An Empirical Study on the Carbon Emission Reduction Effect of the Digital Economy in Zhejiang, China

Abstract

Given the simultaneous background of growing ‘dual-carbon’ ambitions and the rapid expansion of the digital economy, it is critical to understand the mechanisms by which the digital economy influences carbon emissions. This understanding is critical for overcoming regional green development gaps and facilitating the implementation of the national ‘dual-carbon’ policy. Using panel data from 11 prefecture-level cities in Zhejiang Province from 2012 to 2023, this study employs a two-way fixed-effects model and a mediation effect model for empirical analyses to evaluate the carbon emission reduction effect of Zhejiang’s digital economy objectively. The findings show that the digital economy directly cuts carbon emissions and has a spatial spillover effect, while indirectly facilitating emission reduction via the intermediary pathway of innovation efficiency. However, the mediating effect of industrial structure alteration is statistically insignificant. Regional heterogeneity research suggests that carbon reduction impacts are more prominent in economically developed cities (e.g., Hangzhou and Ningbo), whereas the effects are modest in less-developed areas, despite the digital economy’s potential for reduction. This study provides an empirical framework for local governments to establish distinct carbon reduction policies, as well as practical insights into how to align the development of digital economy with low-carbon goals.

1. Introduction

Extreme weather occurrences have increased as a result of the global increase in greenhouse gas emissions. As a result, there is increasing global agreement in favor of low-carbon growth strategies. China, a significant actor in the world economy, has formally announced its ‘dual-carbon’ goal, which is to become carbon neutral by 2060 and attain a carbon peak by 2030. The Action Plan for Achieving Carbon Peak by 2030, which aims to use the digital economy as a catalyst for lowering carbon emissions, is one of the policy documents that support this objective. After manufacturing and agriculture, China’s digital economy is a vital economic sector that is expected to grow to RMB 39.2 trillion in 2020, or 38.6% of the country’s GDP. Technology integration has been found to be an essential way to improve energy efficiency, and by 2030, digital technology is predicted to help reduce carbon emissions in society as a whole by 12–22%. China has launched major projects like the ‘East Counts, West Counts’ program and implemented strategic plans like the Outline for the Implementation of the Strategy of Network Strengthening of the State in recent years. These programs demonstrate China’s dedication to carbon reduction and sustainable development.

Leading the way in the development of China’s digital economy is Zhejiang Province. An astounding RMB 3.57 trillion in added value was produced by the province’s digital economy in 2021, making up a sizeable 48.6% of the province’s GDP. Zhejiang is now the fourth-largest contributor to China’s digital economy as a result. Over the last five years, the digital economy’s core sector has grown at an average annual rate of 13.3%, contributing RMB 834.83 billion in value-added. The industry’s standing as an essential part of Zhejiang’s economic structure has been strengthened by this expansion. Even if Zhejiang’s digital economy is in a highly developed state, there is still a lot of room for expansion; therefore, continuous efforts are needed to support its superior development. Furthermore, can Zhejiang Province reduce its carbon emissions through the digital economy? What impact does Zhejiang Province’s digital economy have on lowering carbon emissions? Is there a cross-regional spatial spillover effect of carbon emission reduction in the digital economy? These topics merit more discussion.

Using panel data from 2012 to 2023, the current work uses Zhejiang Province as a case study and creates a static panel model as well as a mediation effect model. The study performs a thorough examination of the precise effects of the growth of the digital economy on the reduction of carbon emissions, as well as the underlying mechanisms, from the viewpoints of benchmark regression, mediating effect, spatial spillover effects, heterogeneity analysis, and so forth. The study’s conclusions could influence Zhejiang Province’s strategic plans and policies. This paper’s potential innovations are as follows: First, the entropy method is used to objectively measure the level of digital economic development in Zhejiang Province’s cities; second, the mechanism of the digital economy’s impact on reducing carbon emissions in Zhejiang Province is examined from the standpoint of innovation efficiency and industrial structure upgrading. Thirdly, this article thoroughly examines the internal characteristics of the digital economy’s impact on reducing carbon emissions in Zhejiang Province’s cities from the standpoints of spatial econometric analysis and heterogeneity analysis.

2. Literature Review

Research on the digital economy has gained popularity in academia in recent years. Numerous scholarly works concentrate on research subjects such as ‘digital economy’, ‘carbon dioxide emissions’, ’economic growth’, ‘energy consumption’, and ‘power consumption’, as illustrated in Figure 1. Research pertaining to the digital economy-driven decrease in urban carbon emissions can be broadly divided into three categories, according to a thorough analysis of the body of existing literature on the topic: research on the digital economy, research on the reduction of carbon emissions, and research on the influence of the digital economy on the reduction in question.

The main focus of research on the digital economy is its conceptual framework, defining traits, methods of measurement, and mechanisms of influence. There is still disagreement in the academic community over how to conceptualize it. For example, Guan et al. emphasized the critical importance of ICT technology [1], Chen et al. concentrated on data elements and the platform economy [2], and Wang et al. highlighted the innovation-driven character of digital technology [3]. Scholars have treated the idea from a variety of approaches. All things considered, the digital economy is acknowledged as a new economic paradigm based on digital technology that promotes value creation by reorganizing resources and increasing efficiency. It is distinguished by traits like intertemporality, permeability, and synergy. The majority of the existing work looks at how it affects industrial structure, economic growth, and environmental impacts [4,5,6,7]. The value-added accounting method and the complete index system method are the two main approaches to measuring the digital economy that are currently covered in the literature. For example, Wang and Li used the five-dimensional entropy approach to create a thorough assessment model of the digital economy [8]. Similar to this, Cai and Niu compiled industry estimates to determine the value contributed by each sector in the digital communication sector [9]. Studies on the mechanisms of influence show that, despite notable regional heterogeneity [10], the digital economy promotes economic development through channels like energy structure optimization [11], increased innovation efficiency [12], and the impact of market integration [13]. Interestingly, the digital economy’s spatial spillover impact has two distinct characteristics, which may prevent green development in nearby areas [14].

The methods for monitoring carbon emissions and the factors influencing their reduction are the main focus of research on carbon emission reduction. The emission factor method and the apparent emission accounting method are the two most common measurement techniques. While the apparent emission accounting approach incorporates nighttime lighting data to create a machine-learning model for assessment, improving the comparability of intertemporal data [15], the emission factor method is mostly dependent on energy consumption data [16]. Scholars have identified several important factors that contribute to the decrease in carbon emissions, including governmental regulation [17], technical innovation [18], industrial transformation [19], and green finance [20]. Wang et al. also emphasized how the digital economy promotes a multifaceted synergistic mechanism for reducing carbon emissions by utilizing the ‘energy efficiency-industrial structure-technological progress’ routes [3].

There are three main points of view in the scholarly discussion of the connection between the digital economy and carbon emission reduction. According to the first viewpoint, the digital economy makes it easier to reduce carbon emissions. For example, Meng et al. discovered that the digital economy significantly lowers carbon emission intensity [21], while Jiang et al. showed that a 1% rise in the digital economy correlates with a 0.08–0.09% decrease in carbon emissions [22]. According to the second viewpoint, there is a non-linear correlation between carbon emissions and the digital economy. Fan et al. suggested a double-threshold carbon reduction impact [16], while Li and Wang contended that this relationship takes the form of an inverted U [23]. According to the third viewpoint, excessive digitization may have a rebound impact, as proposed by Bai [24], even though the digital economy can help reduce carbon emissions within certain bounds.

The effects of the digital economy on reducing carbon emissions have been established by a large body of literature, which provides useful references for further study. However, there are still a lot of unanswered questions about how the digital economy affects carbon emissions. Notably, the majority of the research that is now available concentrates on the national level, with little empirical study conducted in particular regions, especially those that have advanced significantly in terms of the digital economy. Furthermore, thorough research addressing regional variability is lacking. There is ongoing debate over the spatial effects and mechanisms via which the digital economy affects carbon emissions. In order to fill these gaps, the current study uses Zhejiang Province as a case study to empirically examine the regional traits and transmission mechanisms underlying the benefits of the digital economy on emission reduction in certain locations. The purpose of this study is to offer a scientific foundation for the creation of tailored governmental policies.

3. Theoretical Analysis and Research Hypotheses

3.1. Direct Effect of Digital Economy on Carbon Emission Reduction

Urban carbon emissions are expected to be significantly impacted by the development of the digital economy. Environmentally sustainable development could be made easier by digital technology, which could lead to further advancements in these technologies. Du (2023), Jiang S. and Jia F.(2023), Yang G. et al. (2023) have demonstrated that the development of the digital economy has a significant role in promoting carbon emission reduction through relevant empirical research [25,26,27]. In particular, the following five factors may indicate how the digital economy directly affects the decrease in carbon emissions.

Firstly, it causes old industries to be replaced. Sectors with fewer carbon emissions than traditional industries, like cloud computing, artificial intelligence, and new energy vehicles, are growing as a result of the digital economy’s expansion. As a result, carbon emissions are being reallocated as the digital economy develops, with some moving from established industries to these new ones.

Secondly, the digitalization of energy management has been accelerated by the emergence of the digital economy. Innovative technical approaches and procedures for reducing carbon dioxide emissions have emerged as a result of the extensive integration of information technology across multiple sectors. Smart grids and energy management platforms are examples of digital technologies that facilitate enhanced energy management, increase the efficiency of energy use, reduce energy losses, and help accomplish carbon emission reduction goals.

Thirdly, it is critical to develop sustainable modes of transportation. Digital technologies, including reservation platforms, improve the operational efficiency of urban transit networks and encourage more effective travel modes in the field of public transportation. Furthermore, a collaborative transportation ecosystem that prioritizes societal needs and uses digital technology to optimize the allocation of transportation resources—thus lowering the frequency of car usage and overall energy consumption—is fostered by the sharing economy and online car rental services, which are supported by the digital economy.

Fourth, it is critical to encourage low-carbon consumption. Customers may now enjoy more convenient and effective environmental protection services, like waste sorting and energy management, thanks to digital technology. Customers may better understand their carbon footprint thanks to this access, which encourages them to adopt more ecologically friendly consumption habits.

Achieving carbon neutrality is the fifth imperative. The development and expansion of the carbon trading market are facilitated by the digital economy, which also speeds up the execution of carbon reduction programs and improves the efficiency of the carbon market. Consequently, this encourages businesses to reduce their carbon emissions. To precisely monitor and track corporate carbon emissions and enable carbon trading, technologies like blockchain and the Internet of Things (IoT) can be used. In the end, this helps achieve carbon neutrality by ensuring equitable and effective management of carbon emissions.

However, it is important to understand that not all digitalization scenarios are good for lowering carbon emissions and improving energy efficiency. The digital economy’s explosive growth could potentially create new carbon emission sources. For instance, it is projected that the manufacturing and disposal of electronic equipment, as well as the energy usage of data centers, will result in considerable carbon emissions. Furthermore, the rapid growth of the digital economy is anticipated to lead to the emergence of a number of carbon-intensive industries, including the production of electronic goods and the construction of Internet infrastructure, both of which will have an effect on carbon emissions.

Hypothesis 1.

The growth of the digital economy has a direct impact on lowering carbon emissions.

3.2. Indirect Effects of the Digital Economy on Carbon Emission Reduction

Some scholars believe that the development of the digital economy has an indirect carbon emission reduction effect. Song and Liu (2024) believe that the digital economy can promote carbon emission reduction through green technology innovation [28]. Wang et al. (2025) found that the impact of the digital economy on carbon emissions first increased and then decreased under the regulation of energy efficiency and green innovation level [29]. Miao et al. (2022) proposed that there is a non-linear relationship between the digital economy and innovation efficiency [12]. There are four major areas where the digital economy’s contribution to reducing carbon emissions through innovative efficiency is most noticeable.

Firstly, smart technology has a big impact. Through the use of technologies like big data and artificial intelligence, the digital economy improves the capacity to precisely capture market dynamics and identify possible business opportunities and innovation areas. By enabling businesses to automate energy usage monitoring and implement intelligent energy management, these solutions help lower carbon emissions.

Secondly, in today’s digital environment, the sharing economy plays a crucial role. By facilitating the sharing economy model, the digital economy has the ability to overcome geographic limitations and establish a virtual innovation ecosystem that accelerates the sharing, application, and exchange of technology and information. In turn, this process speeds up and improves the efficiency of invention. By creating a norm for reducing carbon emissions from idle items and providing personal carbon credit accounts, Alibaba’s Idle Fish, for instance, was named a ‘Green Development Service Demonstration Case’ at the 2023 TISC, promoting low-carbon and sustainable development.

Thirdly, the ideas of remote work and virtualization have become essential components of this paradigm. The digital economy makes it easier to integrate the supply chain, production chain, and sales chain by optimizing the industrial chain’s organizational structure. Businesses can share resources and innovate collaboratively thanks to this connectivity. The growing use of remote work and virtualization, which have been shown to reduce invention costs, shorten innovation cycles, and generate higher-quality innovations, is a result of this trend.

Fourth, one of the most important aspects of the digital economy’s impact on environmental sustainability is the growth of carbon markets. The digital economy has created new market spaces and commercial opportunities by redefining the old competitive landscape through innovative business and service models.

In conclusion, there are new chances and prospects for improving innovation efficiency and lowering carbon emissions in the digital economy. It can propel digital industrialization and industrial digitalization, optimize top-level design, and improve the digital economy’s governance structure, all of which will help achieve carbon neutrality.

Additionally, through industrial digitization and digital industrialization, the digital economy can promote the shift from a conventional industrial structure to a low-carbon and green industrial framework, thereby promoting the reduction of carbon emissions [30]. In light of this viewpoint, the following theories are proposed in this research:

Hypothesis 2.

The mediating variable of innovation efficiency helps the digital economy reduce carbon emissions.

Hypothesis 3.

The upgrading of industrial structure acts as a mediating variable in the digital economy to reduce carbon emissions.

4. Research Design

4.1. Model Construction

4.1.1. Fixed Effects Model

This study aims to quantify the impact of the digital economy on Zhejiang Province’s efforts to reduce carbon emissions. In order to do this, the following fixed effect model was created:

$$\ln C{e}_{it}={\beta }_0+{\beta }_{1}Dig{e}_{it}+{\beta }_i{Z}_{it}+y_i+{\delta }_t+{\epsilon }_{it}$$

(1)

The variables in this model are as follows: ln Ce_it represents the amount of carbon emissions; Dige_it represents the degree of development in the digital economy; Z_it represents the control variables; i represents the city; t represents the year; and ε_it, δ_t, and y_i represent the random error term, the time effect, and the individual effect, respectively.

4.1.2. Mediation Effects Model

This research uses Jiang’s proposal for the test of the mediating effect [31] to further examine the indirect impact of the digital economy on carbon emissions through innovation efficiency and upgrading of industrial structure. It circumvents the estimation bias caused by the stepwise regression’s potential endogeneity in the conventional mediating effect model. Additionally, it builds the following model and chooses the two-step mediating effect approach for testing:

$$\ln C{e}_{it}={\alpha }_0+{\alpha }_{1}Dig{e}_{it}+{\alpha }_i{Z}_{it}+y_i+{\delta }_t+{\epsilon }_{it}$$

(2)

$$Me{d}_{it}={\phi }_0+{\phi }_{1}Dig{e}_{it}+{\phi }_i{Z}_{it}+y_i+{\delta }_t+{\epsilon }_{it}$$

(3)

Here, the variables i and t stand for the city and year, respectively, while the meanings of the remaining variables are in line with Equation (1). Med_it is the mediating variable. According to the updated evaluation criteria, the simultaneous significance of the regression coefficients, α₁ and φ₁, in models (2) and (3) is to be used to establish whether a moderating effect is present.

4.2. Variables Selection

4.2.1. Explanatory Variable

With a focus on emission intensity, this study investigates how Zhejiang Province’s carbon emissions are impacted by developments in the digital economy. According to Cong et al. [32], three separate components are aggregated to determine the total carbon emissions (Ce). Part 1: Direct carbon emissions from transportation, building, industrial production, forestry, agriculture, land use changes, and waste disposal in urban areas are included in the first component. Part 2: Indirect emissions that happen outside of cities and are mostly caused by external energy purchases like electricity, heating, or cooling are taken into account in the second component. Part 3: Carbon emissions that are not included in Parts 1 and 2 make up the third component. The natural logarithm of carbon intensity (lnCe) is used to measure total carbon emissions.

4.2.2. Core Explanatory Variables

The degree of digital economic development (Dige) is the main explanatory variable that this article looks at. To evaluate the degree of digital economic progress, this study employs a multimodal methodology that includes the comprehensive index method, principal component analysis, information entropy method, and gray correlation analysis. Based on the groundbreaking research of He (2023) and Xie et al. (2023) [33,34], this study presents a novel assessment index system, as shown in

Table 1. Indicator system for evaluating the level of development of the digital economy.

First-Level Index	Second-Level Index	Measurement of Secondary Indicators
Level of development of the digital economy	Internet-related industry outputs	Total telecommunication services per capita
	Internet-related industry practitioners	Percentage of employees in computer services and software
	Cell phone penetration	Cell phone subscribers per 100 population
	Digital inclusive financial development	Digital Inclusive Finance Index

. The expansion of the Internet and the advancement of digital inclusive finance are two crucial factors that interact to form this system. The digital economic development index for every city in Zhejiang Province is then calculated using the entropy approach.

4.2.3. Control Variable

In this study, a comprehensive set of five control variables influencing carbon emissions is utilized: (1) GDP per capita (Agdp), represented as the logarithm of the ratio of GDP to the year-end resident population; (2) the level of urbanization (Urban), defined as the ratio of the urban population to the year-end resident population; (3) the degree of industrialization (Ind), expressed as the ratio of the added value of the secondary industry to GDP; (4) education expenditure (Edu), measured as the proportion of education expenditure to local budgetary expenditure; and (5) energy consumption (Ec), calculated as the logarithm of urban per capita electricity consumption.

4.2.4. Intermediary Variable

Innovation efficiency (Ie) and industrial structure upgrading (Isu) have been identified as mediating variables in this study. Innovation efficiency can be quantified through multiple metrics, including patent application data, research and development (R&D) investment, the presence of scientific and technological talent, enterprise innovation capacity, scientific and technological output, and the rate of technology transformation. These indicators collectively capture the multifaceted nature of innovation capacity and efficiency at the urban level. Patent data are a critical indicator of urban innovation efficiency, reflecting cities’ accomplishments and activities in scientific and technological innovation. According to Fan [16], the per capita patent authorization within a city’s strategic emerging industries serves as a metric for assessing innovation capacity. This is calculated using the following formula: Patent application authorization per 100 people = (Patent application authorization/End-of-year resident population of the city) × 100. Furthermore, the advancement of industrial structure, which can be achieved through the optimization of the economic structure and enhancement of energy use efficiency, is instrumental in promoting carbon emission reduction. The ratio of employment in the tertiary sector to that in the secondary sector is utilized to evaluate the progress in industrial structure upgrading.

4.3. Data Sources

The dataset utilized in this study comprises panel data for Zhejiang Province spanning the years 2012 to 2023. Industrial production data were extracted from the China Industrial Statistical Yearbook, while data pertaining to agriculture, forestry, and land use were obtained from the China Agricultural Statistical Yearbook and the China Forestry and Grassland Statistical Yearbook. Waste disposal data were sourced from the China Environmental Statistical Yearbook. Additionally, data on employment, electricity, heating, and cooling were derived from the China Energy Statistical Yearbook, the China Urban Statistical Yearbook, and other pertinent publications. The digital financial inclusion index data were obtained from the Peking University Digital Financial Inclusion Index [35]. All remaining data were sourced from the Zhejiang Statistical Yearbook, with any missing data addressed through linear interpolation and mean value methods. The descriptive statistics of the variables are presented in

Table 2. Descriptive statistics of variables.

Variable	Observations	Mean	Std. Dev.	Minimum	Maximum
lnCe	132	3.699	0.154	2.589	4.000
Dige	132	1.170	0.667	0.279	3.764
Agdp	132	2.150	0.361	1.315	2.886
Urban	132	0.661	0.076	0.464	0.842
Ind	132	3.802	0.145	3.341	4.015
Edu	132	0.185	0.039	0.104	0.276
Ec	132	3.827	0.153	3.545	4.323
Ie	132	0.434	0.199	0.108	1.004
Isu	132	1.057	0.689	0.204	3.565

.

5. Analysis of Empirical Results

5.1. Analysis of the Current Situation of the Digital Economy and Carbon Emission Reduction in Zhejiang

5.1.1. Status of Digital Economy Development in Zhejiang Province

As a leading economic hub in China, Zhejiang has experienced significant growth in its digital economy, driven by major e-commerce companies such as Alibaba and Ant Group. In recent years, the province has positioned itself as a leader in the development of China’s digital economy. According to the Zhejiang Province Digital Economy Development Report 2024, the digital economy in Zhejiang has reached a total value of RMB 4.33 trillion, representing a 10.1 percent increase from the previous year [36]. Notably, the e-commerce sector contributed RMB 3.3 trillion, accounting for 40 percent of the province’s total economy and reflecting an 8.4 percent year-on-year increase [37]. Concurrently, the revenue of the Internet industry amounted to RMB 822.8 billion, demonstrating a 12 percent year-on-year growth. In the context of science and technology innovation within the digital economy, Zhejiang Province’s ‘415X’ advanced manufacturing clusters have significantly facilitated the digital transformation of the manufacturing sector, resulting in considerable economic advantages.

Simultaneously, the ‘315’ science and technology innovation framework has further accelerated the rate of scientific and technological advancements in Zhejiang Province, thereby enhancing its level of digitalization. Nonetheless, the rapid progression of Zhejiang’s digital economy is accompanied by substantial challenges, including a shortage of skilled labor, increasing data security concerns, and a persistent digital divide between urban and rural areas. These issues require immediate attention and resolution to ensure the sustained growth and success of Zhejiang’s digital economy.

5.1.2. Status of Carbon Emission Development in Zhejiang Province

As an economically developed coastal province in China, Zhejiang Province has adopted a low-carbon development orientation. The province has improved its ‘dual-carbon’ financial support policy, with the aim of promoting industrial upgrading and green and low-carbon development. This objective is pursued by leveraging the ‘dual-carbon’ policy and scientific and technological innovation. In recent years, there has been a substantial decline in the growth rate of carbon emissions. As of 2023, significant accomplishments have been realized, including the creation of 30 low-carbon pilot counties, 379 provincial-level green low-carbon factories, and 1181 low-carbon villages, alongside a renewable energy installation rate of 42.2%. Additionally, 38 carbon-inclusive projects have been documented, collectively contributing to a notable reduction of 521,000 tons in carbon emissions [38].

Despite these advancements, the province continues to encounter challenges in reducing carbon emissions, largely attributed to the regional concentration of ‘plate correlation’ and the spatial differentiation characterized by ‘polarization at both ends’. In Zhejiang Province, areas with high carbon stock sinks are predominantly situated in the southern region, closely linked to the quality of forests and water resources. In contrast, regions with high carbon emission sources are primarily located in the northern part of the province, significantly associated with the total population and economic activity. Urban centers have emerged as major sources of carbon emissions.

5.2. Base Regression Analysis

Table 3. Benchmark regression results.

Variant	lnCe
	(1)	(2)	(3)	(4)	(5)	(6)
Dige	−0.063 (−0.76)	−0.104 (−1.18)	−0.130 (−1.43)	−0.147 * (−1.69)	−0.153 * (−1.69)	−0.149 * (−1.70)
Ec		0.343 *** (3.72)	0.384 *** (4.31)	0.252 ** (2.04)	0.427 * (1.86)	0.472 * (1.84)
Edu			1.246 (1.46)	2.038 ** (2.06)	1.969 * (1.92)	2.225 ** (2.14)
Agdp				0.393 (1.58)	0.433 * (1.81)	0.419 * (1.75)
Ind					−0.312 (−1.08)	−0.355 (−1.15)
Urban						0.967 (1.00)
Constant	3.589 (18.65)	2.371 *** (5.32)	1.985 *** (4.15)	1.749 *** (3.67)	2.292 *** (4.90)	1.748 ** (2.44)
Urban fixed	Yes	Yes	Yes	Yes	Yes	Yes
Year fixed	Yes	Yes	Yes	Yes	Yes	Yes
Obs	132	132	132	132	132	132
R2	0.299	0.317	0.325	0.348	0.357	0.364
F	7.91	13.74	14.65	13.50	14.12	13.64

The p-value is a statistical measure used to determine whether the relationships between each independent variable (explanatory variable) and the dependent variable (response variable) in a model are statistically significant. *** means p < 0.01, ** means p < 0.05, * means p < 0.1. Standard errors in parentheses.

demonstrates the direct impact of the digital economy on carbon emissions. As control variables are incrementally introduced, the regression coefficient of the variable Dige changed from insignificant to significantly negative at the 10% level. The F value increased from 7.9 to 13.64, which passes the 1% significance level test. This indicates that the model regression results are generally good and that the digital economy has a considerable effect on reducing carbon emissions, thus supporting Hypothesis 1. In Zhejiang Province, the digital economy significantly contributes to carbon emission reduction, primarily through a multidimensional mechanism. The underlying reasons for this phenomenon include the following: (1) The digital economy has been demonstrated to enhance green technological innovation in Zhejiang Province through the utilization of cloud computing, artificial intelligence, and big data, thereby improving the province’s production efficiency and energy utilization. (2) The digital economy encourages the development of innovative business models and strengthens existing industries, facilitating Zhejiang Province’s transition to a low-carbon industrial framework. (3) The digital economy can enhance the flow of factors and optimize resource allocation within Zhejiang Province, thereby reducing resource mismatch and waste.

Moreover, the digital economy can support the establishment of a systematic carbon management system in Zhejiang Province, thereby increasing the effectiveness of the implementation of the ‘dual-carbon’ policy. Additionally, the digital economy can transform the consumption patterns and lifestyles of residents, indirectly promoting carbon emission reduction in Zhejiang Province.

5.3. Analysis of Intermediation Effects

An empirical analysis was performed utilizing the mediation model developed in the preceding section to investigate whether innovation efficiency and industrial structure transformation serve as mediating variables in the relationship between the digital economy and carbon reduction. The findings of this analysis are presented in

Table 4. Intermediation effect regression results.

Variant	(6)	(7)	(8)
	LnCe	Ie	Isu
Dige	−0.149 * (−1.70)	0.099 ** (2.36)	0.071 (0.44)
Ec	0.472 * (1.84)	−0.372 ** (−2.46)	−3.365 *** (−5.12)
Edu	2.225 ** (2.14)	0.083 (0.10)	5.944 *** (2.77)
Agdp	0.419 * (1.75)	−0.159 (−1.23)	0.220 (0.52)
Ind	−0.355 (−1.15)	−0.140 (−0.79)	1.043 (1.45)
Urban	0.967 (1.00)	1.452 *** (2.75)	2.235 (1.07)
Constant	1.748 ** (2.44)	1.480 *** (2.67)	7.608 *** (3.35)
urban fixed effect	Yes	Yes	Yes
Year fixed effects	Yes	Yes	Yes
Obs	132	132	132
R2	0.364	0.862	0.811
F	13.64	40.34	25.54

The p-value is a statistical measure used to determine whether the relationships between each independent variable (explanatory variable) and the dependent variable (response variable) in a model are statistically significant. *** means p < 0.01, ** means p < 0.05, * means p < 0.1. Standard errors in parentheses.

. Upon controlling for temporal and individual effects, the impact of the digital economy on carbon emissions was found to be significantly negative at the 10% significance level. The F value is 13.64, which indicates that the model regression results are generally good. This suggests that advancements in the digital economy contribute to the reduction of carbon emissions. The impact of the digital economy on innovation efficiency is significantly positive at the 5% significance level, suggesting that the advancement of the digital economy facilitates the enhancement of innovation efficiency. The F value is 40.34, which shows that the model regression results are generally good. Conversely, the impact of the digital economy on industrial structure transformation is not significant at the 10% significance level, indicating that the digital economy does not substantially promote industrial structure transformation. From these findings, a preliminary conclusion can be drawn: innovation efficiency seems to serve as an intermediary in the process of carbon reduction within the digital economy. The digital economy indirectly achieves carbon emission reduction through its influence on innovation efficiency, thereby confirming Hypothesis 2. In contrast, industrial structure transformation does not appear to serve as a significant intermediary in the carbon reduction process within the digital economy, leading to the non-verification of Hypothesis 3.

The mediating effects on innovation efficiency can be attributed to the digital economy’s ability to overcome geographical constraints, thereby enhancing access to and dissemination of information. This capability has the potential to create numerous opportunities and resources that can drive social development. Additionally, the digital economy fosters the establishment of open innovation ecosystems, enabling firms to collaborate more effectively with external partners. Considering the significant information and knowledge requirements inherent in innovation, digital technology facilitates the rapid acquisition and sharing of information, thereby promoting innovation. Moreover, digital technology expedites the process of information exchange, which is beneficial for the incubation and development of innovation. Furthermore, digital technology has been demonstrated to significantly lower transaction costs through mechanisms such as online payments, electronic contracts, and other pertinent tools. In conclusion, the evolution of the digital economy offers a broader and more diverse platform for innovative activities, while simultaneously fostering improvements in innovation efficiency. The capacity for innovation is crucial in protecting China’s emerging industries and holds substantial importance for carbon emission reduction. Generally, cities with higher innovation capacities tend to provide a more favorable environment for reducing carbon emissions. These cities benefit from considerable advantages in scientific and technological innovation output, the ability to transform knowledge, and other related factors. They are often capable of implementing more advanced energy-saving and emission-reduction technologies within the framework of the digital economy, which is more conducive to reducing carbon emissions. As a result, these cities possess a higher baseline for carbon emission reduction, advantageous external conditions, and a more pronounced inhibitory effect.

5.4. Robustness Check

To further validate the reliability of previous findings, this study employs four robustness testing methods: adjusting control variables, reducing sample size, substituting explanatory variables, and replacement estimation method.

Initially, adjustments are made to the control variables. Specifically, two control variables—Urban and Ind—are removed, and two new control variables, Openness and Population, are introduced. Openness is defined as the ratio of total exports to GDP, while Population is calculated using the logarithm of the city’s year-end resident population. The regression outcomes are detailed in column (8) of

Table 5. Robustness test regression results.

Variant	(8)	(9)	(10)	(11)	(12)	(13)
	LnCe	LnCe	So2	Sm	LnCe	LnCe
Dige	−0.158 * (−1.67)	−0.148 * (−1.67)	−5.505 *** (2.99)	−5.241 ** (2.53)	−0.156 * (−1.77)	−0.172 (−1.27)
Agdp	0.421 * (1.69)	0.498 * (1.88)	−9.269 (−1.11)	−18.359 (−1.51)	−0.044 (−0.19)	0.704 ** (2.54)
Urban		1.108 (0.90)	−82.928 *** (−3.37)	−59.722 * (−1.74)	−0.439 (−0.49)	4.180 (1.17)
Ind		−0.435 (−1.04)	4.313 (0.50)	25.643 ** (2.16)	0.162 (0.56)	−0.486 (−1.24)
Edu	1.074 (0.92)	2.957 * (1.80)	93.142 ** (2.37)	152.577 ** (2.37)	0.457 (0.33)	7.349 * (1.82)
Ec	0.594 *** (2.98)	0.458 * (1.92)	−5.277 (−0.70)	−13.633 (−1.53)	−1.189 * (−1.69)	0.106 (0.47)
Open	−0.323 (−1.38)
Pop	1.47 ** (1.98)
Constant	−8.606 * (−1.70)	1.773 ** (2.52)	65.759 ** (2.20)	−10.068 (−0.30)	8.083 *** (2.97)	0.307 (0.15)
urban fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Year fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Obs	132	108	132	132	60	72
R2	0.366	0.257	0.865	0.748	0.591	0.358

The p-value is a statistical measure used to determine whether the relationships between each independent variable (explanatory variable) and the dependent variable (response variable) in a model are statistically significant. *** means p < 0.01, ** means p < 0.05, * means p < 0.1. Standard errors in parentheses.

. At a 10% significance level, the effect of the digital economy on carbon emissions remains significantly negative, thereby confirming the robustness of the benchmark regression.

Secondly, it is important to acknowledge that certain samples have been excluded from this study. Hangzhou and Ningbo are the principal cities in Zhejiang Province, each striving to become the ‘No. 1 City of National Digital Economy’ and the ‘City of National Manufacturing Individual Champions’, respectively. These cities are pivotal to Zhejiang’s economic development, with their GDPs ranking among the top two in the province, at RMB 2005.9 billion and RMB 164.53 billion, respectively, which are significantly higher than those of other cities in the region. As a result, this study has chosen to exclude Hangzhou and Ningbo, given their comparatively advanced digital economy development, in order to reduce the sample size prior to analyzing the impact of the digital economy on carbon emissions. The regression results presented in column (9) of

Table 6. Spatial SDM estimation results.

Variant	(14)	(15)	(16)
	Direct	Indirect	Total
Dige	−0.256 ** (−2.47)	−0.575 *** (−3.51)	−0.831 *** (−3.58)
Agdp	0.025 (0.20)	−0.605 ** (−2.19)	−0.579 ** (−2.34)
Urban	1.104 (0.83)	−9.630 ** (−1.99)	−8.526 ** (−2.08)
Ind	−0.428 (−1.45)	0.069 (0.07)	−0.359 (−0.36)
Edu	0.448 (0.45)	11.392 *** (5.75)	11.840 *** (6.62)
Ec	0.642 *** (2.94)	0.701 (1.18)	1.344 * (1.73)
urban fixed effect	Yes	Yes	Yes
Year fixed effects	Yes	Yes	Yes
rho			−1.360 *** (−5.19)
Log L			117.270
Obs	132	132	132

The p-value is a statistical measure used to determine whether the relationships between each independent variable (explanatory variable) and the dependent variable (response variable) in a model are statistically significant. *** means p < 0.01, ** means p < 0.05, * means p < 0.1. Standard errors in parentheses.

, derived after the sample reduction, indicate that the impact of the digital economy on carbon emissions remains significantly negative at the 10% significance level. This finding underscores the robustness of the benchmark regression.

Thirdly, the explanatory variables are replaced. Considering that the emission indicators related to the digital economy include not only carbon emissions but also industrial soot emissions, industrial sulfur dioxide emissions, industrial wastewater emissions, etc., this paper uses sulfur dioxide emission intensity (So2) and soot emission intensity (Sm) to replace the explanatory variables to test the validity of the conclusions regarding variable replacement. The intensity of sulfur dioxide emissions (So2) is expressed as the ratio of sulfur dioxide emissions to GDP in each city, and the intensity of soot emissions (Sm) is expressed by the ratio of soot emissions to GDP in each city. The regression results are presented in Column (10) and Column (11) of Table 5. The impact of the digital economy on sulfur dioxide and soot emissions remains significantly negative at the significance level of 5%. These results suggest that the advancement of the digital economy can also effectively reduce sulfur dioxide, soot, and other waste gas emissions, thus affirming the robustness of the previously discussed empirical findings.

Fourth, the estimation method is replaced. Considering that the digital economy may produce cross-regional spatial spillover effects, which, in turn, affect urban carbon emissions, this paper further uses spatial econometric models for robustness analysis. The spatial Dubin model (SDM) constructed is as follows:

$$\ln C{e}_{it}=\rho W\ln C{e}_{it}+{\alpha }_{1}Dig{e}_{it}+{\theta }_{1}WDig{e}_{it}+{\alpha }_i{Z}_{it}+{\theta }_{i}W{Z}_{it}+y_i+{\delta }_t+{\epsilon }_{it}$$

(4)

where W is the spatial weight matrix. This paper uses the spatial geographic distance weight matrix. θ₁ and θ_i is the spatial lag coefficient vector of digital economic variables and control variables; ρ is the spatial autoregressive coefficient. The meaning of the other variables is the same as in the above Formula (1). After the spatial correlation test, LR test, and Wald test, this paper adopts the spatial SDM model of two-way fixed effect. The measurement results are shown in Table 6. The measurement results based on the spatial geographical distance weight matrix show that the regression coefficient rho passes the significance test at the 1% level, indicating that the carbon emissions of prefecture-level cities in Zhejiang Province are not only affected by the development of the digital economy in the city but also by the carbon emissions of neighboring cities and the development of their digital economy. The regression coefficients of the direct and indirect effects of the digital economy Dige are significantly negative, indicating that the digital economy can have a significant carbon emission reduction effect on both the city and neighboring cities.

5.5. Analysis of Regional Heterogeneity

Considering the documented variations among cities in terms of economic conditions, workforce, policies, and advancements in science and technology, it is reasonable to anticipate that the factors influencing carbon emissions will also exhibit diversity. This implies potential heterogeneity among cities regarding the impact of the digital economy on carbon emissions. To investigate this impact across different urban contexts, this study classifies 11 cities into two distinct categories based on their levels of economic development and GDP. Hangzhou, Ningbo, Wenzhou, Shaoxing, and Jiaxing are designated as City Type I, reflecting their higher levels of economic development relative to the average in Zhejiang Province. Conversely, Taizhou, Jinhua, Huzhou, Quzhou, Zhoushan, and Lishui are classified as City Type II, due to their relatively lower economic development.

The regression analysis of carbon emissions for City Type I, as presented in column (12) of Table 5, reveals a significantly negative estimated coefficient for the variable ‘Dige’ at the 10% significance level. This finding implies that a 1% increase in the digital economy corresponds to an approximate 15.6% reduction in carbon emissions. In contrast, column (13) of Table 5 details the regression outcomes for City Type II, where the estimated coefficient for the primary explanatory variable, ‘Dige’, is −0.172. However, the regression results for City Type II do not achieve statistical significance at the 10% level, indicating that the impact of the digital economy on carbon emission reduction in this city type is not statistically significant. In contrast, urban areas classified as type I exhibit a higher degree of economic development, and the advancement of their digital economies plays a more pronounced role in reducing carbon emissions. The enhanced capacities of these developed cities, as demonstrated by their state-of-the-art large-scale data centers, sophisticated network communication infrastructure, and versatile computing platforms, establish a strong foundation for the digital transformation of traditional industries. Enterprises can capitalize on these advancements by utilizing the capabilities of these facilities to drive the digital transformation of conventional sectors. This transformation may involve the integration of advanced digital technologies for resource management and the optimization of energy consumption. Additionally, individuals have the opportunity to adopt more environmentally sustainable production and lifestyle choices, thereby achieving the goals of cost reduction, efficiency improvement, and carbon emission reduction.

6. Conclusions and Recommendations

6.1. Conclusions of the Study

This research employs a distinctive panel dataset, covering 11 prefecture-level cities in Zhejiang Province from 2012 to 2023, to conduct a comprehensive examination of the intrinsic relationship between the digital economy and carbon emission reduction. The influence of the digital economy on carbon emissions is empirically assessed utilizing a two-way fixed effects model. Furthermore, the mediating effects of innovation efficiency and industrial structure transformation are evaluated through a mediating effect model. The analysis yields the following conclusions.

Initially, the impact coefficient of the digital economy on carbon emissions in Zhejiang Province is estimated to be approximately −0.149. This suggests that the development of the digital economy positively contributes to the reduction of carbon emissions in the region, thereby effectively facilitating carbon emission mitigation. Following a series of robustness tests, which included adjustments to control variables, reductions in sample size, and substitutions of explanatory variables, the research findings remained consistent. The results of the heterogeneity analysis show that the carbon emission reduction effect of the digital economy in economically developed cities such as Hangzhou, Ningbo, and Wenzhou is more significant, while the carbon emission reduction effect of the digital economy in economically underdeveloped cities such as Lishui, Quzhou, and Zhoushan is not significant.

Secondly, the results of the mediation effect analysis indicate that the digital economy can improve the carbon emissions of a city through the variable of innovation efficiency, while the mediating effect of industrial structure transformation in the carbon emission reduction process of the digital economy is not obvious. The results of the spatial econometric analysis show that there is a significant spatial spillover effect in the development of the digital economy in Zhejiang Province. The carbon emissions of prefecture-level cities are not only affected by the development of the digital economy in their own cities, but also by the carbon emissions of neighboring cities and the development of their digital economy.

6.2. Suggestions for Countermeasures

In light of the aforementioned research conclusions, the following recommendations are put forward in this paper.

It is essential to harness the potential of the digital economy to mitigate carbon emissions. Zhejiang Province should strategically exploit the opportunities afforded by the expansion of the digital economy and align effectively with the national digital economy’s primary development initiative. This requires investment in the digital industry, comprehensive optimization of the digital innovation environment, and the implementation of targeted policies. Given the wealth of academic resources in Zhejiang Province, it is vital to prioritize the cultivation and recruitment of talent within the digital economy sector to enhance the province’s innovation capacity and core competitiveness. Simultaneously, the province should focus on developing innovative industry clusters by establishing smart factories and digital workshops to foster the growth of digital industry clusters. The deployment of advanced digital technologies is crucial for improving the efficiency of social governance, thereby facilitating the province’s digital transformation and intelligentization.

Secondly, to mitigate carbon emissions by leveraging the digital economy, it is imperative to focus on enhancing innovation efficiency. In this regard, Zhejiang Province should prioritize the following strategies: (1) the reinforcement of significant scientific and technological research initiatives and project development; (2) the increase in investment in research and development within the scientific and technological sectors; (3) the support and promotion of innovative enterprises and projects; (4) the facilitation of collaboration between research institutions and enterprises; and (5) the advancement of the transformation and practical application of scientific and technological innovations. Simultaneously, it is imperative for the province to prioritize the establishment and enhancement of innovation platforms and carriers, thereby bolstering its capacity for scientific and technological innovation and its ability to deliver innovative solutions. This initiative should be complemented by the development and refinement of an intellectual property protection mechanism to safeguard the legitimate rights and interests of innovation achievements, thereby stimulating the innovative enthusiasm of enterprises and individuals. In conclusion, Zhejiang Province should enhance its innovation efficiency by leveraging the advantages of innovation curatorship, optimizing institutional mechanisms, and strengthening scientific and technological innovation. This strategic approach will provide substantial support for the advancement of the digital economy and the reduction of carbon emissions.

In conclusion, it is imperative to address the disparities in digital economy development levels among urban centers within the province. To achieve this, cities should consider adopting the digital industry development practices exemplified by Hangzhou and Ningbo. Specifically, it is recommended that they expedite the implementation of data-driven development strategies and industrial innovation strategies. Additionally, cities should accelerate the advancement of their data economies. Collaborative efforts are also encouraged to promote the high-quality development of the digital economy across Zhejiang Province. Conversely, cities should tailor their digital economy development strategies to capitalize on their unique characteristics and enhance policy support. For example, the policy guidance model employed by Ouhai District in Wenzhou City, which facilitated the establishment of the ‘digital port’ digital industrial park, has successfully attracted numerous upstream and downstream eco-enterprises within the data industry. The establishment of the ‘digital port’ digital industrial park holds the potential to transform into a regional digital industry cluster, thereby playing a crucial role in the advancement of the digital economy. Furthermore, achieving interoperability in registration, payment, examination, diagnosis, treatment, and patient information can be facilitated through the digital transformation of urban and rural healthcare systems. This involves creating a digital countryside, advancing digital trade, and promoting the equitable distribution of digital resources between urban and rural areas, as demonstrated by the initiatives in Taishun County, Wenzhou City. Additionally, establishing a cooperative platform for digital economy development is crucial for fostering seamless collaboration and knowledge exchange between cities at different stages of digital economic advancement. Such a platform should enable the unobstructed flow of digital resources, expertise, and technology across municipalities. Moreover, implementing a monitoring and assessment mechanism for the digital economy’s development is essential to maintain an understanding of the progress and disparities among cities. This mechanism will provide a basis for the formulation and adjustment of relevant policies.

6.3. Discussion, Limitations, and Future Research Directions

The research findings in this paper offer a useful guide for developed regions in eastern China to reduce carbon emissions using digital technology. They can also serve as a guide for policy-making in comparable cities in China’s coastal regions and the Yangtze River Delta. The following are this paper’s limitations: The analysis of the intermediary mechanism focuses on innovation efficiency and industrial structure transformation, and has not yet included potential paths like energy structure optimization and the diffusion of green technologies. The sample only covers Zhejiang Province, and it is necessary to further verify the applicability of the conclusions to the underdeveloped areas in central and western China. In order to create a more comprehensive impact mechanism model, the first step is to extend the sample to the national inter-provincial and county levels and examine the regional gradient differences in the emission reduction effects of the digital economy. The second step is to add multi-dimensional mediating variables like digital governance capability and green total factor productivity.