industrialization has a stronger promoting effect on the green economic efficiency. The digital economy development better facilitates the green economic efficiency in regions with high digital economy development levels and Big Data experimental areas. The digital economy development promotes green economic efficiency through human capital, industrial structure upgrading, and technological innovation; industrial structure upgrading has a stronger mediating effect, reaching 40%. The digital economy development facilitates the regional green economic efficiency and significantly promotes green economic efficiency in neighboring regions through spatial spillover effects.

1. Introduction

Since China’s reform and opening up, the country’s achievements in economic development have been evident; however, the traditional industrial growth path, which has long relied on high input, consumption, and capital accumulation, has resulted in substantial resource waste and environmental pollution. This contradicts both the concept of green development for which the Chinese government has been advocating and the current inevitable trend of sustainable global economic development. Therefore, under resource-based and environmental constraints, China urgently needs to seek new dynamic energy sources to transform its economic development model and move forward on a green development path.

Further, the digital economy, with digital technology and data elements at its core, has become an important part of both the global and Chinese economies by virtue of its high diffusion, scale effect, and network effect, and has widely penetrated other economic fields. The rapid development of the digital economy has attracted the attention of the Chinese government, and in January 2022, the State Council issued the “Fourteenth Five-Year Plan” for the development of the digital economy. In this new phase of challenges and opportunities, strengthening and expanding China’s digital economy is important for building a digital China and achieving sustainable, efficient, and secure economic development. According to the “China Digital Economy Development Report (2022)” released by the China Academy of Information and Communication Technology, China’s digital economy was projected to reach CNY 45.5 trillion in 2021, accounting for 39.8% of the GDP and representing an increase of 16.5 times, compared to 2005 (CNY 2.6 trillion). Thus, the digital economy’s role as a both a “stabilizer” and a “gas pedal” in China’s national economy has become more apparent.

Therefore, an important question to ask is whether the digital economy, as a new dynamic, can focus on the current requirements for green and sustainable development and have a significant impact on China’s green economic efficiency. In addition, the questions of what heterogeneous factors might interfere with this impact, what mechanisms underly its influence, and whether a spatial relationship exists between the digital economy development and green economic efficiency, require further investigation. Clarifying these questions will further supplement and enrich the literature on the digital economy and provide new ideas and methods for improving the efficiency of China’s green economy, which has important implications for the green transformation of China’s economy and exploration of the path of coordinated regional development.

The remainder of the paper is organized as follows. Section 2 is a literature review on digital economy development and green economic efficiency. Section 3 provides a theoretical analysis and proposes the research hypotheses. Section 4 describes the research model, variables, and data, and Section 5 details the empirical analysis. Finally, Section 6 presents the research conclusions and policy recommendations.

2. Literature Review

2.1. Digital Economy Development

Previous research on the digital economy development has mostly focused on two aspects: the measurement and impact effect of the digital economy. To measure the digital economy development’s mainly direct measurement, an indicator system and satellite account methods have been used [1]. With the continuous enrichment of the connotation of the digital economy development, the amount of research using the indicator system method is increasing. Due to the limited data availability and accounting methods, the digital industrialization and industrial digitization are mainly chosen to measure the digital economy development. The impact of the digital economy development is comprehensive and complex. At the macro level, most scholars have focused on the relationship between the digital economy development and economic growth. The digital economy development can promote economic growth and enhance the economic quality, and it has become an important theme in software development in most countries and regions [2]. The digital economy promotes economic growth by enriching factor sources, improving the factor allocation efficiency and capital deepening effects [3], and improving the total factor productivity through technological innovation and diffusion effects, which promotes the high-quality economic development in China [4]. Further, the digital economy development has been shown to play a positive role in international trade, to foster economic growth in Africa [5]. Moreover, the digital economy development is also becoming a new driver of economic growth in Thailand; however, digital technologies have yet to be fully utilized there and much room for improvement remains [6].

At the meso level, most scholars have primarily explored the relationship between the digital economy development and industrial structure upgrading. The digital economy development has a positive effect on both the quantity and quality of industrial structure upgrades, which it accelerates by enhancing regional innovation [7], promoting urban technological progress, and increasing human capital [8]. The digital economy development has an important impact on the internal structure of global value chain upgrades for China’s service industry [9]. At the micro level, digital technology advancement facilitates an increased employee mobility, creates new high-quality jobs, and accelerates the transformation of employment structures [10,11]. The digital economy development has also had a significant impact on female employment in Turkey, where an increase in credit card e-commerce transactions has facilitated an increase in female employment by 0.13 units, while an increase in the Internet penetration has increased female employment by 0.33% [12].

2.2. Green Economic Efficiency

Green economic efficiency is a comprehensive indicator that considers economic growth, resource consumption, and environmental pollution, and is an important driver of a sustainable economic and social development [13]. At present, research on green economic efficiency mainly focuses on two aspects: measurement and influencing factors. In terms of measurements methods, the most common are the stochastic frontier analysis and data envelopment analysis (DEA). Notably, most scholars have preferred the DEA method because its derived models are able to simultaneously evaluate multiple output or input factors, which is an advantage over the stochastic frontier method [14,15]. However, with further research, some scholars found that traditional DEA models are mostly radial and angular and do not consider the slack improvement factor. Thus, the slacks-based measure (SBM) or super-SBM, which can account for non-desired outputs, such as environmental costs, is chosen to measure the green economic efficiency [16,17]. In terms of the influencing factors, industrial structure upgrading [18], environmental regulation [19], foreign direct investment [20], and informatization levels [21] have shown significant positive effects on green economic efficiency. However, industrialization, new urbanization construction, and government intervention can have a suppressive effect on the green economic efficiency [22]. Further, manufacturing agglomeration [23], high technology industry agglomeration [24], and environmental information disclosure [25] have non-linear relationships with green economic efficiency.

2.3. Digital Economy Development and Green Economic Efficiency

There is little research that directly discusses the relationship between the digital economy development and green economic efficiency. Digital infrastructure, digital industry, and digital integration have all demonstrated significant positive effects on the regional total factor productivity [26]. The digital economy can directly drive high-quality green development, with industrial restructuring and green technological innovation, as important mediating mechanisms [27]. Further, although the digital economy can significantly reduce the carbon emission intensity, it also promotes increases in carbon emissions per capita, and therefore, economic growth, financial development, and industrial structure upgrading can mediate the relationship between the digital economy and carbon emissions [28]. A few studies have also shown that the digital economy development in China has increased carbon emissions because of improved energy efficiency [29]. In addition, based on city-level data in China, one study found that the digital economy development has a positive effect on the green economic efficiency improvement, with technological innovation being an important facilitating factor; however, geographic location heterogeneity was also observed [30]. Findings in the current literature provide a good foundation for the present study from the city level, but ignore the possible spatial relationship between the digital economy development and green economic efficiency, which needs to be further explored.

This study can make several possible contributions. First, in terms of research perspectives, unlike previous research, this study incorporates the digital economy development and green economic efficiency into the same research framework for an in-depth analysis that provides a new perspective for the study of related topics with certain novelty and topicality. Second, in terms of research methods, this study measures the digital economy development levels by constructing a comprehensive index system from two dimensions: industrial digitization and digital industrialization. Green economic efficiency is measured by selecting a super-SBM that accounts for environmental costs and other non-desired outputs, which is more comprehensive and accurate than either the stochastic frontier method or DEA. Third, regarding the research content, a mediating effect model is selected to clarify the mechanism through which the digital economy development affects the green economic efficiency from the perspectives of human capital, industrial structure upgrading, and technological innovation. In addition, a spatial Durbin model is used to explore the spatial correlation between the digital economy development and green economic efficiency, which can further enrich and expand the existing literature.

3. Theoretical Analysis and Hypothesis Development

The mechanism of the effect of the digital economoy development on the green economic efficiency is mainly described in three aspects: direct impact, indirect impact and spatial impact (Figure 1).

3.1. Direct Impact of the Digital Economy Development on the Green Economic Efficiency

The direct impact of the digital economy development on the green economic efficiency is mainly manifested in three aspects: improved enterprise resource utilization efficiency, updated government environmental supervision means, and an enhanced social environmental protection concept. First, in terms of enterprise resource utilization efficiency, market participants can quickly and accurately match market supply and demand, thereby reducing search and transaction costs, improving the information selection efficiency, reducing resource input and consumption [31], and improving the green economic efficiency. Cloud computing technology and digital technologies, such as data mining, can help producers fully explore consumer demand preferences, reasonably locate market demand, appropriately formulate production plans, and continuously improve the resource utilization efficiency. However, through digital network supply platforms, enterprises can help improve the matching efficiency between the upstream and downstream of the supply chain, promote the division of labor, and improve enterprise transaction and resource allocation efficiency [32].

Second, regarding the government environmental supervision means, digital technology has greatly reduced the problems of insufficient supply, backward means, and low efficiency in the government’s traditional environmental supervision mode [33]. In particular, remote sensing technology, Big Data, and cloud computing facilitate the real-time dynamic monitoring of environmental data, such as air quality, river water quality, and pollution discharge [34,35], which has improved the government’s ability to perceive early warning signs in relation to pollution sources. The accuracy and effectiveness of environmental regulation has been significantly improved, playing an active leading role in improving green economic efficiency.

Finally, in terms of social environmental protection concepts, digital media can provide new methods and channels for the public to learn about environmental protection and apply environmental protection concepts through information-sharing between the government and the public. Further, online environmental monitoring and interactive data distribution are conducive to the formation of an interactive communication mechanism between the government, enterprises, and the public, to collaboratively manage pollution emissions through supervision, public opinion, and suggestions [36], thereby improving green economic efficiency. In view of the above, the following hypothesis is proposed:

Hypothesis 1:

The digital economy development significantly contributes to green economic efficiency.

3.2. Indirect Effects of the Digital Economy Development on Green Economic Efficiency

3.2.1. Mediating Effect of Human Capital

In the initial stage of the digital economy development, the birth of new industries provides a large number of jobs for the labor force throughout society. To gain the competency necessary for some knowledge and skill-intensive jobs, relevant workers improve their comprehensive abilities through “learning by doing” or “vocational training and continuing education” [37]. With the widespread use of digital technologies, the key role of digital talent has become increasingly prominent, further stimulating human capital investment [38,39], especially with respect to the cultivation of digitally integrated talent. In the digital era, the existing education system focuses more on the cultivation of professional, complex, and practical talent, in term of discipline construction, professional settings, and social practices [40], which is conducive to breaking traditional disciplinary barriers, enhancing the interconnection between digital knowledge and skills and different disciplines and specialties, and laying a solid talent foundation for the transformation of digital innovation research results. In addition, digital education has an irreplaceable role in promoting educational equity and achieving education for all and lifelong education in other areas, especially various search engines, online learning, artificial intelligence, and real-time translations. This greatly broadens the sources, channels, and types of knowledge available to the general public, and thereby promotes increases in the size, skill level, and resource allocation efficiency of the entire workforce. In turn, human capital enhancement facilitates the use of foreign clean production and pollution control technologies [41], which can provide the necessary support for local enterprises to conserve energy and cut pollution emissions. In addition, high-quality human capital is more conducive to the formation of unified environmental values and beliefs in society [42], which improves the balance in the relationship between economic development and natural environmental protection, and promotes green economic efficiency. However, there is a significant threshold effect of the differences in human capital levels on the green total factor productivity [43]. Accordingly, the following hypothesis is proposed:

Hypothesis 2a:

The digital economy development contributes to the improvement of human capital, which in turn contributes to green economic efficiency.

3.2.2. Mediating Effect of Industrial Structure Upgrading

The digital economy uses digital technology to reduce search, transaction, and replication costs; expand the boundaries of the division of labor in the industrial chain; change production relations; and accelerate the integration between industries and the internal structural adjustment of industries. With the deep integration of digital technology and market economy, the digital economy development has been expanding in scale, and through digitalization, networking, and intelligent transformation in traditional industries, it has driven the mutual integration of traditional and digital industries [44], changing the traditional industrial and production models and gradually moving toward green transformation. Moreover, the digital economy helps accelerate the rational allocation of resources and the synergistic division of labor among industries, gradually eliminating traditional industries with high energy consumption, emissions, and pollution through industrial association, integration, and innovation, and thereby promoting industrial structure upgrading. In addition, the zero marginal cost feature of digital information incubates high-tech, high-value-added, and high-growth modern industries [45], which have the significant advantages of a higher production efficiency, better resource allocation efficiency, and more advanced production technology, and reduce the use of energy sources, such as oil, and decrease pollutant emissions, such as carbon dioxide, in the production process. Further, the effects of conserving energy, reducing emissions and optimizing resource allocation through industrial structure upgrading [46,47] significantly contribute to the green economic growth, which in turn becomes a key means of the green economic efficiency improvement. Accordingly, the following hypothesis is proposed:

Hypothesis 2b:

The digital economy development is conducive to the upgrading of industrial structure, which in turn promotes the green economic efficiency.

3.2.3. Mediating Effect of Technological Innovation

The digital economy, which is dominated by the Internet, cloud computing, and Big Data, has its own technological properties [48] and enhances technological innovation through scale and diffusion effects. Innovation is part of information processing to a certain extent, and the essence of the digital economy is that it is a medium for exchanging information, processing diverse and decentralized information, and increasing innovative activities [49]. Further, digital technology optimizes the cross-border allocation of innovation resources, changes innovation processes and models, shortens the innovation product cycle, thereby improving the efficiency of innovation results transformation. The digital economy improves the market resource allocation capacity through various micro-level mechanisms [50], reduces the risk of information asymmetry between supply and demand, weakens barriers to the circulation of new products in the market, and reduces the costs of technological innovation for enterprises. Moreover, as the main body of technological innovation, improved innovation levels for enterprises are conducive to saving resources and reducing unit undesired output, which promotes green economic efficiency. In addition, rapid digital economy development has promoted the development of the “industry–academia–research” linkage. Innovation subjects, primarily from research institutions, are gradually transformed for interconnection and cooperation among enterprises, governments, universities, and other innovation subjects, which allows innovation resources to be effectively allocated, thereby promoting improvements in technological innovation. Further, the technology spillover and linkage effects [51] generated by technological innovation have significant roles in promoting green economic efficiency. Accordingly, the following hypothesis is proposed:

Hypothesis 2c:

The digital economy development facilitates technological innovation, which in turn promotes green economic efficiency.

3.3. Spatial Impact of the Digital Economy Development on the Green Economic Efficiency

Data, as a new type of production factor, has the typical characteristics of high speed and replicability, compared to the traditional factors, such as labor and capital, which is conducive to resource-sharing between different regions. The digital economy uses digitalized knowledge and information as key production factors and modern information networks as important carriers, which is conducive to breaking spatial and temporal restrictions and administrative barriers and enhancing the breadth and depth of inter-regional economic exchanges and cooperation. The advancement of digital technologies, such as the Internet, Big Data, blockchain, and cloud computing, greatly enhances data mobility and the accessibility of data, and the costs of spatial and temporal dissemination decrease geometrically [52]. With the aim achieving “Digital China”, construction and green transformation development, occupying the high ground of digital technology, has become a new competitive goal for the Chinese government’s performance at all levels [53]. Further, strategic interactions between regional governments indicate that the effects of the digital economy development will not be limited to enhancing the green economic efficiency in only one region, but will have an important impact on neighboring regions through spatial spillover effects. Accordingly, the following hypothesis is proposed:

Hypothesis 3:

The digital economy development will benefit the green economic efficiency, not only in the local region but also in neighboring regions through spatial spillover effects.

4. Methods and Data

4.1. Models

4.1.1. Baseline Model

To test the direct impact of the digital economy development on green economic efficiency, the following benchmark model was constructed [54]:

$$GE{E}_{it}={\alpha }_0+{\alpha }_{1}Di{g}_{it}+{\alpha }_{2}Contro{l}_{it}+{\mu }_i+{\upsilon }_t+{\epsilon }_{it}$$

(1)

where $i$ and $t$, respectively, represent cities and years, $GEE$ is the explanatory variable green economic efficiency, $Dig$ is the explanatory variable digital economy development, $contro{l}_{it}$ is a set of control variables, $\mu i$ is an individual fixed effect, $\upsilon t$ is a time fixed effect, and $\epsilon it$ is a random disturbance term.

4.1.2. Mechanism Test

Section 3 indicates that human capital, industrial structure upgrading, and technological innovation may be important mechanisms through which the digital economy development influences green economic efficiency, and this study uses a mediating model with a “three-step approach” to test them [55]. The specific formula is as follows:

$$M_{it}={\beta }_0+{\beta }_{1}GE{E}_{it}+{\beta }_{2}Contro{l}_{it}+{\mu }_i+{\upsilon }_t+{\epsilon }_{it}$$

(2)

$$GE{E}_{it}={\gamma }_0+{\gamma }_{1}Di{g}_{it}+{\gamma }_2{M}_{it}+{\gamma }_{3}Contro{l}_{it}+{\mu }_i+{\upsilon }_t+{\epsilon }_{it}$$

(3)

For the three-step approach of the intermediary model, in the first step, if ${\alpha }_1$ is significantly positive, it means that the digital economy development is conducive to promoting green economic efficiency. In the second step, if ${\beta }_1$ is significantly positive, it means that the digital economy development has a significant positive impact on human capital, industrial structure upgrading, and technological innovation. In the third step, if ${\gamma }_2$ is significantly positive in Equation (3) after adding the mediating variables, it indicates that the digital economy development promotes green economic efficiency through human capital, industrial structure upgrading, and technological innovation.

4.1.3. Spatial Durbin Model

Following a series of spatial econometric ex ante tests, such as the Hausman test, the LM and Wald tests, the spatial Durbin model was chosen to analyze the impact of the digital economy development on green economic efficiency. The specific equation is as follows [56]:

$$\begin{array}{l}GE{E}_{it}=\theta 0+\rho 1{\displaystyle {\sum }_{i=1}^{n}WijY}+\theta 1Dig+{\rho }_2{\displaystyle {\sum }_{i=1}^{n}WijDig}\\ +{\theta }_{\kappa }{\displaystyle \sum Xit}+{\rho }_{\kappa }{\displaystyle {\sum }_{i=1}^{n}WijXit+\mu i+\upsilon t+\epsilon it}\end{array}$$

(4)

where $W$ denotes the spatial weight matrix. The spatial Durbin model was mainly selected for this study for the geographic distance weight matrix ($W_1$), the 0–1 adjacency matrix ($W_2$), and the economic geographic distance weight matrix ($W_3$). The remaining variables are consistent with the baseline regression model.

4.2. Explanatory Variables

4.2.1. Construction of the Digital Economy Development Index System

As noted above, the digital economy is characterized by high diffusion, penetration, and integration, and has a crucial impact on macro, meso, and micro economies, which makes it difficult to accurately measure the development level of China’s digital economy using a single indicator dimension. Thus, this study references the practices of previous scholars [57] and combines the White Paper on China’s Digital Economy Development Report (2020) and the statistical classification of the digital economy and its core industries issued by the National Bureau of Statistics of China, to measure the digital economy development index at the provincial level, in China, using the entropy value method, based on digital industrialization and industrial digitization. The details are shown in

Table 1. Construction of China’s digital economy development index system.

Main Index	Primary Index	Secondary Index	Variable Selection
Digital Economy Development	Digital Industrialization	Internet and Telecommunications	Internet broadband access ports/million
			Ratio of the total telecom business to value added of a tertiary industry
			Fixed telephone penetration rate
		Electronic Information Manufacturing	Computer, communication and other electronic equipment manufacturing employment
			Ratio of the main revenue of computer, communication, and other electronic equipment manufacturing industries to the main business revenue of industrial enterprises above the scale
		Software and Information Technology Services	Ratio of the software business income to value added of a tertiary industry
			Number of employees in the information transmission, software, and information technology services
			Number of software companies
	Industry Digitization	Digital Talent	Number of degrees awarded per 10,000 people
			Number of full-time college teachers per 10,000 people
		Digital Infrastructure Investment	Information transmission, computer services, and software industry fixed asset investment (CNY billion)
			Length of long-distance fiber optic cable lines (km)
		Digital Trading	Ratio of new product sales revenue to main business revenue of industrial enterprises above the scale
			Express delivery business volume (million pieces)
			Ratio of the subtotal original insurance premium income to the value added of a tertiary industry

.

4.2.2. Spatial and Temporal Evolutionary Characteristics of the Digital Economy Development

All samples in this study were divided into eastern, central, and western regions, according to the criteria of the National Bureau of Statistics of China (In this paper, the eastern, central, and western regions are divided, according to the criteria set by the China Statistical Yearbook 2006 and the Seventh Five-Year Plan adopted at the Fourth Session of the Sixth National People’s Congress. Among them, the eastern region includes Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan, Liaoning, and 11 provincial-level administrative regions; the central region includes Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan, Jilin, Heilongjiang, and eight provincial-level administrative regions; the western region includes Inner Mongolia, Guangxi, Sichuan, Chongqing, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Tibet, and 12 provincial-level administrative regions). The results in Figure 2 indicate that overall, China’s digital economy development shows a significant incremental trend from 2011 to 2019. By region, the eastern region is far ahead in the digital economy development, the central region is second, and the western region is slightly behind. Only the eastern region is higher than the national average. In addition, from 2011 to 2019, the digital economy development gap between the eastern and central regions increased from 0.15 to 0.23, and the digital economy development gap with the western cities increased from 0.15 to 0.26. Meanwhile, the development gap between the central and western regions gradually expanded after 2015. China’s digital economy is gaining momentum over time and becoming an important engine for the high-quality economic development in China. However, the increasingly widening digital economy development gap between regions and its impact on sustainable regional development deserve further scholarly attention.

To further explore the spatial and temporal evolutionary characteristics of China’s digital economy development, the natural breakpoint method in the spatial analysis module of ArcGIS10.2 software was used to divide the digital economy development index into four levels, from high to low: early zone, advance zone, catch-up zone, and lagging zone. Three cross-sectional datasets were selected for visualization in 2011, 2015, and 2019 (Figure 3).

Over time, the development pattern of China’s digital economy has changed from the “single-point” sporadic distribution with the early zone as the core, to the “cluster” development with the early, promotion, and catch-up zones, and the lagging zone increasingly showing a “contiguous” distribution pattern. Specifically, in 2011, only three eastern provinces in the regions of Beijing, Shanghai, and Guangzhou showed a “single-point” sporadic distribution; the catch-up and lagging areas were mainly distributed in the central and western regions. In 2015, the number of early areas rose to five, of which, Jiangsu and Zhejiang jumped from the promotion area to the early area, and the rest of the promotion area formed a “cluster” development pattern. In 2019, China’s digital economy development was increasingly concentrated in the eastern and central provinces, with the early, promotion, and catch-up zones forming a “cluster” development trend, and Sichuan jumped from the catch-up zone to the promotion zone again, becoming the “highlands” of the digital economy development in the western region, while most provinces in the northwest, northeast, and southwest remained in the lagging zone. The development level of China’s digital economy can be seen to have improved significantly; however, there are unbalanced development patterns among the regions, which deserves further research attention.

4.3. Explained Variables

4.3.1. Measurement of the Green Economic Efficiency

Referring to Tone [16], this study used the super-SBM approach for the measurement of green economic efficiency. This method has two significant advantages. First, it relaxes the constraint of the same-proportional improvement for each factor, allowing each factor to adjust the improvement according to the real situation and data characteristics. Second, it relaxes the constraint that the efficiency value of the effective decision units ≤ 1 and achieves comparable effective decision units. The specific formula is described below.

Suppose there is a decision unit and each decision unit contains $m$ unit input ($x$ ∈ $R^m$), $S_1$ unit desired output ($y^g$ ∈ $R^{s_1}$) and $S_2$ unit non-desired output (y^b ∈ $R^{s_2}$), where the matrix $X$, $Y^g$, $Y^b$, is defined by $X=[x_1,\dots ,x_n]\in R^{m\times n}$, $Y^{\mathrm{g}}=[y_1^g,\dots ,y_n^g]\in R^{s_1\times n}$, $Y^b=[y_1^b,\dots ,y_n^b]\in R^{s_2\times n}$, $x_i$ > 0, $y_i^g$ > 0, $y_i^b$ > 0, and the set of production possibilities is denoted as $P=\{(x,y^g,y^b)\left|x\right.\ge X\theta ,y^g\ge Y^g\theta ,y^b\le Y^b\theta ,\theta \ge 0\}$. Based on the set $P$, the non-desired output is included in the SBM, at which point the efficiency value of the decision unit is as follows:

$$\begin{gathered} \rho =\min \frac{1-\frac{1}{m}{\displaystyle \sum _{i=1}^m\frac{s_i^{-}}{x_{i0}}}}{1+\frac{1}{s_1+s_2}\left({\displaystyle \sum _{r=1}^{S_1}\frac{S_r^g}{y_{r0}^g}+}{\displaystyle \sum _{r=1}^{S_2}\frac{S_r^g}{y_{r0}^b}}\right)} \\ s.t.\left\{\begin{array}{l}{x}_0=X\theta +S^{-}\\ y_0^g=Y^g\theta -S^g\\ y_0^b=Y^b\theta -S^b\\ S^{-}\ge 0,S^g\ge 0,S^b\ge 0,\theta \ge 0\end{array}\right. \end{gathered}$$

(5)

where, $x_0,y^g,y^b$ is the decision unit, and, $S^{-}$, $S^g$, $S^b$ is the slack of the input elements, the desired output, and the non-desired output. When $\rho$= 1, the decision unit is completely efficient; when 0 $\le$ $\rho$ < 1, the decision unit experiences an efficiency loss and the input-output needs to be further optimized. Currently, Equation (5) is a nonlinear model, and to measure the efficiency value more reasonably, this study used the Charnes–Cooper method to transform it into a linear model. The specific equation is as follows:

$$\begin{gathered} \tau =\min t-\frac{1}{m}{\displaystyle \sum _{i=1}^m\frac{S_i^{-}}{x_{i0}}} \\ s.t.\left\{\begin{array}{l}1=t+\frac{1}{S_1+S_2}\left({\displaystyle \sum _{r=1}^{S_1}\frac{S_r^g}{y_{r0}^g}}+{\displaystyle \sum _{r=1}^{S_2}\frac{S_r^b}{y_{r0}^b}}\right)\\ x_{0}t-X\mu +S^{-}\\ y_0^{g}t=Y^g\mu -S^g\\ y_0^{b}t=Y^b\mu -S^b\\ S^{-}\ge 0,S^g\ge 0,S^b\ge 0,\mu \ge 0\\ t>0\end{array}\right. \end{gathered}$$

(6)

At this point, the efficiency value, based on Equation (6) will produce the absolute efficiency state of multiple decision units, and the super-SBM can clearly identify the various factors affecting the efficiency value to enhance the accuracy and rationality of the efficiency measurement. The measurement formula is as follows:

$$\begin{gathered} {\rho }^{*}= \min \frac{\frac{1}{m}{\displaystyle \sum _{i=1}^m\frac{\overline{x_i}}{x_{i0}}}}{\frac{1}{S_1+S_2}\left({\displaystyle \sum _{r=1}^{S_1}\frac{{\overline{y_r}}^g}{y_{r0}^g}+{\displaystyle \sum _{r=1}^{S_2}\frac{{\overline{y_r}}^b}{y_{r0}^b}}}\right)} \\ s.t.\left\{\begin{array}{l}\overline{x}={\displaystyle \sum _{j=1,\ne k}^n{\theta }_j{x}_j}\\ {\overline{y}}^g={\displaystyle \sum _{j=1,\ne k}^n{\theta }_j{y}_j^g}\\ {\overline{y}}^b={\displaystyle \sum _{j=1,\ne k}^n{\theta }_j{y}_j^b}\\ \overline{x}\ge x_0,{\overline{y}}^g\le y_0^g,{\overline{y}}^b\ge y_0^b,\theta \ge 0\end{array}\right. \end{gathered}$$

(7)

where ${\rho }^{*}$ is the green economic efficiency value and the specific indicators are selected, as described below.

The input factors are selected from three perspectives: resources, labor, and capital. Considering the importance of global energy supply problems, energy input represents the resource input factors, and labor and capital input represent the non-resource input factors. The desired output indicators are selected from three perspectives: overall economic development, generalization of the economic results, and the optimization of the environment. The overall economic development is expressed in terms of GDP and deflated by the price index in 2011. The universal economic outcome is expressed in terms of the average wage of urban residents. The environmental optimization is expressed in terms of the area of park green space. The non-desired output, which mainly considers the environmental pollution indicators in the economic development process, is expressed as industrial sulfur dioxide, wastewater, and soot emissions. Specific indicators are shown in

Table 2. Construction of the green economic efficiency index system.

Type	Index	Specific Content
Input elements	Energy input	Total energy consumption (million tons of standard coal)
	Labor input	Number of employees in units, private and self-employed, at the end of the year
	Capita input	Capital stock (CNY billions)
Expected outputs	Economic development	Gross domestic product (CNY billion )
	Benefit fairness	Average wage of urban residents (CNY)
	Environment optimization	Park green space area (hectares)
Non-expected outputs	Sulfur dioxide	Sulfur dioxide emissions (million tons)
	Industrial wastewater	Industrial wastewater discharge (million tons)
	Industrial fume and dust	Industrial fume emissions (m3)

.

4.3.2. Spatial and Temporal Evolutionary Characteristics of the Green Economic Efficiency

Figure 4 shows that, between 2011–2019, China showed an overall upward trend in green economic efficiency. By region, the eastern region ranked first in green economic efficiency, followed by the central region, and then the western region at the bottom. Among them, only the eastern region had a green economic efficiency value above the national average, showing some similarities with the regional differences in the digital economy development. However, the difference is that the central region has a smaller gap between the green economic efficiency and the national average, and a larger gap with the western region, and the central region is in a catch-up situation.

Figure 5 shows the spatial and temporal evolution characteristics of China’s green economic efficiency in 2011, 2015, and 2019. For analysis, the natural breakpoint method was used to classify the green economic efficiency into four levels from high to low: high-value area, good area, average area, and lagging area. In 2011, the four levels of green economic efficiency in China showed a decreasing distribution pattern from east to west, and the high-value areas were all distributed in the eastern region, with a clear polarization development trend. In 2015, the number of high-value and lagging areas decreased significantly, and the number of good and average areas increased significantly, indicating that green economic efficiency in the central and western regions improved significantly, and the development gap with the eastern region was gradually reduced, showing a balanced development. In 2019, China’s green economic efficiency was centered on the two high-value zones of Beijing–Tianjin–Hebei and the Yangtze River Delta, which led the surrounding good and general zones to form a “cluster-type” development pattern. However, three western provinces and regions—Gansu, Ningxia, and Inner Mongolia—fell from average to lagging regions, leading to an increase in the number of lagging regions and a contiguous development pattern, with the risk of widening gaps with other regions.

In summary, certain similarities can be observed in the spatial and temporal distribution characteristics of the digital economy development and green economic efficiency. From 2011 to 2019, the levels of the digital economy development and green economic efficiency in China increased, and both showed a radiating trend that spread from the high-level areas to the surrounding areas. In terms of the spatial distribution, most regions in China are at low or medium-low levels of digital economy development and green economic efficiency, and the area shares continued to expand, showing a contiguous development pattern. The low level is mainly concentrated in Xinjiang, Qinghai, Gansu, Ningxia, and Inner Mongolia, and the medium-low level is mainly concentrated in Henan, Hubei, Hunan, Anhui, Chongqing, and Shanxi. Medium-high and high levels are mainly distributed in the eastern coastal region, with the Beijing–Tianjin–Hebei and the Yangtze River Delta regions as the core, radiating out to the surrounding areas, to form a “cluster” situation.

4.4. Mechanism and Control Variables

4.4.1. Mechanism Variables

Regarding the mechanism of human capital (hc), the human capital enhancement provides a solid talent foundation for the transformation of digital innovation research results and is conducive to the formation of consumer environmental awareness, which has an important impact on the green economic efficiency. It is expressed as the proportion of the number of students enrolled in general higher education institutions to the total regional population. Regarding the industrial structure upgrading (isu), the industrial structure adjustment and optimization are important support conditions for green and high-quality economic development, expressed as the proportion of the added value of the tertiary industries to that of the secondary industries. Finally, technological innovation (ti) is a strong driving force for the green transformation and development, and this study emphasizes the independent innovation capability, expressed as the R&D expenditure.

4.4.2. Control Variables

With reference to some research [8,14], the following control variables were selected. Regarding the population size (pop), population growth can create economies of scale, promote the division of labor and technological progress, and facilitate the development of economic transformation. The level of economic development (agdp) in a region is an important basis for improving the green economic efficiency, expressed as the logarithm of the regional GDP per capita. Regarding government behavior (gov), theoretically, the scale of fiscal spending is reasonable and necessary within a certain range; however, excessive government intervention or a lack of fiscal spending efficiency may have a negative impact on green development. In terms of the degree of external openness (open), in recent years, China’s economic take-off has been largely attributed to the implementation of the reform and opening-up policy, especially under the current goal of high-quality economic development. It is particularly important to improve the level of opening-up, which is represented by the proportion of actual foreign investment in the GDP. Regarding urbanization (urban), the higher the level of urbanization, the better the industrial structure, population quality, and development concept, which is conducive to the improvement of the urban green economic efficiency. The descriptive statistics are shown in

Table 3. Descriptive Statistics.

Variables		Observations	Mean	Standard	Minimum	Maximum
Explained variables	Green economic efficiency (GEE)	270	0.2941	0.1358	0.0649	1.0000
Explanatory variables	Digital economy development (Dig)	270	0.2543	0.1670	0.0135	0.9843
Mechanism variables	Human capital (hc)	270	0.0086	0.0050	0.0006	0.0214
	Industrial structure upgrading (isu)	270	1.2561	0.6925	0.5271	5.0221
	Technology innovation (ti)	270	0.0053	0.0071	0.0001	0.0473
Control variables	Population size (pop)	270	3.5618	0.3194	2.7545	4.0548
	Economic development level (agdp)	270	0.6985	0.3102	0.2304	4.6733
	Government behavior (gov)	270	0.2467	0.1019	0.1103	0.6269
	Degree of external openness (open)	270	0.0597	0.1150	0.0025	0.8965
	Urbanization (urban)	270	1.7485	0.0883	1.5441	1.9523

.

4.5. Data

Following the principles of scientificity, comprehensiveness, and reliability of data sources, this study used 30 provincial-level administrative regions (including provinces, autonomous regions, and municipalities directly under the Central Government) in China from 2011–2019 as the research sample (Tibet, Hong Kong, Macao, and Taiwan are excluded from the study, owing to the desirability of data, and the final total sample was 270). Data are mainly from sources, such as the China Statistical Yearbook, China Energy Statistical Yearbook, China Environmental Statistical Yearbook, China Digital Economy Development White Paper, Digital China Index Report, and EPS Database, and the missing data are substituted with the social statistical bulletin data of each province. In testing whether a high correlation exists between variables, the variance inflation factor (VIF) analysis produced a coefficient of 3.62, which is less than the critical value of 10. Thus, the problem of multiple co-linearities between variables can be excluded, indicating that the variables selected in this study are reasonable.

5. Empirical Analysis

5.1. Baseline Regression

The regression results in Columns (1) and (2) of

Table 4. Direct impact of the digital economy development on green economic efficiency.

	Baseline Regression		Robustness Tests
			Substitution of Explanatory Variables	Lag 1 Period	Lag 2 Periods
	(1)	(2)	(3)	(4)	(5)
Dig	0.3920 ***	0.4703 ***	0.1076 ***	0.4987 ***	0.5033 ***
	(0.0642)	(0.1462)	(0.0273)	(0.1144)	(0.0931)
pop		−0.0120	−0.0225	0.2420	0.738
		(0.0082)	(0.0541)	(0.5458)	(0.5713)
agdp		0.0023	0.0091 *	0.0181 **	0.0088 **
		(0.0042)	(0.0049)	(0.0065)	(0.0037)
gov		−0.5091 **	−0.1026	0.2276	0.0847
		(0.1963)	(0.1047)	(0.2004)	(0.1616)
open		0.2835 ***	0.6011 ***	0.2995 ***	0.2710 ***
		(0.0927)	(0.0675)	(0.0678)	(0.0494)
urban		0.5932 ***	0.4207 ***	−0.3482	0.4847 ***
		(0.1734)	(0.1404)	(0.3558)	(0.1435)
Individual fixed effects	NO	YES	YES	YES	YES
Time fixed effects	NO	YES	YES	YES	YES
N	270	270	270	270	270
R2	0.5387	0.6650	0.7501	0.6962	0.7014

Note: *, **, and *** denote the 10%, 5%, and 1% significance levels, respectively.

show that the coefficients of Dig are significantly positive at the 1% level under the double fixed effects model with or without the control variables included. This indicates that the digital economy development has a significant positive impact on the green economic efficiency; thus, Hypothesis 1 is supported. The results for the control variables show that both the level of opening up to the outside world and urbanization are conducive to the green economic efficiency. This may be because, in the process of gradually expanding the degree of opening up to the outside world, China has introduced advanced production technologies and organizational concepts to improve the production efficiency and reduce resource waste and environmental pollution, by increasing investment. China has also been continuously strengthening exchanges and cooperation with other countries in green technology innovation, green infrastructure construction, and green product production, which promotes the green economic efficiency. Improved urbanization levels are conducive to the further concentration of various resources, improving the overall operation quality of the region and promoting green economic efficiency.

5.2. Robustness Tests

5.2.1. Replacement of the Explanatory Variable Measurement Indicators

In this study, the variable of the total telecommunication services (tel) is re-selected as a measure of digital economy development for the regression analysis. The results in Column (3) of Table 4 show that the coefficient of Dig is significantly positive at the 1% level, which is consistent with the sign and significance of the baseline regression results, indicating that the digital economy development is indeed conducive to the green economic efficiency improvement.

5.2.2. Lagged Effects Test

Considering the possible lagged effects of the digital economy development, all variables in the model, except Dig, are lagged 1 and 2 periods, respectively, and the regression analysis is conducted again. The results in columns (4) and (5) of Table 4 show that the coefficient of Dig is significantly positive for both the lag 1 and lag 2 periods, and the absolute value of the coefficient increases continuously. To some extent, this indicates that the long-term impact of the digital economy development on green economic efficiency may be greater than the short-term impact.

5.3. Endogeneity Discussion

Although the robustness test alleviates the possible endogeneity problem in this study to some extent, it cannot avoid the errors arising from the omitted variables and reverse the causality. Thus, this study attempts to identify the instrumental variables to further corroborate the robustness of the baseline regression results. Considering that the instrumental variables need to meet the requirements of “strict exogeneity” and “strong correlation,” the regional telephone ownership in 1984 is selected as the instrumental variable for the digital economy development. When the instrumental variable is based on cross-sectional data, it is difficult to apply to the panel data regression problem; thus, this study further borrowed from Nunn et al. [56] to construct an interaction term between the cross-sectional data (1984 regional telephone ownership) and the time series data (national Internet investment) as an instrumental variable (IV) for the 2SLS regression. The first-stage regression results in columns (6) and (7) of

Table 5. Endogeneity analysis results.

	Stage 1		Stage 2
	(6)	(7)	(8)	(9)
IV	0.0784 ***	0.0619 ***
	(0.0101)	(0.0126)
Dig			0.3703 ***	0.5092 ***
			(0.1000)	(0.1202)
pop		0.7730 ***		0.0196
		(0.1218)		(0.0384)
agdp		0.1064 ***		0.0018
		(0.0179)		(0.0062)
gov		−0.0156 ***		−0.2140 **
		(0.0019)		(0.1004)
open		0.0702		0.5393 ***
		(0.0805)		(0.0580
urban		−0.2330		0.2374 *
		(0.5472)		(0.1303)
Control	NO	YES	NO	YES
Individual fixed effects	YES	YES	YES	YES
Time fixed effects	YES	YES	YES	YES
Gragg–Donald Wald F	36.78	36.78	—	—
N	270	270	270	270
R²	0.5291	0.6923	0.7725	0.8034

Note: *, **, and *** denote the 10%, 5%, and 1% significance levels, respectively.

show a significant positive relationship between the instrumental variables and digital economy development, with or without, the control variables included. The Gragg–Donald Wald F value is 32.11, which is greater than the critical value of 10, thus rejecting the original hypothesis of the weak instrumental variables and indicating that the instrumental variables constructed in this study are reasonable. The second-stage regression results in columns (8) and (9) are all consistent with the baseline regression results, indicating that the omitted variables and reverse causality did not cause an endogeneity problem in this study.

5.4. Heterogeneity Analysis

5.4.1. Analysis of the Regional Heterogeneity

The differences in the economic scale, resource endowment, and policy support exist among the regions in China, which may interfere with the effects of the digital economy development on the green economic efficiency. Thus, the sample is further divided into eastern, central, and western regions for the grouped regressions. The results in columns (10) and (12) of

Table 6. Heterogeneity analysis results (I).

	Regional Heterogeneity			Heterogeneity in the Digital Economy Development Levels
	East	Central	West	High Level	Low Level
	(10)	(11)	(12)	(13)	(14)
Dig	0.3894 ***	0.2108 **	0.1229	0.4876 **	0.0211
	(0.0951)	(0.0762)	(0.1925)	(0.1960)	(0.0432)
Pop	−1.9059 **	0.0039	−1.0985	0.0908	−0.0006
	(0.6599)	(0.0045)	(1.6914)	(0.8133)	(0.0055)
Agdp	−0.0011	0.0000	0.8894 *	0.0110	0.0094 ***
	(0.0009)	(0.0017)	(0.4765)	(0.0108)	(0.0019)
Gov	−0.2729	−0.2276 **	1.0587	0.0633	0.0064
	(0.1613)	(0.1028)	(0.6479)	(0.1738)	(0.0812)
Open	0.3803***	0.2010	−0.3840	0.2724 **	0.3309 *
	(0.1161)	(0.1436)	(0.6307)	(0.0998)	(0.1896)
Urban	0.4491	0.2758	−2.2379	−0.7998	0.2539
	(0.2979)	(0.2343)	(1.4666)	(0.8690)	(0.1418)
Individual fixed effects	Yes	Yes	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes	Yes	Yes
N	99	72	99	162	108
R2	0.7030	0.9316	0.8546	0.6536	0.8484

Note: *, **, and *** denote the10%, 5%, and 1% significance levels, respectively.

show that the digital economy development significantly contributes to the green economic efficiency in the eastern and central regions, but the effect is not significant in the western region. As noted in Section 4, the level of the digital economy development in the eastern region is higher than that in the central and western regions. The development stage is relatively mature, and it accounts for a higher proportion of the economic structure and has a greater influence; thus, it has the most significant impact on the green economic efficiency. The central region has the second highest level of digital economy development, with a relatively good economic development foundation, and although the influence is smaller than that of the eastern region, it still makes a significant contribution to the green economic efficiency. Two reasons may explain the insignificant impact of the digital economy development on the green economic efficiency in the western region. First, the digital economy in this region is still in the initial development stage and no scale effect has emerged; thus, its ability to influence the green economic efficiency is weak. Second, the economic structure of the western region is lagging behind that of the eastern and central regions, with a high proportion of traditional industries and more constraints on the development of green transformation, which may offset the digital economy’s promoting effect.

5.4.2. Heterogeneity Analysis of the Digital Economy Development Levels

In this study, the digital economy development index mean values are used to divide the samples into high- and low-level groups for the regression analysis. The results in columns (13) and (14) of Table 6 show that the digital economy development significantly contributes to the green economic efficiency, only in the high-level group; this effect is not significant in the low level group. This is because, as shown by the spatial pattern of the digital economy development in China in the previous section, the high-level group is generally located in the eastern and central regions, and the results of the regional heterogeneity analysis show that digital economy development in the eastern and central regions has a significant promoting effect on the green economic efficiency. Further, a higher level of digital economy development indicates stronger advantages in the digital infrastructure investment, Internet construction, telecommunications industry development, and digital innovation level, which greatly enhances green economic efficiency. Conversely, the promotion of information technology and industry by digital economy development leads to rapid increases in the energy consumption, which increases carbon emissions. Thus, when the digital economy development level is low, it is difficult for its positive effects on the green economy to surpass those negative effects, resulting in a non-significant impact on the green economic efficiency.

5.4.3. Dimensional Heterogeneity Analysis

The heterogeneous effects of the industry digitization and the digital industrialization on the green economic efficiency are examined separately, by downscaling the digital economy development indicators. The results in columns (15) and (16) of

Table 7. Heterogeneity analysis results (II).

	Dimensional Heterogeneity		Policy Heterogeneity
	Industry Digitization	Digital Industrialization	Experimental Group	Control Group
	(15)	(16)	(17)	(18)
Dig	0.1865 ***	0.3699 ***	0.4735 ***	0.1235
	(0.0426)	(0.0662)	(0.1410)	(0.1332)
pop	1.0214 *	−0.2099	−0.1299	−0.0167 **
	(0.5637)	(0.8531)	(1.8531)	(0.0059)
agdp	0.0171	0.0110	0.0119	−0.0027
	(0.0126)	(0.0124)	(0.0114)	(0.0035)
gov	0.2498	−0.4276 ***	0.0948	−0.1633
	(0.2795)	(0.1212)	(0.2645)	(0.1089)
open	0.3291 ***	0.2648 ***	0.3565 ***	0.1762 ***
	(0.0908)	(0.0910)	(0.1024)	(0.0239)
urban	−0.6078	−0.6213	−0.8048	−0.5850
	(0.4359)	(0.4995)	(0.9271)	(0.4045)
Individual fixed effects	Yes	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes	Yes
N	270	270	90	190
R2	0.5810	0.6632	0.6406	0.7012

Note: *, **, and *** denote the 10%, 5%, and 1% significance levels, respectively.

, show that both the industry digitization and digital industrialization promote green economic efficiency. However, comparing the magnitude of the two coefficients reveals that the digital industrialization provides a stronger contribution to the green economic efficiency, compared with industrial digitization. Thus, by converting digitalized knowledge and information into production factors, integrating information technology innovation with management and business model innovation, continuously giving rise to new industries and business models, and finally forming digital industrial chains and clusters, the current conflict between China’s economic and social development and environmental protection is more likely to be alleviated, indicating an effective method for improving green economic efficiency.

5.4.4. Policy Intensity Heterogeneity Analysis

At present, the digital economy is still a new industry and strongly depends on policy support. In 2015, the Chinese government issued the “Action for Promoting Big Data Development,” proposing to further accelerate the deployment of the Big Data industry, summarizing a set of experience in the development of the Big Data industry that could be replicated nationwide. In addition, the government successively set up the “Big Data Comprehensive Pilot Zone” in 10 provinces and regions, including Guizhou, Beijing, Tianjin, Hebei, Henan, Inner Mongolia, Liaoning, Chongqing, Shanghai, and Guangdong, and arranged for the deployment of seven aspects of data resource management and sharing, data center integration, data resource application, data element flow, Big Data industry clustering, Big Data international cooperation, and Big Data system innovation, to promote the digital economy development. Therefore, the study uses the Big Data pilot zones as the basis for grouping and divides the sample into experimental and control groups for the regression analysis, to examine the differences in the impact of the digital economy development on green economic efficiency under different levels of policy intensity.

The results in columns (17) and (18) of Table 7 show that the digital economy development significantly contributes to the green economic efficiency in the pilot areas, but the effect on the non-pilot areas is not significant. The reason for this may be that the digital economy development in the pilot areas has a stronger momentum from receiving national policy support and effective supervision, which is conducive to creating a scale effect and has an important impact on the regional economy. Further, the establishment of Big Data pilot areas can facilitate the development of a regional electronic information industry, promote the transformation of economic structure through industrial digitization and digital industrialization, and ultimately improve green economic efficiency improvement.

5.5. Mechanism Analysis

5.5.1. Digital Economy Development, Human Capital, and Green Economic Efficiency

First, the mediating mechanism of human capital is examined. The results in columns (20) and (21) of

Table 8. Results of the mechanism of action analysis.

	GEE	hc	GEE	isu	GEE	ti	GEE
	(19)	(20)	(21)	(22)	(23)	(24)	(25)
Dig	0.4703 ***	0.1056 ***	0.2073 **	1.1298 ***	0.2868 ***	0.5302 ***	0.3034 ***
	(0.1462)	(0.0011)	(0.1005)	(0.3117)	(0.0746)	(0.0808)	(0.0789)
hc			0.1071 ***
			(0.0166)
isu					0.1624 **
					(0.0778)
ti							0.1618 *
							(0.0906)
pop	−0.0120	0.0021 ***	0.0048	1.1430 ***	0.1736 *	2.9146 ***	−0.3925
	(0.0082)	(0.0003)	(0.0258)	(0.0604)	(0.0868)	(0.9190)	(0.8416)
agdp	0.0023	0.0000	0.0025	−0.0132	0.0044	0.0088	0.0082 *
	(0.0042)	(0.0001)	(0.0044)	(0.0185)	(0.0036)	(0.0093)	(0.0042)
gov	−0.0591	−0.0055 **	−0.1031	2.8925 ***	−0.5288 *	−0.6141 **	0.3613
	(0.0963)	(0.0020)	(0.1166)	(0.7550)	(0.2624)	(0.2584)	(0.2473)
open	0.2835 ***	−0.0011 *	0.2746 ***	0.5257 **	0.1982 **	−0.0373	0.3071 ***
	(0.0927)	(0.0006)	(0.0933)	(0.2356)	(0.0853)	(0.0589)	(0.0548)
urban	−0.5932	0.0113 **	−0.5024	0.7343	−0.7124	1.7253 ***	−0.9238
	(0.5734)	(0.0055)	(0.4899)	(1.2376)	(0.4518)	(0.3839)	(0.6392)
Individual fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	270	270	270	270	270	270	270
R2	0.6650	0.6884	0.6680	0.7436	0.7429	0.8743	0.7188

Note: *, **, and *** denote the 10%, 5%, and 1% significance levels, respectively.

show that the digital economy development has a significant positive effect on green economic efficiency by enhancing human capital. This indicates that human capital is a mediating mechanism for the effects of the digital economy development on the green economic efficiency. An analysis of the economic results shows that the digital economy development enhances human capital by 0.1056 units, which in turn promotes the green economic efficiency by 0.1071 units. Thus, the mediating effect size of human capital is 0.0113 (0.1056 × 0.1071), accounting for approximately 2.4% of the total effect (0.0113/0.4703). In the era of the digital economy development, the birth of new industries has forced the level of human capital throughout society to increase, which has provided a foundation of talent for the enterprises’ green technology innovation and pollution-control technology use, and helped enterprises to improve their production efficiency and mode transformation. Further, the comprehensive development of high-quality talent is more conducive to the formation of a unified concept of environmental protection in society, which, in turn better balances the relationship between economic development and protection of the natural environment.

5.5.2. Digital Economy Development, Industrial Structure Upgrading, and Green Economic Efficiency

Next, the mediating mechanism of the industrial structure upgrading is examined. The results in columns (22) and (23) of Table 8 show that the digital economy development has a significant positive impact on the green economic efficiency by accelerating the industrial structure upgrading. This indicates that the industrial structure upgrading is a mediating mechanism in the relationship between the digital economy development and green economic efficiency. Analyzing the economic results shows that the digital economy development enhances industrial structure upgrading by 1.1298 units, which in turn promotes green economic efficiency by 0.1624 units. Thus, the mediating effect size of industrial structure upgrading is 0.1834 (1.1298 × 0.1624), accounting for approximately 40.0% of the total effect (0.1834/0.4703). These findings suggest that using digital technology to accelerate industrial integration; facilitating industrial linkage and industrial innovation; eliminating traditional industries with high energy consumption, emissions, and pollution; and achieving industrial structure transformation and upgrading can enhance green economic efficiency.

5.5.3. Digital Economy Development, Technological Innovation, and Green Economic Efficiency

Finally, the mediating mechanism of technological innovation is tested. The results in columns (24) and (25) of Table 8 show that the digital economy development has a significant positive effect on the green economic efficiency by promoting technological innovation (i.e., technological innovation is a mediating mechanism for the relationship between the digital economy development and green economic efficiency). An analysis of the economic results shows that the digital economy development enhances technological innovation by 0.5302 units, which in turn promotes green economic efficiency by 0.1618 units; thus, the mediating effect size of technological innovation is 0.0858 (0.5302 × 0.1618), accounting for approximately 18.2% of the total effect (0.0858/0.4703). The digital economy’s technological attributes are conducive to the society-wide improvement of technological innovation, while the sharing nature of the digital economy reduces the risk of information asymmetry between supply and demand, which in turn stimulates the enterprise technological innovation and ultimately achieves green economic efficiency.

The above findings demonstrate that the digital economy development promotes the green economic efficiency through the mechanisms of action of human capital, industrial structure upgrading, and technological innovation, and the intermediary effect of the industrial structure upgrading is stronger, which is the key for an important focus point to play the digital economy development to promote the green transformation development of China’s economy.

5.6. Analysis of the Spatial Spillover Effects

Based on model (4), the spatial Durbin model is selected to analyze the spatial relationship between the digital economy development and green economic efficiency. The results in

Table 9. Spatial spillover effect analysis results.

	${\mathit{W}}_1$	${\mathit{W}}_2$	${\mathit{W}}_3$
	(26)	(27)	(28)
Dig	0.4895 ***	0.5033 ***	0.4732 ***
	(0.0623)	(0.0564)	(0.0603)
W × Dig	0.1875 ***	0.1006 ***	0.1611 ***
	(0.0568)	(0.0212)	(0.0500)
Direct effect	0.6823 ***	0.6000 ***	0.5913 ***
	(0.1072)	(0.1069)	(0.1034)
Indirect effect	0.1398 **	0.2053 ***	0.1399 ***
	(0.0551)	(0.0550)	(0.0534)
Control	Yes	Yes	Yes
N	270	270	270
Log-lik	30.6794	39.6977	36.5495
R2	0.6046	0.7330	0.5154

Note: **, *** denote the 5%, and 1% significance levels, respectively.

show that the coefficients of Dig are significantly positive, at the 1% level, under all three spatial weight matrices. This indicates that the digital economy development is conducive to the green economic efficiency improvement, which is consistent with the benchmark regression results. Notably, the coefficient of Dig in the spatial model is larger than that of Dig in the benchmark regression, indicating that the explanatory power of the digital economy development in the improvement of the green economic efficiency is stronger, after considering the spatial relationships between variables.

Further observation reveals that the coefficients of W × Dig are significantly positive under all three spatial weight matrices, indicating the possibility of a spatial spillover relationship between the digital economy development and green economic efficiency. However, the simple point estimation results may not be able to accurately explain the spatial spillover effects between regions, and are more likely to lead to wrong conclusions. Therefore, the partial differential method [57] is chosen to further verify the influence of a region’s independent variable on its dependent variable by direct effects, and the influence of a region’s independent variable on the dependent variable in other regions by indirect effects. Under all three spatial weight matrices, the decomposition effects indicate that the digital economy development is conducive to improving, not only a region’s green economic efficiency, but also that of neighboring regions through spatial spillover effects. Thus, Hypothesis 4 is supported. In short, it makes full use of the spatial spillover effect of the digital economy development, strengthens ties and cooperation with neighboring regions, and forms a development synergy to achieve the overall regional green economic efficiency improvement.

6. Conclusions and Policy Recommendations

6.1. Conclusions

As an important strategic emerging industry in China, the digital economy is a new opportunity to achieve a green economic transformation and development. Using Chinese provincial panel data from 2011–2019, this study constructs comprehensive digital economy and a green economic efficiency indicator systems to explore the impact of the digital economy development on green economic efficiency. First, the results show that the levels of the digital economy development and green economic efficiency in China increased during the study period, both showing a radiating trend spreading from high-level regions to the surrounding regions [58]. Regarding the spatial distribution, most regions in China are at low or medium-low levels of the digital economy development and green economic efficiency; however, the area keeps expanding, showing a contiguous development pattern. The low levels are mainly concentrated in Xinjiang, Qinghai, Gansu, Ningxia, and Inner Mongolia, and the medium-low levels are mainly concentrated in Henan, Hubei, Hunan, Anhui, Chongqing, and Shanxi. The middle and high levels are mainly distributed in the eastern coastal region, with the Beijing–Tianjin–Hebei and Yangtze River Delta regions making up the core, leading the surrounding areas to form a “cluster” agglomeration. Second, the digital economy development has a catalytic effect on green economic efficiency [30]. This conclusion still holds after replacing the explanatory variables to measure the indicators, considering the lagged effects and possible endogeneity. Third, the heterogeneity analysis shows that the digital economy development promotes the green economic efficiency in the eastern and central regions, while the effect on the western region is not significant. Compared with the digitalization of industries, the digital industrialization has a stronger promoting effect on the green economic efficiency. In addition, the digital economy development is more conducive to green economic efficiency improvement in regions with high levels of digital economy development and Big Data experimental areas. Fourth, mechanism analysis shows that digital economy development promotes green economic efficiency improvement through human capital, industrial structure upgrading, and technological innovation, and industrial structure upgrading has a greater mediating effect. Finally, digital economy development is not only conducive to green economic efficiency improvement in local regions but also has a significant promoting effect on green economic efficiency in neighboring regions through spatial spillover effects [52,59].

6.2. Policy Recommendations

First, great importance should be attached to the development of China’s digital economy. It is necessary to continue to solidly promote the construction of new infrastructure with digitalization as the core, such as artificial intelligence, 5G networks, and the “broadband China” strategy, to provide a solid foundation for the digital economy development. It will be further necessary to actively encourage the flourishing of digital industries; to deeply optimize production, management, operation, sales, and other aspects of decision-making; and realize a circular, efficient, and intensive economic development model.

Second, the development gap between regions must be correctly understood. In recent years, the digital economy has developed rapidly; however, a gap remains between development levels and speeds in different regions. If local governments do not fully understand the danger of the “digital divide” and allow the gap to widen, disadvantaged groups, such as the populations of the western and remote regions, are likely to fall behind on the “digital track”. Therefore, less developed regions need to take the initiative to strengthen their economic ties with developed regions, to “catch-up” through competition, cooperation, and learning; accelerate the pace of the digital economy development; and help improve their regional green economic efficiency.

Third, human capital, industrial structure upgrading, and technological innovation must be fully utilized in the context of digital economy development, to enhance the green economic efficiency. Workers’ digital literacy and skills should be vigorously enhanced to alleviate the current shortage of professional digital talent in traditional employment positions. Accelerate the high degree of integration between the existing education system and digital economy development, and can provide a solid talent foundation for the transformation of digital innovation research results and development of urban green transformation. It is necessary to rely on the digital technology iteration and upgrade to improve the production efficiency and resource utilization of traditional industries, reduce production costs and pollution emissions, and realize the digital, intelligent, and green development of traditional industries. The innovative application should be expanded to stimulate the vitality of enterprise technological innovation, improve the capacity of independent innovation, and realize the improvement of green economic efficiency in China.

Fourth, it is necessary to expand the radiation effect of the digital economy development and drive the green economic efficiency of neighboring regions. It is necessary to continue to promote the spatial balance of the digital infrastructure allocation between regions and use administrative means and market forces to stimulate data resources and elements to be traded across regions. High-level regions should further solidify their leading roles, form regional growth poles, and guide the diffusion of relevant elements to neighboring regions. At the same time, the surrounding regions also need to identify their own advantages and form complementary resources with high-level regions to promote regional green economic efficiency in a shared digital development.

Although the study has enriched and supplemented the relevant research on the digital economy development and green economic efficiency, and provided theoretical references for the digital economy-enabled green development in China, there are still some limitations in the study that need further improvement. First, due to the availability of data, the digital economy development is measured only from two aspects of the industry digitization and digital industrialization, and to a certain extent, the impact of digital inclusive finance and the digital innovation capability on the green economic efficiency is neglected. Follow-up studies need to further collect data, enrich the connotation of the digital economy development, and improve the measurement of digital economy development level. Second, the study does not consider the dynamic impact of the digital economy development on green economic efficiency, and it is difficult to capture the cyclical pattern of the digital economy development, which can be tested using the dynamic effect model in subsequent studies. Third, the digital economy development promotes green economic efficiency in neighboring regions through spatial spillover effects. Due to the limitations of the provincial-level data, the study does not explore the geographical boundaries of the spatial spillover effects, and thus cannot measure the optimal scope of the spatial spillover effects generated by the digital economy development, which can be specifically verified by subsequent studies using prefecture-level city data.