The Impact of the Digital Economy on Green Development Efficiency in the Pearl River Delta Region: Internal Mechanisms and Empirical Tests

Abstract

Against the backdrop of promoting high-quality and sustainable economic development, the digital economy is regarded as a key driver for enhancing green efficiency. To thoroughly analyze its driving role, this paper constructs a benchmark framework of “digital economy → transmission mechanism → green development efficiency.” Based on panel data from nine cities in the Pearl River Delta region, it employs a two-way fixed effects model to empirically examine the impact of the digital economy on green development efficiency and further expands the analysis to include influencing mechanisms and heterogeneity. The results indicate that the digital economy has a significant positive impact on the efficiency of green development in the Pearl River Delta region. Innovation of green technology and upgrading of industrial structure emerge as two key ways in which the digital economy improves the efficiency of green development. Further heterogeneity analysis reveals that for cities with moderate digital economic development, the marginal benefit of the digital economy in promoting green development efficiency is higher. Simultaneously, the digital economy’s promotional effect on green development efficiency is more pronounced in high-tier cities. Accordingly, this paper proposes prioritizing institutional development and cost reduction to enhance efficiency. This should be achieved through differentiated institutional supply, strengthened transmission pathway empowerment, optimized resource allocation, reduced transmission costs, amplified effects of leading cities, and maximized marginal benefits of leading cities, ultimately achieving comprehensive improvements in green development efficiency across the entire region.

1. Introduction

In 2012, China formally designated the construction of an ecological civilization as a national strategic objective, placing it on a par with sustainable economic development. By prioritizing ecological conservation and pursuing green development, China is spearheading a comprehensive green transformation of its socio-economic development. Currently, the Pearl River Delta is striving to build a green ecological urban agglomeration, its exploration and practices provide an important sample for regional sustainable development. Expanding the perspective globally, the synergistic transformation of green and digital transitions has become a common trend [1]. The World Bank explicitly states in its reports that digital technology is a powerful tool for addressing environmental challenges and enhancing resource productivity [2]. Correspondingly, as a core carrier in the digital economy, information and communication technology can provide potential pathways for achieving green growth by enabling smart energy management and optimizing logistics systems [3]. Therefore, viewing the digital economy as a key engine for improving green development efficiency in China, particularly in an economically dynamic region like the Pearl River Delta, is not only consistent with domestic development strategies but also aligns with the direction of global cutting-edge exploration [4].

The existing scholarship widely acknowledges a robust positive correlation between digital economic expansion and green development efficiency. The theoretical basis of this nexus lies in the intrinsic capacity of the digital economy to optimize resource allocation and catalyze technological innovation [5]. This perspective aligns with the view that ICT acts as a critical enabler, systematically improving the efficiency of energy and resource management [4]. Empirical evidence derived from efficiency measurement models further substantiates that the digital economy bolsters regional energy utilization and pollution abatement capacities through these established channels [6]. From the perspective of spatial linkage, the digital economy fosters synergistic improvements in regional green efficiency by intensifying the spatial spillover of green technologies across cities. This phenomenon validates the “enabling” characteristic of digitalization and finds theoretical support in the diffusion of innovations theory [7]. The theory posits that the dissemination of new technologies generates spatial externalities via social networks; the digital economy, functioning as a general-purpose technology, exemplifies this by diffusing green benefits [8]. Moreover, a multi-dimensional analysis reveals distinct effects across different scales. Macro-level analysis reveals a distinct “core–periphery” spatial gradient in green transformation efficacy, characterized by significant disparities between cities that depend on resources and those that do not [9]. At the meso-level, digitalization indirectly elevates industrial green ecological efficiency by fostering green industries and restructuring traditional energy-intensive sectors [10]. Finally, at the micro-level, digital technological innovation enhances the efficiency of green development by reducing innovation costs and shortening return cycles in corporate green R&D investments [11].

Overall, while the extant research has established a multi-dimensional analytical framework, there remains a paucity of targeted studies on the Pearl River Delta region, which exemplifies coordinated economic and ecological development [12]. As China’s core manufacturing hub, the Pearl River Delta has long grappled with the tension between industrial expansion and ecological constraints. Simultaneously, it serves as the core hinterland of the Greater Bay Area. It boasts a nationally leading foundation for the digital economy and maintains high standards for ecological conservation. Investigating the influence of the digital economy on the efficiency of green development enables precise identification of digital technologies’ role in energy conservation, emissions reduction, and industrial upgrading. This provides a practical pathway for the Pearl River Delta to transition from “scale-driven growth” to “green and efficient growth”. Furthermore, research in this field will establish replicable models for digital technologies to empower the integrated green growth of the Bay Area. This supports the Greater Bay Area’s ambition to become an internationally leading bay area and a world-class urban cluster [13]. Based on this, this paper employs data from the Pearl River Delta region spanning 2011 to 2023 as its sample to conduct both theoretical and empirical analyses. It investigates whether and how the digital economy drives green development in the Pearl River Delta, thereby proposing targeted policy recommendations. The following innovations are presented in this paper: First, by sampling nine cities in the Pearl River Delta region, it addresses the existing literature gap in empirical studies at the city cluster level, expanding the regional applicability of conclusions regarding the digital economy’s empowerment of green development. Second, it optimizes the existing analytical framework by integrating endogeneity treatment with regional heterogeneity analysis, thereby refining the empirical research logic that links the efficiency of the digital economy to green development. Third, it refines practical pathways for the digital economy to empower green development by integrating regional characteristics of the Pearl River Delta, providing theoretical and empirical support for regional extensions of the existing literature. The research conclusions offer precise empirical references for promoting “digital + green” transformation in the Pearl River Delta and similar urban agglomerations.

2. Theoretical Hypotheses

2.1. Direct Impact of the Digital Economy on Green Development Efficiency in the Pearl River Delta

The core metrics for green development efficiency are “resource consumption intensity per unit of economic output” and “degree of environmental damage.” Its fundamental requirement is to directly reduce resource inputs and pollution emissions without weakening economic growth momentum [14]. The digital economy directly impacts core indicators of green development efficiency by leveraging the unique attributes of its data elements, the precise empowerment of digital technologies, and the integration capabilities of digital platforms. This creates a direct pathway for driving “reduced consumption, decreased pollution, and enhanced efficiency” [15]. Specifically, the extensive implementation of digital infrastructure and the advancement of digital industrialization can significantly reduce information asymmetry between regions. This facilitates the diffusion of green technologies, knowledge, and best practices, thereby directly improving regional resource utilization efficiency and pollution control capabilities [16]. Moreover, the embedding of digital technologies can directly optimize production processes and energy management systems, achieving source reduction in energy consumption and emissions. This efficiency gain does not depend on other structural transformation processes, constituting the most direct green contribution [17]. In-depth analysis reveals that the digital economy’s impact on ecological efficiency exhibits a single threshold effect based on its own development level. Only after surpassing a specific threshold does its positive green impact become fully activated and significantly amplified [18]. It has been confirmed by research using interprovincial panel data from China that a nonlinear pattern of positive marginal efficiency is followed by the digital economy’s effect on China’s industrial green total factor productivity [19]. Additional empirical evidence confirms that this nonlinear promotion manifests as a positive acceleration effect, particularly as digital infrastructure matures, significantly accelerating the release of green benefits [20].

Some of the existing literature suggests that the impact of digitalization on green development efficiency may exhibit a nonlinear U-shaped characteristic. During the early stages of digital development, the application of digital technologies primarily focuses on scale expansion. The accompanying construction of digital infrastructure and equipment upgrades can increase energy consumption and pollution emissions. At this stage, scale effects dominate, and the digital economy may inhibit green development efficiency. Digital technologies only empower technological innovation, optimize industrial structures, and enhance environmental governance intelligence once they have crossed a certain threshold. This leads to the conservation of energy and the reduction in pollution, thereby boosting the efficiency of green development. However, it is important to remember that the research for this paper was done in the Pearl River Delta region. As one of the areas in China with the most advanced digital economic development, Pearl River Delta cities had already laid the groundwork for digital construction and begun pilot explorations by 2008. Overall, since 2013, the digitalization of PRD cities has moved beyond the initial stage of digital development. Particularly after 2015, the application of digital technologies shifted from “scale expansion” to “quality enhancement.” The scale effects characteristic of early digitalization have gradually diminished, with technological and structural effects becoming the dominant forces. Given that the U-shaped influence characteristic of the digital economy on green development efficiency is not prominent in the sample from the Pearl River Delta region, whereas a positive linear relationship between the two is more distinctly evident, this paper derives the core research hypothesis H1: The level of digital economic development can significantly and positively drive the green development efficiency of the Pearl River Delta region.

2.2. Mechanism Analysis of the Impact of the Digital Economy on Green Development Efficiency in the Pearl River Delta

The digital economy, propelled by breakthroughs in digital technology and its integrated applications, encompasses production, circulation, distribution, and consumption. It optimizes economic structures, enhances resource allocation efficiency, and promotes the synergy between equity and efficiency. As a manifestation of continuous technological innovation and iteration, the digital economy is underpinned by innovation theory, which serves as its critical theoretical foundation. From the perspective of green technological innovation, green innovation efficiency can be indirectly enhanced by the digital economy through mechanisms such as the acceleration of the flow of innovative technologies via the scale effects of information and communication technologies (ICTs), strengthening intellectual property protection, and fostering industry–academia–research collaboration. This mechanism is in line with the “Porter Hypothesis,” which suggests that the right policy pressure (which can be viewed as the efficiency pressures and opportunities brought about by digital technologies in this context) can lead to an “innovation compensation” effect [21]. Specifically, digital technologies reduce the uncertainty and costs associated with green technological research making enterprises more willing to engage in green innovative activities [22]. Domestic research has found that green technological innovation is a key factor in the way that the digital economy increases green total factor productivity [23]. The applicability of this mechanism at the regional level will be further validated by empirical findings from the Pearl River Delta region in this study. Consequently, this paper advances Research Hypothesis 2: Green development efficiency is enhanced by the digital economy through the boosting of green technological innovation.

Functioning as a transformative driver, the digital economy systematically reshapes the industrial landscape, thereby accelerating the transition toward industrial upgrading and modernization. This process involves a fundamental reorganization of industrial ecosystems, where digital integration acts as the primary mechanism for structural optimization. This transformation is intrinsically aligned with the globally recognized concepts of “economic servitization” and “global value chain (GVC) upgrading.” The underlying mechanisms operate through two primary channels: First, the digital economy directly amplifies the scale and caliber of the service sector. By fostering novel business paradigms—such as platform economies and the sharing economy—it accelerates the evolution of industrial architecture [24]. Second, digital technologies enhance information transparency and coordination efficiency. This empowers firms to penetrate deeper into the GVC and ascend to higher-value segments, thereby phasing out energy-intensive, low-value-added production in favor of green, intensive growth models [25]. Empirical evidence suggests that digitalization fosters the expansion of high-end industries, leading to optimized resource allocation and improved energy efficiency, which subsequently drives the improvement of green total factor productivity (GTFP) [26]. Furthermore, an in-depth analysis of the relationship between the digital economy and GTFP shows that the former has a substantial impact on the latter, with optimizing industrial structure being a key channel through which this influence is transmitted [27]. Additionally, the inherent transparency of digital technology mitigates information asymmetry, compelling traditional industries to undergo green transitions and facilitating industrial upgrading. This process establishes a self-reinforcing virtuous cycle: “industrial upgrading → efficiency gains → green transformation,” which cumulatively elevates green development efficiency. Based on this theoretical framework, we formulate Hypothesis 3: The digital economy boosts green development efficiency by accelerating the modernization of industrial structures.

3. Methods

3.1. Measurement of the Digital Economy Development Level in the Pearl River Delta Region

3.1.1. Measurement Method of the Comprehensive Digital Economic Development Levels in the Pearl River Delta Region

Drawing on the existing literature [28], this paper constructs a measurement system for digital economic development levels across five dimensions: digital infrastructure, human capital investment, industrial output scale, terminal application penetration, and digital financial support. Five indicators are selected: internet penetration rate, number of internet-related personnel, internet-related output, mobile internet users, and inclusive digital financial development. This measurement system aligns with the theoretical essence of the digital economy while meeting the data availability and empirical reliability requirements at the prefecture-level city scale. It also represents the most representative indicator combination for measuring urban digital economies in the current domestic and international literature. Furthermore, utilizing extreme value entropy-weighted legal weights, a scoring method for calculating the digital economic development levels of nine cities in the Pearl River Delta region is constructed to measure the relative development levels of digital economies across these cities.

The specific calculation process is as follows:

First, the original indicator data are standardized using the min–max normalization method. Suppose there are x indicators and j regions, forming a data matrix $X=\left\{x_{ij},i=1,2,\cdots ,5;j=1,2,\cdots ,9\}\right.$. The standardized matrix is acquired through range standardization $Y=\left\{y_{ij},i=1,2,\cdots ,5;j=1,2,\cdots ,9\right\}$. The standardization formulas for positive indicators and negative indicators are as follows:

Positive indicators:

$$y_{ij}={\displaystyle \frac{x_{ij}-\min \left(x_i\right)}{\max \left(x_i\right)-\min \left(x_i\right)}}$$

(1)

Negative indicators:

$$y_{ij}={\displaystyle \frac{\max \left(x_i\right)-x_{ij}}{\max \left(x_i\right)-\min \left(x_i\right)}}$$

(2)

Subsequently, we determine the weight of each indicator using the range entropy weight method. Entropy acts as a metric for quantifying a system’s orderliness: higher order within the system corresponds to greater information entropy, while lower order results in smaller information entropy. The entropy weight method derives indicator weight coefficients from the information content of each indicator’s observed values. Its core principle involves computing the entropy weight of each indicator by linking information entropy to the indicator’s degree of variation—this process yields more objective indicator weights. The following section details the operational steps of the calculation:

$${\mathrm{p}}_{\mathrm{ij}}={\displaystyle \frac{{\mathrm{y}}_{\mathrm{ij}}}{{\displaystyle \sum _{j=1}^m}{y}_{ij}}}$$

(3)

$${\mathrm{e}}_{\mathrm{i}}=-{\displaystyle \frac{1}{\ln n}}\times \sum _{j=1}^m{p}_{ij}\ln \left(p_{ij}\right) i=1,2,\dots ,5$$

(4)

The entropy value of the i-th indicator is denoted by e_i; the proportion of the j-th city within the i-th indicator relative to that indicator is represented by P_ij.

Next, we will calculate the weights for each indicator:

$$W_i={\displaystyle \frac{1-e_i}{5-{\displaystyle \sum e_i}}}\hspace{0.33em}· i=1,2,\dots ,5$$

(5)

Finally, the comprehensive scores for each evaluation indicator are calculated:

$$F_j=\sum _{i=1}^5{W}_i*y_{ij}\hspace{0.33em}j=1,2,\cdots ,9$$

(6)

3.1.2. Indicator Selection

Drawing on the existing literature [28] and considering the specific context of the Pearl River Delta region, the present research selects the following indicators (detailed in Table 1).

Table 1. Indicator system for the comprehensive development level of the digital economy in the Pearl River Delta Region.

Primary Indicator	Secondary Indicator	Tertiary Indicator	Indicator Property
Composite Index of Digital Economic Development	Internet Penetration Rate	Internet Users per 100 People	+
	Digital Workforce Scale	Share of Employees in Computer Services & Software Sectors	+
	Digital Economic Output	Per Capita Telecommunication Business Volume	+
	Mobile Internet Users	Mobile Phone Subscribers per 100 People	+
	Inclusive Development of Digital Finance	China Digital Financial Inclusion Index	+

Note: + indicates a positive indicator.

Within the digital infrastructure dimension, the internet penetration rate is employed as the core indicator, operationalized by the tertiary indicator “the number of internet users per 100 people”. As the internet forms the foundational infrastructure for the digital economy, this indicator directly measures the diffusion of digital technologies in a given area. It thus functions as a fundamental metric for assessing the “reach capability” of the digital economy.

The number of internet-related personnel employed reflects human resource investment in the digital industry, using the tertiary indicator of the proportion of employees in computer services and software. Computer services and software constitute the core sectors of digital industrialization. The proportion of their employees directly indicates the degree of human capital concentration and the level of specialization within a region’s digital industry, making it a key indicator for measuring the “industrial depth” of the digital economy. Simultaneously, high-quality professionals in the digital industry are central to digital technology innovation and application, with their scale and proportion determining the developmental potential of the digital economy.

Internet-related output captures the output dimension of the digital industry, measured by the tertiary indicator “per capita total telecommunication business volume.” Total telecommunication business volume encompasses the operational output of digital infrastructure, including fixed-line communications, mobile communications, and internet data services. The per capita figure adjusts for population size differences, accurately reflecting the operational efficiency and service capacity of regional digital infrastructure. It therefore serves as a key metric for assessing the output efficiency of the digital economy.

The number of mobile internet users in the digital economy reflects the application status of digital infrastructure; we use the tertiary indicator of mobile internet users per 100 people. This figure shows how widely mobile technology is used in a region and acts as a key sign of how convenient and accessible the digital economy is for the public.

The inclusive development of digital finance helps us understand the connection between the digital economy and its financial system. This is quantified by the tertiary indicator “Digital Financial Inclusion Index,” which assesses three aspects: the range of financial access, the frequency of service usage, and the complexity of digital adoption. This index offers a thorough view of the inclusiveness and innovation driving the sector’s digital shift, making it a typical feature of the digital economy’s progress. A quintessential representation of the digital economy’s role in enabling the real economy, it underscores the sector’s transformative potential.

The dataset used here comes from multiple official sources: local statistical yearbooks and bulletins for Guangdong cities, the national China Urban Statistical Yearbook, and the Peking University Digital Finance Research Center. We selected the period 2011–2023 based on actual data availability. For missing data points, we applied linear interpolation to maintain continuity. Statistical checks show that the missing rate is roughly 2.1%, which is significantly lower than the 5% empirical limit, so the data quality remains high. To remove the influence of different units and scales, all indicators were processed using min–max normalization, mapping the values into the [0, 1] interval.

3.1.3. Measurement Results of the Digital Economy Development Level in the Pearl River Delta Region

The study conducted supplementary tests from two dimensions. These were internal robustness and external validity. This was done to enhance the validity of the composite indicators and the reliability of the measurement framework.

First, sensitivity analysis using the “stepwise elimination method” revealed that the sub-indices constructed after removing any single foundational indicator maintained Pearson correlation coefficients exceeding 0.88 with the original composite index. This demonstrates the balanced structure of the measurement system, its insensitivity to individual indicators, and its outstanding internal robustness.

Second, validation through an alternative indicator set demonstrated a correlation coefficient of 0.62 between the new index and the original index, which was highly statistically significant. This confirms that the indicators selected in this study align with commonly used measurement methods in the academic community, ensuring strong external validity. In summary, the measurement framework presented in this paper combines robustness and validity, providing solid support for the reliability of the underlying data.

Based on the aforementioned methodology for measuring digital economic development and the selected indicators, the specific numerical values for the digital economic development levels of nine cities in the Pearl River Delta region were ultimately calculated. The empirical findings are presented in Table 2.

Table 2. Level score of the digital economy development level in the Pearl River Delta Region.

City	Shenzhen	Zhuhai	Dongguan	Guangzhou	Zhongshan	Foshan	Huizhou	Zhaoqing	Jiangmen
2011	0.264	0.100	0.164	0.100	0.075	0.069	0.025	0.011	0.017
2012	0.290	0.115	0.175	0.110	0.087	0.086	0.039	0.029	0.030
2013	0.388	0.156	0.207	0.152	0.110	0.101	0.053	0.038	0.040
2014	0.417	0.174	0.197	0.149	0.122	0.104	0.059	0.044	0.052
2015	0.381	0.191	0.198	0.147	0.136	0.104	0.065	0.047	0.056
2016	0.412	0.202	0.194	0.164	0.128	0.100	0.070	0.036	0.073
2017	0.375	0.244	0.229	0.189	0.133	0.108	0.078	0.066	0.066
2018	0.455	0.252	0.198	0.226	0.135	0.114	0.082	0.075	0.069
2019	0.418	0.301	0.196	0.225	0.136	0.111	0.087	0.084	0.069
2020	0.470	0.335	0.229	0.253	0.149	0.122	0.094	0.100	0.074
2021	0.541	0.405	0.272	0.288	0.172	0.142	0.111	0.126	0.089
2022	0.615	0.526	0.321	0.324	0.204	0.167	0.123	0.154	0.100
2023	0.702	0.639	0.382	0.366	0.234	0.191	0.145	0.189	0.112
Average Level	0.441	0.280	0.228	0.207	0.140	0.117	0.079	0.077	0.065

As shown in Table 2, the digital economy development index values for all nine cities in the Pearl River Delta region demonstrated steady annual growth between 2011 and 2023, indicating significant and sustained progress in the region’s digital economy over the past decade. Among them, Shenzhen recorded the highest average digital economy development level (0.441), with its 2023 measurement reaching 0.702, maintaining a leading position in the region. Jiangmen had the lowest average (0.065), with its 2023 measurement at only 0.112, indicating a relatively lagging level. From a city-level perspective, the development gap between leading cities like Shenzhen and Zhuhai and trailing cities such as Zhaoqing and Jiangmen is pronounced, highlighting the unevenness in the region’s digital economic development.

3.2. Measurement of Green Development Efficiency in the Pearl River Delta Region

3.2.1. Measurement Method of Green Development Efficiency in the Pearl River Delta Region

A review of the existing literature reveals that most scholars typically employ the conventional Data Envelopment Analysis (DEA) model to measure green development efficiency. However, the DEA model assumes that desirable outputs increase and undesirable outputs decrease proportionally, which does not align with actual production conditions. Moreover, its failure to account for slack variables presents significant limitations. Consequently, the traditional Slack-Based Measure (SBM) model, in which slack variables are incorporated into the objective function, has been introduced by some scholars. Although the traditional SBM model effectively addresses the slackness of input–output variables, it still suffers from the limitation of distorted results when all measured efficient decision units yield an efficiency value of 1. Therefore, this paper adopts the Super-SBM model, enabling the model to further distinguish decision units with an efficiency value of 1, thereby providing more precise efficiency evaluation and decision support.

The mathematical formulation of the super-efficiency model is expressed as follows:

$$\begin{array}{l}\rho =\min {\displaystyle \frac{\frac{1}{m}{\displaystyle \sum _{i=1}^m\frac{\overline{x}}{x_{ik}}}}{\frac{1}{s_1+s_2}\left({\displaystyle \sum _{l=1}^{s_1}\frac{{\overline{y}}_l^d}{y_{l_0}^d}}+{\displaystyle \sum _{k=1}^{s_2}\frac{{\overline{y}}_k^u}{y_{k_0}^u}}\right)}}\\ \mathrm{s}.\mathrm{t}. \left\{\begin{array}{l}\overline{x}⩾{\displaystyle \sum _{j=1,j\ne j_0}^n{x}_{ij}}{\lambda }_j;{\overline{y}}^d⩽{\displaystyle \sum _{j=1,j\ne j_0}^n{y}_{lj}^d}{\lambda }_j;{\overline{y}}^u⩾{\displaystyle \sum _{j=1,j\ne j_0}^n{y}_{kj}^d}{\lambda }_j\hfill \\ {\overline{y}}^d⩽y_{lj}^d;{\overline{y}}^u⩾y_{kj}^d\hfill \\ {\lambda }_j⩾0,i=1,2,\cdots ,m;j=1,2,\cdots ,n;l=1,2,\cdots ,s_1;k=1,2,\cdots ,s_2\hfill \end{array}\right.\end{array}$$

(7)

In the equation, each city represents a decision-making unit (DMU), where n denotes the number of DMUs. Each DMU consists of input m, desired output $s_1$, and undesired output $s_2$. x, $y^d$, and $y^u$ represent the slack variables for inputs, desired outputs, and undesired outputs, respectively. λ_j denotes the weight vector (envelope multiplier) for inputs and outputs. $\rho$ represents the green development efficiency value, where a higher value indicates a higher level of green economic efficiency.

3.2.2. Indicator Selection

Green development efficiency (GDE) is conceptualized as a composite economic metric that harmonizes socio-economic progress with ecological preservation. Unlike traditional efficiency measures, GDE must simultaneously capture the desirable outputs of economic expansion and account for the undesirable outputs (environmental externalities) generated during the production process. Regarding input variables, the extant literature on GDE measurement typically identifies capital, labor, and energy as the primary production factors. Grounded in new economic growth theory, capital and labor constitute the fundamental drivers of production. However, to align with the principles of sustainable development, it is imperative to incorporate ecological constraints and resource consumption into the input framework. Consequently, drawing on established methodologies, this study selects capital stock, labor force, and energy consumption as the input indicators. Concerning output variables, the Super-SBM model is employed due to its capability to handle both desirable and undesirable outputs within a unified framework. Specifically, considering the regional characteristics of the Pearl River Delta (PRD), given the region’s developmental priorities, Gross Domestic Product (GDP) is selected as the desirable output to represent economic output. In contrast, to account for ecological costs, discharges of industrial by-products—specifically wastewater, waste gas, and solid waste—are classified as undesirable outputs. Table 3 provides a comprehensive summary of the input–output indicators used to assess the PRD’s green development efficiency (GDE).

Table 3. Evaluation system for green development efficiency in the Pearl River Delta Region.

Primary Indicator	Secondary Indicator	Tertiary Indicator and Unit
Input	Capital Input	Fixed Asset Investment (100 million yuan)
	Labor Input	Total Workforce in Primary, Secondary, and Tertiary Sectors (10,000 persons)
	Resource Input	Total Urban Water Supply (10,000 cubic meters)
		Total Electricity Consumption of the Whole Society (100 million kWh)
		Developed/Built-up Area (square kilometers)
Output	Desirable Output	Gross Domestic Product (GDP) of Each City (100 million CNY)
	Undesirable Output	Industrial Sulfur Dioxide (SO2) Emissions (100 million cubic meters)
		Industrial Wastewater Discharge (100 million tons)
		Industrial Solid Waste (Soot/Dust) Emissions (10,000 tons)

The raw data for our indicators comes from official publications, specifically the China Environmental Statistical Yearbook, Guangdong’s city-level emission bulletins, and the China Urban Statistical Yearbook. The sample period covers 2011 to 2023, selected to ensure data continuity and availability. To handle missing values in the original dataset, we applied linear interpolation. Given that the missing rate is low, this approach preserves the integrity of the data and ensures that our results remain reliable.

3.2.3. Measurement Results of Green Development Efficiency in the Pearl River Delta Region

Utilizing the established assessment framework and chosen metrics, the green development efficiency scores for individual cities within the Pearl River Delta are derived. The complete set of results is provided in Table 4.

Table 4. Green development efficiency in the Pearl River Delta Region.

City	Shenzhen	Foshan	Dongguan	Guangzhou	Zhongshan	Zhuhai	Zhaoqing	Jiangmen	Huizhou
2011	1.002	0.722	1.006	0.300	0.287	0.185	0.173	0.179	0.151
2012	1.015	0.630	1.026	0.307	0.325	0.179	0.175	0.174	0.155
2013	0.829	0.241	0.234	0.322	0.204	0.176	0.170	0.165	0.164
2014	1.013	0.246	0.246	0.333	0.224	0.183	0.179	0.163	0.169
2015	1.004	0.251	0.261	0.346	0.227	0.187	0.182	0.165	0.165
2016	0.669	0.257	0.274	0.361	0.234	0.200	0.191	0.167	0.170
2017	1.009	0.268	0.293	0.378	0.258	0.231	0.195	0.180	0.175
2018	1.000	0.270	0.316	0.392	0.276	0.245	0.199	0.175	0.177
2019	1.011	0.377	0.366	0.375	0.234	0.287	0.207	0.189	0.167
2020	1.036	0.419	0.350	0.400	0.235	0.287	0.206	0.185	0.160
2021	1.003	1.001	0.434	0.445	0.265	0.318	0.296	0.202	0.180
2022	0.961	1.002	0.451	0.435	0.269	0.328	0.297	0.205	0.188
2023	1.018	1.011	0.429	1.002	0.286	0.340	0.308	0.232	0.189
Average Level	0.967	0.515	0.437	0.415	0.256	0.242	0.214	0.183	0.170

Table 4 reveals significant disparities in green development efficiency across the Pearl River Delta region from 2011 to 2023. Shenzhen maintained an average green economic development efficiency value of 0.9668 during this period, exceeding 1 in all years except 2013, 2016, and 2022, indicating consistently high efficiency. Foshan, Dongguan, and Guangzhou recorded green economic development efficiency values above 1 in only a few years, with their annual average efficiency ranging between 0.4 and 0.6, indicating a medium efficiency level. Meanwhile, Zhongshan, Zhuhai, Zhaoqing, Jiangmen, and Huizhou all maintained average green development efficiency values within the lower range of 0.2 to 0.4. Evidently, Shenzhen’s green development efficiency has created a significant gap compared to other cities.

3.3. Baseline Regression Model

In accordance with the preceding theoretical analysis, in order to elucidate the underlying mechanism through which the digital economy exerts its influence on the efficiency of green development, the present paper draws upon the extant research [29] and a two-way fixed effects model is constructed as follows:

$${\mathrm{gie}}_{it}={\alpha }_0+{\alpha }_1{\mathrm{de}}_{it}+{\alpha }_2{X}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

(8)

The econometric specification is defined as follows: the indices i and t correspond to the city (cross-sectional unit) and year (time dimension). The dependent variable gie measures green development efficiency, while the core independent variable de indicates the level of digital economy development. $X_{it}$ represents a vector of control variables, ${\alpha }_0$ is the intercept, and ${\alpha }_1$, ${\alpha }_2$ are the estimated coefficients. Furthermore, ${\mu }_i$ captures individual fixed effects, $V_{\mathrm{t}}$ accounts for time fixed effects, and ${\epsilon }_{it}$ is the stochastic error term.

3.4. Mediating Effect Model

To empirically verify the transmission pathways of green technological innovation and industrial structure optimization, the following model is established in this paper by referencing established research frameworks [30]:

$$M_{it}={\beta }_0+{\beta }_1{\mathrm{de}}_{it}+{\beta }_2{X}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

(9)

$${\mathrm{gie}}_{it}={\gamma }_0+{\gamma }_1{ \mathrm{de}}_{it}+{\gamma }_2{M}_{it}+{\gamma }_3{X}_{it}+{\mu }_i+v_t+{\epsilon }_{it}.$$

(10)

Among these, $M_{it}$ denotes the mediating variables, which in this paper are green technological innovation (gti) and industrial structure upgrading (ins). ${\beta }_1$ represents the regression coefficient, indicating the impact of digital economic development on the mediating variables. The interpretation of other variables remains consistent with Equation (8).

Equation (9) examines the impact of levels of digital economic development on the mediating variable, while Equation (10) examines the joint impact of these levels and the mediating variable on green development efficiency.

3.5. Variable Descriptions and Data Sources

3.5.1. Core Variables

(1) To assess green development efficiency (gie), a super-efficiency SBM model is utilized in this research [14]. Within this framework, input factors encompass capital stock, labor force, and energy consumption. The desirable output is proxied by the gross economic output of cities in the Pearl River Delta (PRD), while undesired outputs are quantified using data on industrial “three wastes” (wastewater, waste gas, and solid waste).

(2) For the digital economy development level (de), a multi-dimensional index system is established based on the entropy weight method. Five specific indicators are selected to capture the digital landscape: internet penetration rate, the volume of internet-related employment, internet-related output value, mobile internet user base, and the progress of digital financial inclusion. These metrics collectively evaluate the digital economic status of the nine PRD cities.

3.5.2. Control Variables

The study incorporates a number of control variables. Specifically, the scale of the population (pop) is captured using the log of registered residents. Economic development (gdp) is measured by taking the logarithm of per capita GDP. To reflect fiscal behavior, government investment intensity (gov) is calculated as the share of fixed asset investment within total fiscal spending. Additionally, human capital (hcl) is determined by the ratio of enrolled higher education students to the total population.

3.5.3. Mediator Variables

To examine the transmission mechanisms, green technology innovation (gti) and industrial structure upgrading (ins) are introduced as mediators. Green technology innovation is measured via the logarithmic value of total green patent grants. Industrial structure upgrading is determined by a specific formula that aggregates the value-added shares of the primary, secondary, and tertiary sectors, weighted by coefficients 1, 2, and 3, respectively.

Based on the empirical analysis requirements, this study constructs a panel dataset covering cities in the Pearl River Delta from 2011 to 2023. The primary data are compiled from official publications, including the Guangdong Statistical Yearbook and the China Urban Statistical Yearbook, supplemented by the commercial Guotai’an (CSMAR) database. To address sporadic missing observations, appropriate interpolation techniques were applied. Table 5 provides the descriptive statistics for all variables used in the analysis, as defined and processed above.

Table 5. Descriptive statistics of variables.

Variable	Mean	Std. Dev.	Min	Max
Green Development Efficiency (GIE)	0.378	0.284	0.151	1.036
Digital Economy Level (DE)	0.182	0.140	0.011	0.702
Green Tech Innovation (GTI)	9.950	1.275	6.701	12.541
Industrial Structure (INS)	2.444	0.131	2.205	2.723
Population Size (POP)	5.818	0.580	4.663	6.963
Economic Development Level (GDP)	12.006	0.740	10.343	13.185
Government Investment Intensity (GOV)	4.062	2.156	0.937	13.528
Human Capital Level (HCL)	0.043	0.041	0.006	0.144

4. Results

4.1. Benchmark Regression Results

In the empirical testing, the study conducted preliminary examinations. These included correlation analysis, multicollinearity tests, F-tests, and Hausman tests. This was to ensure the scientific validity and effectiveness of the analysis. Ultimately, a fixed effects model was selected, and regression analysis was performed using panel data. Table 6 presents the baseline regression outcomes. Specifically, Column (1) presents the results of the OLS regression, which is positive and significant at the 1% level, indicating that the digital economy has a positive promoting effect on green development efficiency. Column (2) shows the results after incorporating fixed effects based on the OLS regression, which remain positively significant. Column (3) reports the results with both fixed effects and control variables included, and the positive significance persists, further demonstrating that the digital economy can significantly enhance green development efficiency. In summary, the digital economy has a positive impact on the efficiency of green development, thus confirming Hypothesis 1.

Table 6. Baseline regression results.

Variable	GIE
	(1)	(2)	(3)
de	1.30 ***	1.46 ***	1.36 ***
	(8.90)	(9.02)	(5.76)
pop			0.14 ***
			(5.29)
gdp			0.20 ***
			(6.19)
gov			0.04 ***
			(3.83)
hcl			−1.68 ***
			(−4.40)
Control	No	No	Yes
Fixed Effects	No	Yes	Yes
$R^2$	0.41	0.48	0.75

Note: *** indicates significance at the 1% level, ** indicates significance at the 5% level, * indicates significance at the 10% level. Values in () represent t-values. The same applies to the table below.

4.2. Endogeneity Test

While the digital economy enhances the efficiency of green development, regions with strong green development also benefit from greater policy support, funding, and technology, which in turn promotes digital economic growth. Simultaneously, other relevant variables affecting green development efficiency may have been overlooked, leading to omitted variable bias and potential endogeneity issues. To address this, this paper adopts the instrumental variables approach, referencing the existing research [31], selecting the number of fixed telephones in each city of the Pearl River Delta region in 1984 as the core explanatory variable. First, the 1984 fixed telephone network constituted a core component of China’s early telecommunications infrastructure. Its deployment density directly influenced the subsequent penetration and adoption of digital technologies, exhibiting a high correlation with current digital economic development levels, thus satisfying the correlation requirement. Second, the layout of fixed-line telephones in 1984 was primarily driven by historical policies, such as the Ministry of Posts and Telecommunications’ national communications network plan and the deployment of communications facilities during the Third Front Construction period. This historical context does not influence current socio-economic development levels, thereby satisfying the exogenous condition. To mitigate potential bias arising from reverse causality, the Two-Stage Least Squares (2SLS) method was employed, with the results detailed in Table 7. The first-stage regression, shown in Column (1), confirms that the chosen instrument is highly relevant to the endogenous regressor, as the coefficient is statistically significant at the 1% level. Moreover, the underidentification test (Anderson canon. corr. LM) rejects the null hypothesis of “underidentification,” and the weak instrument test (Cragg-Donald Wald F) also rejects the null hypothesis of “weak instruments,” suggesting that the instrumental variables selected in this paper are valid. In Column (2), the second-stage estimation reveals that even after addressing endogeneity, the digital economy exerts a statistically significant positive influence on green development efficiency at the 1% level; notably, the magnitude of this impact is even more pronounced compared to the baseline model. In conclusion, utilizing the historical stock of fixed-line telephones as an exogenous instrument successfully addresses the potential endogeneity issues between the digital economy and green efficiency. Consequently, the conclusion that digital economic growth fosters urban green development remains statistically sound and credible.

Table 7. Endogeneity tests results.

Variable	(1) First Stage (DE)	(2) Second Stage (GIE)
IV1	0.00 ***
	(0.00)
de		2.37 ***
		(0.34)
Anderson canon. corr. LM	58.21
	[0.00]
Cragg-Donald Wald F	98.03
	{16.38}
Control	Yes	Yes
Fixed Effects	Yes	Yes
$R^2$	0.89	0.65

Note: *** indicates significance at the 1% level. Values in () represent t-values. Values inside [] are p-values; Values inside {} are critical values of the Stock–Yogo test at the 10% significance level.

4.3. Robustness Test

To improve the strength of the regression findings, this paper conducts a series of robustness tests using the following four approaches. The results of these tests are presented in Table 8. Firstly, control variables were added. In Column (1), openness to the outside world (ope) was added as a new control variable, while the others remained unchanged. The regression analysis was repeated, and the core explanatory variable remained significantly positive at the 1% level. Secondly, lagging was applied for one period. Column (2) regressed the core explanatory variable (de) with a one-period lag, yielding positive significance at the 1% level. Thirdly, the tails were trimmed. Following the methods of the existing literature [32], Column 3 trims the tails of the dependent variable (gie) and the core explanatory variable (de), and then regresses the trimmed sample. The coefficient for digital economic development levels passes the 1% significance level. Fourthly, shortening the sample period: Following the implementation of national policies in 2015, China’s digital economy experienced rapid growth. Therefore, Column (4) shortens the sample period to 2015–2023 and re-conducts the regression analysis. A positive and significant effect at the 1% level was still shown by the results. The findings of the robust test are in line with those of the benchmark regression, which confirms the reliability of the regression findings.

Table 8. Robustness tests results.

Variable	(1) Add Var.	(2) One-Period Lag	(3) Winsorization	(4) 2015–2023
de	1.48 ***			0.86 ***
	(5.49)			(5.04)
ope	−7.20
	(−0.92)
L.de		1.72 ***
		(6.58)
de_w			0.36 ***
			(2.72)
Control	Yes	Yes	Yes	Yes
Fixed Effects	Yes	Yes	Yes	Yes
$R^2$	0.75	0.77	0.70	0.74

Note: *** indicates significance at the 1% level. Values in () represent t-values.

4.4. Heterogeneity Analysis

4.4.1. Heterogeneity in the Degree of Digital Economic Development

Based on the heterogeneity in the level of digital economic development and using the results from the previous section, which measured the comprehensive digital economic development level of the Pearl River Delta region via the entropy method, the region was divided into cities with advanced digital economic development and those with moderate digital economic development, using the median as the cutoff point, for separate regression analysis. Cities with advanced digital economic development include Shenzhen, Zhuhai, Dongguan, and Guangzhou, while those with moderate digital economic development include Zhongshan, Foshan, Huizhou, Zhaoqing, and Jiangmen. As detailed in Table 9, Columns (1) and (2), the core explanatory variables in both columns pass the 1% significance level. However, in cities with moderate digital economic development, the promotional effect (coefficient of 3.45) is stronger than in cities with advanced digital economic development (coefficient of 2.08). This indicates that the digital economy exhibits a “latecomer advantage” in enhancing the efficiency of green development. A likely reason is that regions with less established green transition pathways face weaker traditional path dependencies, enabling them to more directly adopt mature digital-green technologies. Furthermore, cities with advanced digital economic development generate technological spillovers to these regions, thereby achieving greater marginal improvement benefits.

Table 9. Heterogeneity tests results.

Variable	(1) High Digital Economy Development	(2) Low Digital Economy Development	(3) High-Tier Cities	(4) Low-Tier Cities
de	2.08 ***	3.45 ***	2.08 **	0.24 ***
	(5.08)	(2.68)	(2.25)	(2.85)
Control	Yes	Yes	Yes	Yes
Fixed Effects	Yes	Yes	Yes	Yes
$R^2$	0.86	0.70	0.86	0.75

Note: *** indicates significance at the 1% level, ** indicates significance at the 5% level. Values in () represent t-values.

4.4.2. Heterogeneity Based on City Tier

According to the “Guangdong Territorial Spatial Plan (2021–2035)” and the State Council’s approval of city master plans, Pearl River Delta cities are clearly classified as megacities, super-large cities, and ordinary cities. This paper categorizes megacities and super-large cities as high-tier cities and the rest as general-tier cities for separate regression analysis. High-tier cities include Guangzhou, Shenzhen, Foshan, and Dongguan. General-tier cities include Zhuhai, Huizhou, Zhongshan, Jiangmen, and Zhaoqing. The results are shown in Columns (3) and (4) of Table 9. The digital economy significantly enhances the green development efficiency of both high-tier and general-tier cities at the 1% significance level. However, the driving effect of the digital economy on green development in high-tier cities (coefficient of 2.79) is stronger than that in general-tier cities (coefficient of 0.24). This reflects the “Matthew Effect” [33], indicating that higher-tier cities, leveraging their more robust innovation ecosystems, superior industrial structures, and stronger resource aggregation capabilities, can efficiently convert the dividends of the digital economy into tangible outcomes for green development. Conversely, general-tier cities may struggle to fully unlock the green potential of digital technologies due to relative deficiencies in digital infrastructure, high-end talent, and supportive policies, resulting in weak synergies between the two. This divergence signals the risk of widening the “digital-green divide.”

4.5. Mediation Effect Regression Test

To elucidate the underlying mechanisms through which the digital economy influences green development efficiency, a mediation analysis framework was established. The empirical findings are detailed in Table 10. Column (1) examines the impact of the digital economy on green technology innovation. The results passed the 1% significance level, indicating that the digital economy positively promotes green technology innovation. Column (2) incorporates green technology innovation in order to examine the joint impact of the digital economy and green technology innovation on green development efficiency. This result also passes the 1% significance level, and the regression coefficient for the digital economy (1.37) is smaller than that for the benchmark regression model (1.76, as shown in Column 3 of Table 6). This suggests that green technology innovation partially mediates the effect, with a mediation effect of 0.39. Column (3) examines the impact of the digital economy on the upgrading of industrial structures. The results surpass the 1% significance level, suggesting that the digital economy promotes industrial structure upgrading. Column (4) incorporates industrial structure upgrading to examine the joint impact of the digital economy and industrial structure upgrading on green development efficiency. These results also passed the 1% significance level, and the regression coefficient for the digital economy (1.36) was smaller than that for the benchmark regression model (1.76, as shown in Column 3 of Table 6). This suggests that upgrading the industrial structure has a partial mediating effect with a mediation effect of 0.40. Collectively, these results substantiate that the digital economy fosters green development efficiency via two distinct channels: stimulating green technological innovation and facilitating industrial structure optimization. Consequently, Hypotheses 2 and 3 receive empirical support.

Table 10. Mediation effect test results.

Variable	(1) GTI	(2) GIE	(3) INS	(4) GIE
de	1.36 ***	1.37 ***	0.52 ***	1.36 ***
	(2.67)	(5.19)	(5.88)	(5.71)
Control	Yes	Yes	Yes	Yes
Fixed Effects	Yes	Yes	Yes	Yes
$R^2$	0.99	0.75	0.97	0.75

Note: *** indicates significance at the 1% level. Values in () represent t-values.

5. Discussion

5.1. Interpretation of the Direct Influence of the Digital Economy over Green Development Efficiency

The results of baseline regression, endogeneity treatment, and robustness tests in this paper consistently demonstrate that the development level of the digital economy has a statistically significant and robust positive driving effect on the green development efficiency of the Pearl River Delta region, confirming Hypothesis 1. This conclusion theoretically echoes the classic discourse on the digital economy acting as “engines of growth” that drive economic transformation through permeation and restructuring [34]. In reality, it highly aligns with the regional characteristics of the Pearl River Delta as a national pioneer in the digital economy and a main front for green transformation in manufacturing. Its direct mechanism of action is primarily manifested in two key dimensions: factor allocation optimization and production paradigm innovation.

From the perspective of factor allocation, data, as a new key factor of production, with its characteristics of high mobility and strong permeability help optimize resource allocation within the Pearl River Delta urban agglomeration, alleviating constraints imposed by regional development imbalances. In the baseline regression, both the level of regional economic development and the degree of government intervention showed significant positive moderating effects, confirming that a solid material foundation and effective policy design can significantly enhance the green empowerment effect of the digital economy. However, the significant negative relationship shown by the human capital variable reveals a “structural contradiction” that demands urgent attention: the current stock or knowledge structure of human capital in the region may not yet align with the skill requirements of the green digital transformation. This implies that future policies need to focus on promoting reforms in the education and training systems to cultivate interdisciplinary talent with both digital skills and green knowledge, thereby translating human capital potential into green development momentum.

From the perspective of the production paradigm, the integrated application of digital technologies such as the Internet of Things, big data, and artificial intelligence is driving a profound transformation of traditional production modes towards intelligence, precision, and servitization. By enabling real-time monitoring and intelligent regulation embedded throughout the production process, digital technologies can directly improve energy and resource efficiency, reducing pollution emissions at the source. Their technological inclusivity also significantly lowers the barriers and costs of green upgrading for small and medium-sized enterprises (SMEs), especially the densely distributed manufacturing SMEs in the Pearl River Delta. Furthermore, the positive effect brought by population agglomeration reflects the positive role of economies of scale and knowledge spillovers in green innovation, but also suggests that urban development needs to utilize digital economic tools to optimize spatial layout and industrial synergy, preventing the congestion costs that may arise from excessive agglomeration.

In summary, this research confirms that the digital economy is a core driving force for enhancing green development efficiency in the Pearl River Delta. It does not function in isolation but is deeply embedded in the regional economic system through the dual pathways of “optimizing factor allocation” and “innovating the production paradigm.” This process is simultaneously shaped and constrained by multiple factors, including the macroeconomic foundation, government governance actions, spatial distribution of the population, and the structure of human capital. This research offers a comprehensive and structural approach to understanding the green effects of the digital economy.

5.2. Theoretical Extension and Policy Implications of Regional Heterogeneity

The findings of this study offer a new theoretical perspective for understanding the complex mechanisms of green transformation in the digital era. First, the “latecomer advantage” (with stronger promotional effects) observed in regions with moderate digital economic development corroborates the theory that latecomers can achieve rapid catch-up through technological imitation and learning [35]. However, the relative weakening of this effect in advanced digital economic development regions may suggest path dependence arising from “technology–institutional” co-evolution. That is, without sustained institutional guidance, technological progress may become locked into existing pathways, hindering systemic green transformation [36]. Second, the “Matthew Effect” based on urban hierarchy profoundly reveals the critical role of regional “innovation ecosystems.” The powerful driving force of high-tier cities stems from their robust innovation networks and factor markets, which constitute the “social capacity” for efficiently transforming digital technologies [37]. The relative lag of general-tier cities reflects the core tenet of the “appropriate technology” theory: technological effectiveness is highly dependent on local supporting conditions and absorption capacity [38]. Therefore, the synergy between the digital economy and green development is fundamentally a conditional process highly dependent on localized “digital-green” ecosystems.

Based on this theoretical understanding, policy formulation should shift toward building a precise governance system characterized by “tiered coordination and city-specific approaches.” First, for high-tier cities (such as Shenzhen and Guangzhou), implement a “leadership-radiation” strategy, encouraging them to focus on breakthrough green digital technology R&D. Through establishing cross-regional innovation alliances, institutionalize the diffusion of technology, knowledge, and models to surrounding areas. Second, for general-tier cities, implement an “adaptation-leap” strategy centered on cultivating localized absorption and conversion capabilities. Invest in “appropriate” digital-green technologies aligned with local industries and strengthen targeted skills training to help these cities integrate into regional innovation chains. Third, establish a collaborative governance mechanism at the provincial level. This involves promoting the integration of factor markets such as data and green certificates, and exploring the design of horizontal ecological compensation mechanisms. These measures aim to prevent the solidification of the “digital-green divide,” ultimately fostering a regional development pattern characterized by “cooperative advancement and shared benefits.”

5.3. Pathway Analysis of the Impact Mechanism

The digital economy enhances the efficiency of green development in two key ways, as revealed by this paper’s use of a mediation effect model: through green technological innovation and industrial structure upgrading. This finding provides both micro-level and macro-level explanations for understanding the green enabling role of the digital economy.

From a micro-level perspective, the digital economy acts as a catalyst for green technological innovation. Advanced digital infrastructures, including big data analytics and cloud computing, effectively mitigate the uncertainty and trial-and-error costs inherent in green R&D processes. By compressing innovation cycles and reducing payback periods, these technologies trigger an “innovation compensation” effect within firms, offsetting the costs of green transition. Empirical evidence from the Pearl River Delta (PRD) region corroborates this: the digital economy indirectly yet significantly elevates regional green efficiency by cultivating a fertile ecosystem for green innovation, a finding consistent with the existing domestic literature.

From a macro-level perspective, the mediating role of industrial structure upgrading underscores the digital economy’s systemic capacity to reconfigure regional economic architectures [26]. Beyond merely spawning low-carbon business models (e.g., platform and sharing economies) that drive economic sophistication, the digital economy fundamentally enhances information transparency and coordination efficiency across industrial chains. This reduction in transaction costs enables enterprises to integrate deeper into global value chains (GVCs) and climb toward higher value-added segments, effectively phasing out energy-intensive, low-value production. Consequently, this structural transformation fosters a “digital technology–industrial upgrading–green transformation” virtuous cycle [21], facilitating a shift toward intensive, green development paradigms.

5.4. Research Limitations and Prospective Research Directions

While this research offers substantial empirical value, it is essential to recognize its inherent constraints, which simultaneously point toward avenues for subsequent scholarly inquiry. A primary concern pertains to data resolution: the assessment of green technological innovation and industrial structure optimization is currently restricted by the accessibility of granular data. To achieve a more precise quantification, future research should consider leveraging enterprise-level patent data and technology transformation indicators. Such micro-level data would allow for a more nuanced characterization of the quality and efficacy of green technological advancements. Second, concerning model specification: This study primarily examines the linear nexus between the digital economy and green development within the Pearl River Delta (PRD). However, the relationship may exhibit complex nonlinear dynamics, such as threshold effects or spatial spillovers, which were not fully explored. Future investigations, particularly in regions with varying digital maturity, should test for these potential nonlinearities. Third, regarding geographical scope: The findings are derived specifically from the PRD, a region with an advanced digital economic development. Consequently, the external validity of these conclusions to less developed regions requires further verification. Future studies should conduct cross-regional comparative analyses to uncover potential regional heterogeneity in how the digital economy influences green development. Finally, given the rapid evolution of the digital economy, its dynamic interplay with green development necessitates longitudinal tracking. Long-term panel data analysis would provide deeper insights into the evolving mechanisms of this relationship over time.

6. Conclusions and Recommendations

6.1. Conclusions

China is currently at a critical stage in its efforts to advance high-quality development alongside its dual carbon goals. Digital technologies, such as the Industrial Internet of Things (IIoT) and artificial intelligence (AI), are accelerating their penetration into industrial sectors. The digital economy is emerging as the core driver of green development efficiency. This paper conducts an empirical analysis using relevant data from the Pearl River Delta region between 2011 and 2023. The results show that the digital economy exerts a significant positive effect on green development efficiency as a new engine driving social development. This finding remains robust after stability tests were conducted using methods such as adding control variables, applying a one-period lag treatment, tail trimming, and shortening the sample period. However, a study of the differences between cities shows that those with moderate digital economic development have higher marginal benefits in terms of how efficiently they use green technology. This suggests that there is something called a “latecomer advantage”, which means that cities that start later can still achieve success. Compared to general-tier cities, high-tier cities with larger populations and more developed digital infrastructure demonstrate a stronger capacity to enhance green development efficiency through the digital economy. Furthermore, the mediation effect model examined how the digital economy enhances green development efficiency via two pathways: boosting green technological innovation and accelerating industrial restructuring.

6.2. Practical Policy Suggestions

Based on empirical analysis findings, to further enhance the digital economy’s driving role in the Pearl River Delta’s green development, this paper proposes the following recommendations:

First, implement differentiated institutional supply at the systemic level to strengthen enabling transmission pathways and amplify the effects of leading cities. Improve incentive mechanisms for the integrated innovation of digital and green technologies. For cities with strong digital foundations and large-scale digital economies (such as Guangzhou, Shenzhen, Foshan, and Dongguan), introduce specialized assessment indicators for “digital-green innovation leadership.” For cities with weaker digital foundations, prioritize evaluating the effectiveness of technology application implementation. Given the current state of cities with weak digital infrastructures, it is important to introduce differentiated policies for integrated development in order to avoid simply replicating the institutional models of cities with advanced digital economies. Refine intellectual property protection and commercialization systems for green technology innovation. Leveraging the Guangzhou–Shenzhen–Hong Kong–Macao Science and Technology Innovation Corridor, establish an institutionalized process: “digital computing power support → green core technology R&D → patent commercialization → enterprise application.” This breaks down technology transfer barriers, ensuring that the digital economy efficiently empowers green technological innovation. Create cross-city digital-green innovation coordination mechanisms, clarifying innovation roles between core cities (Guangzhou, Shenzhen) and peripheral cities to avoid redundant R&D and resource waste. Introduce specialized regulations for the digital economy to drive green industrial upgrading, specifying timelines and energy consumption standards for digital transformation in high-energy-consuming sectors (e.g., traditional manufacturing in the Pearl River Delta). Link the effectiveness of digital-enabled low-carbon industrial transformation to corporate environmental credit ratings, energy consumption indicators, and credit access, compelling traditional industries to transition toward greener and digital operations while accelerating industrial restructuring and upgrading.

Second, optimize resource allocation from a cost–benefit perspective to reduce transmission costs and amplify the marginal benefits of leading cities. Establish a cost-sharing system for green technology innovation, primarily covering the high upfront R&D investments of enterprises through fiscal subsidies and green credit. Reduce the cost of green technology conversion by leveraging digital technology to build a Pearl River Delta green technology transfer platform. This platform will facilitate precise matching between research institutions and enterprises, cutting out intermediaries and lowering the costs of technology introduction and implementation. Promote “lightweight, low-cost” digital-green transformation solutions for industries to prevent enterprises from undertaking blind full-process overhauls. Optimize supply chain and industrial chain layouts to reduce logistics and energy consumption costs in industrial collaboration, thereby enhancing the green benefits of industrial upgrading. Prioritize fiscal, financial, and digital resources toward cities with stronger digital economies and larger scales, focusing support on their green technology R&D and high-end industrial digital transformation. Concurrently, these cities should bear a greater share of the construction costs for regional public digital-green platforms. Encourage leading cities to export digital-green technologies and their experience of transformation to peripheral cities, sharing the green benefits derived from industrial upgrading and technological innovation. Guide peripheral cities to proactively absorb technological and industrial spillovers, thereby reducing their own transformation costs. Cities with weaker digital economies should promote low-cost, adaptable green technologies, optimize resource allocation and green governance models, and focus particularly on lowering transformation costs for small and medium-sized enterprises. The green marginal advantages of the digital economy will be fully leveraged by this, and the overall efficiency of green development across the Pearl River Delta region will ultimately be enhanced.