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Article

Digital Transformation and Green Innovation of Energy Enterprises

by
Yutong Liu
* and
Peiyi Song
School of Economics and Management, Communication University of China, Beijing 100024, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(9), 7703; https://doi.org/10.3390/su15097703
Submission received: 10 March 2023 / Revised: 4 May 2023 / Accepted: 7 May 2023 / Published: 8 May 2023
(This article belongs to the Special Issue Green Innovation: The Power Source of Enterprise Sustainable Development)
Browse Figure
Versions Notes

Abstract

:
The era of the digital economy has ushered in a new development opportunity for the energy industry, and the role of digitalization in the green and low-carbon transformation process of the energy industry has received increasing attention. Based on the panel data of 55 energy enterprises in China, this study explores the mechanism by which energy enterprises’ digital transformation impacts enterprise green innovation from the perspective of dynamic capability and adopts the double-fixed-effects regression model to empirically analyze the impact of energy enterprises’ digital transformation on enterprise green innovation. The study explores the mediating role of dynamic capability between energy enterprise digital transformation and enterprise green innovation and conducts heterogeneity analysis. The empirical results show that there is a significant positive correlation between the digital transformation level and the green innovation level of energy enterprises. The mechanism test shows that the digital transformation of energy enterprises can promote their green innovation ability by improving their dynamic capability. Heterogeneity analysis shows that the digital transformation of energy enterprises has a significant promotional effect on the green innovation of state-owned enterprises but has no significant effect on non-state-owned enterprises. The results of this study provide a reference for promoting the green development of enterprises, enhancing the green and low-carbon transformation of the energy industry and realizing the sustainable development of enterprises.

1. Introduction

Climate change is a major global challenge facing humankind today, and an active response to climate change is an inherent requirement for a country to achieve sustainable development. As China is an important player in building a market-oriented green technology innovation system, energy enterprises should not only maintain the impetus for innovation but also take environmental protection into account in their development. Considering that the carbon emissions of energy enterprises account for a very high proportion of national carbon emissions, these enterprises’ development goals, paths, plans and implementation of relevant work are important factors in the realization of China’s dual-carbon goals. Therefore, how to develop and enhance the green innovation ability of energy enterprises has become an important issue. In the era of the digital economy, digital transformation provides a new research perspective for enterprises’ green innovation. The green and low-carbon transformation of the energy industry is the only way to rapidly achieve the dual-carbon goal. In this process, digitization is playing an increasingly important role, and technological power and cutting-edge technologies will also play key roles. Digital transformation provides an important entry point for energy enterprises to improve their innovation ability. On the one hand, digital transformation can play an “enabling role”, enabling enterprises to adopt new ways of working, cultivate digital capabilities [1] and utilize advanced data analysis tools to enhance enterprises’ ability to identify new opportunities and process innovation [2,3], thus driving product, service and business model innovation. On the other hand, digital transformation can play the “empowering role” to promote knowledge search and reorganization [4], accelerate knowledge and technology spillover [5] and thus improve the absorptive capacity and creativity of employees, which is conducive to improving innovation ability. In addition, digital transformation can also bring cost advantages, scale advantages and industrial supporting advantages to enterprises, which helps enhance the competitiveness of enterprises.
In recent years, technological change and enterprise digital transformation in the digital era have become frontier topics in research on the digital economy [6,7,8]. Under the constraints of the two-carbon target, the pressure for the low-carbon transformation of traditional energy enterprises is increasing, the business profit model of new energy enterprises is changing and enterprises that are adapting to the needs of the new energy structure, such as energy storage, are still in the initial stage. It is imperative for digital empowerment to enhance the green low-carbon transformation of the energy industry. Currently, the process of transformation from informatization to digitalization in the energy industry is accelerating gradually. In this context, it is of great significance to explore how the digital transformation of energy enterprises affects their green innovation so that these enterprises can seize development opportunities of digital transformation, promote green innovation and realize green development and green transformation to achieve high-quality sustainable development. This is also the main purpose of this study. On this basis, this study takes Chinese energy industry enterprises as research objects, and the panel data of 55 energy enterprises in China from 2010 to 2020 are adopted to empirically analyze the impact of the digital transformation of energy enterprises on enterprise green innovation. In testing the robustness of the empirical results, the influence of heterogeneity is further analyzed in combination with the characteristics of enterprises. The main issue of this study is to clarify the role of the digital transformation of energy enterprises in enterprises’ green innovation and reveal the importance of the digital transformation of energy enterprises in the low-carbon transformation and high-quality development of the energy industry.

2. Literature Review and Research Hypothesis

2.1. Enterprise Digital Transformation

Scholars have conducted in-depth research on enterprise digital transformation from different angles. Most previous studies have examined enterprise digitization from the perspective of technology enablement. Early digital transformation is considered to be an ongoing process of using digital technologies in daily organizational life [9]. Some scholars believe that enterprise digitalization is the application of mobile Internet, embedded devices and other digital technologies and equipment to enterprise business [10]. Others believe that enterprise digitalization is not only the application of digital technology [11], but also the process of enterprise organizational reform. If the organizational structuring process is not established according to the market economy and the laws of science and technology, this failure will eventually affect the possibility of digital transformation [12,13]. From the perspective of enterprise strategy, some scholars believe that digital transformation fundamentally has nothing to do with technology but instead is a matter of strategy. The digital transformation of large and mature enterprises has become a strategic priority on the leadership agenda [14,15,16,17,18,19,20,21]. In addition, to achieve digital transformation, enterprises should pay attention to the use of existing digital assets, with the strong support of the organizational structure and culture and the help of diversified digital platforms [22,23,24,25,26].

2.2. The Impact of Enterprise Digital Transformation on Enterprise Innovation Ability

In recent years, scholars have conducted much research on the impact of enterprise digital transformation on enterprise innovation ability. Compared with the traditional industrial economic environment, against the background of the digital economy, the environmental characteristics of enterprises have changed greatly. Enterprises need to cope with the complex and changeable environment to maintain their competitive advantages, which imposed higher requirements on their dynamic innovation ability [27,28]. Previous studies have concluded that enterprises should pay attention to the important role of various dynamic capabilities, especially the mutual achievement of digital-related dynamic capabilities and the enterprise ecosystem. The management of ecosystems with dynamic capabilities can effectively improve the performance of service innovation [29]. The role of dynamic capability is closely related to the characteristics of the environment in which an enterprise is situated. These studies emphasize that the enterprise should maintain its interaction with the external environment, which will have even greater value in the environment of rapid change. Recently, scholars have focused on the impact of various dynamic digital capabilities on service innovation, such as the positive impact of big data analysis capabilities on service innovation [30]. To ensure the smooth progress of digital transformation, enterprises need corresponding reorganization and updates in organizational structure and corporate culture to promote the improvement of their dynamic capabilities [31,32,33]. Some scholars have concluded that effective interaction between digitalization and servitization can have a positive impact on enterprise innovation. However, an empirical study found that the interaction between low-level digitalization and high-level servitization has a significant negative impact on corporate performance [34]. Digital transformation positively influences dynamic capability, and innovation capability, as a part of dynamic capability, is also affected by digital transformation. From the perspective of organizational change, digital transformation can be regarded as a process of enterprises using digital technology to promote the transformation and innovation of their production service operational mode [35,36].
Regarding the relationship between digital investment and innovation performance, at the enterprise level, some scholars have noted that at a given level of innovation expenditure, enterprise IT input is closely related to innovation output [37]. Others have found that information technology has a significant positive impact on enterprise innovation performance [38], and digital technology can promote business model innovation [39]. Through the implementation of digital investment, enterprises can improve the skill level of human resources and thus enhance their stock and quality of human capital [40]. Some scholars have conducted empirical research on Chinese manufacturing enterprises and found that technological human capital has a significant positive impact on enterprise innovation performance [41]. Digital technology can solve problems of employee redundancy and inefficiency, help employees gain relevant new knowledge and experience and ultimately contribute to the learning capacity of the organization. The skills, knowledge and experience acquired by employees will enable them to adapt quickly to disruptive and structural economic changes in the digital economy and accelerate digital transformation within the organization. If the skills of the employees are matched with the organization’s technical needs, the enabling effect will be further amplified, enabling the enterprise to gain competitiveness and creating higher enterprise value [23,42,43,44,45]. Digital business models and intelligent manufacturing modes can quickly respond to consumer demands and improve the ability to cope with changes in the external market environment, thus establishing the competitiveness of product development and innovation for enterprises [46,47]. Reid and Miedzinski (1996) proposed that enterprise green innovation has a valuable symbiosis function, and the special symbiotic relationship between eco-oriented innovation and economy-oriented innovation in green innovation can promote the improvement of ecological and economic performance and ultimately achieve the optimal allocation of resources. Therefore, enterprises should be encouraged to practice the sustainable development strategy of “harmonious coexistence” and take the initiative in green innovation [48].

2.3. Influencing Factors of Green Innovation

From the perspective of influencing factors of green innovation, the literature suggests that the main determinants of enterprise green innovation are the marketization mechanism, regulation and political influence. There are two ways to motivate enterprises to carry out green innovation: one is to force them to do so through environmental regulation, and the other is to encourage them through market mechanisms. In terms of demand, scholars made use of a sample study of 2000 enterprises in Ireland and found that the environmental awareness of the public and consumers’ conceptualization of green can influence the green innovation of enterprises [49]. In terms of regulation, strict environmental regulation will affect enterprises’ environmental responsibility and promote their green innovation. Porter [50] proposed that environmental regulation can promote enterprises’ green technology innovation. Subsequently, Portugal [51] tested the relationship between environmental regulation and enterprise green innovation and found that the positive impact of environmental regulation on enterprise green transformation was gradually weakened. In addition, a large number of studies have focused on the effect of government subsidies on new energy industry policies, such as the impact of government production subsidies on corporate profitability [52].

2.4. Sustainable Growth for Energy Enterprises

Energy industry enterprises are focused on sustainable growth. Based on the current trends, challenges and market demands of the energy industry, some scholars have proposed the use of a game theory tool that allows energy enterprises to form many possible parameters of salary optimization structure and maximizes the utility of employees by motivating them in the communication department of energy enterprises [53].
In the context of the rapid development of science and technology, enterprises in the energy industry continue to introduce new technologies, goods, services and organizational mechanisms in their activities to survive, improve their competitive position, enter new markets and gain greater profits. Considering the industry characteristics of fuel and energy enterprises, some scholars put forward a method to evaluate the inclusive social responsibility status of enterprises in the energy industry and study and predict the potential negative impact of the innovative activities of enterprises in the energy industry on society and the environment to improve their competitiveness [54].
Based on the above, the digital transformation of energy enterprises has great potential in the application of green innovation. This study identifies at least two deficiencies in previous research. First, there have been few studies, particularly quantitative analyses, on the impact of enterprise digital transformation on enterprise green innovation. Second, most previous studies have been based on all industries and have not considered the particularity of the energy industry. In contrast to green innovation in the service industry, green innovation in the energy industry is the integration of manufacturing and service, and energy enterprises have relatively limited service resources and capabilities, so it is urgent for them to overcome barriers to service innovation and improve their level of green innovation through digital capabilities. In addition, there is a lack of research on the heterogeneous impact of the digital transformation of energy enterprises on the green innovation development of state-owned and non-state-owned enterprises. Therefore, this study combined previous research, using the panel data of 55 Chinese energy enterprises from 2010 to 2020 to empirically analyze the influence of the current development level of the digital transformation of energy enterprises on enterprise green innovation. Additionally, it explored the specific influence mechanism of enterprise digital transformation on enterprise green innovation. For enterprises with different attributes, this influence shows heterogeneity. This study also analyzes the heterogeneity of different enterprise characteristics. These research results have theoretical value and important practical significance in the digital transformation of energy enterprises and the formulation of green development policies, the realization of green development, sustainable development path selection and other aspects and provide an important reference for the realization of digital transformation and green development of energy enterprises. Based on the above analysis, this paper proposes research Hypothesis H1 and H2:
Hypothesis 1 (H1).
The digital transformation level of energy enterprises has a promotional effect on their green innovation.
Hypothesis 2 (H2).
The digital transformation of energy enterprises promotes the improvement of enterprises’ green innovation level by improving their dynamic capability.
The rest of the article is organized as follows: Section 3 describes the research methods. The double-fixed-effects regression model is introduced to verify the impact of the digital transformation of energy enterprises on enterprise green innovation. Based on the benchmark regression analysis, endogeneity and robustness test, the influence mechanisms of the digital transformation of energy enterprises on the promotion of enterprise green innovation are examined in Section 4. This step is followed by analyzing the influence of heterogeneity based on the characteristics of different firms, and the results of the models are discussed. The conclusions and some practical suggestions for achieving the green low-carbon transformation and upgrading of the energy industry are outlined in Section 5.

3. Research Methods

3.1. Model Setup

The ordinary least squares (OLS) model adopted by most previous studies considers only the explanatory factors of enterprise innovation based on cross-sectional data but ignores some missing variables in real development as well as the disruption of enterprise green innovation caused by individual and temporal changes. In reality, a variety of factors affect enterprise green innovation, and these factors change over time. Therefore, to ensure the accuracy of the analysis, this study includes time fixed effects in the model. At the same time, different enterprises show differences in unobservable factors that do not change over time, such as geographical location and consumption habits. Therefore, this study adds individual fixed effects to the model. To verify the impact of enterprise digital transformation on energy enterprise green innovation, this study constructs a double-fixed-effects regression model. The econometric model is formulated as follows:
GIit = β0 + β1DTit + βZit + θt + γi + εit
To further test whether energy enterprise digital transformation has an impact on enterprise green innovation level by influencing dynamic capability (DC), the following intermediary effect test model is designed in this study:
DCit = β0 + β1AIit + βZit + θt + γi + εit
GIit = β0 + β1DTit + β2DCit + βZit + θt + γi + εit
where i is enterprise; t is the year; and GIit, the explained variable, represents the green innovation performance of i enterprise in t years. DTit, an explanatory variable, represents the digital transformation level of i enterprise in t years. DCit is an intermediary variable representing the dynamic capability of enterprise i in year t; β is the parameter to be estimated; Zit represents other control variables that affect enterprises’ green innovation; εit is a random disturbance term; θt is the time fixed effect; and γi is an individual fixed effect.

3.2. Description of Variables

3.2.1. Explained Variable

The explained variable in this study is the green innovation of energy enterprises (GI), and the number of green patent applications of enterprises is selected as the proxy variable.
Some scholars have selected the number of green patent grants to reflect the green innovation strength of enterprises in the belief that the number of green patent grants reflects the ability of enterprises to innovate green technology. Previous studies have measured enterprise green innovation mainly from the perspective of input and output. In this study, the green innovation input of energy enterprises is not selected to measure the green innovation level of enterprises because the problems of long lag time, high risk and high uncertainty when innovation input exists may lead to overestimation of the green innovation level of enterprises [48]. Considering that the number of green patent grants is easily affected by related factors, it takes time for green innovation activities to change from initial input to final output. In the process of patent application, the technology in question has already been applied to enterprise production, which can reflect the level of enterprise green innovation in a timely and accurate manner. Therefore, considering the availability of data and referring to previous studies [38], this study selects the number of green patent applications of enterprises in the current year to measure green innovation, as this metric accurately reflects the actual innovation ability of new energy enterprises. According to the national patent law, green patents can be subdivided into green invention patents, green utility model patents and green appearance design patents according to the type of innovation. Therefore, the number of patent applications in this study is the total number of invention patents, utility model patents and design patents.

3.2.2. Explanatory Variable

The explanatory variable of this study is energy enterprise digital transformation (DT), and the specific measurement index is the energy enterprise digital transformation level. Enterprise digitization is realized by spending a large amount of money to introduce advanced digital technology, such as hardware technology, software technology and network technology. Specifically, it includes blockchain, big data, cloud computing, artificial intelligence, Internet of Things and other new-generation information technologies [55]. This study holds that the digital transformation of enterprises is a major strategy of enterprise development in the new era, and such characteristic information is easier to reflect in guiding corporate annual reports. The usage of vocabulary in enterprise annual reports can reflect the strategic characteristics and future prospects of enterprises and reflect the business philosophy and development goals of enterprises to a large extent. Therefore, it is feasible and scientific to describe the transformation degree in terms of word frequency statistics related to “digital transformation” in the annual reports of listed enterprises. In view of the comparability and availability of energy enterprise-level data, the frequency of words related to digital transformation in the annual reports of listed enterprises is used to measure the level of energy enterprise digital transformation. This study uses Python to collect the keywords of digital transformation in the annual reports of enterprises. The keywords of digital transformation refer to big data technology, blockchain technology, artificial intelligence technology, cloud computing technology and digital technology application. The higher the frequency of the word “digital transformation”, the higher the level of digital transformation of the enterprise.

3.2.3. Intermediary Variable

In this study, dynamic capability (DC) was introduced into the research framework to explore the mediating role of dynamic capability between energy enterprise digital transformation and enterprise green innovation, and dynamic capability was divided into absorptive capability and innovation capability. In terms of absorptive capability, this study refers to existing practices and measures R&D investment intensity, i.e., the ratio of R&D investment to operating revenue of sample enterprises from 2010 to 2020 [56]. Innovation capability is measured by the proportion of R&D personnel in the sample enterprises. The dynamic capability (DC) of an energy enterprise is the sum of its absorptive capability and innovation capability.

3.2.4. Control Variables

The enterprise digital transformation level is only one of the complex and diverse factors affecting energy enterprise green innovation. Based on previous studies, the control variables selected in this study at the enterprise level are enterprise age (Age), enterprise size (Size), ownership concentration (Share), total asset turnover (TAT) and management expense rate (MER) [57].
  • Enterprise age (Age): the number of years since the establishment of the enterprise, with the difference between the current year and the establishment year of the enterprise represented by logarithm;
  • Enterprise size (Size): the natural logarithm of the total annual assets of the enterprise;
  • Ownership concentration (Share): the number of shareholding by the largest shareholder of the enterprise/the total number of shares of the enterprise;
  • Total asset turnover (TAT): operating revenue/average total assets;
  • Management expense ratio (MER): the ratio of the enterprise’s current administrative expense to its operating income.
In summary, the theoretical framework of this study is shown in Figure 1.

3.3. Sample Selection and Data Sources

This study discusses the impact of the digital transformation of Chinese energy enterprises on enterprise green innovation. The data of listed enterprises in China’s energy industry are the research object, and the data sample interval is 2010–2020. A sample of industries with industry code D44 (Electricity and Heat Production and Supply industry) was selected from the Guidance on Industry Classification of Listed Enterprises (Revised 2012) of the China Securities Regulatory Commission. The data on green patent applications of energy enterprises used in this econometric model are from the SIPO patent database of the National Intellectual Property Office, supplemented by the CSMAR database and Wind financial platform database. In terms of explanatory variables, both the word database and word frequency of enterprise digital transformation are from the CSMAR database. After the green innovation data of energy enterprises were matched with the data of listed enterprises in the CSMAR database, the data were cleaned, including the removal of enterprises with incomplete green innovation data, listed ST and delisted enterprises and enterprises with missing financial data. In terms of intermediary variables, the data are from the CSMAR database. In terms of control variables, supplementary relevant data are from the CSMAR database and Wind financial platform database. To reduce the heteroscedasticity problem and multicollinearity between data, natural logarithm processing was adopted for enterprise age (Age), enterprise size (Size) and ownership concentration (Share) when constructing the model. Considering that the word frequency of patent application and digital transformation may be 0 in the current year, the natural logarithm was taken by adding one to it. Based on data availability and consistency, data for 55 energy enterprises in China from 2010 to 2020 were selected to construct panel data. To avoid the interference of extreme values with the results, the 1% and 99% points of the continuous variables were winsorized with a reduced tail, and the panel data of 605 observation samples from 55 enterprises were finally obtained. The descriptive statistics of the above variables are shown in Table 1. Eviews provides powerful tools for complex data analysis, regression and prediction. Data analysis in this study was performed using Eviews 12 software.

4. Results and Discussion

4.1. Benchmark Regression Analysis

To comprehensively study the effect of the digital transformation of energy enterprises on enterprise green innovation, this study considers possible heteroscedasticity in the model. After the Hausmann test, the sample data have individual fixed effects and time fixed effects. Therefore, the double-fixed-effects regression model calculated by the ordinary least square method (OLS) is adopted for the empirical test of Model (1). Eviews 12 software is used for empirical analysis. The benchmark regression results presented in Table 2 show the impact of enterprise digital transformation on enterprise green innovation. Column (1) is the regression result of individual and time fixed effects considering only the core variable of digital transformation of energy enterprises; Column (2) is the regression results of controlling the individual fixed effect on the model considering all control variables included in the econometric model; Column (3) is the regression results of double fixed effects on the model. The coefficients of the explanatory variables in Columns (1), (2) and (3) of Table 2 are close, indicating that the model estimation results are robust. The results of the double-fixed-effects model show that the regression coefficient of the digital transformation level of energy enterprises on green innovation is 0.1274, which is significant at 1%, indicating that the digital transformation level of energy enterprises has a positive promotional effect on the green innovation of enterprises. This conclusion verifies Hypothesis 1, which indicates the importance of the digital transformation of energy enterprises for the low-carbon transformation of enterprises and the development of the energy industry.

4.2. Endogeneity and Robustness Test

4.2.1. Endogeneity Test

The influence of green innovation on the digital transformation of energy enterprises is very limited. However, to effectively solve the endogeneity problem caused by bidirectional causality that may exist in the model, the original explanatory variable is replaced by the delayed explanatory variable one period behind for the endogeneity test, and ordinary least squares (OLS) regression is again conducted to partially avoid the influence of a reverse relationship. The regression results of the instrumental variables reported in Columns (1) to (3) of Table 3 show that the digital transformation of energy enterprises has a significant promotional effect on enterprise green innovation at the 1% significance level, which is consistent with the results above, indicating that the research results are credible.

4.2.2. Robustness Test

Considering that the administrative levels of municipalities are quite different from those of other cities, the economic and social development environment of enterprises may also differ, thus affecting the regression results. To further ensure the effectiveness of the influence of the digital transformation of energy enterprises on enterprise green innovation, enterprise samples from Beijing, Shanghai, Tianjin and Chongqing are excluded and a stability test is conducted. After controlling the fixed effects of region and year, OLS regression is again carried out. The regression results reported in Columns (4) to (6) of Table 3 are compared with the benchmark regression results of Table 2. The coefficients of the explanatory variables and control variables of the model are basically consistent in terms of direction, size and significance level, indicating that the regression results in this study are robust.

4.3. Mechanism Test

Regression analysis has shown that the digital transformation of energy enterprises can improve their green innovation ability, but the mechanism of the impact of digital transformation on green innovation needs to be further tested. This study examines the influence mechanism of digital transformation on green innovation from the perspective of dynamic capability.
Table 4 lists the results of the role of dynamic capability (DC), an intermediary variable, in the relationship between digital transformation and green innovation. Comparing the estimation results of fixed effect models with or without the inclusion of dynamic capability (DC) reveals that the coefficient of the digital transformation variable in each model is significantly positive. Column (1) is listed as the baseline regression result. Dynamic capability is added to the econometric model to test the mediating effect. Column (2) reports the role of dynamic capability as an intermediary variable. The influence of enterprise digital transformation on dynamic capability is significantly positive, indicating that enterprise digital transformation effectively promotes enterprise green innovation. According to the regression results shown in Column (3), the influence coefficient of dynamic capability on enterprise green innovation is 0.1171, which is significant at the 1% level, and the influence of digital transformation on enterprise green innovation is significantly positive. Thus, the dynamic capability of enterprises is improved through digital transformation, and the intermediary effect of digital transformation promoting the improvement of an enterprise’s green innovation level through improving its dynamic capability is established; that is, digital transformation drives the improvement of an enterprise’s green innovation level by promoting the improvement of its dynamic capability. This conclusion verifies Hypothesis 2 and illustrates the mechanism by which the digital transformation of energy enterprises influences green innovation.

4.4. Further Analysis Based on Enterprise Characteristics

There are great differences in the digitization level among different energy enterprises, which may lead to differences in their digitization level and regional digitization construction. Therefore, this study divides the sample enterprises into state-owned enterprises and non-state-owned enterprises according to their attributes. The regression results are shown in Table 5. After control variables are added, the digital transformation of state-owned enterprises has a significant positive promotional effect on enterprise green innovation. For non-state-owned enterprises, this effect is not significant, indicating that the digital transformation level of such enterprises has no significant impact on their green innovation.
Generally, the digital transformation of energy enterprises is more conducive to improving the green innovation of state-owned enterprises than of non-state-owned enterprises. The possible reason is that compared with non-state-owned enterprises, state-owned enterprises have a richer resource base and market resources and can more easily obtain government subsidies and external financing. They can obtain more mature policy support and legal and financial consulting services for green innovation and have sufficient economic strength to develop the digital economy, thus accelerating the improvement of their innovation ability. Green innovation should be promoted among these enterprises. This conclusion illustrates the heterogeneity of the impact of enterprise digital transformation on enterprise green innovation with different attributes, which enhances the reliability and generalizability of the conclusion.

5. Conclusions

With the rapid development of the digital economy, a new digital pattern has emerged in the green innovation of energy enterprises. The transformation of the energy industry by digital technology has entered a new phase. The process of transformation from informatization to digitalization in the energy industry is accelerating gradually. Under the constraints of the dual-carbon goal, energy enterprises face both opportunities and challenges, and it is imperative that digital empowerment boost the green low-carbon transformation of the energy industry. Exploring how the digital transformation of energy enterprises affects their green innovation is of great significance to seize the development opportunities of digital transformation and promote the green innovation of enterprises to achieve sustainable development. First, this study uses panel data to measure the digital transformation level of energy enterprises based on the keyword frequency of digital transformation, constructs a double-fixed effect model and empirically tests the impact of digital transformation on the green innovation of energy enterprises. Second, to overcome the possible endogeneity problem in the model, the core explanatory variable is lagged one stage and used as the instrumental variable to conduct the endogeneity test on the benchmark regression results, and the robustness test is conducted by shrinking the sample of enterprises. In addition, the study empirically tests the mechanism of the digital transformation of energy enterprises on green innovation. Finally, the heterogeneous effects of digital transformation on green innovation for enterprises with different enterprises attributes are further analyzed. The results show that the digital transformation level of energy enterprises has a significant impact on green innovation. The influence of the digital transformation level differs among enterprises with different characteristics. The digital transformation of energy enterprises has a significant positive effect on the green innovation of state-owned enterprises but has no significant effect on non-state-owned enterprises. The conclusions help us understand the specific mechanism by which the digital transformation of energy enterprises influences enterprise green innovation, explain the role of the digital transformation of energy enterprises on their green innovation to a certain extent and reveal the importance of the development of the energy industry. From the perspective of practice, the conclusions of this study will help guide the sustainable development of enterprises, promote the green transformation and low-carbon development of the energy industry and have positive significance for society and the economy. Although this study examines the mechanisms by which energy enterprises’ digital transformation influence enterprise green innovation from the perspective of dynamic capability, it does not carry out an in-depth discussion of how its impact mechanisms work. Future researchers should conduct more in-depth analyses of this aspect.
From the development process of the world’s energy industry, scale and intelligence in the process of technological progress are the inevitable trend of future development. Promoting the green and low-carbon transformation of the energy industry through digital empowerment is of great significance. Therefore, this study proposes the following suggestions:
(1) Vigorously develop digital technologies for the energy industry. Energy enterprises should actively deploy energy transformation, attach importance to the research and development of low-carbon technologies, and actively study and apply digitalization and other related technologies.
(2) Vigorously promote enterprise digital transformation in the energy field as an important way to promote the green and low-carbon transformation of the energy industry. China should deepen the integrated and innovative development of digitalization and intellectualization in the energy sector and promote the large-scale development, allocation and efficient utilization of new energy to drive the sustainable development of energy manufacturing and related industries.
(3) Focus on the mutual empowerment of Internet digital technology and energy production and consumption. China should actively build a new energy digital economy platform, solve the difficulties and pain points in information resources through the deep integration of new generation information technology and new energy business, build a new energy ecosystem, promote the joint development of the upstream and downstream parts of the new energy industry chain and realize the sustainable development of energy enterprises.

Author Contributions

Conceptualization, Y.L. and P.S.; methodology, Y.L.; software, Y.L.; validation, Y.L. and P.S.; formal analysis, Y.L.; investigation, Y.L. and P.S.; resources, Y.L. and P.S.; data curation, Y.L.; writing—original draft preparation, Y.L. and P.S.; writing—review and editing, Y.L. and P.S.; visualization, Y.L.; supervision, Y.L. and P.S.; project administration, P.S.; funding acquisition, P.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (No. U21B20102).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 07703 g001 550
Figure 1. Theoretical model.
Figure 1. Theoretical model.
Sustainability 15 07703 g001
Table
Table 1. Descriptive statistics of sample data.
Table 1. Descriptive statistics of sample data.
Variable CategoryVariable NameVariable DescriptionNMeanSDMinMax
explained variableGIgreen innovation of energy enterprises6050.32010.78830.00004.3175
explanatory variablesDTenergy enterprise digital transformation6050.43520.67310.00002.8904
intermediary variableDCdynamic capability of an energy enterprise6050.42680.81920.00004.4947
control variablesAgeenterprise age6052.92790.31460.69323.4657
Sizeenterprise size60523.40031.393720.416226.8060
Shareownership concentration6053.62180.47612.49984.4534
TATtotal asset turnover6050.38260.22040.02411.7409
MERmanagement expense ratio6050.06010.05010.00150.3299
Table
Table 2. Regression results of the benchmark.
Table 2. Regression results of the benchmark.
VariablesGI
(1)(2)(3)
DT0.1179 ***
(2.5956)		0.1378 ***
(3.0989)		0.1274 ***
(2.7570)
Age 0.7039 ***
(3.7439)		0.5091 *
(1.4871)
Size −0.1141
(−1.1983)		−0.1299
(−1.1336)
Share −0.0881
(−0.5407)		−0.1113
(−0.6775)
TAT −0.2732
(−1.2677)		−0.3423 *
(−1.5625)
MER −1.9293 *
(−1.5694)		−1.8227 *
(−1.4699)
constants0.2688 ***
(9.2934)		1.4090
(0.6595)		2.4565
(0.9792)
individual fixed effectYesYesYes
time fixed effectYesNoYes
R-squared0.61270.61160.6170
N605605605
Robust t-statistics in parentheses, *** p < 0.01, * p < 0.1.
Table
Table 3. Endogeneity table and robustness tests.
Table 3. Endogeneity table and robustness tests.
VariablesGI
(1)(2)(3)(4)(5)(6)
L. DT0.1286 ***
(2.5604)		0.1590 ***
(3.2139)		0.1381 ***
(2.6790)
DT_E 0.0919 **
(2.1979)		0.1171 ***
(2.8529)		0.1026 ***
(2.4118)
Age 0.7566 ***
(3.4214)		0.4344 ***
(0.9485)		 0.4630 ***
(2.7064)		0.1393
(0.4701)
Size −0.1352
(−1.2660)		−0.1464
(−1.3530)		 −0.0929
(−1.0232)		−0.1174
(−1.2711)
Share −0.1073
(−0.5791)		−0.1490
(−0.7915)		 0.0280
(0.1886)		−0.0074
(−0.0493)
TAT −0.2502
(−1.1246)		−0.3182
(−1.4092)		 −1.1642
(−1.5358)		−0.2369
(−1.1258)
MER −2.1460 *
(−1.6521)		−2.0906 *
(−1.5906)		 −1.6424 *
(−1.5358)		−1.6599 *
(−1.5508)
constants0.2921 ***
(10.0191)		0.6616
(0.7671)		3.2225
(1.1183)		0.1684 ***
(6.2093)		0.9937
(0.4893)		2.6859
(1.1462)
individual fixed effectYesYesYesYesYesYes
time fixed effectYesNoYesYesNoYes
R-squared0.64630.64570.64990.55090.54030.5548
N550550550484484484
Robust t-statistics in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.
Table
Table 4. Mechanism test.
Table 4. Mechanism test.
VariablesGIDCGI
(1)(2)(3)
DT0.1274 ***
(2.7570)		0.1392 ***
(2.6885)		0.1171 ***
(2.5231)
DC 0.0741 **
(1.9239)
control variablesYesYesYes
constants2.4565
(0.9792)		−3.5471
(−1.2616)		2.7194
(1.0851)
individual fixed effectYesYesYes
time fixed effectYesYesYes
R-squared0.61700.55460.6196
N605605605
Robust t-statistics in parentheses, *** p < 0.01, ** p < 0.05
Table
Table 5. Heterogeneous effect of energy enterprise green innovation influenced by enterprise digital transformation.
Table 5. Heterogeneous effect of energy enterprise green innovation influenced by enterprise digital transformation.
VariablesGI
State-Owned EnterprisesNon-State-Owned Enterprises
(1)(2)(3)(4)(5)(6)
DT0.1256 ***
(2.5705)		0.1270 ***
(2.5892)		 0.1647
(1.1823)		0.2113
(1.1866)
L. DT 0.1561 ***
(2.8337)		 0.2768 *
(1.5302)
Age 1.7026 ***
(2.9487)		1.5690 **
(2.1653)		 0.3297
(0.6220)		−0.0276
(−0.0355)
Size −0.1176
(−1.1205)		−0.1361
(−1.1677)		 −0.2685
(−0.7590)		−0.4145
(−0.9792)
Share −0.1979
(−0.9089)		−0.3030
(−1.2495)		 −0.4060
(−1.3414)		−0.4376
(−1.2016)
TAT −0.3809 *
(−1.6340)		−0.3694 *
(−1.5457)		 −0.3254
(−0.4086)		−0.6770
(−0.7722)
MER −1.1416
(−0.8525)		−1.4433
(−1.0184)		 −6.5327 *
(−1.5749)		−10.4132 **
(−2.2856)
Constants0.2495 ***
(8.5145)		−1.0859
(−0.3663)		0.1160
(0.0337)		0.4313 ***
(2.8678)		7.6468
(0.9350)		12.4369
(1.2907)
individual fixed effectYesYesYesYesYesYes
time fixed effectYesYesYesYesYesYes
R-squared0.58550.59600.63550.86090.88280.8838
N561561510444440
Robust t-statistics in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.
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Liu, Y.; Song, P. Digital Transformation and Green Innovation of Energy Enterprises. Sustainability 2023, 15, 7703. https://doi.org/10.3390/su15097703

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Liu Y, Song P. Digital Transformation and Green Innovation of Energy Enterprises. Sustainability. 2023; 15(9):7703. https://doi.org/10.3390/su15097703

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Liu, Yutong, and Peiyi Song. 2023. "Digital Transformation and Green Innovation of Energy Enterprises" Sustainability 15, no. 9: 7703. https://doi.org/10.3390/su15097703

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