The Impact and Mechanism of the Digital Economy on Carbon Emission Efficiency: A Perspective Based on Provincial Panel Data in China

Abstract

The regional carbon emission efficiency (RCEE) of 30 provinces in mainland China from 2011 to 2019 was calculated using a super-slack-based measure (Super-SBM) model. Then, using the system generalized method of moments (system GMM) model, spatial Durbin model (SDM), and mediating effect model, we examined the direct effect, spatial effect, and influence mechanism of the digital economy (DE) on RCEE. It was found that DE significantly promoted regional RCEE, but had a negative effect on RCEE in provinces with a high economic correlation. The mechanism studies showed that DE improved RCEE by reducing the energy intensity and promoting industrial upgrading and green technology innovation. Regional heterogeneity analysis found that DE significantly improved RCEE in eastern provinces, but not in central and western provinces. While RCEE in economically developed areas was improved by DE, it was decreased in economically underdeveloped provinces. This paper provides some empirical and theoretical references for the development of DE to improve RCEE.

1. Introduction

With the development of cities and industries, many climate chain reactions that are defined by climate warming have become more severe. Increasing human activity and energy demands have contributed to increasing carbon dioxide emissions [1,2]. China has promised to vigorously battle climate change, commit to energy saving and emission reduction, and honor the Paris Agreement by reaching its carbon peak by roughly 2030 and becoming carbon neutral by 2060. This objective shows that, as China’s capacity to sustain its environment declines, the traditional development model of ignoring environmental costs is no longer viable. Instead, full consideration should be given to RCEE, which views carbon emissions (CEs) as an undesirable output [3]. By analyzing the reasons for efficient or inefficient regional RCEE, RCEE can indeed be enhanced through the use of facilitating elements and the elimination of inhibiting factors. To decrease CO₂ and increase energy efficiency would accomplish eco-friendly and robust growth of the economy. Therefore, each region can start by improving RCEE to meet emission reduction targets by reducing CE.

The worldwide web, big data, and the use of the cloud are examples of a new breed of informatics that involves things that have developed quickly in recent years, ushering in the digital era and giving rise to DE, which has, in turn, become one of the key development poles in the whole of the country’s economy, and is manifesting its power and momentum at an unprecedented scale and speed [4,5]. DE, also known as the information economy, is essentially a type of economy characterized by the explosion of data, computing in the cloud, the world wide web, and the three components of productivity (means of production, objects, and workers), which are the key to digital empowerment, increased productivity, and smart manufacturing. Future investment will be focused on building and upgrading new generation information infrastructure as 2G falls behind, 3G imitates to catch up, 4G synchronizes, and 5G surpasses [6], which calls for an increase in production efficiency and total factor allocation efficiency to further unleash social output while raising societal investment in the development of DE. A large number of reports have existed in China and other countries to conduct pertinent research and analysis on the status of construction of DE, and this research is crucial for achieving a DE with a high-quality and rapid development, as well as for boosting the nation’s overall economic power. However, there is a dearth of research on how the DE affects China’s RCEE and its mode of action. In summary, intending to successfully achieve the double carbon target, investigating the impact and mechanism of DE on RCEE is of major practical importance, to lead China and some other countries to alter the style of development, improve the industrial structure, and alter the growth momentum and thus build DE to accomplish the sustainable development of energy efficiency, a low-carbon economy, and emission reduction.

The following factors primarily highlight the essay’s possible dedications. First, the literature on the impact of DE on RCEE is expanded by using the system GMM model and SDM to learn more about how DE affects RCEE. Secondly, this research examined the varied effects of DE on RCEE in various geographic regions and economic development levels, which provides empirical references for future DE to encourage the creation of an economy with low CE following regional and economic development level needs. Finally, the mediating effect model is also employed to study the mechanism of influence of De on RCEE from the perspectives of energy intensity, industrial structure upgrading, and technological innovation. This model serves as a point of reference for future research on how DE affects RCEE.

2. Research Background and Research Hypothesis

2.1. Research Background

To fulfill China’s goal of achieving carbon neutrality by 2060, it is necessary to develop RCEE, which is receiving more and more attention from academics throughout the world. The definition of RCEE, its measurement, and its affecting elements are the three main topics that have been prioritized in previous related studies in terms of study direction and perspective. RCEE is typically split into two categories: single-factor RCEE and full-factor RCEE. Single-factor RCEE is categorized into three basic groups. The first is carbon productivity, expressed as the ratio of GDP to CE [7]. The second is the carbon index, expressed as the ratio of overall energy use to overall CE [8]. The third category is carbon intensity, which is CE per unit of GDP growth [9]. Given that only the relationship between energy and economic efficiency can be represented by a single element, the significance of economic growth cannot be fully understood without taking into account the effects of other factors such as capital, labor, and technology [10]. As various inputs are taken into account, researchers have started to apply data envelopment analysis (DEA) and stochastic frontier analysis (SFA) techniques to measure total factor carbon efficiency. According to previous research, while openness level, government involvement, business ownership structure, technological advancement, and business size have been beneficial for RCEE, economic size, industrial structure, and rate of urbanization have more detrimental consequences [11,12].

In the course of history, DE has been successively presented as an information economy, the internet economy, and other forms, so it is difficult to provide a simple definition from a certain aspect or aspects, and domestic and foreign studies still have not formed a unified concept definition of DE [13]. Because of the ongoing development of information technology, the digital industry continues to develop. The continuous integration of digital elements and traditional factors of production also promotes the pace of digitization of traditional industries. In the process of the advancement of DE, the division of labor within the enterprise is gradually clarified, and the management system is gradually improved, which, on the one hand greatly promotes the rapid growth of the economy, and, on the other hand, reduces the loss of resources and improves the efficiency of resource use [14]. According to the data from the China ICT Academy, China made a breakthrough in the progression of DE in 2021, with an overall scale of 45.5 trillion CNY, an increase of 16.2% compared with the previous year, accounting for 39.8% of the gross national product. The scale of digital industrialization was 8.3 trillion CNY, up 16.2% from the previous year, accounting for 39.8% of the GDP. The scale of digital industrialization was 8.3 trillion CNY, an increase of 11.9% compared with the previous year, accounting for 7.3% of the GNP, building a solid foundation for China’s economic development; the scale of industrial digitization reached 37.2 trillion CNY, an increase of 17.2% compared with the previous year, accounting for 32.5% of the GNP; and the results of the digital transformation of the agricultural, industrial, and service sectors were remarkable, which has become a key engine for China’s economic development [15]. Growing in importance is the role of DE in the national economy, so academics have given it considerable attention, and the study of its impact on low-carbon development has also become a research hotspot.

2.2. Research Hypothesis

2.2.1. The Direct Impact of the DE on RCEE

With the growth of DE, international interest in the field of DE research has increased. The existing research mostly focuses on how DE affects the economy and the environment. First, the economic effects of DE are discussed and analyzed from various perspectives, including macro, micro, and scale measurement, concerning their impact mechanisms. From a macro perspective, new input factors such as digital information generated by the construction of DE will impact how well regional resources are distributed and will thus have a positive impact on economic development, innovation efficiency, and total factor productivity, which have become the core drivers for the region to attain high-quality economic growth [16,17,18]. Barata (2019) argued that higher income and job growth brought on by the construction of DE would be able to lower poverty and inequality in the long run, as these benefits will further strengthen long-term sustainable national economic growth [19]. Using panel data collected nationally from 2008 to 2018, Dong et al. (2022) found that the growth of DE greatly decreased the intensity of the nation’s CE, and that economic expansion, financial deepening, and modernization of the industrial structure acted as a mediating factor between the two [20]. Studies conducted from a micro perspective are more detailed and have clearer impact mechanisms. Some studies start from the effects of DE on reducing micro-individual-firm costs, arguing that the main impacts of DE on firms are the reductions in marginal, search, tracking, transportation, and validation costs [21]. There is additional research on how DE affects a firm’s output efficiency and innovation capacity perspective, confirming that information technology not only has a favorable impact on several aspects of firms’ R&D investment, product design, and process improvement, but also promotes a firm’s innovation initiative [22]. A small amount of literature has been studied in terms of increasing the likelihood of entrepreneurship, arguing that internet use can increase entrepreneurship rates through, among other things, information channels and social interaction effects, and in doing so, stimulate economic dynamism and bolster economic growth [23,24]. The consequences of DE on the environment have steadily come under academic scrutiny in recent times. According to Zhou et al. (2021), East China is especially impacted by the growth of China’s DE in terms of lowering haze pollution. Haze pollution can be reduced by changing the energy system and promoting innovation [25]. Li et al. (2021) claimed that between 2003 and 2018, the level of cooperation between the environmental system and the DE system expanded and fluctuated. The development of DE greatly lowered PM2.5 [26]. Xu et al. (2022) thought that through the effects of green development and inventive development, the digital economy reduces environmental degradation [27]. Many scholars have examined the impact of DE on the low-carbon economy [28,29,30]. Studies have been conducted, for instance, on how DE affects low-carbon evolution. A thorough index of low-carbon industry development and an evaluation system for the amount of DE development was created by Wang et al. (2012); the empirical research revealed that DE has a substantial driving influence on the growth of the low-carbon industry [31]. Li et al. (2021) investigated the relationship between energy structure, DE, and CE empirically. The findings showed that CE is significantly driven by the coal-based energy structure and that as DE advances, this relationship steadily weakens [32]. Chen (2022) thought that the DE decreased resource misallocation and enhanced resource efficiency, which, in turn, decreased corporate carbon emission intensity [33]. Third, market drivers and government regulations improved and hindered the ability of the digital economy to reduce corporate carbon emission intensity. By combing through the literature, there have been many relevant studies on RCEE and DE both in China and other countries in recent years, but rarely have they been included in the same framework for analysis. Considering the current study findings, DE can promote economic growth and reduce CE, which promotes the growth of a low-carbon economy. Therefore, Hypothesis 1 is put forward.

Hypothesis 1 (H1).

DE significantly influences the RCEE.

2.2.2. Indirect Impact of the DE on RCEE: Perspectives on Reducing Energy Intensity

The implementation of digital technologies has had a tremendous impact on reducing emissions across all industries, especially in the energy industry. Both the supply and demand sides of the energy system concurrently reflect the effect of DE. On the supply side, digital technologies keep track of data from the energy production chain to reduce production risks, forewarn workers about them, and boost the effectiveness of the traditional fossil energy sector’s production. Digital technologies thus ensure the secure and effective operation of energy systems and lower the level of environmental harm [34,35]. On the demand side, in areas such as self-examination, satellite navigation, entertainment systems, critical infrastructure, and transportation systems, digital technology can be used to improve the effectiveness of energy consumption and usage throughout society. Energy efficiency is improved through the use of digital modes of transportation in areas such as intelligent homes, automobiles, and appliances [36]. By dematerializing human activities and communication, DE also lowers the demand for energy and raw resources [37]. For instance, online offices decreased public travel and hence decreased energy use during the New Crown Pneumonia pandemic. The information integration effect of DE generally lessens the informational asymmetries between demand and availability. Calcagnini et al. (2016) also reached a similar conclusion that increased energy intensity causes pollution [38]. As a result, high domestic energy consumption continues to be a significant barrier to RCEE advancement. The incorporation and infiltration of DE in the domain of the area of consuming energy would help to concurrently improve the efficiency of energy usage among demand and availability, effectively promoting a low-carbon economy. Hypothesis 2 is considered in this work considering the analysis presented above.

Hypothesis 2 (H2).

The advancement of DE improves RCEE by lowering energy intensity.

2.2.3. Indirect Impact of the DE on RCEE: Perspectives on Upgrading the Industrial Structure

According to Guan et al. (2022), the amount and caliber of newly constructed industrial structures are significantly impacted by the expansion of DE [39]. The examination of the mechanisms demonstrates that by elevating the level of regional innovation, DE can speed up the transition and modernization of industrial structures. Liu et al. (2022) argued that DE positively influenced China’s Green Total Factor Productivity (GTFP) when viewed dynamically over the long term, and the modernization of industrial structures was a major transfer mechanism for DE to support GTFP [40]. Xi and Zhai (2022) argued that natural resource inequality causes differing levels of economic development and industrial structure modernization in different regions of China, as well as varying effects on environmental pollution [41]. Environmental pollution is positively impacted by economic growth and the modernization of the industrial structure, and these two variables have an inverted U-shaped relationship. The panel threshold model’s findings demonstrate that improving the industrial structure can reduce the beneficial effects of economic expansion on environmental pollution. According to Zhao et al. (2022), industrial structure upgrading in China has risen progressively, and through increasing energy efficiency, it has a large spatially negative association with CO₂ emissions [42]. The following possibilities are given in light of the analyses just mentioned.

Hypothesis 3 (H3).

DE indirectly enhances RCEE by promoting industrial structure upgrading.

2.2.4. Indirect Impact of the DE on RCEE: Perspectives on Green Technological Advancement

Miller and Wilsdon (2001) noted that DE is a major force behind technological advancement and represents a technological revolution [43]. Almost all industries’ value chains have undergone a fundamental business change as a result of digital technologies [44]. Alam and Murad (2020) found that using technology more effectively could advance the creation and application of renewable energy [45]. Through precise 3D modeling of environmental and geographic factors, digital technologies accelerate the development of renewable energy and increase R&D effectiveness. Digital technology has been used to create new energy sources that are similar to conventional fossil fuel sources. These tools help employees perceive data more precisely, forecast weather changes, and utilize cleaner energy more often. Additionally, by facilitating the transformation of the energy consumption structure and helping governments regulate the overall supply of energy through cross-subsidies and price controls, governments may more effectively reduce CE [46]. Xie et al. (2021) measured the RCEE of 59 nations between 1998 and 2016 using a super-SBM model, and then they examined the various effects of technological advancement on the RCEE of countries with various levels of efficiency [47]. Technological advancement will encourage RCEE to make considerable improvements. Additionally, has been shown that the interaction of technological development and energy intensity has intricate effects on RCEE, highlighting the necessity of swiftly converting scientific and technological advancements into productivity to lessen the negative environmental consequences of emissions and pollutants. The fourth theory is suggested in this study based on the analyses shown above.

Hypothesis 4 (H4).

DE advances RCEE by encouraging the development of green technologies.

3. Study Design

3.1. Model Construction

The dynamic panel data make the dependent variable from the previous period an independent variable in the equation, indicating factors that the model did not account for and making the model more realistic. The explanatory variable and the random disturbance term may be related. The dynamic panel data model is characterized by a high level of heteroscedasticity, autocorrelation, and individual effects. The problem can often be solved using two methods. One is to increase accuracy by correcting the estimator derived from the generic static model and lowering the estimation error [48,49]. Another option is to directly estimate the model using the generalized method of moments (GMM), which can yield reliable estimation results all at once. To determine how DE and RCEE are related, this research chooses the system GMM model [50], which can increase the effectiveness of parameter estimation.

$${\mathrm{RCEE}}_{it}={\beta }_0+{\beta }_{1}RCE{E}_{it-1}+{\beta }_{2}D{E}_{it}+{{\displaystyle \sum }}_{i=3}^n{\beta }_n{X}_{it}+{\epsilon }_{it}$$

(1)

where $\mathrm{i}$ and $\mathrm{t}$ represent city and year, respectively. RCEE is the dependent variable describing the performance of CE within the essay. DE is the primary factor that explains the traits of the digital economy. $\mathrm{X}$ is the control variable that contains the marketization process (mark), urbanization rate (urb), and openness level (open). $\epsilon$ is the random error term.

Traditional econometric models ignore the spatial dependence of variables and assume that variables are spatially independent of one another. The spatial regression models fully take into account the dependence between spatial units, thus modifying the traditional econometric model. There are the three current spatial models. The Spatial Lag Model is typically applied when the explained variable exhibits spatial dependence. The SDM model is used when both the explained and explanatory variables have spatial dependence, whereas the spatial error model is used when only the residual term has spatial dependency. In order to do additional analysis, this study incorporates a spatial econometric model while taking spatial considerations into account. The emergence of DE in a region not only affects local CE performance, but also has an impact on nearby areas or areas with closer connections due to spatial spillover effects. To examine the spatial impacts of DE on RCEE in each province, related to the study of LeSage et al. (2010) and Zeng et al. (2022) [51,52], SDM in this research looks like this.

$${\mathrm{RCEE}}_{\mathrm{it}}=\mathsf{\rho }\mathrm{W}\cdot {\mathrm{RCEE}}_{\mathrm{it}}+{\mathsf{\alpha }}_1{\mathrm{DE}}_{\mathrm{it}}+{\mathsf{\alpha }}_2\mathrm{W}\cdot {\mathrm{DE}}_{\mathrm{it}}+{\displaystyle \sum }{\mathsf{\beta }\mathrm{X}}_{\mathrm{it}}+{\displaystyle \sum }{\mathsf{\theta }\mathrm{WX}}_{\mathrm{it}}+{\mu }_{\mathrm{t}}+{\delta }_t+{\mathsf{\epsilon }}_{\mathrm{it}}$$

(2)

where ρ is the lagged regression coefficient of the spatial explained variables; ${\mathsf{\alpha }}_1$ is the coefficient of explanatory variables of DE; ${\mathsf{\alpha }}_2$ is the product coefficient of DE; W denotes the economic spatial weight matrix; and θ is the coefficient of the control variable and the spatial economic matrix, which better describe the level of economic correlation across areas. The economic spatial weight matrix was chosen for this work because the research goal may have spillover effects between regions with frequent trade and investment or substantial economic correlation.

To gain insight into the inner mechanism between DE and RCEE, this research alludes to the study of Zhao et al. (2021) [53] and constructs the mediating effect model as follows.

$$RCE{E}_{it}={\alpha }_0+{\alpha }_{1}D{E}_{it}+{\alpha }_n{X}_{it}+{\epsilon }_{it}$$

$$M_{it}={\phi }_0+{\phi }_{1}D{E}_{it}+{\phi }_n{X}_{it}+{\epsilon }_{it}$$

$$RCE{E}_{it}={\vartheta }_0+{\vartheta }_{1}D{E}_{it}+{\vartheta }_2{M}_{it}+{\vartheta }_n{X}_{it}+{\epsilon }_{it}$$

(3)

where M is an intermediate variable that includes energy intensity (Energy), industrial structure improvement (Ind), and technological advancement effect (Tech), and the remaining variables were set to be the same as in Equation (1).

3.2. Variable Measures and Descriptions

3.2.1. Explanatory Variables

As shown in

Table 1. RCEE measurement system for 30 provinces in mainland China.

	Indicator Name	Proxy Variables	Unit of Measure
Input elements	Capital	Capital stock (K)	Billion
	Workforce	Employment in the three industries (L)	10,000 people
	Energy	Energy consumption (E)	million tons of standard coal
Output elements	Actual output	Real GDP (GDP)	Billion
Non-desired output elements	CE	CE (CO2)	million tons

, this paper employed the DEA Solver Pro 5.0 software to construct an RCEE measurement system using the Super-SBM model and drew inspiration from the research of Ge et al. (2022) [1]. RCEE was measured from 2007 to 2019. The “perpetual inventory approach” was utilized to compute the capital stock using 2006 as the base date. The overall workforce across the three industries for the current year was chosen to be measured for labor force indicators. Each province’s GDP served as the predicted production, and to account for price fluctuations, the real GDP of each region was deflated by using 2006 as the base year. The measurement was made under the IPCC Guidelines for National Greenhouse Gas Inventories from 2006, and the unwanted output factor was the CE of each province.

3.2.2. Core Explanatory Variables

Digital economy (DE): The comprehensive index of DE was computed using the entropy value technique, and it served as the main explanatory factor in this research. Drawing on the index system for the evaluation of Wang et al. (2021) [54], the construction of digital infrastructure, the development dimension of digital technology application, and the production service dimension of DE were the three criteria used to determine the level of DE, and

Table 2. Comprehensive index system of DE.

	Evaluation Dimension	Indicator System
The Level of DE	The construction of digital infrastructure	Internet usage percentage
		Port access to the Internet
		The ratio of people using cell phones
		Users of mobile devices as a whole
	The construction of digital technology application	Internet browsers
		Quantity of Domains
		Quantity of websites
	The construction of digital production services	Computer and software workers as a proportion of the urban population
		Overall telecom services per capita
		Revenue from the software industry as a share of GDP
		E-commerce purchases and sales as a percentage of GDP
		Digital Finance Development Index

displays the specific indexes.

3.2.3. Mediating Variables

Energy intensity (Energy): Energy intensity, also known as integrated energy consumption intensity, is a crucial barometer of economic growth [55], and it is computed by dividing each province’s total energy consumption by its GDP according to the research by Wu et al. (2016) [56]. By translating the actual regional usage of fossil fuels such as oil, coal, and natural gas into similar standard coal and adding them together, it is possible to determine each province’s overall energy consumption.

Industrial structure upgrading (Ind): The tertiary industry’s growth rate significantly increased in this stage of industrial development due to the economy’s ongoing growth and the ongoing restructuring of the industrial structure. According to Liu et al. (2021) [57], the ratio of the value added by the tertiary sector to that of the secondary sector in this study was used to gauge the upgrading of the industrial structure.

Technological innovation (Tech): To gauge technical innovation, the innovation indicator from the Chinese Cities and Industries Innovation Capability Report was used [58]. The benefits of this indication are as follows: First, the bias brought on by duplicate counting of innovation inputs and outputs was first corrected by using the value of patents as the innovation output data. Second, employing the patent renewal model to calculate the anticipated value of patents over a range of years also addressed the issue that the quality and prospective worth of patents are not effectively reflected by the number of patents when the innovation level is measured directly by this metric.

3.2.4. Control Variables

In the empirical study, to increase the validity of the research, this paper selected the marketization process (mark), urbanization rate (urb), and openness level (open) as the control variables. Among them, the marketization process (mark) regarding the research of Zeng et al. (2021) [59] was expressed using the marketization index; urbanization rate refers to the study of Li and Ma (2014) [60] and is expressed as the percentage of the population that does not work in agriculture in each province. Referring to the research of Wang et al. (2022) [61], the openness level was calculated as the overall import/export ratio to GNP, and its proportion size indicates the degree of openness.

3.3. Data Sources and Descriptive Statistics

The National Bureau of Statistics website, the websites of regional statistical offices, the China City Statistical Yearbook, the China Energy Statistical Yearbook, the Statistical Yearbook of Municipalities and Provinces, the China Marketization Index Database, and the publication of the Digital Inclusive Finance Development Index by the Peking University Digital Finance Research Center served as the basis for the entire article. This study used the linear interpolation method to fill in the gaps created by missing data for specific years in various provinces, and

Table 3. Descriptive statistics of main variables.

Variable	Obs	Mean	Std.Dev	Min	Max
RCEE	270	0.433	0.265	0.005	1.217
DE	270	0.245	0.129	0.040	0.703
mark	270	6.721	1.956	2.067	11.639
urb	270	0.572	0.122	0.311	0.896
open	270	5.598	1.545	−0.301	7.724

displays the descriptive statistics for the key variables.

4. Empirical Analysis

4.1. Baseline Regression Analysis

Table 4. Baseline regression and regional heterogeneity regression results.

RCEE	(1) GMM	(2) GMM	(3) FE	(4) FE
L.RCEE	0.508 ***	0.176 ***
	(0.006)	(0.028)
DE	0.844 ***	0.853 ***	1.573 ***	0.942 ***
	(0.021)	(0.096)	(0.078)	(0.109)
mark		0.097 ***		0.020 **
		(0.010)		(0.008)
urb		0.535 ***		0.688 ***
		(0.116)		(0.093)
open		−0.127 ***		−0.014 *
		(0.007)		(0.008)
_cons		0.026 ***	0.048 **	−0.245 ***
		(0.033)	0.021	(0.042)
AR(2)	0.388	0.317
Hansen	0.633	0.851
N	240	240	270	270

*** p < 0.01, ** p < 0.05, * p < 0.1, robust standard errors in parentheses.

(1) and (2) shows the empirical results of using the system GMM model to examine the impact of DE on RCEE, and it was found that DE significantly promoted RCEE at the 1% level. Hypothesis 1 holds. The one-period lagged term of RCEE was positively noticeable at the 1% level on the current period RCEE, then the residual term following differencing exhibited first-order autocorrelation but not second-order autocorrelation, which met the requirement of systematic generalized moment estimation. Considering the findings of the Hansen test, none of the instrumental variables were invalid and there was no over-identification.

Regarding the control variables, all control variables were significant at the 1% level. The degree of marketization (mark) significantly improved RCEE throughout the whole country. This could be because the deepening of marketization reform breaks down barriers between markets, bringing more technology exchanges, learning effects between enterprises, and the progress and diffusion of environmental protection technologies leading to a decrease in CE. The urbanization level (urb) had a favorable impact on RCEE, probably because increased urbanization will result in shifting excess agricultural labor to non-agricultural industries, which will favorably affect the expansion and intensification of agricultural production, as well as the efficient use of resources and the industrial structure while generating economies of scale, making it possible to increase the output per unit of CE, which encourages growth in RCEE. Openness level (open) had a significantly negatively effect on RCEE at a national level, possibly because the import and export trade of goods increased China’s carbon intensity and emissions, and overall the bottom-line racing impact of allowing access to the outside was greater than the environmental gain effect of trade. Industries with high pollution levels were relocated from nations with rigorous environmental restrictions to China, which has laxer environmental regulations, and China became a pollution refuge for developed countries [62]. Table 4 (3) and (4) displays the effect of DE on RCEE calculated using the fixed effects model. As can be observed, there was a strong positive correlation between DE and RCEE, suggesting that a rise in DE would greatly advance RCEE.

4.2. Regional Heterogeneity Analysis

Regression analyses for the eastern, central, and western regions of China are exhibited in

Table 5. Regional heterogeneity regression results.

RCEE	(1) East	(2) Midwest	(3) High Economic Development Level	(4) Low Economic Development Level
DE	1.160 ***	−0.218	1.036 ***	−0.420 *
	(7.18)	(−0.99)	(0.142)	(0.240)
mark	0.0380 *	0.0221 **	0.006	0.019 **
	(2.50)	(2.89)	(0.012)	(0.009)
urb	0.819 ***	−0.027	0.860 ***	−0.096
	(6.09)	(−0.22)	(0.136)	(0.121)
open	−0.069 **	0.020 *	−0.017	0.029 ***
	(−3.31)	(2.47)	(0.016)	(0.009)
_cons	−0.210 *	0.163 **	−0.329 ***	0.222 **
	(−2.09)	(2.69)	(0.091)	(0.060)
N	108	162	135	135
Adj R-sq	0.818	0.711	0.833	0.602

*** p < 0.01, ** p < 0.05, * p < 0.1, robust standard errors in parentheses.

(1) and (2), and the test findings demonstrate that DE in the eastern region had a notable influence on RCEE, which is under the regression results for the whole province: however, the impact of DE on RCEE in the central and western regions was not notable, which may be for the reason that compared with the western region, the development of digital technology was more mature and the “new infrastructure” was relatively complete. Through green technology innovation, industrial upgrading and energy intensity reduction can be achieved earlier to improve RCEE. However, the midwest regions are speeding up the development of new infrastructure, and DE’s effects on energy savings and pollution reduction have not yet been demonstrated. The cut-off sample according to the median of per capita GDP was divided into high degree of economic region samples and low degree of economic development samples. Based on the relationship between DE and RCEE, we conducted a regression analysis, and the empirical results are shown in Table 5 (3) and (4). It can be seen that DE in the high economic development level significantly increased RCEE; however, the low economic development level of 1% reduced RCEE. The possible reason for this is that the construction of DE must be based on the improvement of new infrastructure, which requires the consumption of a large number of energy products during the construction process, leading to a significant increase in carbon emissions. Lower-level economic development areas cannot neutralize the CE brought about by “new infrastructure”. This may continue the crude development mode of high pollution and high consumption, which will bring more carbon emissions along with GDP growth, thus reducing RCEE.

4.3. Robustness Tests Based on SDM

Within this work, we explored the geographical clustering status of the model’s explanatory and explained variables through Moran’s I and utilized it to assess the spatial autocorrelation of the variables, drawing on the study of Meng et al. (2022) [63]. As shown in

Table 6. Moran’s I for DE and RCEE.

Year	Moran’s I of DE	Year	Moran’s I of RCEE
2011	0.552 ***	2011	0.626 ***
2012	0.521 ***	2012	0.638 ***
2013	0.463 ***	2013	0.646 ***
2014	0.497 ***	2014	0.662 ***
2015	0.455 ***	2015	0.678 ***
2016	0.433 ***	2016	0.597 ***
2017	0.437 ***	2017	0.628 ***
2018	0.392 ***	2018	−0.036
2019	0.365 ***	2019	0.615 ***

*** p < 0.01.

, for the observation period of 2011–2019, the observed values of DE and RCEE were positive, and both could reject the null hypothesis of no spatial connection at the 1% level of significance (except for RCEE in 2018). This shows that the observed values for the two had a significant positive spatial association.

Through some tests, we chose the best spatial economic model and determined whether spatial autocorrelation existed [51]. SAR passed LM statistic and its robust variant. Second, the applicability of SDM was evaluated using the Wald test. LR statistic was constructed to verify once again that SDM could not degrade into the spatial autoregressive model and the spatial error model. The results of the above tests are shown in

Table 7. Identification and testing of spatial econometric models.

Method Tests	Statistical Value	Method Tests	Statistical Value
LM–lag	64.728 ***	Wald–SAR	85.21 ***
LM–lag(robust)	2.113	Wald–SEM	19.38 ***
LM–error	105.474 ***	LR–SAR	36.39 ***
LM–error(robust)	42.859 ***	LR–SEM	27.40 ***

*** p < 0.01.

, with SDM as the most appropriate model.

The empirical studies are presented in

Table 8. Spatial Durbin model regression results.

RCEE	(1)	(2)	(3)	(4)
DE	1.202 ***	1.089 ***	0.884 ***	0.918 ***
	(0.0691)	(0.0927)	(0.0984)	(0.009)
mark		0.0183 ***	0.0153 ***	0.022 ***
		(0.00533)	(0.00530)	(0.007)
urb			0.237 **	0.199 *
			(0.111)	(0.112)
open				−0.012 *
				(0.007)
W × DE	−0.253 *	−0.0298	−0.397 *	−0.411 **
	(0.144)	(0.202)	(0.210)	(0.208)
W × mark		−0.0381 ***	−0.0533 ***	−0.063 ***
		(0.0131)	(0.0133)	(0.019)
W × urb			0.713 ***	0.749 ***
			(0.212)	(0.217)
W × open				0.013
				(0.015)
rho	0.557 ***	0.614 ***	0.483 ***	0.494 ***
sigma2_e	0.0116 ***	0.0106 ***	0.0100 ***	0.010 ***
N	270	270	270	270

*** p < 0.01, ** p < 0.05, * p < 0.1, robust standard errors in parentheses.

and are based on the tests mentioned above. This research used SDM for regression analysis. The main effect coefficient of DE was 0.918, at a 1% level, which was substantial and favorable for RCEE, and shows that hypothesis 1 held true. DE’s spillover coefficient was negative and notable, suggesting that a province’s level of DE had a detrimental effect on the RCEE of another province with high economic significance; it may have been challenging to share the results of low-carbon technology innovation between provinces as a result of the regional safeguarding of intellectual property rights, making the development of a low-carbon economy in the province greatly aided by the rise in DE while hindering the construction of a low-carbon economy in a province with high economic relevance. Among the control variables, the degree of marketization had a notable favorable effect on the RCEE of this province, which was in accordance with the previous empirical results, but it had a detrimental impact on the RCEE of economically connected provinces, probably due to the existence of a certain competition between provinces. Urb had a positive effect on the RCEE of the province and economically connected provinces. The openness level had a profoundly adverse impact on the RCEE of the province, but not on the RCEE of the economically related provinces.

4.4. Test of Mediating Effect

The regression outcomes of the mediating effect model are displayed in Table 8, where

Table 9. Mediating effect test.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	RCEE	Energy	RCEE	Tec	RCEE	Ind	RCEE
Energy/Tec/Ind			−0.145 ***		3.40 × 10−5 **		0.00319 ***
			(0.0226)		(1.49 × 10−5)		(0.000767)
DE	0.942 ***	–0.721 **	0.895 ***	4766 ***	0.780 ***	45.09 ***	0.798 ***
	(0.109)	(0.301)	(0.110)	(453.0)	(0.130)	(8.632)	(0.112)
mark	0.0198 **	–0.229 ***	−0.0156 *	−78.14 **	0.0224 ***	0.346	0.0186 **
	(0.00821)	(0.0185)	(0.00846)	(33.99)	(0.00823)	(0.648)	(0.00797)
urb	0.688 ***	1.394 ***	0.890 ***	1420 ***	0.640 ***	87.30 ***	0.410 ***
	(0.0927)	(0.237)	(0.0913)	(383.9)	(0.0944)	(7.315)	(0.112)
open	–0.0143 *	0.000137 **	−2.75 × 10−5	−46.72	−0.0127	−1.698 **	−0.00884
	(0.00834)	(6.14 × 10−5)	(2.24 × 10−5)	(34.52)	(0.00830)	(0.658)	(0.00819)
_cons	–0.245 ***	1.663 ***	–0.0564	−833.9 ***	−0.216 ***	184.4 ***	−0.832 ***
	(0.0422)	(0.106)	(0.0536)	(174.6)	(0.0437)	(3.326)	(0.147)
N	270	270	270	270	270	270	270
year	control	control	control	control	control	control	control
$R^2$	0.799	0.629	0.835	0.545	0.803	0.697	0.811

*** p < 0.01, ** p < 0.05, * p < 0.1, robust standard errors in parentheses.

(1)–(3) provide the outcomes of a test to determine whether the DE has a mediating influence on the energy intensity of RCEE. The DE has a significant and positive impact on the RCEE, according to regression (1), which is consistent with previous findings. Regression (2) demonstrates that DE considerably reduced energy intensity at the 5% level, and regression (3) showed that energy consumption intensity decreased RCEE and DE raised RCEE, which suggests that DE can increase RCEE by reducing the energy intensity, validating Hypothesis 2. The intermediary effects of technological innovation of DE on RCEE are shown in Table 9 (1), (4), and (5). Regression (4) showed that the DE significantly promoted technological innovation, and regression (5) showed that technological innovation was positively significant for RCEE, and DE is was positively significant for RCEE, indicating that DE could promote technological innovation and thus enhance the partial mediating effect of RCEE, thus verifying hypothesis 3. Table 9 (1), (6), and (7) show the outcomes of the intermediary effects of DE on the industrial structure upgrade of RCEE. Regression (6) shows that DE had a positive notable effect on the industrial structure upgrade(Ind), regression (7) showed that Ind has a noticeable beneficial effect on RCEE, and DE continued to have a favorable effect on RCEE, which indicates that DE can improve the industrial structure upgrade and thus enhance the intermediary effect of RCEE, thus verifying hypothesis 4.

5. Conclusions and Implications

We examined whether DE may serve as a key for understanding energy saving and decarbonization. It has great theoretical and applied usefulness to strengthen RCEE in the background of dual carbon and high-quality development in China. Given this, this work measured the RCEE of 30 provinces in China from 2011 to 2019 according to the Super-SBM method and then investigated the relationship and influence mechanism between DE and RCEE through the system GMM, SDM, and mediating effect model. We discovered that (1) DE improves RCEE; (2) the mechanism study shows that the DE improves RCEE by reducing energy intensity and promoting industrial upgrading and green technology innovation; and (3) an examination of regional heterogeneity reveals that DE greatly raises RCEE in the eastern provinces, but not in the middle or western regions. DE improves RCEE in provinces with a high degree of economic development but decreases RCEE in provinces with a low degree of economic development. As a result, the following recommendations for policy are made in this study.

First, we firmly grasped the important strategic opportunity period for the construction of DE. Efforts should be made to promote the construction of digital infrastructure, digital technology applications, and digital production services to solidify the foundation of DE. Simultaneously, we should accelerate the promotion of digital technology and knowledge popularization in the less developed regions in the central and western regions, and the government and enterprises can carry out cross-regional digital technology application and production management experience exchange, digital knowledge popularization education, and various initiatives to reduce the disparity in development between provinces and regions and raise China’s DE standards generally. DE’s potential to aid when a low-carbon economy expands following regional needs should be utilized. Eastern regions should accelerate the construction of digital labor platforms under the premise of steadily realizing DE development to promote green technological innovation, while central and western regions should focus on encouraging DE development under the general principle of controlling systemic financial risks and appropriately and actively broadening digital investment and financing channels.

Secondly, we should promote economic transformation and upgrade through digitalization, make up for the shortcomings of industries around the country with the support of Internet technology, and encourage the general improvement and modernization of China’s industrial structure. Agriculture should actively encourage the use of digital monitoring systems and online sales methods in the process of planting, producing, and selling agricultural products. In industry, we should continue to modernize and improve existing production equipment and encourage the coordinated allocation of production factors. On the service side, first, we should increase investment in R&D and invention to lay the foundation for digital technology for all regions. Second, we should keep advancing the growth of e-commerce and service industry integration, and link agriculture and industry digitization with the service industry. We should guide real economy enterprises to take the initiative to apply and upgrade digital production equipment. We should encourage the development, application, and promotion of enterprise intelligent big data systems; enhance the ability and level of digital application at the front, middle, and back ends of production; and promote low-carbon economic development with digital as the new driving force.

Third, we should strengthen regional technology linkages and resource sharing o solve the difficulties faced by DE in promoting the construction of a low-carbon economy. China’s DE started late and is now in a rapid development stage, and the phenomenon of uneven development is still more prominent. At the early stage of development, certain barriers are formed in information, knowledge, technology, and innovation factors to safeguard their development, which hinders resource sharing and is ultimately detrimental to the overall coordinated development of the region. In the digital era, cooperation is an inevitable choice for regional development. To this end, we should break the information technology barriers between provinces and regions, eliminate institutional barriers, smooth the flow of innovation factors, optimize the reasonable layout of resources in the region, strengthen the sharing of digital resources and talent exchange, accelerate technology dissemination and information exchange, and give full play to the development “dividends” brought by DE.