The Impact of Digital Economy Development on Improving the Ecological Environment—An Empirical Analysis Based on Data from 30 Provinces in China from 2012 to 2021

Abstract

The rapid progress in science and technology has ushered in a new era of organized and efficient development within the digital economy. China has repeatedly emphasized the need for high-quality development that prioritizes ecological conservation. The central challenge is to balance economic growth with environmental protection, ensuring sustainable development. Understanding the environmental impact of the digital economy is critical for achieving green growth in China. This paper investigates the relationship between the digital economy and ecological protection, using data from 30 provinces and cities in China between 2012 and 2021. Through empirical analysis, including a two-way fixed effect model, mechanism analysis, regional difference analysis, and robustness tests, the study found a significant negative correlation between the digital economy and environmental pollution. This indicates that the development of the digital economy can effectively improve the ecological environment. In the information age, seizing the opportunities presented by the digital economy is crucial. By deepening the digital industry and leveraging digital technologies, China can enhance enterprise production, promote innovation, and create a positive feedback loop between economic development and environmental optimization. However, it is essential to recognize regional disparities in digital economy development and work to narrow these gaps, ensuring balanced and sustainable growth across the country.

1. Introduction

Research Background

The rapid advancement of information technology and the Internet has positioned the digital economy as a key driver of industrial growth, corporate innovation, and efficiency improvements in China. China’s 2035 Vision Goals emphasize enhancing digital infrastructure and leveraging the digital economy to promote social progress, transform the real economy, and ensure coordinated development across sectors.

However, an overemphasis on GDP growth and a development model characterized by high input, energy consumption, and emissions have led to significant ecological challenges. In 2021, data from the Ministry of Ecology and Environment revealed that 121 cities in China, accounting for 35.7% of those surveyed, had substandard air quality, with acid rain affecting 369,000 square kilometers, or 3.8% of the country’s area. This environmental degradation not only threatens public health but also hinders sustainable economic progress.

To address these issues, the 20th National Congress has prioritized a “green transformation” of development methods, focusing on energy conservation, green and low-carbon industries, and sustainable development. Concurrently, the 14th Five-Year Plan underscores the need to accelerate digital development and innovation as central components of China’s modernization.

The current focus is on promoting green development through the digital economy and technological innovation. Given the many factors influencing environmental protection, it is crucial to clarify the relationship between national strategies and ecological preservation. Understanding the intrinsic link between digital economic development and environmental sustainability is essential for formulating a development plan that aligns with China’s specific conditions, ensuring a balanced approach to growth and ecological conservation.

2. Literature Review

2.1. Definition of Digital Economy

2.1.1. Concept of Digital Economy

The digital economy, first introduced by Tapscott in 1996 [1], involves economic activities and transactions facilitated by digital technologies and platforms. Since its inception, the concept has evolved to become a core component of modern economic systems.

Research on the digital economy typically falls into two perspectives:

Industrial Economy Perspective: This perspective views the digital economy primarily through the lens of digital industries. Scholars in this camp focus on how digital technologies process data and information to produce digital products. For example, some scholars emphasized the role of digital industrialization and digitization in driving industrial structure upgrading. They found that while digital industrialization provides foundational benefits, and has a more pronounced impact on upgrading industrial structures [2,3,4].

New Economic Activity Perspective: This broader view sees the digital economy as a transformative force affecting all aspects of economic activities. It is characterized by the integration of digital knowledge, information, and ICT to enhance overall efficiency. The G20 Hangzhou Summit (2016) and the China Academy of Information and Communications Technology (2021) describe the digital economy as an economic system driven by digital inputs and technologies that optimize economic structures and processes.

2.1.2. Measurement of the Digital Economy

The measurement of the digital economy is a critical area of research, given its increasing significance in global economic structures [5]. Various methodologies have been developed to quantify the scope and assess the development of the digital economy. This literature review explores the primary approaches used by scholars to measure the digital economy, including the value-added measurement method, indexing methodologies, and the development of satellite accounts.

The value-added measurement method is one of the most direct approaches for quantifying the scale of the digital economy. It involves identifying the industries related to the digital economy and measuring the value they add to the overall economy. This method allows for a precise calculation of the economic contribution of digital sectors. The value-added approach has its roots in earlier economic research, notably the work on the knowledge economy [6] and on the information economy [7]. These studies laid the groundwork for contemporary methods of measuring the digital economy.

Indexing is another widely adopted method for measuring the digital economy. This approach involves creating a comprehensive evaluation system that incorporates multiple aspects of the digital economy, resulting in indices that reflect its development. Scholars developed a digital economy development index to assess regional progress in China [8]. Their index was based on three dimensions: digital infrastructure, digital industry, and digital integration. This index enabled them to measure the level of digital economy development across 30 provinces from 2015 to 2018, offering insights into regional disparities and trends. Other researchers have contributed to the field by developing similar indices [9,10,11]. These indices allow for both horizontal and vertical comparisons of digital economic development, making them valuable tools for analyzing trends over time and across different regions.

Satellite accounts are an emerging methodology used to quantify the scale of the digital economy. This approach involves integrating digital economy metrics into national accounts to provide a clearer picture of its economic impact. Some scholars have explored the use of satellite accounts to measure the digital economy. Their work demonstrates how satellite accounts can be used to systematically track the contributions of digital sectors within the broader economy, offering a more integrated view of economic performance.

Overall, the methodologies for measuring the digital economy—value-added measurement, indexing, and satellite accounts—provide robust frameworks for understanding its scope and impact. The value-added method offers direct quantification of economic contributions, while indexing methodologies allow for comprehensive evaluations across various dimensions of the digital economy. Satellite accounts, meanwhile, integrate these metrics into national accounts, providing a holistic view of the digital economy’s role in economic development.

2.2. Research on the Relationship between the Digital Economy and Industrial Structure Upgrading

The digital economy plays a vital role in transforming and upgrading industrial structures, as various studies suggest [12]. Some scholars found that digital industrialization and industrial digitization are key drivers of this transformation, with the latter having a more pronounced impact on industrial upgrades [13]. They demonstrated that integrating the electronic information industry with the textile sector significantly modernized the textile industry, boosting its global competitiveness. Similarly, some scholars emphasized that advancements in digital infrastructure and innovation have greatly modernized the manufacturing sector [14]. The research was analyzed data from 275 cities, concluding that more developed digital economies experience greater industrial upgrades [15]. It was further noted that the digital economy’s impact on industrial quality varies by region, depending on local economic conditions and digital adoption levels [16].

2.3. Research on the Relationship between the Digital Economy and Technological Innovation

The digital economy plays a pivotal role in enhancing innovation at both the local and regional levels [17]. It was found that the digital economy not only boosts local innovation performance but also generates positive spillover effects for neighboring cities. Similarly, some scholars demonstrated that the integration of the digital economy with market forces significantly improves corporate innovation performance, with technological diversification and market integration driving innovation [18]. It was further observed that the digital economy accelerates regional innovation by facilitating the accumulation of human capital and increasing investment in research and development (R&D) [19].

Through threshold regression analysis, some scholars identified that the digital economy can not only directly affect regional innovation by promoting the deep integration of information technology and various fields of society, but also indirectly promote technological innovation through corresponding mechanisms and reducing various costs [20,21].

The digital economy also supports advancements in green technology innovation, particularly when combined with environmental regulations [22]. It was found that environmental legislation plays a crucial role in driving corporate green technology innovation. The digital economy amplifies this effect by providing support through information technology and market incentives, thereby enhancing resource efficiency and optimizing industrial structures [23].

In examining the impact of the digital economy on green total factor productivity, some scholars focused on resource-based cities [24]. Their study revealed that the digital economy significantly boosts overall productivity in these cities by improving resource utilization efficiency and optimizing industrial structures.

The synchronized development of the digital economy and the green economy is critical for sustainable growth [25]. It was highlighted that the digital economy provides substantial support for advancing the green economy [26]. Together, these sectors drive economic transformation, industry upgrading, and sustainable development [27].

2.4. Research on the Impact of the Digital Economy on Environmental Pollution

The digital economy’s role in reducing environmental pollution is increasingly recognized [28]. It was found that investing in information technology improves resource utilization efficiency and conservation [29]. Similarly, some scholars argued that the integration of ICT in manufacturing significantly reduces energy intensity [30]. It was demonstrated that big data pilot zones have a positive impact on urban air quality through industrial upgrading and technological innovation [31]. Some research also highlighted the role of digital finance in mitigating environmental pollution, especially in regions with less stringent financial regulations [32,33]. It was explored the regional differences in how the digital economy impacts environmental degradation [34]. Their findings suggest that regions with a well-developed digital economy are more effective in reducing environmental pollution due to their advanced technology and stronger environmental management capabilities. Some scholars used data from 277 Chinese cities to analyze the impact of the digital economy on urban environmental pollution [35]. The finding showed that the growth of the digital economy significantly reduces urban pollution, mainly through improved resource efficiency and optimized industrial structures [36,37]. Some scholars examined the effect of the digital economy on industrial wastewater discharge in 281 Chinese cities [38]. Their study found that digital economy growth contributes to reducing industrial wastewater emissions, largely due to the implementation of digital technology in production processes and wastewater treatment.

2.5. Research on the Impact of the Digital Economy on Carbon Emissions

The carbon emissions reduction is largely due to digital technology’s role in improving energy efficiency and promoting the use of clean energy sources [39,40]). Some scholars analyzed panel data from 278 Chinese cities and found that the advancement of the digital economy significantly reduces carbon emissions [41]. It was emphasized the importance of the digital economy in mitigating carbon emissions and facilitating the shift towards alternative energy sources. The research demonstrates that the digital economy contributes to the growth of a low-carbon economy by supporting energy transitions and eco-friendly innovations in businesses. Similarly, some scholars identified a non-linear relationship between the digital economy, energy consumption, and carbon emissions [42]. It was further argued that the digital economy can effectively reduce urban carbon emissions and improve emission efficiency, highlighting its role in enhancing environmental sustainability [43].

2.6. Research on the Impact of the Digital Economy on Environmental Governance

The digital economy improves governance effectiveness and supports the integrated development of both the digital and green economies. Some scholars investigated how the digital economy’s capabilities for precise identification and real-time monitoring can enhance ecological and environmental governance [44,45]. The findings of study of the impact of the digital economy on the development of "zero waste cities" at the provincial level in China, reveal that the digital economy promotes sustainable urban growth by increasing resource recycling rates and optimizing waste management processes [46]. Digital transformation significantly improves environmental governance in mining industries. This highlights the broad potential of digital technologies to enhance environmental practices across various sectors [47].

2.7. Research on the Impact of the Digital Economy on Urban Environment

The digital advancements contribute to more environmentally friendly metropolitan development by improving resource efficiency and reducing pollution levels [48]. Some scholars found that the digital economy significantly enhances urban environmental quality. It was explored how the digital economy supports ecological balance and green transformation in urban areas [49]. The findings emphasizes that digitalization optimizes urban functions, resource use, and reduces environmental pollution, facilitating a greener cityscape [50].

2.8. The Extensive Influence of the Digital Economy

The digital economy not only reduces environmental contamination but also improves overall environmental quality [51]. Some scholars explored the broad impact of the digital economy on environmental quality [52]. They investigated how the digital economy contributes to inclusive green growth. Their study found that advancements in the digital economy support sustainable economic development while minimizing environmental impact. They also highlighted that government environmental regulations positively moderate this relationship, enhancing the benefits of digitalization.

2.9. Literature Review Summary

The current literature provides comprehensive insights into the impact of the digital economy on industrial structure upgrading and technological innovation. It also explores its effects on environmental conservation and green development. Key contributions include improved resource utilization efficiency, optimized industrial structures, and enhanced technological innovation. However, there is no universally accepted standard for quantifying the level of digital economy development in China. Although various methods have been proposed, a standardized measure is still lacking, warranting further research. Existing studies often focus on one aspect of the digital economy—such as ICT, the Internet, or big data—primarily through theoretical analyses. There is a need for more empirical research to understand the environmental effects comprehensively.

3. Theoretical Analysis and Research Hypothesis

3.1. The Development of the Digital Economy Is Conducive to Strengthening Environmental Pollution Control

Environmental pollution control involves promoting green transformation through technological innovation, improving production equipment and conditions, and establishing a robust system for pollution prevention and control [53].

Front-end prevention and control, with the help of data analytics, enterprises can capture detailed and accurate information, enabling them to transform their production processes. This allows for green production and intelligent management through digital interconnection and real-time monitoring. The integration of data with production equipment helps enterprises achieve predictive production, improving efficiency, reducing pollutant emissions, and enhancing front-end prevention and control.

Mid-end process supervision with the digital economy addresses the limitations of traditional regulatory models, such as outdated methods, limited oversight, and low efficiency [54]. It enables the creation of a new environmental regulatory model involving multiple stakeholders. The government use digital technologies to collect and analyze environmental data in real-time, combining this with artificial intelligence for dynamic monitoring and prediction of environmental pollution [55]. Enterprises can leverage digital technologies to collect data on energy-saving and emission reduction, optimizing production processes for precise supervision and improved regulatory efficiency. The digital economy empowers the public with convenient tools for environmental supervision. Digital media allows the public to access environmental data and knowledge, lowering the barrier to understanding environmental quality and enabling cooperation with the government and enterprises in pollution control [56]. Based on this, this paper proposes,

H1. 

The development of the digital economy is conducive to curbing environmental pollution.

3.2. The Digital Economy Improves the Ecological Environment by Enabling Industrial Structure Upgrades

The influence of the digital economy on the upgrading of industrial structure is multifaceted, encompassing its development model, its role in fostering new industries and formats, and its transformative effects on traditional sectors.

The digital economy significantly alters the industrial landscape by shifting from traditional labor-, capital-, and technology-intensive industries to digital-intensive industries. This transition enhances the level of advanced industrial structure, reflecting a shift towards more sophisticated and technology-driven sectors .

Characterized by high innovation, extensive permeability, and robust diffusion, the digital economy can dismantle traditional industry boundaries. It fosters the integration of related industries and the convergence of upstream and downstream sectors, leading to the emergence of new industries and business models. This integration creates opportunities for the development of novel industrial formats that leverage digital technology.

The digital economy uses information and data as key production factors, driving the intelligent and digital transformation of traditional industries, particularly manufacturing. This transformation enhances the coordination of various industrial sectors, stimulates technological innovation, and renews internal business processes. Consequently, it improves the rationalization of the industrial structure and boosts production efficiency.

The optimization and upgrading of industrial structures are crucial for improving ecological efficiency. The digital economy contributes to this by generating new industries and formats. By replacing traditional natural resources and environmental factors with knowledge, technology, and digital tools, the digital economy facilitates high output with low input and minimal pollution. Digital technology enables efficient resource integration and allocation within the industrial chain. The upgrading of traditional industries is also enabled. The integration of digital technology with traditional industries drives their transformation towards medium and high-end sectors. This shift promotes technological innovation, enhances resource utilization efficiency, and ultimately achieves higher output with lower input. This process contributes to improved regional ecological efficiency.

Based on this, this paper proposes:

H2. 

The digital economy can improve the ecological environment by enabling the upgrading of industrial structure.

3.3. Digital Economy Improves Environmental Pollution by Promoting Green Technology Innovation

According to the State Intellectual Property Office, green technology innovation encompasses technologies designed to conserve resources, enhance energy efficiency, control pollution, and achieve sustainable development. It is recognized as an effective approach to mitigating environmental pollution. Digitalization plays a crucial role in advancing green technology innovation through various mechanisms.

Schumpeter’s innovation theory posits that the reorganization of production factors constitutes innovation. Within enterprises, the integration of digital technology with resources such as energy can optimize the allocation of production factors throughout the production and pollution control stages. This integration promotes the reform of production and governance paradigms, driving green technology advancements and innovation in regional enterprises.

Digitalization enables dynamic supervision of production, sales, and management processes within enterprises. It reduces the costs associated with acquiring external knowledge by accelerating the collection of both internal and external information. This includes dynamic data related to energy conservation, emission reduction, and environmental governance, which supports targeted green technology innovation.

Digitalization enhances the efficiency of research and development (R&D) departments by promoting optimal internal resource allocation and enabling the virtualization of the R&D process. This reduces trial-and-error costs and accelerates R&D efficiency, thereby fostering green technology innovation within enterprises.

Digitalization fosters scientific research collaboration among enterprises, government agencies, universities, and other institutions. By facilitating the accumulation and upgrading of human capital, it helps overcome technical barriers and accelerates green technology innovation.

Based on this, this paper proposes,

H3. 

Digitalization can reduce environmental pollution levels by promoting regional green technology innovation.

4. Study Design

4.1. Model Design and Operational Definition of Variables

4.1.1. Model Design

The main issue studied in this paper is the effect of the digital economy development level on the improvement of the ecological environment. Based on the assumptions of this paper, the following benchmark econometric regression model is constructed:

$${Pollute}_{it}={\beta }_0+{\beta }_1{Digit}_{it}+{\beta }_2{Control}_{it}+{\gamma }_i+{\mu }_t+{ϵ}_{it}$$

(1)

Among them, the explained variable ${Pollute}_{it}$ represents the environmental pollution level of region i in year t, which is calculated by the entropy method based on industrial wastewater discharge, industrial sulfur dioxide emissions, and industrial smoke and dust emissions. The core independent variable is represented by ${Digit}_{it}$, the level of digital economic development in region ${Control}_{it}$ i in year t. It is a series of control variables, with ${\gamma }_i$ representing individual fixed effects, ${\mu }_t$ represents the time-fixed effects, and ${ϵ}_{it}$ independent and identically distributed random disturbance terms. This paper mainly focuses on the coefficient ${\beta }_1$ of the ${Digit}_{it}$. If ${\beta }_1$ is significantly negative, it indicates that the level of digital economy development can significantly improve the ecological environment in the region.

4.1.2. Explained Variables

The selection of explained variables in this paper refers to the ecological protection evaluation system constructed by Zhang and Deng [57] and the ecological environment carrying dimension with the largest weight in the system is selected, and finally environmental pollution is used to characterize the effect of ecological protection. Because environmental pollution is a negative indicator, the greater the absolute value of environmental pollution, the worse the effect of ecological protection, and vice versa. At the same time, this paper refers to the research of Qi et al. [58], and selects three specific indicators of industrial wastewater discharge, industrial sulfur dioxide emissions, and industrial smoke and dust emissions to construct the environmental pollution uses the entropy method to assign weights. The specific composition of the explained variables is shown in

Table 1. Environmental pollution level evaluation index system.

Explained Variable	Specific Indicators	Unit	Influence	Weights
Environmental pollution level	Industrial wastewater discharge	10,000 tons	Negative	0.386
	Industrial sulfur dioxide emissions			0.355
	Industrial smoke and dust emissions			0.259

.

4.1.3. Core Explanatory Variables

Digital economy development level (dig) is discussed here. According to the “ 14th Five-Year Plan for Digital Economy Development” released in 2022, the digital economy is the main economic form after the agricultural economy and the industrial economy. It is a new economic form that takes data resources as the key element, modern information networks as the main carrier, and the integrated application of information and communication technologies and the digital transformation of all factors as important driving forces, promoting more unified fairness and efficiency. Referring to the research of Wang [59], this paper measures the development level of China’s digital economy from four dimensions: digital infrastructure, digital industrialization, industrial digitalization, and scientific and technological innovation capabilities. Among them, digital infrastructure is the external condition and foundation for the development of the digital economy, providing technical support and application scenarios for the development of the digital economy; digital industrialization refers to the industrialization, commercialization and marketization of data elements. The development of digital industrialization constantly gives birth to new industries, new formats, and new models, leading and promoting the rapid development and digital transformation and upgrading of all walks of life. Industrial digitalization is the use of digital technology to transform traditional industries in an all-round, all-angle and full-chain manner, promoting the deep integration of digital technology and the real economy; scientific and technological innovation capabilities can promote the evolution of digital technology and are the driving force for the development of the digital economy. The variables were selected mainly based on the principles of scientific and data availability, and a total of 20 secondary indicators were selected. The specific indicator system is shown in

Table 2. Evaluation index system of digital economic development level.

First-Level Indicators	Secondary Indicators	Units	Weights
Digital Infrastructure	Number of Internet access ports	Ten thousand	0.034
	Number of Internet broadband access users	Ten thousand	0.036
	Number of domain names	Ten thousand	0.066
	Mobile phone base station density	Pieces/square kilometer	0.073
	Mobile phone penetration rate	Department/100 people	0.019
	Long-distance optical cable length per unit area	10,000/km	0.064
Digital Industrialization	Software business revenue as a percentage of GDP	%	0.066
	Information technology service revenue as a percentage of GDP	%	0.070
	Number of people employed in information services	10,000 people	0.054
	Telecommunications business as a percentage of GDP	%	0.054
Industrial Digitalization	The proportion of enterprises with e-commerce transactions to the total number of enterprises	%	0.016
	E-commerce revenue as a percentage of GDP	%	0.033
	The number of computers per 100 people in enterprises	/	0.024
	Number of websites owned by every 100 enterprises	/	0.012
	Digital Financial Inclusion Index	/	0.019
Technological Innovation Capabilities	Full-time equivalent of R&D personnel in industrial enterprises above designated size	Person/year	0.066
	R&D expenditure of industrial enterprises above designated size	CNY 10,000	0.062
	Number of R&D projects (topics) of industrial enterprises above designated size	Item	0.070
	Total transaction number of technical contracts	CNY 10,000	0.090
	Number of patent applications granted	Item	0.071

.

4.1.4. Mediating Variables

Based on the above theoretical analysis, the level of digital economic development mainly improves the ecological environment through two paths: enabling the transformation and upgrading of industrial structure and promoting regional green technology innovation. Therefore, this paper selects industrial structure upgrading and green technology innovation as mediating variables for empirical analysis.

(1) The upgrading of industrial structure refers to the process of industrial structure system transformation from a low-level form dominated by labor and capital-intensive industries to a higher-level form dominated by knowledge, technology, and digital-intensive industries. This paper follows the approach of Gan Chunhui et al. (2011) and uses the ratio of the output value of the tertiary industry to the output value of the secondary industry to express it.

(2) Regarding green technology innovation, this paper draws on the ideas of Pang et al. [60], and uses the number of green invention patents applied for in a region in that year to define the region’s green technology innovation capability level.

4.1.5. Control Variables

Of course, there are many factors that affect the level of environmental pollution, and the level of digital economic development is only one of them. To better test the relationship between digital economic development and environmental pollution control, this paper selects the economic development level, foreign direct investment, environmental regulation, and government intervention as control variables. The level of economic development (GDP) is expressed by the per capita GDP of each province. Foreign direct investment (FDI) is the ratio of the product of the total foreign direct investment and the exchange rate of the US dollar to the CNY in a given year to the regional GDP. Environmental regulation (reg) is expressed as the proportion of completed investment in industrial pollution control to the added value of the secondary industry. The degree of government intervention (gov) is measured by the proportion of government expenditure to the regional GDP.

4.2. Data Source and Description

This paper primarily investigates the impact of digital economy development at the provincial level on regional environmental quality, using panel data from 30 provinces, cities, and autonomous regions (excluding Hong Kong, Macau, Taiwan, and Tibet) for the period from 2012 to 2021. The data sources of the digital economy index are mainly the China Statistical Yearbook, the China Electronic Information Industry Statistical Yearbook, and the China Industrial Statistical Yearbook. The data sources of environmental pollution levels are mainly the China Environmental Statistical Yearbook and the China City Statistical Yearbook, and some missing data are supplemented by linear interpolation. Other data come from the China Statistical Yearbook and the China Research Data Service Platform (CNRDS).

Table 3. Descriptive statistics.

Variable	Observations	Unit	Mean	Standard Deviation	Minimum	Maximum
Environmental pollution level	300	-	0.78	0.165	0.211	0.996
The level of development of the digital economy	300	-	0.13	0.101	0.017	0.577
The level of economic development	300	CNY	12,769.645	8145.207	5422.97	48,075
Foreign direct investment	300	%	0.018	0.014	0.0001	0.08
Environmental regulation	300	%	0.003	0.004	0.000085	0.031
Degree of government intervention	300	%	0.25	0.103	0.107	0.643
Advanced industrial structure	300	%	1.283	0.711	0.549	5.297
Green technology innovation	300	Individual	4035.15	5431.926	42	32,269

lists the descriptive statistical results of each variable.

5. Empirical Analysis

5.1. Benchmark Regression

To verify hypothesis H1, this paper uses Stata16 software and adopts a two-way fixed effect model of individuals and years for baseline regression. At the same time, to make the data easier to analyze, all data used for regression analysis are logarithmized. The specific regression results are shown in

Table 4. Benchmark regression.

	(1)	(2)	(3)	(4)	(5)
Variable	Pollute	Pollute	Pollute	Pollute	Pollute
dig	−0.0885 **	−0.0728 *	−0.0815 **	−0.0775 **	−0.0755 **
	(0.0374)	(0.0377)	(0.0373)	(0.0367)	(0.0358)
gdp		−0.240 **	−0.269 **	−0.283 ***	0.0797
		(0.105)	(0.104)	(0.102)	(0.140)
fd			0.0506 ***	0.0503 ***	0.0401 **
			(0.0175)	(0.0172)	(0.0170)
reg				0.0446 ***	0.0401 ***
				(0.0143)	(0.0140)
gov					0.413 ***
					(0.105)
Province	Yes	Yes	Yes	Yes	Yes
Young	Yes	Yes	Yes	Yes	Yes
_cons	−0.682 ***	1.603	2.057 **	2.461 **	−0.357
	(0.103)	(1.002)	(1.001)	(0.993)	(1.235)
N	300	300	300	300	300
R2	0.545	0.554	0.568	0.584	0.605

Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001.

.

Table 4 reports the baseline regression results before adding control variables. Currently, the coefficient of the digital economic development level is −0.0885 and is significant at the 5% level, indicating that there is a significant negative correlation between the development of the digital economy and the level of environmental pollution. Specifically, for every unit increase in the logarithm of the digital economic development level, the logarithm of the environmental pollution level will decrease by 0.085 units accordingly.

The second to fifth columns are the results obtained by gradually adding the economic development level, foreign direct investment, environmental regulation, and government intervention as control variables while using individual and time-fixed effects. With the continuous increase of control variables, compared with the baseline regression results in the first column, the newly obtained values have decreased, but the sign is still negative and significant at the 5% level, which shows that there is still a clear negative correlation between the level of digital economic development and the level of environmental pollution.

The second column shows the regression results of adding the economic development level as a control variable to the model. Currently, the coefficient of the digital economic development level is significantly negative. This shows that for every unit increase in the logarithm of the digital economic development level, the logarithm of the environmental pollution level will decrease by about 0.0728 units. At the same time, it can be found from the second column that there is also a significant negative correlation between the economic development level and the environmental pollution level, which means that while the economic development level increases, the environmental pollution level also shows a downward trend. In the third column, foreign direct investment is added as a control variable. The coefficient of the explanatory variable becomes −0.0815, which is significant at the 5% level. This shows that for every unit increase in the logarithm of the digital economic development level, the logarithm of the environmental pollution level will decrease by about 0.0815 units. There is a significant positive correlation between foreign direct investment and the environmental pollution level, indicating that the increase in the proportion of foreign direct investment will aggravate the degree of environmental pollution. In the fourth column, the coefficient of the digital economic development level becomes −0.0775 after adding environmental regulation as a control variable, which is still significant at the 5% level. However, there is a positive correlation between environmental regulation and the environmental pollution level, which shows that the improvement in environmental regulation has failed to reduce the level of environmental pollution. In the fifth column, we add government intervention as the last control variable, and the coefficient becomes −0.0755, which indicates that for every unit increase in the logarithm of the digital economy development level, the environmental pollution level will decrease by approximately 0.0755 units.

In summary, the results of stepwise regression show that with the development of the digital economy, the degree of environmental pollution can be effectively improved, which is consistent with the analysis of hypothesis H1. The possible reasons are that, on the one hand, the digital economy helps enterprises develop green and clean production, reduce pollutant emissions, and promote the reuse of pollutants; on the other hand, the digital economy provides a medium for the public to participate in environmental supervision, the public’s environmental awareness is increasing, and they are more likely to choose a green and environmentally friendly lifestyle, thereby reducing pollutant emissions.

5.2. Robustness Test

5.2.1. Endogeneity Test

The core variable of this paper, the level of digital economic development, is a comprehensive index covering four dimensions: digital infrastructure, digital industrialization, industrial digitization, and scientific and technological innovation capabilities. Therefore, the integration process may lead to endogenous problems due to measurement errors. In addition, although this paper has controlled multiple variables that may affect the level of environmental pollution, such as the economic development level and environmental regulation, there may be other potential factors affecting ecological efficiency, which were difficult to include in the model during the model construction process. The factors not considered are included in the error term, which may also cause endogenous problems. Therefore, to solve the above problems, based on the experience of previous scholars’ empirical research, this paper chooses to use the core variable lagged one period (ldig) as an instrumental variable for robustness testing. The regression results are shown in

Table 5. Endogeneity test.

	(1)	(2)	(3)	(4)	(5)
Variable	Pollute	Pollute	Pollute	Pollute	Pollute
ldig	−0.0820 **	−0.0662 *	−0.0752 **	−0.0757 **	−0.0706 *
	(0.0377)	(0.0384)	(0.0381)	(0.0375)	(0.0365)
gdp		−0.208 *	−0.229 *	0.256 **	0.136
		(0.110)	(0.109)	(0.108)	(0.148)
fd			0.0473 **	0.0471 ***	0.0402 **
			(0.0184)	(0.0181)	(0.0177)
reg				0.0419 ***	0.0370 **
				(0.0148)	(0.0144)
gov					0.442 ***
					(0.118)
Province	Yes	Yes	Yes	Yes	Yes
Young	Yes	Yes	Yes	Yes	Yes
_cons	−0.647 ***	1.342	1.706	2.189 **	−0.844
	(0.103)	(1.055)	(1.052)	(1.050)	(1.303)
N	270	270	270	270	270
R2	0.543	0.550	0.562	0.577	0.602

Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001.

.

Table 5 reports the results of the endogeneity test. When the control variables are gradually added to the regression from the second to the sixth columns, the coefficients of the instrumental variables are all significantly negative, which is consistent with the original regression results, indicating that the regression results of this paper are reliable.

5.2.2. Replacing Core Explanatory Variables

In order to avoid the randomness of the results caused by variable selection, this paper draws on the research of Liu [61] and selects the proportion of software business revenue in the added value of the tertiary industry (dig1) to replace the level of digital economic development and re-estimates it. The estimation results in column (1) of

Table 6. Test results of replacement explanatory variables.

	(1)	(2)
Variable	Pollute	Pollute
dig1	−0.0409 *
	(0.0221)
dig2		−0.0289 ***
		(0.00917)
control	Yes	Yes
Province	Yes	Yes
Young	Yes	Yes
_cons	−0.637	−0.508
	(1.286)	(1.216)
N	300	300
R2	0.604	0.613

Standard errors in parentheses * p < 0.05, *** p < 0.001.

show that the new explanatory variable and the level of environmental pollution are significantly negatively correlated, and the sign and significance of the regression coefficient have not changed much from the original explanatory variable, which shows that the regression results are robust. At the same time, to ensure that the estimation results will not change significantly due to different measurement methods of the core explanatory variables, this paper uses principal component analysis instead of the entropy method to calculate the new level of digital economic development (dig2) and re-estimates the parameters with the level of environmental pollution. As shown in Table for every unit increase in the level of digital economic development, the level of environmental pollution will significantly decrease by 0.0289 units, indicating that the research conclusions of this paper are reliable.

5.3. Testing of Mediation Mechanisms

The benchmark regression results reflect the results of the combined effects of various mechanism variables. It cannot be ruled out that digital technology will have a suppressive effect on environmental pollution in some mechanisms. Therefore, this section will continue to further analyze and test the specific transmission mechanism of digital economic development on environmental pollution control from the two aspects of industrial structure upgrading and green technology construct the following model.

$${Pollute}_{it}={\beta }_0+{\beta }_1{Digit}_{it}+{\beta }_2{Control}_{it}+{\gamma }_i+{\mu }_t+{ϵ}_{it}$$

(2)

$${Med}_{it}={\alpha }_0+{\alpha }_1{Digit}_{it}+{\alpha }_2{Control}_{it}+{\gamma }_i+{\mu }_t+{ϵ}_{it}$$

(3)

$${Pollute}_{it}={\delta }_0+{\delta }_1{Digit}_{it}+{\delta }_2{Med}_{it}+{\delta }_3{Control}_{it}+{\gamma }_i+{\mu }_t+{ϵ}_{it}$$

(4)

Among them, model (2) is the baseline regression model, and models (3) and (4) are mediation effect test models. Med is the mediating variable, representing the industrial structure upgrading (str) and green technology innovation (ino) in region i in year t, respectively. The other variables are consistent with the above. The specific empirical regression results are shown in

Table 7. Mediation effect test.

	(1)	(2)	(3)	(4)
Variable	Str	Pollute	Ino	Pollute
dig	0.362 ***	−0.0717 **	0.00709	−0.086 *
	(0.0309)	(0.0356)	(0.0613)	(0.0374)
str		−0.186 **
		(0.0866)
ino				−0.0346
				(0.0365)
Control	Yes	Yes	Yes	Yes
Province	Yes	Yes	Yes	Yes
Young	Yes	Yes	Yes	Yes
_cons	4.101 ***	0.803	2.307	−0.277
	(1.438)	(1.339)	(2.114)	(1.238)
N	300	300	300	300
R2	0.618	0.612	0.830	0.607

Standard errors in parentheses * p < 0.05, ** p< 0.01, *** p < 0.001.

.

In column (1) of Table 7,we found that when the digital economy development level is used to regress the industrial structure upgrading, the coefficient of the digital economy development level is 0.362, and it is significant at the 1% level. The results of column (2) show that when the digital economy development level and the industrial structure upgrading are used to regress the environmental pollution level, the coefficient of the digital economy development level is −0.0717, which is significant at the 5% level, and the coefficient of the industrial structure upgrading is significantly negative at the 5% level, which shows that the industrial structure upgrading plays a mediating role between the digital economy level and the environmental pollution level. According to the results of columns (3) and (4) of Table 7, the regression results of the digital economy development level on green technology innovation and the regression results of the digital economy development level and green technology innovation on the environmental pollution level are not significant, which shows that in the empirical analysis of this paper, in the mechanism of digital economy development affecting the environmental pollution level, the mediating effect of green technology innovation is not as significant as was found in previously analyses.

To enhance the robustness of the results of the mediation effect mechanism analysis, we conducted a Bootstrap analysis on the mediating variable of industrial structure upgrading.

Table 8. Bootstrap test results.

Mediating Variables	Industrial Structural Upgrade
rep	1000
95% confidence interval	[0.0584, 0.1574]
Mediation effect value	0.1079 *** (0.0253)

Standard errors in parentheses *** p < 0.001.

lists the results of 1000 samplings.

Table 8 shows that in the test with industrial structure upgrading as the mediating variable, the confidence interval of the indirect effect does not pass through 0, and the indirect effect coefficient is significant at the 1% level, indicating that the mediating effect is significant. The development of the digital economy can improve the ecological environment by promoting industrial structure upgrading, which is consistent with hypothesis H2. However, in the empirical results of this article, the mediating effect of green technology innovation is not significant, so hypothesis H3 is rejected.

5.4. Heterogeneity Test

China has a vast territory, and the economic development level, digital development degree, and pollutant emissions in different regions are different. To further verify whether the inhibitory effect of the digital economy development level on the environmental pollution level is still significant in different regions, this paper divides 30 provinces into three major regions: eastern, central, and western. The eastern cities are Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; the central cities are Shanxi, Jilin, Heilongjiang, Henan, Hubei, Hunan, Anhui, and Jiangxi; the western cities are Inner Mongolia, Chongqing, Sichuan, Guangxi, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. The data of the three major regions are empirically analyzed, and the specific regression results are shown in

Table 9. Heterogeneity test.

	(1)	(2)	(3)
Variable	East	Central	West
dig	−0.286 ***	−0.0999	−0.00818
	(0.107)	(0.0669)	(0.0213)
Control	Yes	Yes	Yes
Province	Yes	Yes	Yes
Young	Yes	Yes	Yes
_cons	17.83 **	−14.25 ***	−0.0692
	(7.327)	(4.362)	(2.308)
N	110	80	110
R2	0.667	0.752	0.823

Standard errors in parentheses ** p < 0.01, *** p < 0.001.

.

From the heterogeneity test results in Table 9, the level of digital economic development in the eastern region has the most significant effect on environmental pollution control. This is mainly because the eastern region has a complete digital infrastructure, a high level of digital technology application and digital innovation, and a good foundation for the digital economy to empower ecological environmental pollution control. In recent years, provinces in the eastern region of China have actively promoted the digital empowerment of ecological environmental governance and achieved good results. Digital technology has achieved good results in driving changes in production methods, lifestyles, and governance methods, continuously broadening the application scenarios of digital technology, and fully activating the vitality of market entities. For example, Zhejiang Province took the lead in digital reform, innovated the ecological environmental supervision and law enforcement model, continuously deepened the practice of “smart” management of the ecological environment, and took the lead in developing an “environmental map” in the country, becoming a pioneer in building a comprehensive collaborative management platform for ecological environmental protection. For the central and western regions, although the coefficient relationship between the level of digital economic development and the level of environmental pollution is negative, it is not significant.

6. Conclusions and Discussions

6.1. Main Conclusions

In contemporary society, the digital economy has emerged as a pivotal goal and direction for economic development. Capitalizing on the opportunities presented by the digital economy is crucial for gaining a strategic advantage in economic advancement. However, for human society to achieve sustainable and healthy development, robust economic support must be complemented by a sound and well-preserved ecological environment. With the continuous expansion of economic activities, the digital economy has experienced rapid growth since the Third Plenary Session of the Eleventh Central Committee, particularly over the past decade. This period has seen an accelerated momentum in the digital economy’s growth. Concurrently, environmental quality has also seen marked improvement. Although the early stages of reform and opening were characterized by ecological degradation and a decline in environmental quality due to factors such as technological limitations and outdated production methods, the ongoing development of the digital economy has contributed to a gradual enhancement of environmental conditions.

To explore the relationship between the digital economy and environmental pollution, this study, building on previous research, selected 20 indicators across four dimensions: digital infrastructure, digital industrialization, industrial digitization, and scientific and technological innovation capabilities. Using the entropy method, the study calculated the development level of the digital economy across 30 provinces in China, excluding Tibet, Hong Kong, Macao, and Taiwan, from 2012 to 2021. The key findings of this study are as follows.

The development of China’s digital economy significantly improves the ecological environment. The baseline regression model, based on two-way fixed effects, demonstrates that higher levels of digital economic development contribute to better ecological governance and lower levels of environmental pollution. This conclusion remains robust even after conducting instrumental variable testing, thereby confirming hypothesis H1.

The advancement of the digital economy contributes to environmental governance by facilitating the upgrading of industrial structures. Through mediation effect modeling, the analysis reveals that an advanced industrial structure partially mediates the relationship between digital economy development and reduced environmental pollution, thus validating hypothesis H2. Although some theoretical analyses suggest that green technology innovation is another important mechanism by which the digital economy could mitigate environmental pollution, this hypothesis was not empirically supported in this study.

There is notable regional heterogeneity in the impact of China’s digital economy on improving the ecological environment. Further heterogeneity analysis indicates significant regional disparities in the effectiveness of ecological governance within the context of digital economy development. Specifically, the eastern region, which has a more established digital economy foundation, exhibits a significant governance effect, whereas this effect is less pronounced in the central and western regions.

This study makes significant contributions to the understanding of the relationship between the digital economy and environmental governance in China. It provides empirical evidence for the positive impact of digital economy development on ecological environment improvement, highlights the role of industrial structure upgrading in mediating this relationship, and underscores the regional heterogeneity in the effects of digital economy development on environmental quality.

6.2. Policy Implications

To advance the digital economy and promote environmental sustainability, it is imperative to strengthen digital infrastructure construction, focusing on energy conservation and emission reduction. The 20th National Congress of the Communist Party of China emphasized the importance of coordinated development between digitalization and environmental greening. As the foundation of the digital industry, robust digital infrastructure is essential for supporting ecological environmental governance and facilitating the growth of the digital economy.

Key initiatives include the acceleration of 5G base station deployment, expansion of optical fiber networks, and development of the Internet of Things (IoT). These advancements will provide the necessary support for the construction of an ecological civilization [62]. In parallel, a comprehensive top-level design for the green development of digital infrastructure is needed. This design should outline strategies for energy conservation and emission reduction throughout the entire lifecycle of digital infrastructure projects, enhance the environmental protection reward evaluation system, and accelerate the implementation of incentive policies.

Although China’s digital economy has developed rapidly overall, significant regional disparities persist, and these differences must be addressed to achieve balanced national development. If these gaps continue to widen, they could hinder the overall planning of environmental initiatives and the improvement in environmental quality across the country. By fostering a development mechanism characterized by complementary advantages and coordinated cooperation, China can ensure that the digital economy becomes a vital tool in strengthening environmental pollution control and promoting sustainable development across all regions.

6.3. Discussion

Considering rapid digital transformation, there is an increasing need to evaluate how these advancements influence social well-being and environmental sustainability. Our expanded discussion explores the balance between technological progress and its broader implications for society. While digital innovation drives efficiency and economic growth, it is crucial to assess whether these advancements sometimes overshadow the integral development of individuals and communities. The pursuit of efficiency and economic gains must be balanced with considerations of social equity and environmental impact. By addressing these concerns, we can ensure that digital transformation contributes not only to economic prosperity but also to the holistic well-being of society. This balanced approach is essential for fostering sustainable development that aligns technological progress with the needs of individuals and communities.

6.4. Limitations and Future Research

This study makes significant contributions to the understanding of the relationship between the digital economy and environmental governance in China. It provides empirical evidence for the positive impact of digital economy development on ecological environment improvement, highlights the role of industrial structure upgrading in mediating this relationship, and underscores the regional heterogeneity in the effects of digital economy development on environmental quality.

However, the study is not without limitations. First, the research focuses primarily on China, and the findings may not be directly applicable to other countries with different economic and digital development contexts. Additionally, the study primarily examines the period from 2012 to 2021, which may not capture long-term trends and emerging challenges in the digital economy’s impact on the environment.

Future research should explore the effects of the digital economy on environmental governance in different countries and regions, considering varying levels of digital infrastructure development and environmental policies. Longitudinal studies that analyze the long-term impacts of digital economy growth on environmental sustainability would also provide valuable insights. Furthermore, future research could delve deeper into the mechanisms through which digital technologies influence environmental outcomes, particularly the role of green technology innovation and other potential mediators not fully explored in this study.