Research on the Impact and Mechanism of Digital Technology on the Synergistic Governance of Pollution and Carbon Reduction

Abstract

Environmental pollution and climate change are significant challenges on the path to sustainable development for human society. This study employs panel data from 30 provinces in China, from 2000 to 2022, to empirically analyzes the impact of digital technology on the synergistic governance of pollution and carbon reduction, as well as its underlying mechanisms. The findings show that digital technology helps optimize corporate management models, enhance environmental governance capabilities, and create a green and low-carbon social atmosphere, thereby significantly improving the level of synergistic governance of pollution and carbon reduction. This conclusion remains robust after replacing the explanatory variable with digital patents, substituting regression models, and controlling for endogeneity issues. However, the effects of digital technology on synergistic governance of pollution and carbon reduction are heterogeneous. Digital technology has a more pronounced positive impact in regions with a solid foundation in environmental governance, distinct green economic characteristics, and mature digital operation models. The mediating effect results indicate that digital technology can achieve synergistic governance of pollution and carbon reduction through two pathways: improving energy efficiency and promoting virtual agglomeration. The conclusions drawn provide insights for relevant stakeholders to fully utilize digital technology to accelerate green production and address climate change.

1. Introduction

Under the context of economic globalization, introducing industries to enhance a nation’s industrialization level has become a major means for most developing countries to develop their economies and improve their citizens’ living standards [1]. However, as industrialization continues to advance, developing countries, while achieving economic growth objectives, have also presented new demands for energy. This has led to a significant increase in the consumption of fossil fuels, primarily oil, coal, and natural gas, which in turn results in the excessive emission of pollutants and greenhouse gases [2]. According to the International Energy Agency (IEA), global energy consumption reached a historic high in 2024. Developing countries accounted for 80% of the new growth in energy demand, with traditional fossil fuels comprising over 54% of this increase. This trend undoubtedly has severe negative impacts on the global environment. Thus, against the backdrop of escalating global environmental degradation, curbing emissions of diverse pollutants and greenhouse gases has become a critical challenge for developing nations.

As the world’s largest developing economy, China has achieved remarkable economic progress. Yet its long-term dependence on energy-intensive, highly polluting industries for growth has simultaneously generated immense environmental protection pressures [3]. According to China’s National Bureau of Statistics (NBS, Figure 1), from 2015 to 2024, China’s total energy consumption has continued to reach new record highs. Although the proportion of fossil fuels steadily declined, they maintained their dominance. In light of this, the Chinese government pledged at the United Nations General Assembly to achieve peak carbon emissions by 2030. In 2022, it enacted the “Implementation Plan for Synergizing Pollution Reduction and Carbon Mitigation”, thereby establishing definitive guidance for the nation’s environmental governance framework. Against this backdrop, research on China’s synergistic governance of pollution and carbon reduction not only advances its transition toward high-quality economic development but also offers actionable insights for other developing economies pursuing environmental sustainability.

2. Literature Review

2.1. The Concept and Calculation Method of Synergistic Governance of Pollution and Carbon Reduction

The theory of collaborative governance transcends the limitations of a single governing entity, emphasizing the importance of collaboration among various actors, including governments, the public, and enterprises. It asserts that the synergistic effects of such collaboration can maximize public interest [4]. Environmental protection is intrinsically connected to the health and well-being of residents, the normal operations of enterprises, and the governance performance of governments, encompassing the shared interests of multiple stakeholders. Integrating environmental protection into the framework of collaborative governance has significant implications for optimizing the coordination of public interests and enhancing governance efficiency [5]. Pollutants and carbon emissions are integral to environmental protection and share a common origin. Since Ayres and Walter first introduced the concept of the co-occurrence effect [6], subsequent studies have explored the relationship between climate change and environmental pollution, revealing that atmospheric pollutants and greenhouse gases exhibit consistent patterns across multiple dimensions, including emission sources, production processes, and spatiotemporal distribution [7]. These findings have gradually increased scholars’ attention to research on how to achieve synergistic governance of pollution and carbon reduction. Currently, research on synergistic governance of pollution and carbon reduction mainly focuses on two aspects: horizontal measurement and influencing factors. In terms of horizontal measurement, some studies utilize methods such as absolute emissions [8], cross-elasticity coefficients [9], and emission reduction percentages [10] to assess the level of synergistic governance in pollution and carbon emission reduction. However, these approaches are limited by oversimplified indicators, which hinder the accurate reflection of the actual governance situation [11]. In response to the limitations, some studies have proposed methods such as coupling coordination degree [12] and Data Envelopment Analysis (DEA) [13]. These approaches involve relatively comprehensive indicators, which can mitigate the limitations of individual indicators. However, each method is subject to specific drawbacks, including the neglect of economic benefits and susceptibility to calculation biases. To address these issues, some studies have enhanced the Data Envelopment Analysis method by integrating the strengths of both the DEA and Slack-Based Measure (SBM) models, thereby proposing a more comprehensive Epsilon-Based Measure (EBM) model [14]. In terms of influencing factors, existing studies primarily focus on the impact of factors such as government policies [15], energy utilization [16], and technological measures [17] on the synergistic governance of pollution and carbon reduction. Chukwuemeka et al. evaluated international agreements and government policies through qualitative analysis, demonstrating their potential efficacy in reducing transportation sector pollution and carbon emissions, albeit constrained by technological, political, and economic factors [18]. Based on on-site investigations of coal fly ash from Chinese thermal power plants, Hou et al. developed three resource valorization pathways aimed at mitigating solid waste pollution and associated greenhouse gas emissions [19]. Darius et al. analyzed the temporal lag between green patent development and observable carbon reduction, demonstrating that advancements in green transportation technologies effectively promote decarbonization in Europe [20]. Additionally, some studies have focused on the digital economy, finding that factors such as the digital economy [21], digital finance [22], and digital infrastructure [23] all have a positive impact on synergistic governance of pollution and carbon reduction. However, there is a lack of research examining the impact of digital technology on such synergistic governance.

2.2. The Impact of Digital Technology on the Economy and Society

Digital technology is a comprehensive technological system based on binary digital signals, which utilizes computer systems, communication networks, software algorithms, and other means to collect, store, transmit, process, and apply information [24]. Given that the digital economy has become a new growth engine for China’s economy, digital technology, as the technological foundation supporting the sustained development of the digital economy, will undoubtedly receive increasing attention from the Chinese government [25]. Through comprehensive support in areas such as policy, funding, and talent, the development level of digital technology in China has rapidly advanced [26]. For example, in the telecommunications sector, according to the China Academy of Information and Communications Technology, the number of 5G base stations installed in China is expected to account for approximately 65% of the global total by 2024. In the field of innovation, according to the “Digital China Development Report”, the number of generative artificial intelligence patents in China is projected to exceed 27,000 by 2024, accounting for approximately 60% of the global total. As the application of digital technology continues to expand in China, its impact on the country’s economic and social development will deepen, thereby offering a new perspective for addressing environmental issues in China. However, despite the growing body of research on the environmental impact of digital technology, the conclusions drawn remain inconsistent. On the one hand, digital technology can contribute to environmental protection by addressing aspects such as energy utilization, environmental monitoring, production activities, and industrial development [27,28]. For instance, Feroz et al. [29] argue that digital technology can drive the digital transformation of industries by optimizing production processes to reduce energy consumption, thereby achieving the goal of reducing pollutants and greenhouse gas emissions. Liu et al. [30] contend that the application of digital technology can facilitate the development of the digital economy, thereby promoting industrial structure transformation and effectively enhancing green total factor productivity. On the other hand, the application of digital technology relies on high-energy-consuming equipment as its hardware foundation, which may result in environmental pollution [31]. For instance, de Sousa et al. argue that digital devices increase energy consumption, thereby leading to higher carbon emissions and contributing to environmental pollution [32]. Thus, the impact of digital technology on the ecological environment still requires further in-depth exploration. At the same time, synergistic governance of pollution and carbon reduction, as a core approach to ecological environmental protection, is closely linked to its implementation effectiveness and technological support. However, existing research lacks a systematic examination of the specific effects of digital technologies on this governance model. Therefore, further investigation into the specific impact of digital technology on synergistic governance of pollution and carbon reduction is needed.

2.3. The Concept and Role of Virtual Agglomeration

Virtual agglomeration has emerged alongside the widespread application of digital technologies such as the internet, big data, cloud computing, and artificial intelligence [33]. It represents an economic activity model based on digital platforms that utilizes productive data elements to shift the gathering of supply and demand parties, along with related enterprises, from physical geographical space to virtual space [34]. Compared to traditional agglomeration, which faces issues like geographical limitations, rising costs, and excessive competition [35], virtual agglomeration overcomes spatial constraints, enabling significantly more efficient and convenient information dissemination and access [36]. Consequently, it offers advantages like lower costs, broader coverage, and higher efficiency [37], effectively alleviating the various problems associated with traditional agglomeration. Regarding how to promote virtual agglomeration, scholars primarily explore areas related to digital technologies. Scholars such as Shen et al. argue that next-generation digital technologies drive the transformation of certain industries and firms towards digitalization, intelligence, and platformization, facilitating a shift from geographical clustering to virtual network agglomeration [38].Through text mining, Chen et al. identified nine factors driving virtual agglomeration in entrepreneurial industries, including the digital economic environment, government policy planning, online interactive services, and virtual platform infrastructure [39]. Johannes et al. propose that information and communication technologies can enhance internal and external corporate communication, aiding regional economic development [40]. Concerning the effects of virtual agglomeration, scholars often examine aspects like environmental protection and technological innovation. For instance, Zhang et al. [41] empirically demonstrate that manufacturing virtual agglomeration has a positive nonlinear impact on enterprise green total factor productivity. Ru et al. [42] found that manufacturing virtual agglomeration positively influences corporate technological innovation, though this effect shows heterogeneity depending on firm age. Reviewing the literature reveals that digital technologies drive virtual agglomeration, which significantly impacts environmental protection and plays a mediating role in the process through which digital technologies affect the environment. However, existing research has not deeply explored this mediating effect. Therefore, when studying the impact of digital technologies on synergistic governance of pollution and carbon reduction, the mediating role of virtual agglomeration needs to be considered.

2.4. The Role of Energy Efficiency

The mediating role of energy efficiency is equally significant and cannot be overlooked. Currently, the world faces severe energy shortages. On the one hand, total energy consumption continues to rise annually due to global economic development and lifestyle changes [43]. On the other hand, the energy consumption structure remains heavily dependent on non-renewable fossil fuels, which have limited reserves. Furthermore, supply instability is exacerbated by political instability in some resource-rich regions [44]. While renewable energy is developing, it faces numerous technical challenges, such as the intermittent and unstable nature of solar and wind power, and the slow technological advancement and low commercialization of nuclear fusion energy [45]. According to the “World Energy Statistical Yearbook”, global primary energy consumption reached 620 exajoules (EJ) in 2023, with fossil fuels accounting for a substantial 84%, while renewable energy usage remained relatively low. In this context, improving energy efficiency becomes critically important. It can both extend the usability period of non-renewable resources and buy time for the research and development of new energy technologies, serving as a key strategy to alleviate energy scarcity. Energy efficiency refers to the ratio of useful energy output to energy input, measuring the rationality of energy utilization [46]. Scholars have extensively studied it in areas such as policy formulation [47], industrial structure [48], and technological improvements [49], with particular attention paid to the impact of digital technologies on energy efficiency [50]. For instance, Lorenzo et al. demonstrated through a comparison of control strategies in a digital twin model of a fuel cell hybrid electric vehicle that digital technology can increase vehicle range by nearly 30 km while effectively managing battery pack load stress [51]. Concurrently, the environmental impact of energy use is also a focus. Nick et al., analyzing questionnaire data from employees in Indonesia’s oil industry, concluded that energy efficiency positively contributes to a sustainable environment, with organizational awareness playing a moderating role [52]. However, some studies suggest that increased energy efficiency may reduce per-unit energy costs, potentially stimulating overall energy demand and consequently leading to environmental pollution [53]. Therefore, energy efficiency likely plays a crucial mediating role in the process through which digital technologies drive synergistic governance of pollution and carbon reduction, though its specific utility requires further validation.

2.5. Literature Gap

Based on the preceding analysis, this study identifies that current research rarely examines the impact effect of digital technologies on synergistic governance of pollution and carbon reduction, while the mediating effects of virtual agglomeration and energy efficiency in this process have been persistently overlooked. Given this gap, and ensuring data availability constraints, this research builds upon existing empirical studies to empirically investigate the impact of digital technologies on synergistic governance of pollution and carbon reduction and its underlying mechanisms, using Chinese provincial panel data [54]. Regarding the measurement methodology for the level of synergistic governance of pollution and carbon reduction, after comprehensively considering the strengths and weaknesses of existing approaches [55], this study first constructs an indicator system from an “input-output” perspective and then employs a modified Data Envelopment Analysis method to measure its level. Furthermore, acknowledging China’s significant regional disparities and varying levels of emphasis on environmental protection across different regions—which may lead to geographical variations in the impact of digital technologies on synergistic governance—this study incorporates heterogeneity analysis to explore these regional differences in digital technology effectiveness. Finally, based on the empirical findings, the study proposes corresponding policy recommendations to provide practical insights for China’s high-quality economic development and environmental protection efforts in other developing countries.

The contributions of this study lie in two key aspects: (1) It approaches the challenge from the perspective of digital technologies, exploring new pathways to enhance the level of synergistic governance of pollution and carbon reduction in China. (2) It pioneers the introduction of “virtual agglomeration” and “energy efficiency” as mediating variables, examining their specific mediating roles in the process through which digital technologies affect synergistic governance of pollution and carbon reduction.

3. Research Hypotheses

3.1. The Direct Effect Mechanism of Digital Technology

The technological substitution theory shows that technological progress can lower pollution-control costs through creative destruction in economic production, thereby cutting pollution emissions. The application, innovation, and development of digital technology not only provide precise, scientific, and feasible decision-making options for market entities’ environmental planning, but also offer technical support for optimizing the top-down environmental regulation mode and enhancing regulatory efficiency. This facilitates the achievement of the dual objectives of reducing air pollutant emissions and controlling carbon emissions [56]. The implementation of synergistic governance of pollution and carbon reduction can be encapsulated into three stages: source prevention, process control, and end-of-pipe treatment. All three stages rely on the support of advanced technologies [57]. In the dimension of process control, innovation-driven green technological progress can reduce unnecessary resource consumption in the production process through optimizing manufacturing procedures, promoting circular utilization of waste, enhancing the combination of input factors, and reducing electricity loss. This, in turn, contributes to atmospheric environmental protection and the enhancement of carbon efficiency. In the industrial production field, digital technology can achieve the synergy of pollution and carbon reduction by optimizing the energy consumption system, improving the energy input structure, and accelerating the replacement of renewable energy, thus promoting the clean-efficient transformation of energy input. In the industrial synergy dimension, digital-enabled smart management can strengthen the connection and coordination among upstream and downstream firms, cross-industry entities, government and businesses, and enterprises and communities. This consequently promotes the reduction in energy intensity per unit of value-added. During the end-of-pipe treatment phase, the adoption of digital technologies enables enterprises to maximize processing efficiency and pollution control capabilities with limited green investment, thereby reducing pollutant and carbon emissions. Take waste purification as an example. Automated systems allow technicians to adjust equipment operating parameters according to the characteristics of various waste materials and pollutants. This approach enhances purification precision and mitigates untreated carbon emissions. In terms of source prevention, digital technology can conduct real-time monitoring across the entire industrial chain and precisely adjust production methods based on demand changes and equipment status, thereby enhancing resource allocation efficiency. Meanwhile, through advanced technologies such as artificial intelligence, cloud computing, and digital twins, which are capable of collecting, organizing, and analyzing vast amounts of environmental information, the capacity for environmental governance can be significantly enhanced. This enables the shift in pollution reduction and carbon reduction from end-of-pipe treatment to source prevention and control in multiple aspects, including top-level design, monitoring and perception, early-warning and forecasting, intelligent decision-making, and emergency response [58].

Digital technology has become a core driver of innovation in urban travel, promoting a green transportation society. Shared bikes, public transport, empowered by digital tech, are increasingly favored by the public. Real-time, accurate bus info-sharing boosts public transport’s capacity utilization. Optimized resource allocation via digital tech cuts private-transport emissions and carbon footprints. The widespread use of the Internet, blockchain and information and communication technologies has promoted the development of second-hand e-commerce and recycling of end-of-life products platforms and has significantly contributed to resource conservation and the reduction of carbon and pollutant emissions. A substantial volume of idle goods(particularly e-waste and used textiles) undergoes circular trading and reuse via platforms, mitigating environmental burdens resulting from direct disposal. Efficient recycling significantly reduces atmospheric pollutants generated by repetitive production and direct incineration, while reducing energy consumption and associated carbon emissions during reproduction processes. Furthermore, this model facilitates the cultivation of cost-saving, minimalism-oriented, and sharing-based sustainable consumption concepts and behavioral paradigms among consumers. In the domain of environmental oversight, digital technology-enabled pollution co-governance networks and environmental information disclosure platforms significantly expand channels for stakeholders (including the public and non-profit organizations) to monitor corporate pollution activities. By integrating multi-stakeholder regulatory capacities, this mechanism fosters a collaborative governance system featuring full-process digital monitoring, cloud-based dynamic analysis, and behavior-oriented incentive-disincentive mechanisms, thereby enhancing incentives for regulatory compliance while constraining environmental violations [59]. Digital government platforms and environmental information disclosure mechanisms strengthen two-way communication channels between authorities and citizens. These instruments effectively enhance public awareness of environmental oversight and stimulate proactive engagement, thereby facilitating the development of a government-enterprise-resident tripartite collaborative governance framework in environmental protection [60]. Accordingly, this study puts forward the following research hypotheses:

H1. 

Digital technologies can promote the synergistic governance of pollution and carbon reduction.

3.2. The Mediating Effect of Energy Efficiency

Environmental economics theories typically regard energy consumption as a direct source of pollution [61]. Pollutants are mainly produced by traditional heavy industries, especially power plants, petrochemical plants, metal smelting facilities, and mechanical manufacturing plants. These sectors generate large quantities of carbon dioxide, soot, and nitrogen oxides. In daily life, fuels such as coal, natural gas, and liquefied petroleum gas (LPG), which are commonly used by residents, can also lead to significant low-altitude emissions of pollutants when combustion is incomplete. As the transport sector develops rapidly, vehicles such as cars, trains, ships and planes, which use diesel, petrol and other petroleum products as the main energy source, have experienced a significant increase in usage duration and ownership in cities. However, the inefficient combustion of petroleum products leads to massive emissions of particulate matter, carbon monoxide, nitrogen oxides, hydrocarbons and greenhouse gases. Moreover, China is currently in a period of rapid industrialization and urbanization. The extensive economic model characterized by high-energy consumption, high-emission, and low-efficiency, which was long used in the early stages of economic development, has already caused a series of environmental problems. China’s energy structure, which is dominated by coal and petroleum products, combined with lagging environmental protection infrastructure, has caused the concentration of sulfur dioxide and particulate matter in atmospheric pollutants to remain high. Thus, improving energy utilization has become a necessary choice for promoting sustainable economic development [62].

To alleviate the negative impacts of energy production and consumption on ecosystems and the environment, there is a need for ongoing exploration of digitally driven technological solutions. Enhancing energy efficiency in the fields of transportation, industrial production, and daily life is a key measure to mitigate ecological impacts from anthropogenic pollution sources. Digitally driven systems are undergoing rapid deployment and application within the power and energy sector. The rapid advancement of modern information technology has catalyzed opportunities for digital transformation and generated ecological benefits, which have effectively facilitated advancements in corporate production technologies and energy management systems, thereby enhancing corporate energy efficiency. At the production stage, manufacturing paradigms represented by smart manufacturing, by enabling automation and flexibility, significantly enhanced process efficiency and controllability, thereby increasing production efficiency and reducing both energy consumption and pollutant emissions. Within the energy allocation domain, smart grids underpinned by digital technologies—defined as infrastructure capable of continuously monitoring and effectively matching energy supply and demand—significantly enhance the grid’s sensing, decision-making, and execution capabilities. These systems facilitate the electrification transition of energy and achieve integrated production and consumption by applying key technologies such as maximizing the integration of distributed energy resources, proactive grid coordination and control, and optimized operation of demand-side resources, alongside aggregating vast amounts of adjustable demand-side resources through the Internet of Things (IoT) and blockchain technologies. Virtual Power Plants (VPPs), which aggregate electricity consumption information, can flexibly configure electricity supply based on user consumption patterns, facilitating coordination between supply and demand sides, and thereby enhancing grid resilience and electricity efficiency [63]. Most critically, the digital transformation of the energy supply system significantly reduces information asymmetry across the entire energy chain—from production and transmission to storage and consumption. This enables flexible regulation of energy integration, facilitates coordinated interaction and mutual support among the grid, load, and energy storage, thereby continuously advancing the energy transition. Finally, the digital transformation of energy systems empowers managers to implement comprehensive monitoring and efficient management of energy production processes. This concurrently ensures stable system operation while driving the transition towards cleaner and low-carbon energy production. In summary, informational and intelligent management and regulation enabled by digital technologies can significantly reduce non-essential energy consumption within economic activities, thereby contributing to addressing the challenges of the energy crisis and environmental pollution [64]. Accordingly, the following research hypotheses are proposed:

H2. 

Digital technologies can promote the synergistic governance of pollution and carbon reduction through the mediating mechanism of improving energy efficiency.

3.3. The Mediating Effect of Virtual Agglomeration

New economic geography’s agglomeration theory focuses on traditional industrial agglomeration. This pattern depicts enterprises with upstream-downstream linkages or competitive relationships agglomerating in specific regions and forming self-reinforcing mechanisms. During this process, the functioning of external economies relies on geographical proximity. With the deepening development of new-generation information technologies like big data, industrial internet, digital twins, and quantum computing, the coordinated promotion of digital industrialization and industrial digitization is bringing profound changes to traditional industrial-agglomeration patterns. This is seen not only in the formation of novel forms of geographical clustering and the decline of traditional ones but also in the emergence of entirely new virtual agglomeration forms. The innovation of information technology is significantly weakening the restrictive role of geographical distance and facilitating the evolution of future industrial organizational forms in the direction of virtual agglomeration [65]. Virtual agglomeration is a new spatial agglomeration model based on network platforms, supported by cloud computing, and accessed via smart terminals. It concentrates data, information, and value on virtual network platforms and has possessed the core characteristics of highly developed geographical agglomeration since its inception. Virtual agglomeration, by enabling real-time information exchange among enterprises and organizations, allows them to overcome geographical constraints and achieve efficient interconnectivity. It weakens the traditional restrictions of spatial association and geographical proximity on industrial agglomeration. Meanwhile, it significantly reduces and even offsets ‘iceberg’ transportation costs and alleviates the congestion—related negative externalities common in traditional geographical agglomeration patterns [66].

The core of urban development is to leverage agglomeration effects. Reducing the negative externalities from population concentration, urban expansion, and industrial agglomeration is crucial for synergistic governance of pollution and carbon reduction. In theory, industrial agglomeration can promote regional green development via technological spillover, shared labor pools, shared economies, and improved matching efficiency [67]. Compared with the closed cooperative network of traditional agglomeration, the ecological network of virtual agglomeration is more open. Any enterprise with productive advantages can become a core node to form a better cooperative network and external relations. The characteristics of virtual agglomeration, such as its boundary-less nature, high permeability, multi-agency, and dynamism, can achieve exponential amplification of the formation mechanisms of industrial agglomeration and its resultant economic and social effects. Digital trade enables firms in the same industry and upstream-downstream-related enterprises to conduct cross-regional transactions, service delivery, and collaborative production at a lower cost. This engenders a large-scale spatially unconstrained agglomeration pattern distinct from traditional geographical agglomeration. It also facilitates seamless integration of production, services, distribution, and consumption, demonstrating a pronounced “distance-reducing” effect. As a key part of digital tech, modern communication technology greatly facilitates information exchange among different entities and effectively reduces the constraints of space and time on creative exchange, knowledge accumulation, and technological innovation. Through knowledge spillover and technology diffusion mechanisms, modern communication technology not only boosts the quality and efficiency of economic and social development but also advances synergistic governance of pollution and carbon reduction [68]. Digital platforms and virtual spaces, transcending physical limits, host numerous enterprises. They strengthen upstream-downstream industrial linkages and foster synaptic connections between unrelated industries. By innovating contractual arrangements, optimizing value distribution, and deepening innovative collaboration, they enhance the operational efficiency, productivity, and resource utilization of cloud-aggregated enterprises. This also promotes the sharing of key elements like pollution control technologies, carbon capture, utilization and storage (CCUS) technologies, environmental equipment, and green innovation talents within a broader scope, thereby improving the efficiency of synergistic governance of pollution and carbon reduction [69]. App—driven platform agglomeration combines market and organizational coordination mechanisms. Through online information gathering, remote work, and virtual collaboration, it promotes resource sharing in industrial chains. At the same time, it cuts carbon emissions from buildings and traffic caused by the separation of workplaces and residential areas. Virtual industrial parks, characterized by borderless office work, deeply integrate technologies like 5G, industrial internet platforms, and BIM/CIM. They unify geographically dispersed yet closely value-and industrially linked enterprises under one governance framework. This shifts production resources and data from scattered points to full-coverage distribution, achieving “geographical dispersion with industrial agglomeration” and reducing the negative externalities of traditional excessive agglomeration. In addition, the resource network built on digital platforms alleviates information asymmetry, curbing redundant construction and resource waste. It also provides efficient channels for equipment and waste material circulation. Relying on an informational collaborative mechanism of total-quantity management, precise allocation, comprehensive conservation, and circular utilization, the network strongly promotes the circulation of intermediates and consumables among enterprises in the agglomeration area and the regeneration of pollutants. This offers a core support for building a circular economy and a low-carbon development model. Accordingly, this study proposes the following hypotheses:

H3. 

Digital technologies can promote the synergistic governance of pollution and carbon reduction through facilitating virtual agglomeration of industries in the cloud.

4. Research Design

4.1. Variable Construction

4.1.1. Dependent Variable

The dependent variable in this study is digital technology. In the quantitative analysis of the synergistic governance level of pollution and carbon reduction, most literature does not directly measure the coordination degree of the synergistic effects of the two types of emissions. Instead, they establish two econometric equations to separately examine whether economic variables can reduce pollutant emissions while reducing carbon emissions. However, this “divide-and-conquer” approach deviates from the connotation of synergistic governance. Conclusions drawn under conditions of differential emission reduction rates and non-unified systems fail to highlight the common benefits of local synergistic governance measures. The absence of synergistic effects and efficiency also prevents policy optimization paths from shifting towards integrated governance, leaving greenhouse gas emission reduction and pollution control in a fragmented state [70]. In the literature on measuring synergistic governance level, the coupling coordination degree model (CCDM), which reflects the thinking of mutual promotion and interconnection, is a commonly used method. This model is widely applied to assess the coordination level between two or more systems by calculating the coupling degree and coordination index [71]. Existing literature has overlooked the emission reduction rates of pollution and carbon, instead focusing on indicators based on absolute emission levels. This approach has several limitations. First, it neglects the economic profit returns from the input of production factors. Second, it lacks basic information on the economy, environment, and energy. Third, it deviates from the connotation of synergistic benefits and comprehensive coordinated interests, failing to reflect the fairness of carbon emissions based on economic foundations.

To address these issues, this study constructs a comprehensive evaluation index system from the perspective of input-output and employs Data Envelopment Analysis (DEA) to measure the synergistic governance of pollution and carbon reduction [72]. The evaluation system constructed based on the existing literature is shown in

Table 1. Evaluation system for the synergistic governance level of pollution and carbon reduction.

Primary Indicator	Secondary Indicator	Specific Description
Input Indicators	Labor	Total employment in society
	Capital	Fixed asset investment stock (with 2000 as the base year)
	Land	Urban construction land area
	Energy	Terminal energy consumption (in standard coal tons)
Output Indicators	Desired Output	Real GDP (with 2000 as the base year)
	Undesired Output	Carbon dioxide, industrial sulfur dioxide

. Specifically The primary indicators consist of input indicators and output indicators, with secondary indicators providing more detailed measures of the primary ones. Specifically, the input indicators include four aspects: labor, capital, land, and energy. These are represented by the total number of employed people in the whole society, the stock of fixed asset investment based on the year 2000, the area of urban construction land, and the total final energy consumption, respectively. The output indicators are divided into desirable output and undesirable output. The desirable output is measured by the real GDP based on the year 2000, while the undesirable output is measured by carbon dioxide emissions and industrial sulfur dioxide emissions.

Data Envelopment Analysis (DEA) based on linear programming and using radial distance functions primarily emphasizes the proportional reduction in input factors or the proportional increase in output factors to measure inefficiency. However, this approach neglects the substitutability between input variables and the characteristics of non-proportional changes, which can lead to the omission of non-radial slack variables. The slack-based measure (SBM) model, an improvement on DEA, incorporates both radial and non-radial slack variables to reflect the evolution of inefficiencies more comprehensively. Nevertheless, this method can result in the loss of original proportional information between target and actual values of inputs or outputs, leading to an underestimation of efficiency scores. Specifically, the projection point used in SBM is the farthest point on the production frontier from the evaluated unit, rather than the optimal path [73]. The epsilon-based measure (EBM) model, which integrates the advantages of both radial distance functions in DEA and non-radial distance functions in SBM, takes into account not only the radial proportion between input target values and actual values but also the differentiated non-radial slack variables among various input indicators. This approach effectively enhances the accuracy and scientific nature of the measurement results [74]. Combining the characteristics of panel data, the paper treats each province (city) as an independent decision-making unit (DMU) to construct differentiated input-output stochastic frontier surfaces for different regions. Meanwhile, following the approach of Pastor and Lovell [75], the study employs global benchmarking technology to address the issues of incomparable efficiency values across different periods and the inability to effectively rank them. Its global production possibility set (PPS) is defined as:

$$PPS=\left\{\begin{array}{l}\left(\overline{x},\overline{y},\overline{z}\right)\left|{\displaystyle \sum _{t=1}^{23}{\displaystyle \sum _{k=1}^{30}{x}_k^t{\lambda }_k^t}}\right.\\ {\displaystyle \sum _{t=1}^{23}{\displaystyle \sum _{k=1}^{30}{y}_k^t{\lambda }_k^t}\ge {\overline{y}}^t}\\ {\displaystyle \sum _{t=1}^{23}{\displaystyle \sum _{k=1}^{30}{z}_k^t{\lambda }_k^t\le {\overline{z}}^t}}\\ {\displaystyle \sum _{t=1}^{23}{\displaystyle \sum _{k=1}^{30}{\lambda }_k^t=1}}\end{array}\right.$$

(1)

In Equation (1), $\left(\overline{x},\overline{y},\overline{z}\right)$ represents the optimal solution of the model, t denotes time, ${\lambda }_k^t$ is the weight, and $x_k^t$, $y_k^t$, $z_k^t$ represent the input factors, desired outputs, and undesired outputs of region k in year t, respectively. Since the efficiency values calculated by the EBM model are distributed within the closed interval of 0 to 1, it is not possible to observe or calculate efficiency values greater than 1. Therefore, following the approach in existing literature, the model is extended to a super-efficiency version [76]. Under the assumption of variable returns to scale, the global Super-EBM model that incorporates undesired outputs is expressed as:

$${\theta }^{*}=\min {\displaystyle \frac{\theta -{\epsilon }_x\left(1/{\displaystyle \sum _{m=1}^M{w}_m^{-}}\right){\displaystyle \sum _{m=1}^M\left(w_m^{-}{s}_m^{-}/x_{mk}\right)}}{\varphi +{\epsilon }_y\left(1/{\displaystyle \sum _{r=1}^R{w}_r^{+}}\right){\displaystyle \sum _{r=1}^R\left(w_r^{+}{s}_r^{+}/y_{yk}\right)+{\epsilon }_z\left(1/{\displaystyle \sum _{p=1}^P{w}_p^z}\right){\displaystyle \sum _{p=1}^P\left(w_p^{z-}{s}_p^{z-}/z_{pk}\right)}}}}$$

(2)

Its constraints are:

$$s.t.\left\{\begin{array}{l}{\displaystyle \sum _{t=1}^{23}{\displaystyle \sum _{j=1\ne k}^{30}{\lambda }_j{x}_{mk}+s_m^{-}}=\theta x_{mk}}\\ {\displaystyle \sum _{t=1}^{23}{\displaystyle \sum _{j=1\ne k}^{30}{\lambda }_j{y}_{rk}-s_r^{+}}=\varphi y_{rk}}\\ {\displaystyle \sum _{t=1}^{23}{\displaystyle \sum _{p=1}^{30}{\lambda }_j{z}_{pk}+s_p^b=}\varphi z_{pk}}\\ {\lambda }_j\ge 0,s_r^{+}\ge 0,s_m^{-}\ge 0,s_p^{z-}\ge 0\end{array}\right.$$

(3)

In Equations (2) and (3), ${\theta }^{*}$ represents the overall efficiency value of the independent decision-making unit, which in this paper is used to indicate the synergistic effect of pollution reduction and carbon emission reduction. $x_{mk}$, $y_{rk}$, $z_{pk}$ represent the input indicators, desired outputs, and undesired outputs of the evaluated independent decision-making unit k, respectively. $s_r^{+}$ is the slack variable for the r-th desired output, $s_p^{z-}$ is the slack variable for the p-th undesired output, and $s_m^{-}$ is the slack variable for the m-th input factor. M, R, and P represent the number of input factors, desired outputs, and undesired outputs, respectively. $\theta$ is the planning parameter for the radial part, ${\lambda }_j$ is the linear combination coefficient, $w_r^{+}$, $w_p^{z-}$ and $w_m^{-}$ are the weights for desired outputs, undesired outputs, and input factors, respectively. ${\epsilon }_x$, ${\epsilon }_y$ and ${\epsilon }_z$ are all key parameters for the efficiency value, $\phi$ is the parameter for output expansion, and j represents the independent decision-making unit.

Based on the above calculation steps, this study ultimately calculates the level of coordinated pollution reduction and carbon reduction governance over the 23-year period from 2000 to 2022. To ensure data transparency while maintaining the coherence of the main text, the calculation results are presented in the Appendix A.

After obtaining the level of synergistic governance of pollution and carbon reduction using the methods described above, this study further explores its spatiotemporal distribution patterns through a combination of graphics and text. Figure 2 presents the spatiotemporal distribution maps of China’s synergistic governance levels in 2000, 2008, 2016, and 2022. The color intensity in the images represents the level of synergistic governance, with deeper colors indicating higher governance levels.

In terms of time, the color shifts from dark to light and then back to dark, suggesting that China’s synergistic governance level of pollution and carbon reduction follows a pattern of initial decline followed by an increase. Specifically, from 2000 to 2016, the colors gradually became lighter, indicating a downward trend in synergistic governance levels. This trend can be attributed to China’s government performance evaluation system and economic growth model during that period. In terms of government performance evaluation, there was an excessive emphasis on economic growth, with GDP, fiscal revenue, and foreign investment placed at the core. As a result, local officials, aiming for smooth career advancement, often prioritized economic growth through large-scale projects and land development, while environmental protection, which would take time to show its positive effects on the economy, was long overlooked by the government, leading to a sustained decline in the level of synergistic governance of pollution and carbon reduction. Regarding the economic growth model, although China’s economy was in a rapid growth phase, it largely relied on excessive input of factors, leading to an extensive growth model characterized by low economic efficiency and negative environmental impacts. For example, during this period, China implemented large-scale infrastructure projects such as the “four trillion yuan economic stimulus plan” and the “resettlement plan for urban renewal”, which positively influenced economic growth but also caused significant environmental damage due to large-scale construction and real estate development. After 2016, the color intensity gradually deepened, indicating a progressive improvement in synergistic governance of pollution and carbon reduction. On one hand, the government performance evaluation system continued to improve, and the importance of environmental protection indicators was significantly strengthened, leading to greater attention to synergistic governance of pollution and carbon reduction at the local level. On the other hand, after decades of rapid economic growth, the contribution of traditional high-energy, high-pollution industries to economic growth continued to weaken. To achieve sustainable economic growth, China needed to shift its development model and reduce reliance on traditional industries, which undoubtedly had positive effects on the environment.

Spatially, by 2022, the synergistic governance level for pollution reduction and carbon reduction in China showed a pattern of being higher in the north and lower in the south, which contrasts with China’s economic distribution. According to the National Statistical Yearbook of China, the GDP of southern China accounted for 65% of the national total in 2022, while northern China accounted for only 35%. One important reason for this inverse relationship between economic development level and governance level is the scale effect. The larger the economic scale of a region, the higher the demand for energy consumption, leading to greater pollutant emissions. Even if energy consumption per unit decreases, as long as the total energy demand is large enough, the absolute amount of emissions can still have a more significant negative impact on the environment. Therefore, the synergistic governance level of pollution and carbon reduction in China shows an inverse pattern compared to its economic distribution.

4.1.2. Independent Variable

The independent variable in this study is digital technology. In measuring digital technology, existing studies commonly adopt approaches such as constructing indicator systems [77] or using digital technology patent counts [78]. While these approaches have some validity, they suffer from significant limitations. For instance, indicator system construction often involves high subjectivity, potentially leading to empirical results that deviate from actual conditions. Similarly, patent data primarily capture innovation levels rather than the practical application of digital technologies. To more objectively capture the level of digital technology adoption across regions, some studies have proposed using robot penetration as a proxy for regional digital technology application [79]. This rationale stems from the fact that robots—as intelligent machines capable of semi- or fully autonomous operation—typically integrate multiple digital technologies (e.g., sensors, the Internet of Things (IoT), edge computing, and artificial intelligence) to perform functions including data collection, cloud-based collaboration, and intelligent decision-making. Thus, robots serve as comprehensive carriers of digital technologies [80]. Moreover, higher robot density in a region indicates stronger market demand for such technologies, offering a direct reflection of regional digital technology application levels. Such heightened demand may further stimulate development in upstream and downstream industries (e.g., semiconductors, motors, maintenance services, and software), thereby generating significant technological spillover effects. These spillovers reduce technology adoption costs and enhance technical capabilities, ultimately sustaining regional digital advancement. Therefore, given the intrinsic digital characteristics of robotic technology and its role in promoting the digital industry, this study uses robot installation density as a proxy variable for digital technology. In terms of the computational methodology, the study employs the moving share method for calculation [81]. Moreover, for robustness checks, digital technology patent counts are used as an alternative proxy to validate the empirical findings.

The specific calculation process of the moving share method is as follows:

$$D{T}_{it}={\displaystyle \frac{Robo{t}_{it}}{Labo{r}_{it}}}={\displaystyle \frac{\left[{\sum }_{j=1}^J\left(W_{ji,t=2000}\times Robo{t}_{jt}\right)\right]}{Labo{r}_{ji,t=2000}}}$$

(4)

In Equation (4), ${Labor}_{it}$ represents the total employment in all industries in city i in year t. $W_{ji,t=2000}$ indicates the proportion of employees in industry j of city i in 2000 relative to the total number of employees in that industry nationwide. Based on the application practice of the shift-share method, this paper uses this proportion as the benchmark share and weight standard for other years. This approach can mitigate endogeneity caused by factors such as “machine replacing human labor,” changes in population size, and industrial structure transformation. Using historical employment shares as weights also provides a certain degree of exogeneity for the rapid development of China’s digital economy in recent years. ${Robot}_{jt}$ represents the number of robots installed in industry j in year t. j denotes the number of industries to be aggregated. ${Robot}_{it}$ indicates the number of robots installed in city i in year t. To better capture the overall application status of robots in China, based on the 14 major industrial categories provided by the International Robot Federation (IRF), this paper also calculates the robot installation density in service industries such as education and urban public services, considering data availability.

4.1.3. Mediating Variables

Energy Efficiency. This study uses single-factor energy efficiency as a proxy variable, which essentially refers to the energy consumption intensity per unit of Gross Domestic Product (GDP).

Virtual Agglomeration. Given the unique nature of virtual agglomeration and considering the definitions of its connotation and extension in existing research as well as data availability, this paper focuses on the agglomeration of the information transmission, computer services, and software industry at the provincial level. The location entropy method is employed to calculate the following equation:

$$Ag{g}_{it}={\displaystyle \frac{v{s}_{it}}{tota{l}_{it}}}/{\displaystyle \frac{vs}{total}}$$

(5)

In Equation (5), ${total}_{it}$ represent the employment in the information transmission, computer services, and software industry and the total employment in all industries in region i in year t, respectively. vs denotes the employment in the information transmission, computer services, and software industry, while total represents the total employment in all industries in the province. ${Agg}_{it}$ is the agglomeration degree of the information transmission, computer services, and software industry. Considering that the permeability, borderless nature, and platform characteristics of virtual agglomeration are significantly different from the geographical constraints of traditional agglomeration, and given that digital technology is the dominant force in the new round of technological revolution and industrial innovation, using the location entropy of the information transmission, computer services, and software industry to measure virtual agglomeration may have strong endogeneity. Therefore, this paper employs the shift-share method to construct a Bartik instrument variable to address the potential causal relationship between virtual agglomeration and digital inputs. Existing studies have used the information intensity of modern service industries to represent the spatial virtuality, data resourcefulness, and digital trade of virtual agglomeration. To fully capture the basic connotation of virtual agglomeration and mitigate endogeneity, this paper uses the information technology service revenue of each region as a weight to calculate virtual agglomeration:

$$V{A}_{it}={I{T}_{it}/ITT}_t\times Ag{g}_{it}$$

(6)

In Equation (6), ${VA}_{it}$ represents the degree of virtual agglomeration in region i in year t. ${IT}_{it}$ denotes the information technology service revenue in province i in year t, while ${ITT}_t$ represents the total national information technology service revenue in year t. Since the National Bureau of Statistics began disclosing data related to this indicator only in 2014, this study uses 2014 as the base year for the shift-share calculation.

4.1.4. Control Variables

To mitigate the bias in model estimates caused by the omission of important variables, this study incorporates seven variables selected from two dimensions—policy support and socio-economic factors—based on relevant literature, in order to control for provincial characteristics. These variables include: Foreign Direct Investment (FDI), measured by the actual foreign investment utilized in each region; Economic Development Level, measured by GDP per capita; Population Density, measured by the number of people per square kilometer; Environmental Governance, measured by the completed investment in industrial pollution control; Transportation Convenience, measured by the number of private cars owned; Industrial Structure, measured by the ratio of the tertiary sector to the secondary sector; and Policy Regulation, represented by a binary variable for the energy rights trading pilot program. This policy, under the premise of controlling total energy use in the region, grants enterprises participating in energy rights trading an initial energy quota, and then uses market mechanisms to guide enterprises to reduce energy input through both source reduction and process control stages, thereby reducing carbon and pollutant emissions generated from energy consumption. Based on the “Pilot Program for the Paid Use and Trading of Energy Rights” issued by the National Development and Reform Commission, samples from Zhejiang, Fujian, Henan, and Sichuan regions after 2017 are assigned a value of 1, while the remaining samples are assigned a value of 0.

4.2. Empirical Strategy

To examine the impact of digital technologies on the synergistic governance of pollution and carbon reduction, as well as the underlying mechanisms, this paper constructs the following panel data model based on the research hypotheses and the definitions of the relevant variables:

$$LnP{O}_{it}=a_0+a_{1}LnD{T}_{it}+a_{2}Contro{l}_{it}+{\mu }_i+{\nu }_t+{\epsilon }_{it}$$

(7)

$$LnGH{S}_{it}=b_0+b_{1}LnD{T}_{it}+b_{2}Contro{l}_{it}+{\mu }_i+{\nu }_t+{\epsilon }_{it}$$

(8)

$$LnC{G}_{it}=c_0+c_{1}LnD{T}_{it}+c_{2}Contro{l}_{it}+{\mu }_i+{\nu }_t+{\epsilon }_{it}$$

(9)

In Equations (7)–(9), $a_0$, $b_0$, and $c_0$ are constant terms, ${\epsilon }_{it}$ is a random disturbance term following a white noise process, ${\upsilon }_t$ is the time fixed effect, ${\mu }_i$ is the individual fixed effect, $a_1$, $b_1$, and $c_1$ are the regression coefficients for digital technology, $a_2$, $b_2$ and $c_2$ are the regression coefficients for control variables, and Control represents the set of all control variables. GHS, PO, and CG denote carbon dioxide emission intensity, industrial sulfur dioxide emission volume, and the synergistic governance of pollution and carbon reduction, respectively. Given that the traditional three-stage mediation effect model has serious endogeneity in causal inference, this paper, based on the theoretical foundations and practical applications of existing research, focuses on the impact of mediating variables on the synergistic governance of pollution and carbon reduction in the research hypotheses. In the empirical analysis section, the emphasis is on examining whether digital technology has a statistically significant impact on mediating variables, whether the direction of the impact is as expected, and finding support from relevant literature to mitigate the endogeneity in the mediation analysis as much as possible. Therefore, the two-stage mediation effect model established in this paper is as follows:

$$LnE{E}_{it}=d_0+d_{1}LnD{T}_{it}+d_{2}Contro{l}_{it}+{\mu }_i+{\nu }_t+{\epsilon }_{it}$$

(10)

$$LnV{A}_{it}=e_0+e_{1}LnD{T}_{it}+e_{2}Contro{l}_{it}+{\mu }_i+{\nu }_t+{\epsilon }_{it}$$

(11)

In Equations (10) and (11), $d_1$, $e_1$ are regression coefficients of digital technologies; $d_0$, $e_0$ are constant terms; $d_2$, $e_2$ are regression coefficients of control variables. The meanings of the remaining symbols are consistent with those in Equation (7).

4.3. Data and Sample Construction

Regarding data sources, adhering to principles of data availability, systematicity, and comparability, this study employs balanced panel data from 30 Chinese provinces and municipalities directly under the central government (excluding Tibet, Hong Kong, Macao, and Taiwan) spanning 2000 to 2022. The number of robot installations related to digital technologies is primarily sourced from the International Federation of Robotics (IRF). Carbon dioxide (CO₂) emission figures originate from the China Carbon Accounting Database. Data for sulfur dioxide (SO₂), control variables, and mechanism variables were primarily sourced from the China Statistical Yearbook, China Energy Statistical Yearbook, China Environment Statistical Yearbook, China Labour Statistical Yearbook, China Population and Employment Statistical Yearbook, and provincial/municipal statistical yearbooks. For the minimal number of missing values encountered, linear interpolation was applied to ensure data completeness.

Regarding data description,

Table 2. Descriptive statistics of variables.

Variable	Observations	Mean	Standard Error	Minimum	Maximum
Digital Technology	690	4.5478	1.8803	2.3026	9.4784
Carbon Emissions	690	−3.9197	0.7795	−6.4653	−2.1132
Sulfur Dioxide	690	12.5233	1.2646	8.3056	14.2473
Synergistic Level	690	0.7005	0.2350	0.0086	1.1705
Population Density	690	7.6076	0.7089	5.3519	8.6793
Transportation Convenience	690	4.9485	1.4935	1.3515	7.6461
Policy Regulation	690	0.0406	0.1975	0	1
Industrial Structure	690	−0.0160	0.3894	−0.6075	1.4268
Economic Development Level	690	10.2657	0.8878	8.3464	11.9658
Environmental Governance	690	2.2959	1.1269	−1.3425	4.4886
Foreign Direct Investment	690	14.2172	1.7329	9.7040	16.7394
Energy Efficiency	690	4.4625	0.5112	1.4347	7.6230
Virtual Agglomeration	690	0.0208	0.0597	0.0001	0.5785

provides a summary of all variables involved in the study. The number of observations for every variable is 660, indicating a complete dataset with no missing values. All standard deviations fall within the range of 0 to 2, signifying relatively low overall data volatility and minimal influence from extreme values. Substantial gaps exist between the maximum and minimum values across variables, reflecting significant regional disparities in China’s economic and social development. For instance, the values for digital technology range from a minimum of 2.3026 to a maximum of 9.4784, while the coordinated governance level ranges from 0.0086 to 1.1705, both demonstrating considerable divergence. This necessitates heterogeneity analysis to clarify the regional variations in the impact of digital technologies. At the same time, the control variable “ Policy Regulation “ is a dummy variable representing pilot policies, where pilot provinces are assigned a value of 1 and non-pilot provinces 0. Its mean value is 0.0406, indicating a relatively small number of pilot provinces and suggesting that the policy rollout remains in the early stages. Additionally, The presence of negative values for variables such as carbon emissions, industrial structure, and environmental investment stems from the application of logarithmic transformations to these respective variables. This procedure was implemented to mitigate potential estimation bias caused by heteroscedasticity.

5. Empirical Results

5.1. Benchmark Results

To further investigate the impact of digital technology on the synergistic governance of pollution and carbon reduction, this study organizes the synergistic governance data calculated using the EBM-DEA model. On this basis, a panel regression model is constructed to empirically examine the effect of digital technology. Regression analysis is adopted because it allows for the control of various confounding variables, enabling a more precise identification of the relationship between digital technology and the synergistic governance of pollution and carbon reduction, and thereby facilitates an assessment of potential causal links between them. The synergistic governance level of pollution and carbon reduction calculated using the EBM-DEA model helps to overcome the limitations of single-indicator measurements, thereby enhancing the reliability of the regression results.

The integration of methodological steps is as follows: First, a comprehensive indicator system is constructed from an input-output perspective, and the EBM-DEA model is employed to measure the level of synergistic governance of pollution and carbon reduction. The temporal and spatial evolution of the governance level is then analyzed. Second, the measured results are used as input data for the regression analysis. Specifically, the level of synergistic governance of pollution and carbon reduction serves as the dependent variable, while the development level of digital technology is used as the core explanatory variable. Control variables include population density, policy regulation, industrial structure, economic development, and environmental investment. All relevant data for the empirical analysis are compiled in the form of a panel dataset. Finally, based on the constructed panel data, the F-test and Hausman test are conducted to justify the appropriateness of using a two-way fixed effects model, which controls for both individual-specific and time-specific effects. In addition, clustered robust standard errors are applied to mitigate the impact of heteroskedasticity, cross-sectional dependence, and autocorrelation, thereby ensuring the robustness of the empirical results.

Following the above approach, this study conducts the regression analysis using a two-way fixed effects model that simultaneously controls for individual and time effects. The regression results are presented in

Table 3. Results of benchmark regression.

Variable	Carbon Emission Intensity		Industrial SO2 Emissions		Collaborative Governance
	(1)	(2)	(3)	(4)	(5)	(6)
Digital Technology	−0.1665 *** (−3.34)	−0.1579 *** (−3.14)	−0.2167 *** (−3.35)	−0.1615 *** (−3.44)	0.0283 * (1.84)	0.0387 *** (2.76)
Control Variable	No	Yes	No	Yes	No	Yes
Individual Effect	Yes	Yes	Yes	Yes	Yes	Yes
Time Effect	Yes	Yes	Yes	Yes	Yes	Yes
Goodness of Fit	0.7216	0.7425	0.8697	0.8894	0.6450	0.6726
F-test	71.78	60.54	184.89	168.88	50.32 ***	43.13
Hausman Test		25.00		91.19		22.71

Note: * and *** indicate significance at the 10% and 1% levels, respectively. The values in parentheses are the t-statistics.

.

As shown in Table 3, when no individual characteristics are controlled for, the regression coefficient of digital technology for synergistic governance of pollution and carbon reduction is 0.0283, significant at the 10% level. After including control variables, the coefficient rises to 0.0387, significant at the 1% level. This indicates that individual characteristics can interfere with regression results, and controlling for socio-economic variables is essential. Echoing the findings of existing literature, the regression results of this study affirm the research hypothesis that digital technology facilitates synergistic governance of pollution and carbon reduction. Technological emission reduction is recognized as the core pathway for pollution source governance. As an emerging technological tool and innovative thinking paradigm, digital technology provides critical opportunities for synergistic governance of pollution and carbon reduction. As stated in Research Hypothesis 1, digital technology and digital transformation can drive the “reduction, reutilization, and harmless treatment” transition at the production end by optimizing enterprise production processes, enhancing pollution monitoring capabilities, and improving waste reutilization rates, thereby reducing sulfur dioxide and carbon emission intensity. As shown in Table 3, the regression coefficients of digital technology for carbon emission intensity and industrial sulfur dioxide emissions are −0.1579 and −0.1615, respectively, and both are significant at the 1% level. This shows that even in isolated emission reduction situations, the environmental benefits of digital technology still play a leading role. Enterprise managers integrate modern equipment, the Internet of Things, and data elements to systematically consolidate production information resources. They also establish intelligent management systems for materials, processes, and products. This promotes the transition of the entire production process toward efficiency and cleanliness, enhancing operational efficiency and achieving the dual dividends of pollutant reduction and decreased carbon emission intensity.

For example, in the steel industry, real-time monitoring of energy consumption data from equipment such as blast furnaces and rolling lines can first be achieved by deploying Internet of Things devices, including sensors and edge computing nodes. Subsequently, a digital twin system can be implemented, utilizing a three-dimensional model of the energy system to collect and visualize energy consumption data. Finally, artificial intelligence algorithms can be employed to process and apply the collected data, enabling the optimal allocation of energy resources throughout the steel production process. This integrated approach contributes positively to pollution reduction and carbon reduction efforts.

5.2. Robustness Tests

Baseline regression results demonstrate that the digital economy not only significantly reduces carbon emissions and industrial sulfur dioxide emission intensity, but also enhances the synergistic emission reduction effect between the two. To enhance the robustness of the above conclusions, this study employs two methods for verification. First, digital patents are used as proxy variables for digital technologies. This paper identifies data on digital invention patent applications of listed companies according to the Classification System of Key Digital Technology Patents (2023) issued by the National Intellectual Property Administration of China, matches them to the provincial level based on the companies’ registered addresses, and finally measures the provincial-level adoption of digital technologies by the total number of digital invention patent applications filed by all listed companies within the jurisdiction. Secondly, the econometric model is replaced. Carbon emissions and atmospheric pollution often exhibit a clustered distribution trend. For instance, the Action Plan for Sustained Improvement of Air Quality issued by China’s State Council in 2023 highlighted the need to prioritize addressing the Beijing-Tianjin-Hebei region and its surrounding areas, the Yangtze River Delta, and the Fenwei Plain. Neglecting the spatial agglomeration of greenhouse gases and air pollutants may lead to biased regression results. Therefore, drawing on the methodologies of relevant literature [82], this study controls the spatial spillover effects of digital technologies on the synergistic governance of pollution and carbon reduction by constructing a contiguity spatial weight matrix. It also addresses endogeneity by using instrumental variables constructed from the interaction terms of explanatory variables and their higher-order lag terms. Finally, the generalized spatial two-stage least squares (GS2SLS) method is employed to estimate the spatial panel model.

As can be seen from

Table 4. Results of robustness test.

Variable	Digital Patents (Invention)			GS2SLS
	CO2	SO2	Collaborative Governance	CO2	SO2	Collaborative Governance
Digital Technology	−0.1574 *** (−3.17)	−0.1785 *** (−3.77)	0.0418 *** (3.06)	−0.1391 *** (−7.88)	−0.0809 *** (−4.03)	0.0219 *** (2.99)
Spatial-rho				0.2822 *** (5.01)	0.2897 *** (5.36)	0.0087 *** (18.57)
Control Variable	Yes	Yes	Yes	Yes	Yes	Yes
Individual Effect	Yes	Yes	Yes	Yes	Yes	Yes
Time Effect	Yes	Yes	Yes	Yes	Yes	Yes

Note: *** indicates significance at the 1% level. The values in parentheses are the t-statistics.

, the fitting results of the two methods show that the regression coefficients of digital technologies for the synergistic governance of pollution and carbon reduction are 0.0418 and 0.0219, respectively, both of which are significant at the 1% level. This finding indicates that the conclusion in the baseline regression section, that digital technologies can promote the synergistic governance of pollution and carbon reduction, is robust.

5.3. Endogeneity Test

The aforementioned robustness checks provide partial confirmation of the reliability of the study’s findings. However, there may be a bidirectional causal relationship between digital technology and the synergistic governance of pollution and carbon reduction. For example, the promotion and diffusion of digital technologies depend largely on the level of infrastructure development in a region. Yet the construction of digital infrastructure itself inevitably leads to carbon emissions and the release of pollutants. Furthermore, government-imposed carbon reduction targets may in turn accelerate the development of digital platforms. In regions facing greater pressure to cut emissions and meet pollution control tasks, local governments may actively seek digital and intelligent transformation solutions to improve environmental governance efficiency in response to higher-level mandates. These scenarios may give rise to endogeneity problems, which could bias the empirical results. Therefore, it is necessary to conduct endogeneity tests to further ensure the robustness of the conclusions.

In terms of methodology, this study adopts a research approach that combines statistical inference with causal inference. Based on the selection of appropriate instrumental variables, the two-stage least squares (2SLS) method is employed for regression analysis. Following existing literature [30,62], this study selects terrain ruggedness as an instrumental variable for digital technology. The validity of this choice lies in the following two aspects: (1) Exogeneity: Terrain ruggedness is determined by natural geological processes such as tectonic movement and water erosion, which occurred during specific geological periods. As a physical geographic feature, it is unrelated to pollution generated by human activities and therefore meets the requirement of exogeneity. (2) Exclusion restriction: First, terrain ruggedness is an objective and largely time-invariant feature of the natural environment, and it has no direct impact on the synergistic governance of pollution and carbon reduction. Second, it can indirectly affect digital technology adoption by influencing the cost of digital infrastructure deployment. Specifically, higher terrain ruggedness leads to higher installation costs, which may reduce the number of installations and thus limit the adoption of digital technologies. As a result, the capacity for environmental governance in such regions may also be constrained. This indirect pathway supports the use of terrain ruggedness as a valid instrumental variable.

In summary, terrain ruggedness is a theoretically and empirically reasonable instrument. However, given that the study is based on balanced panel data and terrain ruggedness changes very little over time, using it directly in a two-way fixed effects model may present measurement challenges. To address this limitation, the study introduces long-distance fiber-optic cable density as a time-varying variable to complement terrain ruggedness. The deployment of fiber-optic cable networks directly affects the adoption of digital technologies and is independent of environmental governance agencies, thereby satisfying both exogeneity and the exclusion restriction. Therefore, the interaction term between terrain ruggedness and long-distance fiber-optic cable density is used as the instrumental variable in this study.

As shown in

Table 5. Results of endogeneity test.

Variable	Digital Technology	Carbon Emissions	SO2	Collaborative Governance
	(1)	(2)	(3)	(4)
Digital Technology		−0.7725 *** (−3.08)	−1.1408 *** (−3.04)	0.3069 *** (2.85)
Instrumental Variable	−0.3126 *** (−3.45)
Control Variable	Yes	Yes	Yes	Yes
Individual Effect	Yes	Yes	Yes	Yes
Time Effect	Yes	Yes	Yes	Yes

Note: *** indicates significance at the 1% level. The values in parentheses are the t-statistics.

, the results of the first stage in column (1) indicate that the regression coefficient of the instrumental variable for digital technology is −0.3126 and significant at the 1% level, which demonstrates that the relevance requirement for the instrumental variable is met. The F-statistic for the weak instrument test is 18.967, greater than the critical value of 16.38 at the 10% level; the LM statistic for the un-identification test is 9.262 and significant at the 1% level. These two tests confirm that the instrumental variable selected in this paper is reasonable and effective. The results of the second stage show that the regression coefficient of digital technology on synergistic governance is 0.3069 and significant at the 1% level. This result indicates that, after controlling for potential endogeneity in the model, the conclusion that digital technology can promote the synergistic governance of pollution and carbon reduction still holds. There is a significant causal relationship between the two.

5.4. Mechanism Analysis

To test the mediating role of energy efficiency and virtual agglomeration in the process of digital technology empowering synergistic governance of pollution and carbon reduction, in accordance with H2 and H3, as well as the constructed two-stage mediating effect test equations, this study again employs the two-way fixed effects model to derive the following results.

As shown in

Table 6. Results of mechanism tests and heterogeneity tests.

Variable	Mechanism Tests		Heterogeneity Tests
	Energy Efficiency	Virtual Agglomeration	Pilot Areas	Non-Pilot Areas
Digital Technology	−0.1006 ** (−2.62)	0.0106 ** (2.32)	0.0241 *** (2.70)	0.0162 (1.31)
Control Variables	Yes	Yes	Yes	Yes
Individual Effects	Yes	Yes	Yes	Yes
Time Effects	Yes	Yes	Yes	Yes

Note: ** and *** indicate significance at the 5% and 1% levels, respectively. The values in parentheses are the t-statistics.

, the regression coefficients of digital technology on energy efficiency and virtual agglomeration are −0.1006 and 0.0106, respectively, and both are statistically significant at the 5% level. This result indicates that digital technology achieves synergistic governance for pollution and carbon reduction through the following two mechanisms: (1) reducing energy consumption per unit of GDP; (2) promoting related enterprises and industries to form virtual agglomeration in the cloud. Research H2 and H3 are thus verified.

From the perspective of energy efficiency, the productivity effect and resource allocation effect of digital technology contribute to enhancing energy efficiency, thereby achieving synergistic governance of pollution and carbon reduction. In terms of improving productivity, Moore’s Law reveals the law of rapid iteration of chip-based, digitalized, and information-based products. This law causes the prices of related products to fall rapidly with the iteration and popularization of technology, which in turn helps manufacturers upgrade and replace energy-intensive and low-efficiency production equipment, ultimately achieving a reduction in energy consumption and an improvement in energy marginal productivity. Moreover, the most prominent characteristic of emerging technologies lies in their differentiated impact on labor: substituting for low-skilled labor while complementing high-skilled labor. Such integration of digitalization, informatization, and intelligentization reduces enterprises’ dependence on labor factors and enables them to efficiently accomplish repetitive tasks that are difficult for human labor to handle quickly (e.g., packaging, sorting, and transshipment). With energy consumption and labor input remaining unchanged, this helps enterprises achieve greater economic output, thereby improving their total factor productivity (TFP). In terms of improving the allocation of energy resources, smart grids, real-time scheduling, and virtual power plants derived from digital technology contribute to the optimal allocation of energy consumption. Digital technologies such as Artificial Intelligence, Industrial Internet of Things, and Big Data Analytics facilitate the sharing and utilization of data elements. By virtue of factor circulation and knowledge-technology spillovers, energy enterprises and power grid companies are able to construct smart management systems that support energy interconnection and global energy allocation. Such systems evolve traditional stove-piped independent architectures and island-style management into unified architecture, unified management, and integrated application, thereby forming capabilities for full-link coordination, collaboration, and optimization. This further promotes the low-carbon transformation of the whole society and improves energy utilization efficiency. Therefore, digital technology has improved the utilization efficiency of electric power operation and maintenance and the consumption side, which contributes to synergistic governance of pollution and carbon reduction.

From the perspective of virtual agglomeration, the development of digital technology provides a favorable opportunity for promoting industrial virtual agglomeration to achieve synergistic governance of pollution and carbon reduction. Industrial Internet, artificial intelligence, humanoid robots, and Light Fidelity (LiFi) will become indispensable key technologies in the future manufacturing industry. Cloud-based spaces built on digital networks are gradually emerging as new spatial forms for the agglomeration of future industrial organizations. With the support of digital technologies, production chains have achieved autonomous management and independent production by virtue of cloud connectivity, industrial robots, and deep learning technologies. Meanwhile, digital networks continue to expand their functions in terms of boundaryless office work, the construction of virtual parks, and the promotion of knowledge spillovers. Thus, production chains and value chains are highly agglomerated in virtual spaces, leading to the gradual weakening of the traditional proximity advantages associated with geographical spatial agglomeration. Meanwhile, the massive volumes of data generated by the integration of digital and physical economies drive the aggregation of information flow, goods flow, capital flow, technology flow, and value flow on digital platforms. By virtue of the open-source community model and the concept of sharing and openness, this aggregation effect facilitates efficient matching of supply and demand. It not only overcomes to the greatest extent the information asymmetry issues derived from industry barriers, trade barriers, and geographical distance but also significantly reduces information search costs. The resulting digital dividends continuously attract enterprises to carry out digital transformation and participate in virtual agglomeration. In the field of knowledge exchange, the decentralized and dynamic characteristics of digital technologies have effectively broken the linear and closed technology transfer model between traditional upstream and downstream industries. This enables difficult-to-code “Tacit Knowledge” to be communicated and exchanged over long distances. The network-like diffusion characteristics of knowledge have significantly reduced the risk of “Technological Dormancy”. Meanwhile, the traditional “point-to-point” technology diffusion model is gradually shifting to a “one-to-many” network structure. The knowledge and technologies acquired by enterprises in virtual communities continue to self-accumulate and strengthen during the agglomeration process, thereby continuously consolidating and developing the industrial organization model based on virtual agglomeration. Furthermore, digital technologies contribute to enhancing the sophistication of professional division of labor, while economies of scope and new business models can elevate the agglomeration degree of both heterogeneous and similar industries in virtual spaces. Overall, under the support of digital technologies, virtual agglomeration significantly amplifies the positive externalities of traditional agglomeration. Unimpeded information flow, technology spillovers, and co-construction and sharing mechanisms guide the reallocation of capital, technology, and labor to high-quality projects facing factor shortages under market mechanisms characterized by enhanced information symmetry. This drives the transformation of traditional Fordist large-scale and process-oriented production models toward flexible production models, thereby reducing intermediate product consumption, improving resource allocation efficiency, and ultimately achieving synergistic governance of pollution and carbon reduction.

5.5. Heterogeneity Analysis

To actively advance the implementation of key tasks outlined in the “Implementation Plan for Synergistic Enhancement of Pollution and Carbon Reduction” and accelerate the formation of a synergistic governance framework, the Chinese government released the first batch of pilot cities for synergistic innovation in pollution reduction and carbon emission reduction in 2024. Based on indicators such as comprehensive environmental data, governance effectiveness, historical foundations, industrial models, and economic realities, the pilot list includes 21 cities covering various urban types—including resource-based, industrial, comprehensive, and ecologically sound cities—characterized by strong representativeness and diverse forms. Therefore, although this policy was introduced in 2024, some pilot areas are already provincial-level key experimental zones. The corresponding industrial parks and application scenarios have accumulated rich experience and developed advanced governance models, boasting profound foundations and distinct foundational advantages. Within their respective provinces, they have initially formed replicable and promotable blueprints for environmental governance. Based on the list of this pilot policy, this study takes 16 provinces (municipalities directly under the Central Government) in China that have demonstration cities(districts) as the experimental group to conduct a sub-sample heterogeneity test.

As shown in the results of the heterogeneity test in Table 6, the regression coefficients of digital technology on the synergistic effect of pollution and carbon reduction in pilot areas and non-pilot areas are 0.0241 and 0.0162, respectively. Specifically, only the coefficient in pilot areas is statistically significant at the 1% level, whereas that in non-pilot areas is not statistically significant. This result indicates that the positive impact of digital technology on pollution carbon reduction is more significant and stronger in regions with a solid foundation in environmental governance, distinct green economy characteristics, and mature digital operation models. Cities in innovation collaborative pilot zones embrace the synergistic enhancement concept. They strive to boost interconnected and coupled progress in key areas such as optimizing the regional industrial structure, adjusting the energy structure, cutting pollutants’ emissions synergistically in key fields, and advancing demonstration and establishment projects. For example, Huzhou City in Zhejiang Province, as an innovative collaborative pilot city, steadfastly implements the manufacturing upgrading strategy and coordinates the advancement of ecological civilization construction and industrial green transformation. By pioneering the industrial carbon efficiency code and establishing an innovative carbon efficiency evaluation system, the city explores and forms a new path for industrialization development with green manufacturing as its core. As an innovative pilot zone, Taizhou Bay Economic and Technological Development Zone in Zhejiang Province prioritizes digital transformation and industrial chain resource integration, constructing a core architecture based on the “Industrial Brain + Future Factory” model. Through three major modules—manufacturing applications, generic technologies, and industrial ecosystems—the architecture expands application scenarios including energy management, equipment operation maintenance, and intelligent inspection, enabling intelligent operations such as smart energy regulation, logistics vehicle scheduling, and predictive maintenance of equipment. This approach effectively reduces pollutant and carbon emissions induced by energy consumption. In the field of energy management, some advanced pilot regions have empowered enterprises to conduct intelligent monitoring and optimization of carbon assets by deploying pan-energy network low-carbon digital and intelligent platforms to enterprises within their jurisdiction. This practice not only positively feeds back into the green and low-carbon development process of the parks but also drives the low-carbon construction of surrounding parks through synergistic effects. Driven by the digital economy and dual-carbon goals, the demonstration effect of innovation collaborative pilot zones will accelerate the formation of green low-carbon production models at the provincial level. Market entities, propelled by policy support and industry best practices, will systematically integrate carbon neutrality principles into the entire lifecycle of planning, construction, management, and operation. Enabled by digital technologies, coordinated transformations will be achieved across three dimensions: intelligent and low-carbon logistics, cleaner energy structures, and digitalized business processes. This ultimately accomplishes synergistic government of pollution and carbon reduction through higher efficiency, lower pollution, and minimal carbon emissions.

6. Discussion

6.1. Research Conclusions

This study examines the impact of digital technologies on the synergistic governance of pollution and carbon reduction, as well as its underlying mechanism, based on panel data covering 30 provinces (autonomous regions and municipalities directly under the Central Government) in China from 2000 to 2022. The findings indicate that the comprehensive penetration of digital technologies in economic and social spheres facilitates the transformation of the economic development paradigm toward intensiveness and refinement; that is, digital technologies significantly promote the synergistic governance of pollution and carbon reduction. This conclusion remains valid even after replacing explanatory variables, altering econometric models, and eliminating endogeneity. The results of the heterogeneity test, which adopts the list of the first batch of pilot cities for synergistic innovation in pollution and carbon reduction issued by the Ministry of Ecology and Environment of China as the criterion for sample division, show that compared with provinces without such pilot cities for synergistic innovation the positive impact of digital technologies on the synergistic governance of pollution and carbon reduction is more significant and stronger in regions with a solid foundation for environmental governance, distinct characteristics of green economy, and mature digital operation models. Mitigating the negative impacts of energy production and consumption on the ecological environment and fully unleashing the positive externalities of industrial agglomeration are non-negligible path mechanisms. The results of the mediation effect test indicate that digital technologies can achieve the synergistic governance of pollution and carbon reduction by improving energy efficiency and promoting the virtual agglomeration of traditional industries in the cloud.

6.2. Policy Implications

Based on the research findings, this study further proposes the following policy recommendations:

(1)

Strengthening Digital Infrastructure Construction: Digital infrastructure is analogous to highways and power grids in the information age; it serves as the foundation for digital technologies to contribute effectively to pollution reduction and carbon mitigation. Governments should increase investment in the construction of 5G networks, data centers, and related facilities—especially in remote and underdeveloped areas—to bridge the digital divide. For example, expanding high-speed broadband networks can promote e-commerce and remote work in rural regions, thereby reducing carbon emissions from commuting and logistics. At the same time, enterprises should be encouraged to use cloud computing and other technologies to enhance the intelligence of energy management and to optimize energy use in production processes.

(2)

Promoting Industrial Digital Transformation: The digitalization of industry is a key step in achieving pollution and carbon reduction. Governments can introduce incentive policies—such as tax reductions and financial subsidies—to support traditional manufacturing enterprises in upgrading their operations through artificial intelligence, the industrial internet, and other digital technologies. These upgrades can improve production efficiency while reducing resource waste and pollutant emissions. For instance, some factories have adopted smart monitoring systems that enable real-time tracking of equipment operation and energy use, helping to detect and resolve potential issues early and thus lower energy consumption and emissions.

(3)

Enhancing Energy Efficiency: Improving energy efficiency is a major pathway to reducing pollution and carbon emissions. Governments should increase awareness and training efforts to raise the understanding of energy efficiency among enterprises and the public. Energy-saving technologies and products should be promoted—for example, by encouraging the adoption of high-efficiency motors and LED lighting to reduce energy use. Additionally, the development of digital energy management systems should be supported. These systems can monitor and analyze real-time energy data, helping enterprises optimize energy allocation and improve overall energy efficiency.

(4)

Establishing a Synergistic Governance Mechanism: Pollution and carbon reduction require the joint efforts of governments, enterprises, and the general public. Governments should play a leading role in building cross-sectoral coordination mechanisms, breaking information barriers, and promoting data sharing and resource integration. For example, a unified environmental monitoring and management platform can enable real-time data sharing between environmental authorities and enterprises, thereby improving regulatory efficiency. Public participation should also be encouraged by organizing environmental education activities and creating public oversight mechanisms, which can raise environmental awareness and engagement, ultimately fostering a social atmosphere of collective action toward pollution and carbon reduction.

6.3. Research Outlook

From an empirical perspective, this study adopts research methods that align with the research context, based on a comprehensive review of existing literature. However, there is still room for optimization in several areas: (1) Data Selection: This study uses provincial-level panel data as the empirical foundation, a common choice in many studies, which has yielded numerous representative findings. However, this approach has certain limitations. For instance, the large geographical scope of China’s provinces results in a lack of precision in data selection, making it difficult to refine empirical conclusions at a micro level. Therefore, future research could adopt city- or county-level panel data to enable more detailed analysis. (2) Measurement of Variables: While this study has selected appropriate measurement methods based on a thorough review of existing literature and data availability, there remain issues of representativeness. For example, the use of traditional location entropy to represent virtual agglomeration presents several problems. First, the calculation of location entropy often relies on administrative boundaries, but virtual agglomeration lacks such boundaries, leading to potential distortions in the traditional calculations. Second, digital platform companies typically operate in multiple markets and generate profits through a variety of business models, which makes it difficult to clearly categorize their industry sectors. This results in location entropy failing to accurately reflect virtual agglomeration. Lastly, virtual agglomeration often experiences significant short-term fluctuations due to platform rule changes, while location entropy calculations depend on annual statistical data, which introduces considerable time lags. Therefore, future research should refine the measurement methods to address these existing limitations. (3) Research Extension: This study primarily investigates the impact of digital technology on the synergistic governance of pollution reduction and carbon mitigation from a macro-level perspective, providing a comprehensive understanding of the positive role digital technologies play in environmental governance. However, it lacks in-depth insight into specific industries. Future research could further refine the analysis to the industry level—such as tourism, agriculture, and others—in order to clarify the environmental benefits of digital technologies across different sectors.

In terms of application, this study has implications for other developing countries and holds international relevance. This is because: (1) Similar National Contexts: China shares several similarities with other developing countries. Many of these nations are in the later stages of industrialization or experiencing rapid urbanization, with energy structures that heavily rely on coal, making it difficult to shift to cleaner energy sources in the short term. These countries face dual pressures from economic growth and environmental protection, along with significant imbalances in socio-economic development and regional disparities. They also struggle with limited capacity and financial resources for pollution control and low-carbon transformation. (2) Common Challenges: Like China, many other developing countries face similar challenges. Their industrial structures are dominated by energy-intensive and high-emission industries, which contribute to both traditional pollutants and greenhouse gas emissions. Additionally, urban traffic, building expansion, and agricultural non-point source pollution exacerbate the compounding effects of pollution and carbon emissions. These countries also have underdeveloped monitoring systems, weak market mechanisms, and low public participation, making single governance measures less effective. (3) Similar Opportunities: The global green and low-carbon industrial landscape is undergoing a fundamental reshaping, while the declining cost of renewable energy, together with the rise in digital technologies and green finance, offers a strategic window for leapfrog development. The scale of international green investment is steadily expanding, with increased targeted support from the Green Climate Fund and multilateral development banks, helping to ease the financial pressures associated with green transitions. Therefore, China shares similar national conditions with many developing countries, which provides comparable contexts for the application of digital technologies in environmental governance across other developing nations. Exploring how China can achieve synergistic governance of pollution and carbon reduction not only holds significant value for the country’s high-quality economic development but also offers valuable lessons for other developing countries seeking similar paths.