The Impact of Enterprise Digitalization on Green Total Factor Productivity: Evidence from Chinese Provinces

Abstract

Digitalization optimizes production processes by promoting information sharing and collaborative operations, thereby enhancing resource allocation efficiency and significantly improving production efficiency. Based on a review of the literature on enterprise digital transformation and regional GTFP, this study employed a panel data model to analyze the impact of enterprise digitalization on regional GTFP from a microeconomic perspective. After conducting robustness and endogeneity tests, we further examined the mechanisms through which enterprise digitalization influences regional GTFP and explored the heterogeneity of these effects. Using panel data from China’s provinces from 2007 to 2022, we arrived at the following conclusions: (1) Despite the tendency for enterprises to make contingent decisions in the digitalization process, enterprise digitalization fundamentally has a significant positive impact on GTFP, as confirmed by robustness and endogeneity tests. (2) The impact of enterprise digitalization on GTFP operates through a moderating effect, where green patents, environmental regulations, and an advanced industrial structure positively reinforce this effect. (3) The effect of enterprise digitalization on GTFP exhibits heterogeneity, with the most pronounced impact being in western China followed by the eastern region, while the effect in the central region is not statistically significant.

1. Introduction

With the increasing challenges posed by global climate change and environmental pollution, green development has emerged as a crucial path for China to achieve high-quality economic growth. Green total factor productivity (GTFP), as a key metric for evaluating the progress of green economic development, indicates enhancements in both resource utilization efficiency and environmental productivity. Nevertheless, China is confronted with the twin challenges of significant environmental pollution and low resource efficiency during its development processes [1]. A critical issue in China’s economic transformation is identifying how to facilitate green and high-quality development through technological innovation and structural optimization. In this context, enterprise digital transformation—characterized by intelligent systems, data-driven decision-making, and integrated solutions—provides an innovative approach to improving GTFP. By streamlining resource allocation and reducing energy consumption, enterprise digital transformation enhances production efficiency while minimizing pollution emissions through intelligent technologies, thereby promoting green industrial upgrading [2,3].

Nevertheless, substantial regional disparities persist in China regarding resource utilization efficiency, environmental governance, and industrial structures [4], resulting in varied impacts of enterprise digital transformation on GTFP. For example, provinces characterized by lower digital transformation levels and a higher dependence on traditional industries may struggle with resource wastage and environmental degradation, whereas regions with advanced technological capabilities may more effectively utilize digital tools to achieve resource conservation and environmental benefits. Additionally, differences in regional policy support, market development, and technological absorption capacity could further influence the dissemination and effectiveness of enterprise digital transformation initiatives. Such disparities not only impede improvements in GTFP but may also hinder China’s ability to achieve its “dual carbon” goals (carbon peak and carbon neutrality) and broader objectives for sustainable development. Consequently, examining the specific pathways and mechanisms by which enterprise digital transformation influences GTFP under varying regional conditions is of significant practical importance.

In recent years, a growing body of research has focused on understanding the factors that influence GTFP. GTFP is shaped by a range of determinants [5,6,7]. For example, Li et al. analyzed data from Chinese provinces between 2009 and 2017 and undertook a comprehensive study of the factors affecting GTFP. Their study reveals that elements such as internet development, human capital, urbanization, energy efficiency, and external dependence play a positive role in enhancing China’s GTFP, whereas government intervention appears to have a detrimental effect on its improvement [8].

Some scholars have investigated the relationship between the digital economy and GTFP, finding that the digital economy contributes to enhanced GTFP, with environmental expenditures exerting a positive influence on this relationship. Moreover, research has identified significant differences in how the digital economy influences GTFP across cities with varying population sizes and geographical locations [9,10].

In the realm of green finance, research utilizing panel data from 30 Chinese provinces has examined how green finance influences GTFP, uncovering that advancements in green finance notably enhance green productivity levels [11]. Furthermore, some researchers have concentrated on the impact of national policies on GTFP [12,13]. For instance, employing a difference-in-differences (DID) model in conjunction with a stepwise regression approach, scholars have assessed both the static and dynamic effects of carbon trading policies on GTFP, along with various mediation effects, discovering that carbon trading policies significantly bolster regional GTFP in China. Other studies have applied the Difference-in-Differences with Sample Selection (DIDS) method to investigate how green credit policies affect total factor productivity, identifying a substantial positive correlation between green credit and corporate total factor productivity [14,15].

In addition to analyzing the determinants of GTFP, some studies have further explored sector-specific GTFP [16,17,18]. For instance, using panel data from 277 Chinese cities spanning the years 2013 to 2019, researchers have investigated how digital economy empowerment influences forestry GTFP. Their results reveal a notable inverted U-shaped relationship between the digital economy and forestry GTFP, suggesting that while digital transformation initially boosts GTFP, it eventually hinders it. Furthermore, the relationship between the digital economy and urban green innovation follows a positive U-shaped pattern, first stifling and then fostering green innovation, which in turn indirectly impacts forestry GTFP [19].

Additionally, empirical studies employing panel data models have analyzed the driving factors behind China’s agricultural GTFP from a carbon emission perspective. These studies reveal that the primary determinants of agricultural GTFP in China are agricultural factor endowments and regional characteristics, with notable regional variations in their impact [20].

Several scholars have explored the impact of digital transformation on GTFP, emphasizing the various mechanisms underlying this relationship [21,22]. At the industry level, studies have mainly concentrated on digital transformation within manufacturing, industrial sectors, and agriculture [23,24,25]. For instance, a study using data from publicly listed firms between 2015 and 2019 found that corporate digital transformation significantly enhances GTFP. Mediation effect tests further indicate that advanced digital technologies improve GTFP by optimizing firms’ internal financial conditions [26].

Another strand of research has examined the relationship between industrial digital transformation and corporate environmental performance [27,28,29]. The findings indicate that the digital transformation of manufacturing firms results in notable improvements in environmental performance, with structural and technological effects acting as the primary transmission channels. Specifically, structural effects play a crucial role in driving the positive environmental impact of information and communication technology penetration. Additionally, empirical studies using panel data from 31 Chinese provinces between 2011 and 2022 have investigated the impact of digital transformation on agricultural production inputs and outputs. These studies conclude that digital empowerment plays a significant role in enhancing agricultural GTFP, primarily by improving technical efficiency. Further analysis reveals that digital transformation reduces land and labor inputs while increasing the expected output and minimizing any undesired output, thereby boosting agricultural GTFP [30].

At the societal level, some scholars have examined the influence of digital transformation on urban GTFP, demonstrating a significant and positive relationship between the two [31]. Mechanism analyses reveal that green technological innovation and industrial structural transformation serve as the two primary pathways through which digital transformation enhances GTFP. Furthermore, studies investigating the role of social digital transformation in China’s GTFP have discovered that digital economy development in the social sector positively influences GTFP growth and generates spatial spillover effects [32,33,34]. Additionally, scholars have analyzed the impact of global value chain (GVC) digital transformation on green growth by calculating the digital economy index of 279 Chinese cities from 2011 to 2019. Their findings reveal that a region’s position and backward participation in the GVC negatively impact green productivity, while forward participation has no significant effect. However, as the level of digital transformation increases, the impact of GVC positioning on green productivity transitions from negative to positive [35]. Related studies have also investigated whether digital transformation strengthens a region’s position within the GVC [36,37]. Furthermore, under carbon emission constraints, research has investigated the role of digital investment in GTFP, finding that digital investment significantly enhances GTFP. Mechanism analyses suggest that this effect operates through improvements in energy efficiency and labor productivity [12].

A comprehensive review of the existing literature indicates that most studies examining the determinants of GTFP focus on factors such as the digital economy, green finance, and urbanization. These macro-scale factors have been widely explored, with scholars examining how the digital economy reshapes production models to influence GTFP and how green finance directs funds toward environmentally friendly and productive activities. However, research specifically addressing the impact of enterprise-level digital transformation on GTFP remains limited. Furthermore, the majority of existing studies adopt macro perspectives, conducting analyses at the city or province level to examine how regional digital transformation trends affect GTFP. In contrast, fewer studies have focused on firm-level mechanisms. Gaining insight into how individual enterprises’ digital transformation strategies enhance or hinder GTFP could provide valuable perspectives. Additionally, the literature is marked by inconsistent conclusions regarding the mechanisms through which digital transformation influences GTFP. Some studies suggest that digital technologies improve GTFP by optimizing resource allocation, while others argue that the initial investment required for digital transformation may impose short-term costs that hinder productivity growth.

Based on the existing literature, the possible marginal contributions of this paper’s research are as follows: (1) This paper explores the impact of digital transformation on GTFP from the perspective of microeconomic mechanisms. (2) It is believed that the moderating effect is the key mechanism through which the digital transformation of enterprises affects GTFP. The structural arrangement of this paper is shown in Figure 1.

2. Research Design

2.1. Research Hypothesis

Enterprise digital transformation has a significant impact on regional GTFP. From a theoretical perspective, this impact is realized through mechanisms such as technological innovation, regional synergy, and knowledge spillovers.

From the perspective of technological innovation, enterprises utilize digital technologies, such as big data, artificial intelligence, and the Internet of Things, to improve the efficiency of research and development (R&D) activities, thereby accelerating technological innovation and facilitating its implementation across various scenarios [38]. With proactive government guidance, enterprises are more likely to concentrate on the R&D and application of green technologies, such as clean energy technologies and energy-saving emission reduction technologies [39]. These applications directly promote the widespread adoption of environmentally friendly production methods. In this context, government interventions that guide firms in applying digital and green innovations can substantially enhance regional GTFP. However, investing in technological innovation also entails short-term cost increases for firms. Therefore, when enterprises make strategic decisions regarding green technology innovation, government funding and guidance should be provided as needed to facilitate improvements in GTFP.

From the perspective of regional synergy, enterprises are directly embedded within industrial chains, forming relatively well-developed network structures. When making strategic decisions, firms must consider their upstream and downstream industrial linkages [40,41,42]. Consequently, digital transformation, particularly for leading firms within a supply chain, fosters greater synergy within the industrial network [43]. This synergy, achieved through enhanced information sharing and resource integration, ultimately improves regional green production efficiency. For instance, enterprises within a region can utilize digital platforms to share environmental protection technologies and resources, collectively enhancing GTFP. Additionally, digital technologies promote collaborative innovation among enterprises, research institutions, and government entities within a region. Such inter-organizational cooperation encourages the development and application of green technologies, thereby further enhancing regional GTFP.

From the perspective of knowledge spillovers, enterprise digital transformation facilitates knowledge sharing and diffusion. Through digital platforms, firms can exchange information on green production technologies and best practices, generating spillover effects that enhance regional GTFP. Furthermore, the digital and green transformation experiences of leading firms can quickly be disseminated through digital platforms, enabling other enterprises to improve their own green production efficiency via learning effects. Thus, enterprise digital transformation promotes the enhancement of regional GTFP via knowledge spillovers. Based on this, we propose the following hypotheses:

Hypothesis 1.

Enterprise digital transformation has a significantly positive impact on green total factor productivity.

In order to more clearly explore the impact of enterprise digital transformation on GTFP, we also need to conduct an in-depth analysis of whether there is a certain positive moderating effect in the process of enterprise digital transformation enhancing GTFP.

Green patents serve as a crucial intermediary in how enterprise digital transformation enhances GTFP. Digital technologies have facilitated the creation and application of green patents by lowering the research and development costs of green technologies, accelerating knowledge dissemination, and optimizing resource allocation. For instance, through the use of artificial intelligence and big data analysis technologies, enterprises can more efficiently identify potential innovation directions for green technologies, thereby reducing the research and development cycle and minimizing resource waste. Additionally, digital platforms have enabled the collaborative research and development as well as the knowledge sharing of green technologies, allowing enterprises to more swiftly transform green patents into tangible productivity, further enhancing GTFP.

The implementation of environmental regulations provides essential institutional support and compliance incentives for enterprise digital transformation. Digital technologies have enhanced the efficiency of regulation implementation by reducing the costs for enterprises to meet environmental standards. For example, enterprises can utilize the Internet of Things and smart sensors to monitor resource consumption and pollution emissions in real time during the production process, ensuring compliance with environmental regulations. At the same time, digital systems can automatically generate environmental reports and compliance records, thereby lowering the compliance costs for enterprises. Moreover, the strict enforcement of environmental regulations also serves as a driving force for enterprises’ green transformation, prompting them to more actively adopt digital technologies to achieve green production and further enhance GTFP.

An advanced industrial structure serves as another critical moderating mechanism in the relationship between enterprise digital transformation and GTFP. Digital technologies have facilitated the evolution of the industrial structure toward high-value-added and green directions by enhancing resource allocation and strengthening the synergy within the industrial chain. For instance, the Industrial Internet and digital twin technologies enable enterprises to better coordinate upstream and downstream resources, thereby reducing transaction costs and minimizing waste. Additionally, digital technologies have promoted the establishment of a green industrial chain. For example, intelligent manufacturing and resource recycling technologies have improved the efficiency of resource utilization and environmental performance. This optimization of the industrial structure not only enhances the production efficiency of individual enterprises but also, through scale effects and spillover effects, further elevates the GTFP of the entire industrial chain. Based on this analysis, we propose the following hypothesis:

Hypothesis 2.

Enterprise digital transformation affects GTFP via a moderating mechanism. Green patents, environmental regulations, and high-level industrial structures positively promote this moderating effect.

Whether the impact of enterprise digital transformation on GTFP shows heterogeneity due to different regions also needs to be considered in this study, because it is directly related to the effectiveness and precision of policy formulation and implementation.

The western region of China may experience the most significant impact of enterprise digital transformation on GTFP. In recent years, this region has benefited from strong policy support, including initiatives like the “Western Development” strategy and the “Belt and Road Initiative”. These policies have provided substantial financial and technical backing for the region’s digital transformation. Additionally, while the western region is endowed with abundant resources, it also faces significant environmental challenges, prompting enterprises to actively embrace digital technologies to achieve greener production processes. Furthermore, the region has effectively leveraged its unique position within the social network and has actively promoted the development of digital technologies, supported by national policies [44,45]. For instance, digital technologies have enabled enterprises to achieve efficient resource utilization and significantly reduce pollution emissions, thereby mitigating environmental negative externalities while enhancing production efficiency. Moreover, the industrial structure in the western region is undergoing transformation, with a growing emphasis on the development of green industries. This creates a positive interaction with the application of digital technologies, further boosting GTFP. Notably, the western region’s relatively low starting point for economic development, particularly in resource-intensive industries, presents opportunities for improvement through the adoption of digital technologies, as these industries often face issues of inefficiency and resource waste.

Given the economic differences across regions, the impact of enterprise digital transformation on GTFP may vary significantly. In the eastern region, which is the most economically advanced in China, enterprises already have a robust foundation for digital transformation, face intense market competition, and encounter high environmental awareness among consumers. These factors have driven the widespread adoption of digital technologies, particularly in enhancing resource utilization efficiency and reducing environmental pollution. However, since digital transformation in the eastern region is relatively mature and the marginal benefits of further technological advancements are limited, the potential to improve GTFP may be constrained, with growth rates slowing compared to the western region. In contrast, the central region, whose economic development falls between that of the east and the west, is primarily characterized by manufacturing and traditional industries. These industries have seen relatively low levels of digital transformation investment and effectiveness, and the central region receives weaker policy support compared to the western region. As a result, the impact of digital transformation on GTFP in the central region may also be limited. Based on this analysis, this paper proposes the following hypothesis:

Hypothesis 3.

The impact of enterprise digital transformation on GTFP shows regional heterogeneity, and the impact on the western region of China is the most significant.

2.2. Model Variable

Based on the theoretical analysis, this study constructs a panel dataset encompassing 30 provinces in China from 2007 to 2022. Considering the significant regional development disparities and the varying economic contexts across different years, this study employed a two-way fixed effects model to analyze the impact of enterprise digital transformation on GTFP. A Hausman test was conducted prior to selecting this model to determine the appropriateness of the fixed effects specification.

The test results support the adoption of a fixed effects model (see Appendix A for detailed results). The specific formulation of the fixed effects model is as follows:

$${\mathrm{g}\mathrm{t}\mathrm{f}\mathrm{p}}_{\mathrm{i},\mathrm{j}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{d}\mathrm{i}\mathrm{g}\mathrm{t}\mathrm{a}\mathrm{l}}_{\mathrm{i},\mathrm{t}}+\sum _{\mathrm{j}=1}^{\mathrm{n}}{\mathsf{\delta }}_{\mathrm{j}}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{j},\mathrm{i},\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{t}}+{\mathsf{\sigma }}_{\mathrm{i}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(1)

In Equation (1), ${\mathrm{g}\mathrm{t}\mathrm{f}\mathrm{p}}_{\mathrm{i},\mathrm{j}}$ represents the green total factor productivity of province i in year t, ${\mathrm{d}\mathrm{i}\mathrm{g}\mathrm{t}\mathrm{a}\mathrm{l}}_{\mathrm{i},\mathrm{t}}$ denotes the level of enterprise digital transformation in province i in year t, ${\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathsf{\delta }}_{\mathrm{j}}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{j},\mathrm{i},\mathrm{t}}$ is a vector of control variables. ${\mathsf{\mu }}_{\mathrm{t}}$ and ${\mathsf{\sigma }}_{\mathrm{i}}$ represent province-specific and time-fixed effects, respectively, while ${\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$ is the random error term.

The variables in Model (1) include the dependent variable, independent variable, and control variables. The measurement methods and economic significance of each variable are explained as follows.

The dependent variable in this study is regional green total factor productivity (GTFP), which is measured using the Sequential Malmquist–Luenberger (SML) index. The SML index is employed to assess the change in GTFP from period $\mathrm{t}$ to $\mathrm{t}+1$. Its fundamental form is expressed as follows:

$${\mathrm{S}\mathrm{M}\mathrm{L}}^{\mathrm{t},\mathrm{t}+1}=\left[{\displaystyle \frac{1+{\stackrel{⃑}{\mathrm{D}}}_0^{\mathrm{t}}({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}},{\mathrm{b}}^{\mathrm{t}};{\mathrm{g}}_{\mathrm{y}};{\mathrm{g}}_{\mathrm{b}})}{1+{\stackrel{⃑}{\mathrm{D}}}_0^{\mathrm{t}+1}({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1},{\mathrm{b}}^{\mathrm{t}+1};{\mathrm{g}}_{\mathrm{y}};{\mathrm{g}}_{\mathrm{b}})}}\times {\displaystyle \frac{1+{\stackrel{⃑}{\mathrm{D}}}_0^{\mathrm{t}+1}({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}},{\mathrm{b}}^{\mathrm{t}};{\mathrm{g}}_{\mathrm{y}};{\mathrm{g}}_{\mathrm{b}})}{1+{\stackrel{⃑}{\mathrm{D}}}_0^{\mathrm{t}}({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1},{\mathrm{b}}^{\mathrm{t}+1};{\mathrm{g}}_{\mathrm{y}};{\mathrm{g}}_{\mathrm{b}})}}\right]$$

(2)

In Equation (2), ${\stackrel{⃑}{\mathrm{D}}}_0^{\mathrm{t}}({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}},{\mathrm{b}}^{\mathrm{t}};{\mathrm{g}}_{\mathrm{y}};{\mathrm{g}}_{\mathrm{b}})$ represents the directional distance function at period $\mathrm{t}; {\stackrel{⃑}{\mathrm{D}}}_0^{\mathrm{t}+1}({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1},{\mathrm{b}}^{\mathrm{t}+1};{\mathrm{g}}_{\mathrm{y}};{\mathrm{g}}_{\mathrm{b}})$ represents the directional distance function at period $\mathrm{t}+1; {\stackrel{⃑}{\mathrm{D}}}_0^{\mathrm{t}+1}({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}},{\mathrm{b}}^{\mathrm{t}};{\mathrm{g}}_{\mathrm{y}};{\mathrm{g}}_{\mathrm{b}})$ represents the directional distance function under the technological conditions of period $\mathrm{t}+1$, using the input–output data of period $\mathrm{t}; \mathrm{a}\mathrm{n}\mathrm{d} {\stackrel{⃑}{\mathrm{D}}}_0^{\mathrm{t}}({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1},{\mathrm{b}}^{\mathrm{t}+1};{\mathrm{g}}_{\mathrm{y}};{\mathrm{g}}_{\mathrm{b}})$ represents the directional distance function under the technological conditions of period $\mathrm{t}$ using the input–output data of period $\mathrm{t}+1$.

In measuring GTFP, both input and output indicators are taken into account. The input factors encompass labor, capital, and energy consumption, which are measured by the number of employed persons, the fixed capital stock (calculated at 2000 constant prices), and energy consumption in each province, respectively. The output factors comprise desirable outputs and undesirable outputs. The desirable output is quantified by gross domestic product (GDP), while the undesirable outputs consist of carbon dioxide emissions and industrial “three wastes” emissions. The data utilized primarily originate from the China Carbon Accounting Database and the China Environmental Statistics Yearbook. The input–output table is presented in

Table 1. Input–output table.

	Indicator	Indicator Description
Input Factors	Labor	Measures the labor input by the number of employed people
	Capital	Measures the capital input by the fixed capital stock (calculated at constant prices in the year 2000)
	Energy Consumption	Measures the energy input by the energy consumption of each province
Output Factors	GDP (Desirable Output)	Measures the desired output by the gross domestic product
	Carbon Dioxide Emissions (Undesirable Outputs)	Measures the undesired output by the carbon dioxide emissions
	Industrial “Three Wastes Emissions” (Undesirable Outputs)	Measures the undesired output by the emissions of industrial wastewater, waste gas, and industrial solid waste

.

The independent variable in this study represents the level of enterprise digital transformation across provinces, which can be indicated by the average digital transformation level of publicly listed companies in each region. Specifically, the digital transformation level of these firms was assessed by the frequency of digital transformation-related keywords found in the “Management Discussion and Analysis” section of their annual reports, divided by the total word count. To facilitate computation, the result was multiplied by 10,000. The digital transformation-related keywords encompassed 78 terms across five dimensions, including big data technology, block chain technology, artificial intelligence technology, digital technology applications, and cloud computing technology (see Appendix B for details).

Given the research objectives, factors that influence regional total factor productivity (TFP) extend beyond enterprise digital transformation and include a variety of additional determinants. Therefore, a set of control variables was incorporated into the panel data model. Based on existing studies [9,46,47,48] and in line with the study’s specific needs, eight control variables were selected, with detailed definitions presented in

Table 2. Definition and measurement of main variables.

Variable Name	Symbol	Measurement Method
Economic scale	lngdp	Natural logarithm of GDP (unit: one thousand RMB 10,000)
Economic development level	lnpgdp	Natural logarithm of GDP per capita (Unit: RMB 1)
Government intervention	intervention	Ratio of general public budget expenditure to GDP
Industrialization level	industrial	Ratio of secondary industry GDP to total GDP
Industrial structure	structure	Ratio of tertiary industry GDP to secondary industry GDP
R&D investment intensity	rad	Ratio of internal R&D expenditure to GDP
Financial development level	finance	Ratio of total loans and deposits of financial institutions at year-end to GDP
Human capital level	hcapital	Ratio of the number of enrolled university students to the total population
green total factor productivity	GTFP	Sequential Malmquist–Luenberger (SML) index
the level of enterprise digital transformation	digital	The average digital transformation level of publicly listed firms within the region

.

2.3. Data Sources and Descriptive Statistics

The sample for this study covers annual data from 30 provinces and municipalities in China (excluding Hong Kong, Macau, Taiwan, and Tibet) for the period spanning 2007–2022. Data on enterprise digital transformation levels were obtained from the China Research Data Services Platform (CNRDS). GTFP data were sourced from the China Carbon Accounting Database and the China Environmental Statistics Yearbook. Control variables were derived from the National Bureau of Statistics of China. (Note: the unit of economic scale and the level of economic development before taking the logarithm is RMB 10,000, and the remaining data are all in percentage terms). The descriptive statistics of the key variables used in this study are presented in

Table 3. Descriptive statistics of key variables.

Variable	Obs	Mean	Std. Dev.	Min	Max
GTFP	480	1.015	0.061	0.771	1.480
digital	480	3.358	3.239	0	18.750
lngdp	480	9.645	0.972	6.579	11.772
lnpgdp	480	10.663	0.588	8.959	12.155
intervention	480	0.235	0.099	0.087	0.643
industrial	480	0.365	0.090	0.097	0.536
structure	480	1.168	0.684	0.500	5.297
rad	480	0.016	0.011	0.002	0.068
finance	480	3.245	1.111	1.445	7.609
hcapital	480	0.020	0.006	0.007	0.044

.

From Table 3, a certain relationship can be observed between enterprise digital transformation and GTFP. The minimum value of GTFP is 0.771, while the maximum value reaches 1.480, indicating a significant range. However, the standard deviation is only 0.061, suggesting that while some regions exhibit exceptionally high or low green productivity, the overall regional disparity remains relatively stable. This highlights the existence of regional development imbalances. For enterprise digital transformation (digital), some observations take a value of zero, which, upon further examination, is found to be primarily from earlier years in less developed regions where the majority of enterprises had not yet initiated digital transformation. While this is a special case, it underscores the comprehensiveness of the data set as it captures the entire development trajectory of enterprise digital transformation—from inception to maturity. The large range of digital transformation values reflects the rapid expansion of enterprise digital transformation during the sample period and also signifies regional disparities in digital transformation levels. The statistical properties of the control variables are generally consistent with the existing literature, confirming their reliability.

In addition, this paper also conducted descriptive statistics on the data of different regions, including the eastern region, the central region, and the western region, as shown in

Table 4. Descriptive statistical data of key variables in the eastern region.

Variable	Obs	Mean	Std. Dev.	Min	Max
GTFP	160	1.035	0.098	0.958	1.480
digital	160	4.696	4.185	0.012	18.750
lngdp	160	10.135	0.980	7.118	11.772
lnpgdp	160	11.067	0.540	9.594	12.155
intervention	160	0.173	0.062	0.087	0.357
industrial	160	0.354	0.116	0.097	0.526
structure	160	1.514	1.014	0.586	5.297
rad	160	0.025	0.015	0.002	0.068
finance	160	3.808	1.456	1.733	7.609
hcapital	160	0.022	0.007	0.012	0.044

,

Table 5. Descriptive statistical data of key variables in the central region.

Variable	Obs	Mean	Std. Dev.	Min	Max
GTFP	144	1.006	0.009	0.964	1.024
digital	144	2.935	2.291	0.021	7.651
lngdp	144	9.778	0.608	8.314	10.972
lnpgdp	144	10.51	0.452	9.472	11.412
intervention	144	0.211	0.047	0.125	0.398
industrial	144	0.391	0.075	0.200	0.536
structure	144	0.978	0.352	0.500	2.315
rad	144	0.014	0.005	0.007	0.026
finance	144	2.767	0.804	1.445	4.806
hcapital	144	0.021	0.004	0.012	0.034

and

Table 6. Descriptive statistical data of key variables in the western region.

Variable	Obs	Mean	Std. Dev.	Min	Max
GTFP	176	1.003	0.030	0.771	1.095
digital	176	2.489	2.442	0.000	10.53
lngdp	176	9.091	0.931	6.579	10.944
lnpgdp	176	10.422	0.535	8.959	11.487
intervention	176	0.311	0.108	0.164	0.643
industrial	176	0.354	0.067	0.223	0.495
structure	176	1.009	0.286	0.554	1.741
rad	176	0.011	0.006	0.003	0.024
finance	176	3.125	0.657	1.688	5.072
hcapital	176	0.017	0.006	0.007	0.033

.

From the data presented in the tables above, it is evident that GTFP in the eastern region is considerably higher than that in the central and western regions overall. Specifically, the average GTFP value in the eastern region is recorded at 1.035, whereas the central and western regions report average values of 1.006 and 1.003, respectively. Additionally, there are notable overall differences in the level of industrial digital transformation across the three regions. The average values for the level of industrial digital transformation in the eastern, central, and western regions are 4.696, 2.935, and 2.489, respectively.

Regarding the control variables, it is apparent that the eastern region exhibits significantly higher values for economic scale (lngdp), economic development level (lnpgdp), industrial structure (structure), and research and development investment (rad) compared to the central and western regions. This aligns with the economic reality, as the eastern region boasts a more developed economy, a more advanced industrial structure, and greater investment in scientific and technological research and development. At the same time, the level of government intervention (intervention) in the eastern region is lower than that in the central and western regions. This may be attributed to the eastern region’s stronger economic foundation, enabling the government to achieve better economic performance with relatively lower fiscal expenditure.

Overall, there are obvious development differences among the eastern, central, and western regions of China. The development level of the eastern region is significantly higher than that of the central and western regions. There are also development differences between the central and western regions, but compared with the differences between the central and western regions and the eastern region, the development differences between the central and western regions are relatively small.

3. Econometric Test

3.1. Baseline Regression

Using the collected provincial-level data, parameter estimation was performed with the specified model, and the findings are summarized in

Table 7. Benchmark regression results.

	(1)	(2)	(3)
	GTFP	GTFP	GTFP
digital	0.005 ***	0.001 *	0.005 ***
	(8.322)	(1.941)	(8.464)
lngdp		−0.006 *	0.097 ***
		(−1.844)	(3.277)
lnpgdp		−0.013 **	−0.042
		(−2.334)	(−1.314)
intervention		0.131 ***	0.030
		(4.472)	(0.660)
industrial		0.382 ***	0.215 ***
		(9.019)	(5.104)
structure		0.098 ***	0.060 ***
		(14.095)	(9.078)
rad		2.586 ***	−1.789 ***
		(7.454)	(−3.100)
finance		−0.016 ***	0.003
		(−4.972)	(0.548)
hcapital		0.018	−1.228
		(0.051)	(−1.564)
cons	0.996 ***	0.933 ***	0.402 **
	(406.411)	(18.443)	(2.237)
Individual Effect	YES	NO	YES
Time Effect	YES	NO	YES
N	480	480	480
Adj. R2	0.8470	0.6924	0.8869

Note: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. The values in parentheses represent the t-statistics.

. Column (1) outlines the regression results, examining the relationship between GTFP and enterprise digital transformation without accounting for control variables or fixed effects. Column (2) illustrates the outcomes when control variables are added to the model, though fixed effects are still excluded. Column (3) displays the results of the full model specification (Equation (1)), which includes both control variables and fixed effects.

As shown in Table 7, enterprise digital transformation (digital) has a significant positive impact on green total factor productivity (GTFP). This positive relationship remains stable even when individual and time effects are controlled. A comparison of Columns (1), (2), and (3) in Table 7 reveals that enterprise digital transformation consistently shows a positive effect on GTFP, irrespective of whether the model includes control variables, fixed effects, or both. Regarding the control variables, most have a significant effect on GTFP, although the extent and direction of their influence vary. Notably, the industrialization level and the industrial structure both demonstrate a significant positive effect on GTFP. R&D investment, however, exhibits a reversed impact on GTFP after controlling for fixed effects, suggesting that failing to account for unobserved heterogeneity may lead to biased estimates. In terms of model fit, Column (1), which only includes enterprise digital transformation as the explanatory variable, explains 84.70% of the variation in GTFP. Column (2), which introduces control variables but excludes fixed effects, has an adjusted R² of 0.6924, indicating a decline in explanatory power to 69.24%. Column (3), which incorporates both control variables and fixed effects, achieves an adjusted R² of 0.8869, demonstrating a substantial improvement in explanatory power. These results emphasize that the inclusion of fixed effects leads to a substantial improvement in the model’s explanatory power, with notable implications for the direction and significance of certain control variables. In particular, the influences of lngdp and rad undergo significant changes, indicating that fixed effects successfully address unobserved individual and time-related heterogeneity.

3.2. Robustness Tests

To verify the robustness of the baseline regression results, this study employed robustness tests by replacing explanatory variables and modifying the sample period. The specific robustness test methods are as follows:

Alternative keyword calculation method. The measurement method for enterprise digital transformation is adjusted by modifying the keyword frequency calculation approach. In the previous definition, the level of enterprise digitalization was measured by dividing the number of digitalization-related keywords by the total word count of the text. The regional enterprise digitalization level was then derived by averaging the digitalization levels of all enterprises within that region. Here, we refined the approach by dividing the number of keywords related to enterprise digitalization by the total number of words in the text to determine the enterprise digitalization level. Subsequently, we averaged the digitalization levels of enterprises within the region to obtain the regional enterprise digitalization level. This alternative measure is denoted as digitalw.

Alternative digital transformation proxy. The level of enterprise digital transformation at the regional level was re-estimated using the average digital intangible assets of publicly listed companies in each region, denoted as digitalc. Specifically, the proportion of software book value in the intangible assets of listed companies in each region was considered as digital intangible assets. The average of digital intangible assets of all listed companies in the region was taken as the digitalization level of the enterprise in that region. In studies on digital assets, intangible assets are often considered to be a viable proxy for measuring digital transformation levels.

Given the substantial economic impact of the COVID-19 pandemic, the sample was re-estimated by excluding data from 2020 to 2022 to ensure that the observed relationship is not driven by pandemic-induced distortions.

Table 8. Robustness test results.

	(1)	(2)	(3)
	GTFP	GTFP	GTFP
digitalw	0.001 ***
	(8.380)
digitalc		0.123 ***
		(4.168)
digital			0.006 ***
			(9.342)
lngdp	0.096 ***	0.090 ***	0.141 ***
	(3.250)	(2.877)	(4.040)
lnpgdp	−0.042	−0.053	−0.058
	(−1.303)	(−1.547)	(−1.543)
intervention	0.029	−0.031	0.053
	(0.618)	(−0.636)	(1.042)
industrial	0.214 ***	0.237 ***	0.101 **
	(5.068)	(5.313)	(2.203)
structure	0.060 ***	0.057 ***	0.045 ***
	(9.043)	(8.085)	(5.380)
rad	−1.787 ***	−1.429 **	−2.642 ***
	(−3.092)	(−2.345)	(−3.876)
finance	0.003	0.003	0.008
	(0.585)	(0.645)	(1.443)
hcapital	−1.300 *	−1.966 **	−1.392
	(−1.656)	(−2.381)	(−1.362)
cons	0.407 **	0.599 ***	0.207
	(2.260)	(3.149)	(1.071)
Individual Effect	YES	YES	YES
Time Effect	YES	YES	YES
N	480	480	390
Adj. R2	0.8866	0.8730	0.8825

Note: *, **, and *** represent the significance levels of 10%, 5%, and 1%, respectively, and the T values are in brackets.

demonstrates that the robustness test provides further confirmation of the reliability of the benchmark regression results, showing that the positive impact of the enterprise digital transformation level on green total factor productivity is robust. As evidenced by the empirical results in Columns (1)–(2) of Table 8, it is evident that regardless of whether we alter the calculation method for the digital transformation level or employ different proxy variables, the impact of the enterprise digital transformation level on green total factor productivity consistently remains positive, demonstrating that the benchmark regression results exhibit high robustness. In Column (3) of Table 8, it is clear that even after excluding the samples affected by the epidemic, the impact of enterprise digital transformation level on green total factor productivity remains robust, indicating that the benchmark regression results are largely unaffected by external shocks such as the epidemic. These findings suggest that whether we adjust the calculation method of the digital transformation level, use different proxy variables, or exclude the sample interval affected by the epidemic, the positive impact of the enterprise digital transformation level on green total factor productivity remains consistently significant. In terms of the model’s explanatory power and robustness, both aspects remain consistently high across different robustness testing methods.

3.3. Endogeneity Test

Concerns about potential reverse causality influencing the accuracy of the estimates led this study to lag the explanatory and control variables by one period. Furthermore, to mitigate potential endogeneity stemming from omitted variables or other factors, the average digital transformation level of other regions in the same year serves as an instrumental variable for enterprise digital transformation. The outcomes of this instrumental variable regression analysis are detailed in

Table 9. Endogeneity test results.

	(1)	(2)
	P.GTFP	GTFP
digital	0.004 ***	0.005 ***
	(7.513)	(8.464)
lngdp	0.095 ***	0.097 ***
	(3.290)	(3.277)
lnpgdp	−0.061 *	−0.042
	(−1.931)	(−1.314)
intervention	0.056	0.030
	(1.289)	(0.660)
industrial	0.219 ***	0.215 ***
	(5.462)	(5.104)
structure	0.055 ***	0.060 ***
	(8.283)	(9.078)
rad	−1.612 ***	−1.789 ***
	(−2.834)	(−3.100)
finance	0.001	0.003
	(0.195)	(0.548)
hcapital	−1.545 *	−1.228
	(−1.945)	(−1.564)
cons	0.629 ***
	(3.675)
Individual Effect	YES	YES
Time Effect	YES	YES
LM statistics		480
N	450	480
Adj. R2	0.9087	0.2947

Note: * and *** represent the significance levels of 10% and 1%, respectively, and the T value is in brackets.

.

As demonstrated in Table 9, the endogenous test results confirm that the positive influence of the enterprise digital transformation level on green total factor productivity remains robust and is not significantly impacted by endogeneity issues. Column (1) of Table 9 presents the fixed-effect model regression results after lagging the explanatory and control variables by one period. The coefficient of the one-period lagged digital transformation (digital_lag) on green total factor productivity (P.GTFP) is 0.004, which is significant at the 1% level. This indicates that even when the variables are lagged by one period, the positive effect of enterprise digital transformation level on green total factor productivity remains statistically significant. Column (2) displays the results from the 2SLS model, where the coefficient of the digital transformation level (digital) is 0.005, also significant at the 1% level. This suggests that after addressing endogeneity concerns through instrumental variables, the positive impact of enterprise digital transformation level on green total factor productivity persists. Furthermore, Table 9 reports LM statistics of 480, which confirms the effectiveness of the instrumental variables and rules out the presence of weak instrumental variables.

4. Mechanistic and Heterogeneity Analysis

4.1. Mechanism Analysis

To further explore the mechanisms by which enterprise digital transformation influences green total factor productivity, this study adopted a two-stage mediation model and interaction term models for analysis.

Before examining whether enterprise digital transformation enhances GTFP by driving green technological innovation, it is crucial for this paper to explore other potential mediating mechanisms through theoretical analysis. These mechanisms primarily involve improvements in energy efficiency, enhanced supply chain coordination, and shifts in consumer demand toward green products. Enterprise digital transformation plays a crucial role in influencing GTFP by improving energy efficiency. From a theoretical perspective, this mediating effect aligns with the principles of “resource efficiency theory”, which posits that digital technologies enhance resource allocation and reduce waste. Consequently, the efficiency of production factors, such as energy, is improved. According to production function theory, advancements in GTFP can be attributed to technological progress and enhanced resource utilization efficiency. Enterprise digital transformation achieves energy efficiency improvements precisely through these mechanisms. Particularly in western China, where energy resources are abundant yet utilization efficiency remains relatively low, the adoption of digital technologies can significantly address this issue, thereby exerting a more pronounced mediating effect on GTFP.

Enterprise digital transformation also indirectly enhances GTFP by improving supply chain coordination. This mediating effect is supported by both the “supply chain management theory” and the “Resource-Based View (RBV)”. Digital technologies optimize the allocation of supply chain resources, thereby enhancing enterprises’ ability to integrate resources and strengthen their competitive advantages. According to the definition of GTFP, improved supply chain coordination reduces resource waste and mitigates negative environmental externalities, thereby significantly boosting green production efficiency. Furthermore, the digital transformation of the supply chain encourages enterprises to transition toward a green supply chain, further enhancing the promotion of GTFP.

Enterprise digital transformation also indirectly enhances GTFP by influencing shifts in consumer demand toward green products. Digital technologies, such as big data analysis and artificial intelligence, enable enterprises to more accurately understand and predict consumer preferences, particularly for green and sustainable products. Through digital marketing and e-commerce platforms, enterprises can effectively communicate the value of green products and attract consumers to choose environmentally friendly and sustainable options. This shift in consumer demand encourages enterprises to adapt their production structures, prioritize green production, and focus on sustainable development, thereby improving resource utilization efficiency and environmental performance. The extant literature has validated these mediating effects, primarily within regional studies. However, these studies differ from the micro-enterprise focus of this paper. Therefore, this study chose to explore the mediating effects of green patents and environmental regulation, highlighting its unique research contributions.

To determine whether enterprise digital transformation enhances green total factor productivity (GTFP) by driving green technological innovation, this study employed the number of green patent applications (Greenap) and the number of granted green patents (Greenob) as mediator variables. The data were standardized for dimensional consistency. The regression results from this analysis are presented in Column (1) and Column (2) of

Table 10. Mechanism analysis results.

	(1)	(2)	(3)	(4)	(5)
	Greenap	Greenob	GTFP	GTFP	GTFP
digital	0.072 ***	0.058 ***	0.006 ***	0.002 **	0.003 ***
	(6.166)	(4.302)	(8.340)	(2.170)	(3.601)
regulation×digital			0.005 **
			(2.070)
regulation			−0.006
			(−0.741)
digtal×industrial				−0.026 ***
				(−5.174)
digtal×structure					0.002 ***
					(5.660)
lngdp	0.837	1.176 *	0.089 ***	0.104 ***	0.094 ***
	(1.467)	(1.785)	(3.000)	(3.599)	(3.301)
lnpgdp	−0.680	−0.615	−0.028	−0.070 **	−0.048
	(−1.093)	(−0.857)	(−0.868)	(−2.193)	(−1.548)
intervention	−1.603 *	−1.421	0.032	0.036	0.034
	(−1.800)	(−1.383)	(0.702)	(0.794)	(0.757)
industrial	0.476	−0.432	0.208 ***	0.140 ***	0.110 **
	(0.587)	(−0.462)	(4.881)	(3.227)	(2.460)
structure	0.024	0.027	0.062 ***	0.035 ***	0.027 ***
	(0.184)	(0.184)	(9.340)	(4.392)	(3.059)
rad	82.262 ***	72.501 ***	−1.384 **	−1.279 **	−1.374 **
	(7.402)	(5.654)	(−2.341)	(−2.247)	(−2.445)
finance	−0.039	0.160	0.002	0.000	0.003
	(−0.403)	(1.444)	(0.480)	(0.056)	(0.560)
hcapital	−115.644 ***	−145.558 ***	−1.496 *	−1.077	−0.682
	(−7.649)	(−8.344)	(−1.903)	(−1.411)	(−0.893)
cons	0.195	−3.326	0.331 *	0.689 ***	0.555 ***
	(0.056)	(−0.833)	(1.830)	(3.761)	(3.158)
Individual Effect	YES	YES	YES	YES	YES
Time Effect	YES	YES	YES	YES	YES
N	480	480	480	480	480
Adj. R2	0.8431	0.7911	0.8884	0.8933	0.8946

Note: *, **, and *** represent the significance levels of 10%, 5%, and 1%, respectively, and the T value is in brackets.

.

To analyze how environmental regulation (regulation) moderates the relationship between enterprise digital transformation (digital) and green total factor productivity (GTFP), this study introduces interaction terms for environmental regulation and enterprise digital transformation. These interaction terms were incorporated into regression model (1), with the results displayed in Column (3) of Table 10. Environmental regulation is measured by the ratio of investment in industrial pollution control to regional GDP.

To determine in which industry structures enterprise digital transformation can most effectively enhance green total factor productivity (GTFP), this study constructed interaction terms between the level of industrialization (industrial), the structure of industries (structure), and the level of enterprise digital transformation. These interaction terms are then included in the regression analysis. The results of this analysis are presented in Column (4) and Column (5) of Table 10.

From Table 10, it is evident that the mechanism by which enterprise digital transformation influences green total factor productivity (GTFP) is reflected in its regulatory effects, with green patents, environmental regulation, and high-level industrial structures all playing a positive role in enhancing this regulatory effect. The specific analysis is as follows: According to Columns (1) and (2) of Table 10, the number of green patent applications (Greenap) and the number of granted green patents (Greenob) are introduced as intermediary variables. The results indicate that digital transformation significantly contributes to GTFP, thereby verifying the mechanism through which enterprise digital transformation promotes green innovation to improve GTFP. From Column (3) of Table 10, the interaction term between environmental regulation and enterprise digital transformation (regulation×digital) is shown to be significantly positive. This suggests that in regions with stricter environmental regulation, enterprise digital transformation can exert a stronger influence in enhancing GTFP. In other words, environmental regulation acts as a guiding force, encouraging enterprises to adopt digital transformation and promote green development. As seen in Columns (4) and (5) of Table 10, the interaction term between the enterprise digital transformation level and the industrialization level (digtal×industrial) is significantly negative, while the interaction term between the enterprise digital transformation level and the industrial structure (digtal×structure) is significantly positive. These findings indicate that in industries with more advanced structures, the digital transformation level of enterprises can play a more positive role in enhancing GTFP.

4.2. Analysis of Heterogeneity

Considering the regional differences in resource endowments and the level of digital transformation, the impact of enterprise digital transformation on green total factor productivity (GTFP) varies across regions. Based on the geographical characteristics of mainland China, the provinces were categorized into three regional samples: east, middle, and west. The heterogeneity across these regions was analyzed by estimating the parameters of model (1) for each sub-sample. The estimated results are presented in

Table 11. Heterogeneity test results.

	Eastern China	Central China	Western China
	GTFP	GTFP	GTFP
digital	0.004 ***	0.001	0.006 ***
	(5.030)	(0.527)	(2.885)
lngdp	−0.116 *	0.065 *	0.122 *
	(−1.682)	(1.816)	(1.700)
lnpgdp	0.033	−0.120 ***	0.022
	(0.478)	(−3.478)	(0.299)
intervention	0.236 *	−0.078 *	0.186 **
	(1.747)	(−1.718)	(2.125)
industrial	0.281 ***	0.069 *	0.214 **
	(2.694)	(1.813)	(2.058)
structure	0.061 ***	0.012	0.011
	(6.552)	(1.390)	(0.384)
rad	−4.446 ***	−0.876 *	2.136
	(−3.674)	(−1.843)	(1.548)
finance	−0.028 ***	−0.016 ***	0.008
	(−2.853)	(−2.800)	(0.921)
hcapital	−7.287 ***	1.743 **	−1.273
	(−4.262)	(2.402)	(−0.726)
cons	1.962 ***	1.632 ***	−0.522
	(3.926)	(11.420)	(−1.069)
Individual Effect	YES	YES	YES
Time Effect	YES	YES	YES
N	160	144	176
Adj. R2	0.9546	0.3099	0.4399

Note: *, **, and *** represent significance levels of 10%, 5%, and 1%, respectively, with t-values in parentheses.

.

From Table 11, it is evident that the impact of enterprise digital transformation on total factor productivity (TFP) varies significantly across regions. Specifically, the effect is strongest in the western region, followed by the eastern region, with no significant impact observed in the central region. A detailed regional analysis reveals the following: in the eastern region, the effect of digital transformation on TFP is significantly positive at the 1% significance level, indicating that enterprise digital transformation substantially enhances TFP in this region. In the central region, the coefficient for digital transformation’s impact on TFP is 0.001, with a t-value of 0.527, suggesting that the effect of digital transformation on TFP in this region is not statistically significant. In the western region, the coefficient for digital transformation’s impact on TFP is 0.006 and is significantly positive with a t-value of 2.885, indicating that enterprise digital transformation has a pronounced and significant effect on TFP in the western region, and this effect is even stronger than in the eastern region.

From the perspective of economics, the influence of enterprise digitalization and regional economic characteristics on GTFP highlights regional disparities in economic development stages, efficiency in resource allocation, and the capacity to absorb new technologies. The notably positive impact of digital transformation on GTFP in the eastern region can be attributed to its advanced economic status, well-established infrastructure, and robust technological absorption capabilities. Companies in this region are more adept at employing digital technologies to streamline production processes and enhance resource allocation efficiency, thereby significantly boosting GTFP. In contrast, the central region exhibits an insignificant effect of digital transformation on GTFP. This is likely due to the region’s current stage of industrialization, where the application of digital technologies is still in its developmental phase. Enterprises in this region may lack the requisite technical expertise or financial backing to effectively utilize digital technologies. On the other hand, the western region demonstrates the most substantial positive impact of digital transformation on GTFP. This can be explained by the region’s relatively underdeveloped economy, where the introduction of digital technologies serves to counterbalance the limitations of traditional production methods. This phenomenon, often referred to as the “latecomer advantage”, results in a more pronounced improvement in productivity.

The heterogeneity in the impact of enterprise digital transformation on GTFP is particularly evident in China’s western region. This can primarily be explained by the seamless integration of rapidly advancing digital technologies with the region’s unique resource endowments. By streamlining resource allocation, improving energy efficiency, and fostering the development of a circular economy, digital technologies have substantially boosted the western region’s production efficiency. Notably, in this resource-rich area, enterprises have effectively harnessed digital tools to achieve both efficient resource utilization and environmental protection. Consequently, while enhancing productivity, these enterprises have also successfully aligned with green development objectives. This synergy between technological advancement and resource availability is especially notable in the western region. Furthermore, the western region offers robust support for enterprise digital transformation through policy backing and a favorable market environment. The nation’s significant investments in the western region, coupled with its green development strategy, have established a conducive policy framework for enterprise digitalization. Additionally, advancements in digital infrastructure within the region have provided the necessary hardware support for enterprise digital applications. Motivated by both policy incentives and market dynamics, enterprises in the western region have experienced the substantial benefits of digital transformation on GTFP earlier than others, thereby demonstrating distinct regional heterogeneity at the national level.

5. Conclusions

Based on the established model and empirical results, this paper draws the following conclusions.

First, enterprise digital transformation has a significant positive effect on regional green total factor productivity. During the process of enterprise digital transformation, costs are incurred due to equipment upgrades and renovations. However, these upgrades also enhance operational efficiency, further promoting the improvement of regional green total factor productivity. Therefore, while enterprises tend to make strategic decisions in the digital transformation process, digital transformation inherently has a significant positive effect on green total factor productivity, which is further confirmed by robustness and endogeneity tests.

Second, three variables—green patents, environmental regulation, and higher-level industrial structures—play a positive role in moderating this relationship. This study finds that the mechanism through which enterprise digital transformation affects green total factor productivity operates via a moderating effect. Further analysis of this moderating effect shows that green patents, environmental regulation, and a higher-level industrial structure positively enhance the impact of digital transformation on green total factor productivity.

Third, the effect of enterprise digital transformation on the improvement of green total factor productivity exhibits regional heterogeneity. This paper considers the impact of enterprise digital transformation and regional economic characteristics on green total factor productivity, which reflects the differences in economic development stages, resource allocation efficiency, and technological absorption capacity across regions. An empirical analysis using separate samples for the eastern, central, and western regions reveals that the positive impact of enterprise digital transformation on green total factor productivity is most pronounced in the western region followed by the eastern region, while it is not significant in the central region.

Based on China’s provincial data, this paper studies the impact of enterprise digital transformation on green total factor productivity. In terms of practical significance, the research of this paper provides a reference for regional differences in policy formulation. That is, policies can be formulated in line with the local situation according to the heterogeneity of different regions. In terms of theoretical significance, this paper analyzes the impact of enterprise digital transformation on regional green total factor productivity from a micro perspective, providing a reference for other similar studies.