Will the Development of the Digital Economy Impact the Clean Energy Transition? An Intermediary Utility Analysis Based on Technological Innovation and Industrial Structure

Abstract

In the context of global warming and the clean energy transition, the rapid development of the digital economy, a highly technology-intensive economic form, has an important impact on the clean energy transition. Examining how the growth of the digital economy has affected the renewable energy transition has broad implications for the creation of national policies, business planning and design, and everyday human behavior. The paper uses a two-way fixed-effect model to empirically investigate the impact of the development of the digital economy on the clean energy transition based on Chinese municipal panel data from 2013 to 2022. It also sorts out the intrinsic mechanism of the digital economy affecting the clean energy transition from a theoretical level. Finally, it tests the indirect effects of technological innovation and upgrading industrial structure using endogeneity analysis and a robustness test. The study finds that (1) digital economy development effectively promotes clean energy transition; (2) the digital economy influences the transformation of renewable energy through two intermediary channels: technological innovation and upgrading of industrial structures; (3) there is geographical variation in how the digital economy affects the growth of the clean energy transformation. Lastly, policy recommendations are provided for boosting investment in digital infrastructure, strengthening the digital technology base, and deepening and broadening the interaction of the digital and real economies.

1. Introduction

Against the backdrop of global warming, fossil energy—especially coal and coal power being a high-pollution and high-emission energy source—has contributed to global warming, and a clean energy transition is essential for environmentally sustainable development. In recent years, as the global economy undergoes rapid changes in both traditional and modern energy sources, nations worldwide have come to an agreement on the importance of developing clean and low-carbon energy. The shift towards clean energy has emerged as the primary development strategy for all countries. This is particularly evident in several developed nations, where renewable energy has become the dominant source for production and consumption [1]. According to Child et al. [2] and Gallo et al. [3], the energy transition is influenced by the demand for and availability of various fuels, moving away from a heavy reliance on biomass and high-carbon fossil fuels toward the widespread adoption of clean, renewable energy. Through big data, artificial intelligence, the Internet of Things, and other technologies, the digital economy can realize the intelligent management of the energy system and improve the efficiency of energy production, transmission, and consumption. For example, smart grids can optimize power dispatch and reduce energy waste through real-time data analysis and prediction.

To tackle the challenges faced during the energy transition and to speed up its progress, numerous researchers have investigated various factors influencing the energy transition from perspectives like economic status [4] and urbanization [5]. Nevertheless, with the swift advancement of the digital economy, there has been a significant shift in consumer behavior, and the demand for traditional energy sources is gradually decreasing, bringing opportunities for energy system transformation.

Empirical evidence shows that the development of the digital economy has had a significant impact on energy markets. Its development has made energy markets smarter and more efficient. In particular, advances in digital technologies have enabled the implementation of smart grids, which help to distribute and utilize energy more efficiently, thereby minimizing waste and losses.

However, in terms of the specific relationship between the development of the digital economy and the clean energy transition, no scholars have yet empirically studied China as a case study and the two as variables. Based on this, scientific research on the impact of the digital economy on clean energy transition is not only conducive to emphasizing the importance of the digital economy and accelerating the swift advancement of the digital economy, but it also supports the investigation of the factors affecting the clean energy transition, helping in reaching the targets of carbon peaking and carbon neutrality on time.

Clean energy transition will undoubtedly promote the realization of carbon emission reduction targets, and examining the factors and pathways that affect the transition to clean energy can offer valuable insights for promoting sustainable environmental development.

In the context of global carbon emission reduction, the existing energy transition related research mainly focuses on the influencing factors of new energy development and the influencing factors of energy structure transition. In terms of the influencing factors of new energy development, Ma Limei et al. found that policy incentives are a key factor in promoting renewable energy innovation by analyzing the data of OECD countries, and this effect is more significant in countries at the early stage of energy transition or slower transition [6]. As for the influencing factors of the energy structure transition, Liu Pingkuo et al. concluded that the transition at the institutional level is fundamental to China’s energy transition, in which four factors, namely, technology penetration, cost and expense, investment incentives, and corporate social responsibility, are important considerations in the current trade-offs of China’s energy transition efforts [7]. Zou et al. found that industrial structure upgrading has a facilitating effect on optimizing the energy consumption structure, but its effect differs significantly between different regions [8]. Based on international experience, Fan Ying et al. proposed the focus and breakthroughs for China to continuously promote energy transformation from four dimensions: policy-driven, innovation-driven, market-driven, and behavior-driven [9].

As the digital economy continues to evolve, certain researchers are starting to explore its effects on energy. Some have discovered that the digital economy can positively influence energy transition, particularly when government governance acts as a mediating factor [10]. She et al. conducted a mechanism test on the carbon emission reduction effect of the development of digital economy and found that the digital economy can realize energy saving and emission reduction by improving energy efficiency and energy structure in both middle-income and high-income countries [11]. Luo et al. found that digital economic development inhibits energy consumption, and digital economy inhibits energy consumption and improves energy consumption structure through regional integration [12].

The current body of literature regarding the influence of the digital economy on the transition to clean energy is still quite limited. There are only a few studies that directly examine the connection between the two, while many others focus on the indirect relationships between various aspects of the digital economy and the clean energy transition. Additionally, there is a scarcity of research investigating how the digital economy affects the clean energy transition and the underlying mechanisms involved.

Based on this, the thesis systematically analyzes the impact of digital economy development on clean energy transition based on the theoretical model of the impact of digital economy on clean energy transition reasonably deduced, and based on the 2013–2022 municipal panel data and bidirectional fixed effect model, and at the same time adopts the mediated effect model to explore the mechanism of its role to serve as a valuable resource for speeding up the shift to clean energy and achieving the “dual-carbon” goal, as well as for the advancement of clean energy initiatives. It provides useful reference for accelerating the clean energy transition and realizing the “dual-carbon” goal.

In comparison to the current literature, this paper makes significant contributions in two main areas: (1) It provides a theoretical explanation of how the development of the digital economy influences the transition to clean energy, and it establishes a theoretical framework that connects digital economic growth with the facilitation of clean energy transition, which not only enriches the connotation and application scenarios of the basic theories of digital economy but also provides a new theoretical support for the promotion of green and high-quality development. (2) Two factors, technological innovation, and the upgrading of industrial structure from an innovation standpoint. It employs a mediation model to examine how these factors influence the relationship between digital economic development and the transition to clean energy. This approach enhances and broadens the research scope within this area.

2. Theoretical Analysis and Research Hypotheses

2.1. Digital Economy and Clean Energy Transition

Initially, the digital economy offers technological assistance for the shift towards clean energy. Zhao et al. [13] conducted an empirical study to explore the relationship between Information and Communication Technology (ICT) and energy efficiency in emerging Asian economies, utilizing data from 1990 to 2019. Their findings indicated that the advancement of ICT can positively influence energy efficiency. The positive effect of ICT development on improving energy efficiency is mainly reflected in the energy conservation and efficient use through technological innovation and system optimization, such as smart grids, system-level optimization and simulation, industrial automation and intelligent manufacturing. Second, the digital economy has inherent advantages and basic features such as inter-temporal information dissemination, data creation and sharing, which can effectively reduce the financial costs of enterprises. At the same time, it provides enterprises with diversified financing channels and improves the mismatch of credit resources, This gradually enhances energy efficiency. Furthermore, digital technologies such as artificial intelligence and big data can significantly drive industrial optimization and advancement, facilitate the effective distribution of production efficiency, and boost energy efficiency [14,15,16]. Based on this, the subsequent hypotheses are suggested:

H1. 

The development of digital economy facilitates the transition to clean energy.

2.2. Way the Digital Economy Influences the Shift Towards Clean Energy

2.2.1. Upgrading of Industrial Structure

Analyzed from the perspective of reducing energy consumption, the digital economy and traditional industries have gradually realized cross-fertilization, accelerated the process of digital transformation of traditional industries [17,18,19], and through the adjustment of the industrial structure, the proportion of digital and technology-intensive industries has risen, which is able to reduce the consumption of traditional energy [20,21]. Analyzed from the perspective of increasing the proportion of clean energy, digital finance promotes inter-regional technology trade and spillover and constantly creates a large number of new industries, new business forms, and new models, which is conducive to the continuous development of clean energy technology and business forms, playing a key role in clean energy transformation. In light of this, the subsequent hypothesis is suggested [22].

H2. 

The advancement of the digital economy encourages the transition to clean energy by enhancing the industrial structure, which in turn supports the shift toward clean energy.

2.2.2. Technological Innovation and Clean Energy Transition

From the viewpoint of businesses, in the age of digitalization and competitive markets, the research and development sectors of digital companies have expedited the creation and implementation of new technologies, including recycling and other environmentally friendly technologies, which have significantly decreased the use of traditional energy sources [23,24]. From the government’s standpoint, the advancement of the digital economy and the Internet not only enhances the efficiency of green total factor energy and energy production technologies but also lowers energy consumption during the production processes of companies [25]. Consequently, the digital economy can facilitate the shift toward clean energy through technological advancements. Based on this, the following hypothesis is suggested:

H3. 

The growth of the digital economy fosters the transition to clean energy by encouraging technological innovation and thereby enhancing energy efficiency and sustainability.

The logical relationship of the research hypotheses is shown in Figure 1.

3. Model Setting and Variable Selection

3.1. Data Source

The study utilizes panel data from prefecture-level cities, municipalities, and autonomous regions across 30 provinces, excluding Tibet, Hong Kong, Macao, and Taiwan, covering the years 2013 to 2022. It employs a two-way fixed-effects regression model for analysis. The pertinent information regarding the variables related to the clean energy transition primarily comes from the China Information Yearbook and the China Statistical Yearbook. To conduct an empirical analysis of how the digital economy affects the clean energy transition, the econometric model is formulated in the following manner [26].

In this article, we examine how the digital economy influences research on the transition to clean energy, choose individual, time double fixed model, the specific model is constructed as follows:

$$CE{T}_{i,t}={\beta }_0+{\beta }_{1}DE{C}_{i,t}+{\beta }_{2}EG+{\beta }_{3}FDI+{\beta }_{4}Open+{\beta }_{5}CL+{\beta }_{6}GS+{\gamma }_i+{\mu }_t+{\epsilon }_{i,t}$$

(1)

where the explanatory variable is CET (Clean Energy Transition); the explanatory variable is DEC (Digital Economy); the control variables are EG (Economic Growth), FDI (Foreign Direct Investment), Open (Trade Openness), CL (Consumption Level), and GS (Government Support); γ_i is an individual fixed effect; σ_t is a year fixed effect; and ε_it is a randomized disturbance term.

Subsequently, on the basis of the research on the effects of the digital economy on clean energy transformation [27], this paper delves deeper into the impact of upgrading industrial structures and technological innovation within the mechanism, with a specific model developed as outlined below:

(1)

Industrial structure upgrading

$$UI{S}_{i,t}={\beta }_0+{\beta }_{1}DE{C}_{i,t}+{\beta }_{2}EG+{\beta }_{3}FDI+{\beta }_{4}Open+{\beta }_{5}CL+{\beta }_{6}GS+{\gamma }_i+{\mu }_t+{\epsilon }_{i,t}$$

(2)

$$CE{T}_{i,t}={\beta }_0+{\beta }_{1}DE{C}_{i,t}+{\beta }_{2}UI{S}_{i,t}+{\beta }_{3}EG+{\beta }_{4}FDI+{\beta }_{5}Open+{\beta }_{6}CL+{\beta }_{7}GS+{\gamma }_i+{\mu }_t+{\epsilon }_{i,t}$$

(3)

where the explanatory variable is CET (Clean Energy Transition); the explanatory variable is DEC (Digital Economy); the mediator variable is UIS (Upgrading of Industrial Structure); the control variables are EG (Economic Growth), FDI (Foreign Direct Investment), Open (Openness to Trade), CL (Consumption Level), and GS (Government Support); and γ_i is an individual fixed effect; σ_t is a year fixed effect; ε_it is the randomized disturbance term.

(2)

technological innovation

$$T{I}_{i,t}={\beta }_0+{\beta }_{1}DE{C}_{i,t}+{\beta }_{2}EG+{\beta }_{3}FDI+{\beta }_{4}Open+{\beta }_{5}CL+{\beta }_{6}GS+{\gamma }_i+{\mu }_t+{\epsilon }_{i,t}$$

(4)

$$CE{T}_{i,t}={\beta }_0+{\beta }_{1}DE{C}_{i,t}+{\beta }_{2}T{I}_{i,t}+{\beta }_{3}EG+{\beta }_{4}FDI+{\beta }_{5}Open+{\beta }_{6}CL+{\beta }_{7}GS+{\gamma }_i+{\mu }_t+{\epsilon }_{i,t}$$

(5)

where the explanatory variable is CET (Clean Energy Transition); the explanatory variable is DEC (Digital Economy); the mediator variable is TI (Technological Innovation); the control variables are EG (Economic Growth), FDI (Foreign Direct Investment), Open (Trade Openness), CL (Consumption Level), and GS (Government Support); and γ_i is the individual fixed effect; σ_t is the year fixed effect; ε_it is the randomized perturbation term.

3.2. Variable Selection

3.2.1. Core Explanatory Variables

The core explanatory variable of the thesis is DEC, and since the research on digital economy in the thesis mainly focuses on the application level, drawing on the idea of Huang and Pan (2022) [21], a total of seven variables have been chosen for the digital economy index system, which is based on two primary indicators: digital industrialization and industrial digitization. The entropy weight method was then applied to compute the digital economy index for prefecture-level cities (including districts and municipalities) across 30 provinces from 2013 to 2022, excluding Tibet, Hong Kong, Macao, and Taiwan due to data availability. The data sources primarily include the China Information Yearbook and the China Statistical Yearbook, with information on digital inclusive finance sourced from the “Peking University Digital Inclusive Finance Index (2011–2022)” [28].

3.2.2. Explanatory Variables

The explanatory variable of the paper is Clean Energy Transition (CET). Taking into account the approaches of Tang et al. (2022) [23] we select a specific definition for our category and represent it by the proportion of non-fossil energy consumption relative to total energy consumption. The data on non-fossil energy consumption were sourced from the Energy Economy Data Platform at the Center for Energy [29].

3.2.3. Mediation Variables

(1)

Upgrading of Industrial Structure (UIS)

Industrial structure upgrading refers to the transformation and upgrading of industrial structure from a lower form to a higher form in the process of economic growth and development [30]. Referring to Li et al. (2020) [24] and Li and Gai (2019) [25], the study employs the ratio of the output value of the tertiary sector to that of the secondary sector as a measure of the advancement in the industrial structure (UIS).

(2)

Technology Innovation (TI)

Arundel and Kabla argue that the number of patents is one of the most appropriate indicators for evaluating regional innovation capacity, and the number of patents granted is the most prevalent and dependable indicator of technological advancement [31]. Considering that design patents only protect the appearance of products, the degree of embedded technological innovation is low. Therefore, the paper adopts the sum of invention patents and utility model patents granted to indicate technological innovation.

3.2.4. Control Variables

The paper selects five control variables that determine the clean energy transition.

First, economic growth (EG). As urbanization rises across various areas, the swift expansion of economic wealth has emerged as a key driver of energy development [32]. Consequently, economic growth is selected as a control variable influencing the shift towards clean energy, which is expressed by GDP per capita [33].

Second, foreign direct investment (FDI). It promotes the consumption of renewable energy by accelerating investment activities in the market [34]. On the other hand, FDI also encourages industrial growth, which does not support the transition to clean energy. As a result, foreign direct investment (FDI) plays a significant role in energy transformation, although the exact nature of its potential effects remains uncertain.

Third, trade openness (Open). Referring to the study of Zhang et al. [30], the proportion of total imports and exports relative to the regional GDP for each area is selected to measure trade openness.

Fourth, consumption level (CL). It is expressed by per capita consumption expenditure.

Fifth, government support (GS). Refer to the research of Fang (2025) [35], it is expressed by using the share of public service expenditures in fiscal expenditures to GDP.

4. Empirical Analysis

4.1. Descriptive Analytics

In this paper, before the analysis, the collected data pre-processed through methods such as shrinkage and elimination to minimize the influence of extreme values and reduce the heteroskedasticity, thereby improving the accuracy and effectiveness of subsequent analyses. Details of the data collection and processing process can be found in Table S1 of the Supplementary Materials. Subsequently, to grasp the overall traits of the sample, this paper uses STATA data processing software to carry out descriptive analysis of the collected data. The results are shown in

Table 1. Digital economy indicators.

Level 1 Indicators	Secondary Indicators
Digital industrialization	Main business income of electronic information manufacturing industry (billion yuan)
	Software business revenue (billions of dollars)
	Total telecommunications business (billions of dollars)
Industrial digitization	Websites per 100 enterprises (number)
	Computers per 100 population (units)
	E-commerce sales (billions of dollars)
	Digital Inclusive Finance Index

below.

Based on the analysis above, the maximum value of Clean Energy Transition (CET) is 0.119, indicating that the use of clean energy has taken up a larger proportion of energy consumption in some regions, and that significant progress has been made in enhancing the energy framework and safeguarding the environment. The minimum value is 0, indicating that there are still some regions with very little clean energy transition and still rely heavily on fossil energy. Overall, the mean value of clean energy transition is 0.013, indicating average clean energy transition about country is still in primary stage. Median is 0.007, slightly lower than the mean value, and the data distribution is tilted to the left, implying that the progress of clean energy transition in most regions has not yet reached the average level, and the transition needs to be strengthened. The standard deviation is 0.018, indicating that although all regions are promoting clean energy transition, there are obvious differences in the progress and strength of transition. The maximum value for Digital Economy (DEC) is 0.142, indicating that some regions have made significant progress in DEC. The minimum value is 0.003, expressing that some regions are still at a very early stage in building the digital economy. Overall, the average value of the digital economy is 0.023, embodying that China’s DEC as a whole is still in its infancy. The median is 0.014, slightly lower than the mean, and the data distribution is tilted to the left, implying that the level of the digital economy in most regions has not yet reached the national average, and there is more room for improvement. The standard deviation is 0.024, signifying that there are clear disparities in the advancement of the digital economy across various regions. The maximum value of Upgrading of the Industrial Structure (UIS) is 3.806, indicating that some regions have achieved remarkable results in optimizing and upgrading their industrial structure, forming a highly developed and diversified industrial system. The minimum value is 0.371, indicating that the industrial structure of some regions is relatively homogeneous. Overall, the mean value of industrial structure upgrading is 1.145, indicating that China’s industrial structure is at a critical stage of transformation and upgrading. The median is 1.012, close to the mean but slightly lower, indicating that there are more regions with improvement of the industrial framework close to the average level. The typical deviation is 0.581, suggesting that there are variations in the advancement of industrial structure upgrading across different regions. The maximum value of technological innovation (TI) is 116,113, indicating that some regions are leading in the field of technological innovation. The minimum value is 33, indicating a relative lack of technological innovation activities in some regions. Overall, the mean value of technological innovation is 10,101.08, indicating that technological innovation has reached a certain scale, but the median value of 2363.5 is much lower than the mean value, indicating that the level of technological innovation in most regions is lower than the national average, and that there is a large room for improvement. The standard deviation is 20,856.6, indicating that different regions have large differences in technological innovation capability.

From the descriptive analysis of the control variables, the maximum value of economic growth (EG) is 180,844, which shows the growth strength of some economically developed regions. The minimum value is 14,876, which shows the relative lag of economic growth in some less developed regions. Overall, the mean value of economic growth is 59,977.4, indicating that the overall economy of China is currently at a medium level of development. The median is 50,467, which is slightly lower than the mean, indicating that the level of economic growth in most regions has not yet reached the national average. The standard deviation is 34,774.39, indicating that there are significant variations in the degree of economic growth among different regions. The maximum value of foreign direct investment (FDI) is 1,300,000, which shows that some regions with a significant level of economic openness and a favorable investment environment have attracted a large inflow of foreign capital. The minimum value is 52, indicating that some other regions still have much room for improvement in attracting foreign investment. Overall, the mean value of foreign direct investment is 95,909.79, suggesting that the country’s attractiveness to foreign investment is at a medium level. The median is 24,952.5, which is lower than the mean, indicating that the scale of FDI in most regions has not yet reached the national average. The standard deviation is 200,187.4, suggesting that there are significant disparities in the scale of FDI in different regions. The maximum value of trade openness (Open) is 1.809, indicating that some regions with a high degree of openness to the outside world are highly dependent on international trade for their economic activities. The minimum value is 0.001, which shows that some inland or relatively closed economic regions have a very low proportion of international trade in their economies. Overall, the mean value of trade openness is 0.203, indicating that China’s overall economy is moderately open. The median is 0.087, much lower than the mean, indicating that the trade openness of most regions has not yet reached the national average. The standard deviation is 0.311, indicating that there are large differences in trade openness among different regions. The maximum value of the consumption level (CL) is 55,162, and the minimum value is 13,054.2, indicating that the consumption level shows obvious regional differences. Overall, the mean value of the consumption level is 25,069.65, indicating that the overall consumption capacity of China is at a medium level. The median is 23,578.4, which is slightly lower than the mean, indicating that the consumption level of most regions has not yet reached the national average. The standard deviation is 8232.837, indicating that there is a big difference in consumption level between different regions. The maximum value of government support (GS) is 0.276, indicating that some local governments have invested more in public services. The minimum value is 0.033, indicating that some local governments invest relatively little in public service expenditures, and may face financial pressure or have different development priorities. Overall, the mean value of government support is 0.089, indicating that the average ratio of public service expenditures to GDP is moderate at the national level. The median is 0.077, slightly below the mean, indicating that the level of government support in most regions has not yet reached the national average. The standard deviation is 0.045, indicating that there are some differences in the level of government support in different regions.

4.2. Correlation Analysis

In order to have a preliminary understanding of the correlation between the variables, in this part, a Pearson correlation analysis was conducted on the variables, and the findings are presented in

Table 2. Descriptive analysis.

Variable	N	Mean	Median	SD	Min	Max
CET	2800	0.013	0.007	0.018	0	0.119
DEC	2800	0.023	0.014	0.024	0.003	0.142
UIS	2800	1.145	1.012	0.581	0.371	3.806
TI	2800	10,101.080	2363.500	20,856.600	33	116,113
EG	2800	59,977.400	50,467	34,774.390	14,876	180,844
FDI	2800	95,909.790	24,952.500	200,187.400	52	1,300,000
Open	2800	0.203	0.087	0.311	0.001	1.809
CL	2800	25,069.650	23,578.400	8232.837	13,054.200	55,162
GS	2800	0.089	0.077	0.045	0.033	0.276

below.

Table 2 above demonstrates the correlation levels between the variables in this paper, and the correlation analysis of the core variables with Clean Energy Transition (CET) shows that the correlation coefficient between Digital Economy (DEC) and Clean Energy Transition (CET) is 0.349, and its significance p < 0.01 indicates that there is a significant correlation between Digital Economy (DEC) and Clean Energy Transition (CET). The correlation coefficient of industrial structure upgrading (UIS) and clean energy transition (CET) is 0.241, and its significance p < 0.01 indicates that there is a significant correlation between UIS and CET. The correlation coefficient between technological innovation (TI) and clean energy transition (CET) is 0.176, and its significance p < 0.01 indicates that there is a significant correlation between technological innovation (TI) and clean energy transition (CET).

From the correlation analysis of control variables with clean energy transition (CET), it can be seen that the correlation coefficient between economic growth (EG) and clean energy transition (CET) is 0.093, and its significance p < 0.01, suggesting that there is a strong relationship between EG and CET.

The correlation coefficient of foreign direct investment (FDI) and clean energy transition (CET) is 0.039, and its significance p < 0.05 indicates the significant correlation between FDI and clean energy transition (CET). The correlation coefficient of trade openness (Open) and clean energy transition (CET) is 0.176 and its significance p < 0.01 indicates that there is a significant correlation between trade openness (Open) and clean energy transition (CET). The correlation coefficient of Consumption Level (CL) and Clean Energy Transition (CET) is 0.192, and its significance p < 0.01 indicates that there is a significant correlation between Consumption Level (CL) and Clean Energy Transition (CET). The correlation coefficient of government support (GS) and clean energy transition (CET) is 0.066, and its significance p < 0.01 indicates that there is a significant correlation between government support (GS) and clean energy transition (CET).

The outcomes of the correlation analysis merely indicate a potential connection from two or two variables, but the mechanism of influence between multiple variables needs to be demonstrated by further regression analysis. Before regression, in order to avoid biased results caused by potential strong correlations between variables, the variables were tested for multicollinearity, as detailed in

Table 3. Correlation analysis.

Variable	CET	DEC	UIS	TI	EG	FDI	Open	CL	GS
CET	1
DEC	0.349 ***	1
UIS	0.241 ***	0.285 ***	1
TI	0.176 ***	0.758 ***	0.258 ***	1
EG	0.093 ***	0.541 ***	0.0200	0.584 ***	1
FDI	0.039 **	0.628 ***	0.210 ***	0.681 ***	0.511 ***	1
Open	0.176 ***	0.534 ***	0.00500	0.501 ***	0.468 ***	0.370 ***	1
CL	0.192 ***	0.629 ***	0.205 ***	0.669 ***	0.783 ***	0.456 ***	0.512 ***	1
GS	0.066 ***	−0.212 ***	0.303 ***	−0.274 ***	−0.586 ***	−0.271 ***	−0.303 ***	−0.382 ***	1

Annotation: *** and ** indicate significance at 1% and 5% levels, respectively.

below.

According to

Table 4. Multiple covariance test.

Variable	VIF	1/VIF
EG	3.713	0.269
CL	3.595	0.278
TI	3.301	0.303
DEC	2.894	0.345
FDI	2.138	0.468
GS	1.816	0.551
Open	1.593	0.628
UIS	1.39	0.719
Mean VIF	2.555	-

below, the average VIF is 2.555, and each variable’s VIF is below the critical threshold of 10. This indicates that there is no significant multicollinearity among the variables, a point that can be further supported by regression analysis.

4.3. Benchmark Regression Results

There are many models for regression analysis, and when conducting the actual analysis, it is often necessary to use different models according to the nature of the panel data, so how to determine the applicable model for the panel data is crucial to the regression results. In this regard, we often use F-test and Hausman test to choose. According to the results of the F-test, we can choose between mixed regression model and fixed effect regression model, while the Hausman test can help us distinguish between the fixed effect regression model and the random effect regression model. The test shows that F (279, 2514) = 10.22 and p = 0.0000 < 0.05, which shows that the fixed effect model outperforms the mixed OLS model in terms of model selection. Another Hausman test found that chi² (3) = 16.93 and p = 0.0007 < 0.05, which shows that the fixed effect model is superior to the random effect model in terms of model selection. Therefore, before the subsequent study is adopted in conducting the research, taking into account the different data units and magnitudes used in this paper may have a significant impact on the results of the study, this paper will be standardized when conducting the study to eliminate the differences in the scale of the information among the data so that data of different magnitudes or units can be analyzed on the same scale.

Table 5. Benchmark regression.

Variables	(1)	(2)	(3)	(4)
	CET	CET	CET	CET
DEC	0.3492 ***	0.4037 ***	0.4881 ***	0.3572 ***
	(19.715)	(12.046)	(18.503)	(10.664)
EG		0.1768 ***	0.0158	0.0841
		(3.511)	(0.483)	(1.628)
FDI		−0.0972 **	−0.2119 ***	−0.0718 *
		(−2.465)	(−9.037)	(−1.844)
Open		0.0664	0.1056 ***	0.0119
		(1.138)	(4.600)	(0.201)
CL		0.1247 ***	−0.1996 ***	−0.2053 ***
		(3.841)	(−4.860)	(−3.813)
GS		0.0071	0.0559 **	−0.0685
		(0.179)	(2.490)	(−1.572)
Constant	1.10 × 10−8	6.51 × 10−9	−0.2155 ***	−0.2821 ***
	(0.000)	(0.000)	(−3.618)	(−5.031)
Individual FE	NO	YES	NO	YES
Year FE	NO	NO	YES	YES
Observations	2800	2800	2800	2800
R-squared	0.122	0.174	0.215	0.205
F	388.7	88.39	50.87	43.15

Annotation: ***, ** and * indicate significance at 1%, 5%, and 10% levels, respectively.

below shows the study of the influence of the DEC on the CET, where column (1) is the study of the impact of the digital economy on the clean energy transition without controlling for other factors and without fixing for individual and time effects. Column (2) is a study of the influence of the DEC on the CET, controlling for other factors and fixing only individual effects. Column (3) is for other factors and fixing only time effects. Column (4) is a study of the impact of the digital economy on the clean energy transition, controlling for other factors and fixing individual and time effects.

As can be seen in column (1) of Table 5, the regression coefficient of Digital Economy (DEC) on Clean Energy Transition (CET) without controlling for other factors and without fixing for individual and time effects is 0.3492, which passes the test of significance at the 1% level. Column (2) shows that the regression coefficient of Digital Economy (DEC) on Clean Energy Transition (CET) is 0.4037, which passes the test of significance at the 1% level, controlling for other factors and fixing for individual effects only. Column (3) shows that the regression coefficient of Digital Economy (DEC) on Clean Energy Transition (CET), controlling for other factors and fixing only the time effect, is 0.4881, which passes the test of significance at the 1% level. Column (4) shows that the regression coefficient of digital economy (DEC) on clean energy transition (CET) is 0.3572, which passes the test of significance at the 1% level, controlling for other factors and fixing the individual and time effects. From the previous analysis, it can be seen that Digital Economy (DEC) has a significant positive effect on Clean Energy Transition (CET) and the regression results are highly consistent with the previous hypothesis. Such a result may be due to the fact that, on the one hand, the booming development of digital economy provides strong technical support for clean energy transition. In the era of digital economy, cutting-edge technologies such as big data, cloud computing, the Internet of Things (IoT), and artificial intelligence have been widely used, and these technologies not only promote the transformation and upgrading of traditional industries but also bring revolutionary changes to the development and utilization of clean energy. For example, through big data analysis, energy demand can be predicted more accurately, optimize energy allocation, and reduce energy waste; cloud computing technology makes the production and distribution process of clean energy more intelligent, and improves the efficiency of energy use; and the application of the Internet-of-Things technology realizes remote monitoring and maintenance of clean energy equipment and reduces operating costs. The integration and application of these technologies provides strong technical support for clean energy transformation and promotes the rapid development of the clean energy industry. On the other hand, the digital economy has created a favorable social atmosphere for clean energy transformation by enhancing public awareness of environmental protection and promoting the popularization of green and low-carbon lifestyles. With the wide application of digital technology, the public’s cognition and understanding of clean energy has been deepening, and the desire and pursuit of a green and low-carbon life become stronger. The formation of this social atmosphere not only promotes the rapid development of the clean energy market but also promotes the continuous innovation and upgrading of clean energy technology.

Simultaneously, the DEC also enhances the engagement and communication between the public, government, and enterprises through channels such as social media and online platforms, promoting the formulation and implementation of clean energy policies that are more in line with public opinion and market demand.

4.4. Robustness Test

To guarantee the dependability and consistency of the research findings, this paper plans to employ two approaches for robustness testing. One approach involves excluding municipalities that are directly governed by the central government, as these areas typically exhibit higher economic development, superior infrastructure, and more abundant policy resources, which may have a significant impact on clean energy transition. Therefore, this paper deletes the samples of municipalities (Beijing, Shanghai, Tianjin, Chongqing) to improve the accuracy and reliability of the study. The results of the analysis are shown in Column (1) of Table 5, which shows that after deleting municipality samples, the regression coefficient of Digital Economy (DEC) on Clean Energy Transformation (CET) is 0.3699, which passes the test of significance at the 1% level. It can be seen that after deleting the group of municipalities that are directly governed by the authority government, the results of the positive impact of the DEC on the clean energy transition (CET) did not change, which is consistent with the previous regression results, indicating that the overall robustness of this study is good. The second method is quantile regression, which is used for robustness testing. This approach verifies whether the impact of the DEC on the clean energy transition remains consistent across different quantiles. This consistency enhances the credibility of the research conclusions. As shown in Table 5, column (2), the DEC has a positive impact on the CET at various quantile points. The regression coefficients are always positive and all of them pass the significance test at the 1% level. It can be seen that after the quantile regression, the positive effect of DEC on CET is significant and stable at different quantile points, which aligns with the earlier regression findings, suggesting that the overall reliability of this study is strong.

4.5. Endogeneity Analysis

In order to avoid the effect of endogeneity among different indicators, this paper uses the generalized method of moments estimation (GMM) with first-order differencing to estimate the model and further test the endogeneity problem. Considering that the digital economy and clean energy transition will not change rapidly in the short term, there may be a certain trend in the digital economy. Considering that there could be a reciprocal causal connection involving the DEC and the clean energy transition, this paper takes the lag period of the core explanatory variable digital economy indicator as an instrumental variable and uses the first-order difference generalized moment estimation method (GMM) to construct a dynamic panel model to further test the endogeneity problem. On the one hand, the current level of clean energy transition will not affect the development of the DEC in the previous period and meet the exogenous requirements of instrumental variables. Conversely, the development of the DEC usually has the characteristics of path dependence and technology accumulation, and there is a strong relationship between the development level of the DEC in the early stage and the current period, which meets the correlation condition of instrumental variables.

In addition, to address the potential issue of endogeneity and over-identification of instrumental variables in differential GMM analysis, the following two tests were conducted. Firstly, an autoregressive) test (AR test) was conducted with the aim of testing whether a model serial correlation exists. It is based on an autoregressive model that verifies the existence of a continuous linear correlation in the data by calculating the autocorrelation coefficient of the data. The purpose of this test is to confirm the appropriateness of the chosen GMM estimation method as well as to verify the presence of serial correlation in the model. In column AR(1) in

Table 6. Robustness test.

Variables	(1)	(2)	(3)	(4)
	Remove	Quantile Regression
	CET	0.25	0.5	0.75
DEC	0.3699 ***	0.3154 ***	0.4030 ***	0.5604 ***
	(10.775)	(17.554)	(20.012)	(21.987)
EG	0.1118 **	0.0371 ***	0.0830 ***	0.1499 ***
	(2.114)	(4.745)	(4.963)	(4.563)
FDI	−0.1048 **	−0.0946 ***	−0.0680 **	−0.2841 ***
	(−2.403)	(−10.514)	(−2.073)	(−17.814)
Open	0.0039	−0.0362 **	−0.1116 ***	0.0226 ***
	(0.065)	(−2.222)	(−14.059)	(4.731)
CL	−0.1891 ***	−0.0725 ***	−0.0177	0.0262 ***
	(−3.449)	(−6.619)	(−0.401)	(4.265)
GS	−0.0641	−0.0048	0.0777 ***	0.3096 ***
	(−1.462)	(−0.476)	(5.734)	(9.730)
Constant	−0.2482 ***
	(−4.302)
Individual FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observations	2760	2800	2800	2800
R-squared	0.211	-	-	-
F	43.89	-	-	-

Annotation: *** and ** indicate significance at 1% and 5% levels, respectively.

, the results of the first order serial correlation test are provided. AR(2) provides the results of the second order serial correlation test. Next, Hansen’s test was conducted to test the validity of the instrumental variables and the consistency of the GMM estimation method. The purpose of this test is to ensure that the instrumental variables fulfill the relevant identification conditions and to verify the reliability of the GMM estimation method.

According to Table 6, it can be seen that the p-value of AR(1) is less than 0.05, which indicates significance, while the p-value for the AR(2) model exceeds 0.05, suggesting that non-significance. This indicates that there is no serial correlation present in the regression, suggesting that the model successfully addresses the endogeneity issue. The result of Hansen test indicates that the p-value of instrumental variables is greater than 0.1, suggesting that the instrumental variables are valid and there is no over-identification problem. The regression coefficient of 0.134 for Digital Economy (DEC) on Clean Energy Transition (CET) passes the test of significance at 10% level. It can be seen that the result that the Digital Economy (DEC) has a positive effect on the Clean Energy Transition (CET) has not changed, which means that the endogeneity problem does not affect the findings above, and the endogeneity test is passed.

4.6. Influence Mechanism Test

Based on the results of the previous study, it is evident that the development of the digital economy can significantly promote the clean energy transformation. But through what specific mechanisms does the digital economy influence the clean energy transformation? The theoretical analysis part puts forward the hypothesis that the impact of digital economic development on clean energy transformation is realized through two channels: technological innovation and industrial structure upgrading. This paper adopts the mediation effect model to verify the two impact channels of technological innovation and industrial structure upgrading.

The core of the two-step test is to assess the influence of the independent variable on the dependent variable and then assess independent variable on the mediating variable. As for effect from the mediator variable on the dependent variable, it is proved by the conclusions of existing studies. Considering that the first step has already been discussed in the Benchmark Regression section, this section will focus on subsequent steps and avoid repeating previously covered content. According to the results of

Table 7. GMM model test.

Variables	(1)
	CET
LCET	0.581 ***
	(7.463)
DEC	0.134 *
	(1.742)
EG	2.04 × 10−8
	(0.393)
FDI	2.68 × 10−9
	(0.343)
Open	−0.00158
	(−0.272)
CL	2.23 × 10−7
	(1.370)
GS	−0.0154
	(−0.442)
Observations	2240
AR(1)	0.000
AR(2)	0.421
Hansen test	0.147

Annotation: *** and * indicate significance at 1% and 10% levels, respectively.

, it can be seen that in column (1), the regression coefficient of digital economy (DEC) to industrial structure upgrading (UIS) is 0.0340, which passes the significance test at the 10% level, which shows that the digital economy has a significant positive impact on industrial structure upgrading, and the second step of the corresponding test is established. At the same time, existing studies have proved that there is a significant positive correlation between industrial structure upgrading (UIS) and clean energy transition (CET), and when the industrial structure is further upgraded (UIS), regional clean energy transition (CET) will also be further developed.

The above results show that the development of the DEC can promote the clean energy transition by promoting the upgrading of the industrial structure. This result is mainly because of the ongoing improvement of the industrial framework and the rapid development of emerging industries driven by the digital economy. This growth not only raises the overall level of wealth in society but also provides an ample source of funding for the investment in green industries such as clean energy. At the same time, with the upgrading of the industrial structure, the energy consumption structure has also undergone significant changes, and the proportion of clean energy in energy consumption has been increasing, laying a solid foundation for the green transformation of the energy structure. In the same way, the regression coefficient of digital economy (DEC) to technological innovation (TI) is 0.2872, which passes the significance test at the level of 1%, which shows that the digital economy has a significant positive impact on technological innovation, and the second step of the corresponding test is established. At the same time, existing studies have shown a positive correlation between technological innovation (TI) and clean energy transition (CET). It is evident that the development of the DEC can promote the clean energy transition by promoting technological innovation This result may be due to the rapid development of cutting-edge technologies like big data, cloud technology, and AI, driven by the digital economy, which not only provide powerful tools for clean energy research and development but also promote continuous innovation and breakthroughs in clean energy technology.

Through the empowerment of the DEC, the production efficiency of clean energy can be improved, the cost can be reduced, and the reliability can be enhanced, thus laying a solid foundation for the widespread application of clean energy. The innovation of clean energy technology is not only reflected in the breakthrough of the technology itself but also in the expansion and deepening of technology application. On the one hand, the innovation of CET has promoted the development of the clean energy industry, making the proportion of clean energy in the energy structure continue to increase, providing strong support for the optimization and transformation of the energy structure. On the other hand, technological innovation has also promoted the commercialization of clean energy technology, making the application of clean energy technology more extensive and deeper, creating favorable conditions for the acceleration of clean energy transformation.

4.7. Analysis of Regional Heterogeneity

Considering that the promotion effect of the DEC on clean energy transition may show heterogeneity in various areas because of the significant differences in the degree of economic development among Chinese regions, the paper divides the sample of the study into three regions, namely, East, Central, and West, according to the provinces they belong to for regression analysis. Among them, 12 provinces and municipalities directly under the central government, including Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Guangxi, and Hainan, are divided into the eastern region; 9 provinces and autonomous regions, including Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan, are divided into the central region; and 9 provinces, namely, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Ningxia, Qinghai, and Xinjiang, autonomous regions and municipalities directly under the central government are divided into the western region, and the results of regional heterogeneity analysis are shown in

Table 8. Impact mechanism test.

Variables	(1)	(2)
	UIS	TI
DEC	0.0340 *	0.2872 ***
	(1.827)	(11.456)
UIS
TI
EG	−0.2683 ***	0.0682 *
	(−9.357)	(1.763)
FDI	0.0615 ***	−0.0492 *
	(2.847)	(−1.690)
Open	0.0353	−0.1237 ***
	(1.072)	(−2.783)
CL	−0.1555 ***	0.5797 ***
	(−5.204)	(14.385)
GS	0.1973 ***	0.0475
	(8.162)	(1.456)
Constant	−0.7331 ***	0.3294 ***
	(−23.567)	(7.849)
Individual FE	YES	YES
Year FE	YES	YES
Observations	2800	2800
R-squared	0.579	0.283
F	229.4	65.92

Annotation: *** and * indicate significance at 1% and 10% levels, respectively.

.

The outcomes are shown in Table 8. For the eastern region, the regression coefficient of the DEC on the clean energy transition (CET) is 0.1696, which passes the significance test at the 1% level. In the central region, the regression coefficient of the DEC on the clean energy transition (CET) is 0.4877, which passes the significance test at 1% level. For the western region, the regression coefficient of the DEC on the clean energy transition (CET) is 0.5584, which passes the significance test at 1% level. It can be seen that the digital economy in the western region has a stronger role in promoting the clean energy transition. Such a result is mainly due to the fact that the western region has a unique advantage in terms of clean energy resources. The western region has plentiful renewable energy sources such as solar energy and wind energy, and the development and utilization of these resources are important for promoting the clean energy transformation. The rapid development of the digital economy has provided strong support for the intelligent and efficient utilization of these resources, thus accelerating the process of clean energy transformation. In contrast, the central and eastern regions may have certain limitations in terms of clean energy resources. Although these regions are also rich in energy resources, the types and quantities of their clean energy resources may be more limited compared to those in the western region. In addition, the central and eastern regions may be more dependent on traditional energy industries, such as coal, oil, and natural gas, in their economic development. While these traditional energy industries drive economic growth, they also put greater pressure on the environment. As a result, the central and eastern regions may face greater challenges and difficulties in promoting the clean energy transition, thus weakening the facilitating role of the digital economy in the clean energy transition.

4.8. Analysis of Heterogeneity in Economic Growth Levels

The level of economic growth reflects the economic vitality and development potential of a region, and some noticeable differences in resource allocation, industrial structure, and policy orientation among regions with different levels of economic growth. These differences may lead to different impacts of digital economy on clean energy transition. Therefore, this paper divides the sample into two groups for in-depth analysis based on the median economic growth level in order to explore the differences in the role of digital economy in different economic contexts, and the specific analysis is shown in

Table 9. Analysis of regional heterogeneity.

Variables	(1)	(2)	(3)
	Eastern	Central	Western
DEC	0.1696 ***	0.4877 ***	0.5584 ***
	(3.137)	(10.596)	(6.235)
EG	0.0764	−0.1082	0.5561 ***
	(1.005)	(−1.268)	(3.478)
FDI	−0.0866 *	0.0675	−0.0781
	(−1.696)	(0.818)	(−0.594)
Open	0.0515	−0.2404 *	0.2607
	(0.674)	(−1.931)	(1.053)
CL	−0.1581 *	−0.0477	−0.4919 ***
	(−1.915)	(−0.426)	(−3.605)
GS	−0.0230	−0.1088 *	−0.0798
	(−0.213)	(−1.748)	(−1.058)
Constant	−0.2857 **	−0.2102	0.1075
	(−2.435)	(−1.608)	(0.533)
Individual FE	YES	YES	YES
Year FE	YES	YES	YES
Observations	1130	1080	590
R-squared	0.181	0.258	0.287
F	14.74	22.24	13.87

Annotation: ***, ** and * indicate significance at 1%, 5%, and 10% levels, respectively.

.

The regression results, as shown in Table 9, show that in high economic growth regions, the regression coefficient of DEC on CET is 0.1849, which passes the significance test at the 1% level. In the low economic growth region, the regression coefficient of the DEC on the CET is 0.5103, which passes the significance test at 1% level. hese findings suggest that the development of the DEC in low economic growth regions has a stronger contribution to the clean energy transition. Such a result is mainly due to the fact that high economic growth regions tend to have more developed economic systems and diversified industrial structures, and these regions may be more complex and diversified in terms of energy consumption and energy structure.

The process of promoting the clean energy transition in the high-economic-growth regions may face more challenges and difficulties, such as the modernization and enhancement of conventional energy sectors, the innovation and implementation of clean energy technologies, as well as the competition and oversight of energy markets. These challenges and difficulties may, to some extent, undermine the facilitating role of the digital economy in the clean energy transition. In contrast, low economic growth regions may be more homogeneous and backward in terms of the economic structure and development stage, with relatively simple energy consumption and the energy structure. These regions may be more dependent on external technical support and financial inputs in promoting the clean energy transition, and the DEC is one of the important ways to provide such support and inputs. Through the application and promotion of digital technologies, low economic growth regions can make more effective use of clean energy resources, improve energy use efficiency, and reduce environmental pollution, thereby accelerating the process of clean energy transition. The relevant data analysis is shown in

Table 10. Heterogeneity analysis of the level of economic growth.

Variables	(1)	(2)
	High Economic	Low Economic
DEC	0.1849 ***	0.5103 ***
	(5.192)	(9.015)
EG	0.1467 ***	0.0973
	(3.124)	(0.452)
FDI	−0.0876 ***	0.3036
	(−2.867)	(1.060)
Open	0.0308	−0.0835
	(0.583)	(−0.563)
CL	−0.0639	−0.2016
	(−1.209)	(−1.498)
GS	0.1432 *	−0.0975
	(1.664)	(−1.542)
Constant	−0.1399	−0.0242
	(−1.592)	(−0.096)
Individual FE	YES	YES
Year FE	YES	YES
Observations	1400	1400
R-squared	0.216	0.243
F	22.27	25.94

Annotation: *** and * indicate significance at 1% and 10% levels, respectively.

below.

4.9. Analysis of Spatial Spillover Effects

In this paper, the reciprocal of the geographical distance between cities is used to construct the spatial weight matrix, and the findings suggest that the Moran index of the DEC on the CET from 2013 to 2022 passes the significance test at the 1% level. After determining the existence of spatial effects, the LM test and the Hausman test were further used in this paper, and the test results rejected the null hypothesis of random effects, so the fixed-effect spatial Durbin model was used. At the same time, the Wald test is also used to analyze whether the spatial Durbin model will degenerate into a spatial autoregressive model or a spatial error model, and the test results show that the study is suitable for the spatial Durbin model, and the final regression results can be seen in

Table 11. Regression results of the spatial Durbin model.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)
					Spatial	Variance	LR_Direct
	Main	Wx	Spatial	Variance	LR_Direct	LR_Indirect	LR_Total
ZDEC	0.331 ***	0.511 ***			0.338 ***	0.713 ***	1.051 ***
	(9.928)	(3.880)			(9.923)	(4.542)	(6.500)
ZEG	0.010	0.009			0.008	0.011	0.019
	(0.167)	(0.063)			(0.133)	(0.071)	(0.138)
ZFDI	−0.063	−0.290 *			−0.063 *	−0.358 *	−0.421 **
	(−1.639)	(−1.875)			(−1.676)	(−1.868)	(−2.114)
ZOpen	0.099	−0.122			0.098	−0.119	−0.021
	(1.584)	(−0.801)			(1.616)	(−0.653)	(−0.122)
ZCL	−0.492 ***	0.485 ***			−0.488 ***	0.479 ***	−0.008
	(−6.192)	(4.204)			(−6.477)	(3.891)	(−0.092)
ZGS	−0.082 *	−0.103			−0.081 *	−0.141	−0.222 **
	(−1.685)	(−1.057)			(−1.716)	(−1.235)	(−2.109)
rho			0.200 ***
			(3.662)
Sigma2e				0.400 ***
				(35.464)
Observations	2520	2520	2520	2520	2520	2520	2520
R-squared	0.218	0.218	0.218	0.218	0.218	0.218	0.218
Number of id	280	280	280	280	280	280	280
Individual FE	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES

Annotation: ***, ** and * indicate significance at 1%, 5%, and 10% levels, respectively.

.

According to the specific results in the table, the indirect effect of the DEC is 0.713, and p < 0.01, indicating that there is a significant positive spillover effect of the DEC on the CET at the 1% significant level. That is, the development of the DEC will increase by 1 unit, which will promote the CET in the surrounding area by 0.713 units. It can be concluded that the DEC can significantly contribute to the CET not only in the region but also in the surrounding areas. The reason for the above results may be that the digital economy itself is based on advanced information technology, and the development process is accompanied by the generation and dissemination of a large number of new technologies. Different regions are not developing in isolation in the field of digital economy but through multiple channels for technological exchanges and cooperation. When the digital economy of a certain region develops rapidly, the new energy management technology of the region can quickly spread to the surrounding areas through technical cooperation and other means. By learning from these advanced technologies, the surrounding areas can improve their own clean energy technology level, thereby accelerating the process of the local clean energy transformation.

5. Conclusions and Recommendations

5.1. Conclusions of the Study

In the context of the “double carbon” strategy, promoting clean energy transition has emerged as a new catalyst for economic growth, a new advantage for national competition, and an important means of reshaping the new pattern of international competition. In the context of the rapid development of the DEC and its continuous integration with economic and social fields, the paper explains the theoretical path of the DEC to promote the clean energy transition from two channels, namely, technological innovation and improvement of the industrial framework. On the basis of measuring the index of DEC development, the two-way fixed effect model, the differential GMM model, the mediation effect model, and the entropy weight method are comprehensively applied to test the effect and the influence mechanism of DEC development on the CET from multiple dimensions.

The conclusions of the study are as follows: First, digital economic development effectively promotes clean energy transformation. Second, technological innovation and industrial structure upgrading are two intermediary channels through which the digital economy affects clean energy transformation. Third, the impact of the DEC on the advancement of the CET has regional heterogeneity.

5.2. Policy Recommendations

The paper not only verifies the impact effect and the influence mechanism of digital economic development affecting the clean energy transformation but also provides a scientific basis for government departments to formulate and implement relevant policies.

First of all, it is essential to further strengthen the technical foundation of DEC development and promote its wider and deeper integration into the real economy. The state should increase investment to accelerate the construction of “Digital China”; especially in promoting the commercialization of 5G, big data, and artificial intelligence applications, etc., to ensure that the technical foundation of the digital economy is strengthened and the dividends brought by the DEC are fully released and consolidated. In addition, the government should actively promote and guide the digital transformation of traditional industries. To achieve this goal, the government can carry out technological transformation for enterprises in terms of technical training and tax incentives so as to lower their technical barriers and reduce their costs. In this way, enterprises can make a better use of digital technology to improve their productivity.

Second, focusing on the objective differences in the level of DEC development between different regions, a differentiated and dynamic digital economy strategy needs to be implemented. First, the government should encourage each region to develop the DEC according to its own economic development and basic conditions. Secondly, for cities in China where the digital economy is not well developed, especially in the west, it is necessary to increase support for these cities, accelerate the construction of digital infrastructure, expand digital coverage, and promote the coordinated development of various regions so as to ultimately achieve the goal of the “clean energy transformation”.

Finally, based on the two main ways of technological innovation and industrial structure upgrading, we should better utilize the effect of the digital economy and explore the path of multi-dimensional promotion of clean energy transformation. On the one hand, the state should increase investment in science and technology education to improve the quality of innovative talents; at the same time, it should also increase the protection of intellectual property rights and provide adequate support in terms of funding, talent, and systems to promote scientific and technological innovation. Simultaneously, it should also further break down the institutional barriers restricting the circulation of production factors and R&D factors in China and realize the smooth circulation of factors. To achieve this goal, it is necessary to open up the circulation channels of factors of production and promote the orderly flow and the effective allocation of factors of production in a shorter period of time.

5.3. Limitations and Future Research

Using Chinese municipal data as an example, this study examines the impact of digital economy development on clean energy transition and investigates the underlying impact mechanisms, providing solutions for environmentally sustainable development as well as lessons for other regions around the world. This study still has the following shortcomings and possible extensions for future research.

(1) In terms of the sample, this study selected Chinese municipal data as the sample. More specifically, in the future, city-level data could be selected for specific studies to obtain more precise and specific results. Similarly, the sample area can also be expanded by selecting macro data from multiple countries’ data to explore more universal laws.

(2) In terms of the intrinsic mechanism, this paper explores the mediating mechanism of the two paths and other variables as well as other types of mechanisms which can be explored in future research to further open the black box of the mechanism of the digital economy affecting the clean energy transition.

(3) In terms of variable selection, the explanatory variables can be further expanded; for example, the digital economy’s first-level indicators of industrial digitization and digital industrialization can be selected as variables for research to explore more specific research directions. It is also possible to replace the explanatory variables and select other energy-related variables, such as carbon emission efficiency and energy efficiency, for research. Alternatively, future research could focus on specific sectors, such as the industrial or agricultural energy transition, to conduct more targeted analyses.