Digitization and Green Technology Innovation of Chinese Firms Under Government Subsidy Policies

Abstract

In the context of the digital economy, digital technology is an important driving force to promote green development and achieve the “dual-carbon goal”. Taking 1746 Shanghai and Shenzhen A-share enterprises from 2015 to 2022 as research objects, we empirically examine the relationship between government subsidies, digital transformation, and corporate green technology innovation. The study shows that (1) there is an inverted “U”-shaped relationship between government subsidies and corporate green technological innovation, while digital transformation plays a mediating role, and there is a difference between the quality and quantity of digital transformation in promoting green technological innovation. (2) Through the analysis of the moderating effect, it is found that market concentration has an obvious inhibitory effect between enterprise digital transformation and green technology innovation. (3) The study, by classifying the nature of enterprises, shows that the promotion effect of digital transformation on green technology innovation is weaker under heavily polluted enterprises than under non-heavily polluted enterprises, but the promotion interval of the relationship between government subsidies and green technology innovation is larger. Therefore, enterprises should make full use of digital technology to inject new impetus into their innovation activities, and the government should fully consider the appropriate space for enterprises to receive subsidies, make reasonable use of the incentive effect of government subsidies, and smooth the information docking channels for government and enterprise subsidies.

1. Introduction

A new wave of scientific and technical revolution is currently thriving, and the wave of digital science is reaching its zenith on a global scale. The fundamental component of the digital economy is the profound integration of digital technology and the market economy, and as businesses are the birthplace of the digital economy, they are intimately tied to the developments and activities of the whole digital market. However, while companies continue to optimize production, there are still problems such as high consumption, high emissions, and high pollution [1]. In order to respond to the policy of high-quality development, it is urgent to promote both the intelligent and green development of enterprises. Green technology innovation, as an important way of green development, not only has an important impact on the high-quality sustainable development of enterprises but also has far-reaching significance in realizing the strategic goal of carbon neutrality. How to promote green innovation is to lead greening with digitalization and drive digitalization with greening, which is the new kinetic energy for high-quality development and green innovation of enterprises [2,3].

In recent years, a large number of scholars have shown that widespread digitalization has fundamentally changed the traditional business and ecosystem of enterprises [4]. Digital technology has democratized the innovation process of enterprises [5], dramatically changed the original industrial structure, and created a new value system [6]. A key pillar for the growth of the digital economy is the close integration of digital technology and economic entities, and the digital transformation of businesses—the backbone of the market economy—will aid in achieving the goal of Digital China [7]. The digital transformation of enterprises is the transformation of traditional business operations and management processes into a way that relies on digital technology and digital tools [8]. Therefore, for traditional enterprises, digitalization is no longer a simple systematization of internal business and management; its essence is business transformation driven by information technology, with the fundamental purpose of enhancing enterprise competitiveness [9].

Research on how digital transformation affects businesses’ high-quality development has also gained popularity in the literature. Research indicates that digital transformation helps businesses better integrate their knowledge and data chains [10], thereby enhancing their data analysis capabilities and optimizing the efficiency of resource allocation, thus creating favorable conditions for improving organizational structure and green technological innovation. Under the wave of digitalization, the digital transformation of enterprises has changed the previous approach of conducting technological innovation only internally and promoted connections and communication among different innovation units. At the same time, digital technologies have provided enterprises with greater flexibility and precision in the research and development process, promoting the development of flatter organizational structures [11]. However, some scholars, drawing on resource dependency theory, argue that digital transformation can consume a significant amount of funds, inadvertently putting financial pressure on firms and affecting their innovation processes. Especially for small firms, engaging in both digital transformation and green innovation at the same time may be constrained by financial issues, which may have a negative impact on firms’ innovation outcomes [12]. In this context, it becomes crucial and important for the government to use fiscal policy to guide and incentivize green innovation in enterprises.

Research on the relationship between government subsidies and enterprises’ green technological innovation can be broadly divided into fiscal expenditure policy research and fiscal revenue policy research [13]. In the research on fiscal expenditure policies, on the one hand, the increase in fiscal expenditure can significantly expand social demand, promote the development of the market economy, and give impetus to the green technological innovation of enterprises [14]. On the other hand, the increase in fiscal expenditure may disrupt the equilibrium of the money market, leading to a decline in consumption and investment levels, thereby causing crowding-out effects on enterprises [15]. However, looking at the development of the market in China, government subsidy policies have played a crucial role in promoting green innovation in the market without causing crowding effects during the period of rapid economic growth. From the perspective of scholars’ research, studies such as Kong Yue et al. based on Grunow’s competition model suggest that government subsidies have a positive promoting effect on new energy enterprises’ R&D investment in green innovation, effectively improving the level of enterprises’ green innovation [16]. Using hierarchical regression analysis at the national level, Zhang Wenqing et al. found through empirical analysis that R&D subsidies play a positive moderating role between environmental regulation and green technological innovation, and there is a positive linear relationship between R&D subsidies and green technological innovation [17]. Based on the data of enterprises listed on the Shanghai and Shenzhen stock exchanges from 2011 to 2020, Xiao Heng’s empirical tests found that fiscal subsidies can effectively alleviate enterprises’ financing constraints, thereby promoting the development of their green technological innovation [18]. Using the PSM model for empirical research, Wu Jingguang et al. found that increasing R&D subsidies can significantly boost enterprises’ R&D investment, and there is a positive relationship between fiscal subsidies and enterprises’ innovation intentions. In the research on fiscal revenue policies, scholars have differing opinions on the impact of environmental regulation [19]. Generally, it can be categorized into three types: firstly, environmental regulation promotes enterprise green innovation; secondly, environmental regulation hinders enterprise green innovation; and thirdly, there is a nonlinear relationship between environmental regulation and enterprise green innovation [20].

In reviewing the literature, it is clear that most scholars have mainly explored the relationship between digital transformation and green technology innovation from a single linear perspective while overlooking the macro-regulatory role of government subsidy policies. However, the key to achieving sustainable development lies in the collaborative development between the government and enterprises [21]. On the one hand, it interprets the conducive conditions for enterprises to engage in sustainable green innovation to ensure their long-term survival in the market. On the other hand, it explains the ability of enterprises to undergo digital and green transformation based on the tax burden [22].

In summary, given the current state of institutional and economic change, this paper aims to investigate the mechanism of government subsidies on green technological innovation of enterprises, particularly from the perspective of the theory of digital transformation, and to confirm the mediating effect of digital transformation and the regulating effect of market concentration. To do this, it will employ the content analysis method to extract information about green technological innovation of Chinese listed companies in 2015–2022. In addition, although some scholars have already confirmed the impact of digital transformation on green technological innovation [23], there has been relatively little research from the perspective of the essence of green technological innovation. This paper incorporates the quality and quantity of green technological innovation into the study, dividing green technological innovation into three parts, total innovation, quality of innovation, and quantity of innovation, using statistical methods for comprehensive analysis. In addition, it compares the differences in innovation between pollution-intensive enterprises and non-pollution-intensive enterprises, thus contributing theoretical insights to both “building enterprise digital transformation systems” and “building enterprise green transformation systems”.

2. Literature Review and Research Hypothesis

2.1. Digital Transformation and Enterprise Green Technology Innovation

Compared with traditional innovation, the uniqueness of green technology innovation lies in its emphasis on the use of cutting-edge technology and advanced concepts to achieve efficient resource utilization and significant pollution reduction, thereby enhancing economic benefits while achieving sustainable development [24]. With the development and transformation of the high-tech and economic environment, enterprises are increasingly focusing on innovation cooperation across organizational boundaries [25]. Green development and open innovation have also become new connotations of green technology innovation.

First, enterprise digital transformation not only lays a solid technological foundation for green technology innovation but also provides a broader innovation platform. The foundation of digitization lies in information technology, and the essence of enterprise digitization is the application of high-tech to transform traditional structures [26]. The rich technological resources provided by digitization form a strong foundation for enterprises to engage in green technological innovation. Emerging technologies are represented by artificial intelligence, blockchain, cloud computing, and big data facilitate innovation sharing among enterprises and even industries [27]. This not only facilitates enterprises to establish innovation networks with external entities for collaborative research and development but also helps enterprises to conduct targeted innovation activities through rapid information sharing and data collection [28].

Furthermore, the digital transformation of companies can optimize the allocation of resources and have a talent attraction effect. On the one hand, digital transformation breaks the boundaries of factors, achieves optimal resource restructuring, and enhances enterprise resilience and operational capabilities. It also improves the efficiency of resource allocation, thereby promoting the efficiency and output of green technological innovation [29]. On the other hand, enterprise digital transformation can improve the level of automation, reduce labor costs, and thus optimize the allocation of labor resources. In addition, digital technology has an attractive effect on high-end talent [30]. Through data analysis, Hoening et al. found that regions or enterprises with higher levels of digitalization tend to attract more high-end talent, thereby facilitating the integration of knowledge and skills within enterprises and providing knowledge capital and human capital for enterprise green technological innovation [31].

Finally, digital transformation facilitates information symmetry among organizations, thereby promoting the establishment of equal and symbiotic relationships among enterprises engaged in collaborative innovation, ensuring the smooth progress of green technology innovation. During the innovation process, enterprises often face the risk of data leakage due to integrity issues. The higher the degree of information asymmetry between enterprises, the higher the cost of coordinating cooperation between them, which may hinder the innovation process of both parties. Digital transformation comprehensively captures enterprise operational information, increasing the visibility of information and reducing information asymmetry [32], thereby facilitating communication and exchange between enterprises and making cooperation more stable and robust. In addition, due to the embeddedness and permeability of digital technology [33], enterprises can become interdependent, fostering harmonious and symbiotic relationships among innovation units, which also provides innovation units with greater innovation resources and advantages over other enterprises [34]. Based on the previous analysis, this paper proposes the following research hypotheses:

H1. 

Digital transformation has a positive impact on companies’ green technology innovation.

2.2. Government Subsidies and Green Technology Innovation

Currently, enterprises often face significant risks, high investment, and long payback periods when engaging in green technology innovation. Failure in innovation not only causes severe economic losses but also dampens the enthusiasm for green innovation. Therefore, it is necessary for the government to promote enterprises’ green technological innovation through fiscal regulation. Although there is a lot of research on this topic, the conclusions are controversial and no consensus has been reached. On the one hand, government subsidies can compensate for the lack of internal innovation drive within enterprises, thereby stimulating the willingness to innovate and encouraging enterprises to increase innovation investment [35]. In addition, government subsidies can have a risk-bearing effect on enterprises [36], thereby reducing innovation risks. Moreover, fiscal subsidies also have a signaling and certification effect on enterprises [37], indicating government recognition and enhancing the credibility of enterprises in the market, thereby improving their financing capabilities.

From the perspective of enterprises themselves, government subsidy policies, with their long-term stability, partially compensate for research and development investments, giving enterprises the confidence to continue innovation investments [38].

From the government’s perspective, providing tax subsidies to enterprises creates an implicit bond between the government and enterprises. This indirectly provides financing advantages and frontier information for enterprises to engage in green technological innovation. At the macro level, the government’s fiscal subsidies encourage enterprises to engage in green technological innovation [39].

From the perspective of enterprise development, with the continuous emergence of market monopolies, enterprises need to optimize specific industries to break through technological barriers and seek market space. The provision of government subsidies injects innovation capital and vitality into enterprises, promoting the fulfillment of basic innovation needs. Based on the previous analysis [40], this paper proposes the following research hypothesis:

H2. 

Government subsidies promote the level of green technological innovation in enterprises.

2.3. Government Subsidies and Digital Transformation

Companies often need to make significant investments during digital transformation to ensure the continuity of their technology research and development activities. In reality, however, demand for such initiatives often exceeds supply due to the long duration, large investment, and high risk associated with digital transformation. In this scenario, governments intervene to prevent market failure by providing targeted fiscal subsidies to incentivize enterprises to undertake digital transformation, thereby ensuring the stability of the economic market. Research by Wang Chunying et al. identified financing issues as the main bottleneck for most enterprises during digital transformation, and government subsidies can significantly alleviate the financing pressure on enterprises [41]. This is because government fiscal subsidies are highly targeted and can be scientifically allocated based on screening, thus easing the financial situation of enterprises while significantly improving the efficiency of digital innovation [42]. Government subsidies play an important role in reducing the risks associated with enterprises’ digital transformation, thereby accumulating the necessary conditions for enterprises to move from quantitative to qualitative transformation. Studies by Li, M et al. revealed that fiscal subsidies not only help enterprises raise funds but also stimulate the intrinsic motivation for enterprise digital transformation [43]. In addition, the government supports the digital transformation of manufacturing companies through tax incentives and other favorable policies that encourage equipment and product iteration and updating, thereby optimizing the value chain of companies and even entire industries. These macroeconomic policies are closely linked to the strategic goals of Digital China. The introduction of fiscal subsidies not only reduces uncertainty during the transformation process, but also enhances enterprise management capabilities, enables more resources to be allocated to R&D innovation areas, and motivates R&D departments to continuously explore and apply new digital technologies [44]. Considering the above comprehensive research, it is clear that there is a close relationship and promotion between enterprise digital transformation, tax subsidies, and green technology innovation. Based on previous analysis, this paper proposes the following hypothesis:

H3. 

The digital transformation of enterprises may act as a mediator between government subsidies and green technological innovation.

2.4. Market Concentration

In order to break through their own bottlenecks and gain a competitive advantage position, enterprises will spend a lot of time and money on innovation, and the market, as the driving force behind the transformation and development of the industry, is closely related to the innovative activities and decisions of individual enterprises. When the market concentration of an industry is high, large enterprises often monopolize a large share of the market. In order to consolidate their position and continue to obtain monopoly profits, enterprises will increase their investment in innovation and enhance their innovation activities, a phenomenon also known as the “Schumpeter effect” [45]. When the market concentration in the industry is low, each enterprise will face fierce competition. In this environment, if the enterprise through innovation activities stands out, not only will it consume a lot of human, material, and financial resources but also, after the success, it will face more uncertainty and competitive pressure [46]. Therefore, in an environment of low market concentration, enterprises are more willing to reduce their own R&D investment, so as to avoid more risks to maintain the existing revenue.

Compared to industries with low market concentration, industries with high market concentration often promote innovation within enterprises. However, in highly concentrated industries, large enterprises hold the lead in core technologies, which are crucial for establishing their position and achieving long-term development. Therefore, the innovation achievements of these enterprises are not shared with others. Enterprises enhance their competitive advantage by introducing technical talents, optimizing research and development environments, and overcoming industry technological bottlenecks through innovation activities [47]. In this closed innovation model, internal innovation remains unaffected by external factors, ensuring the uniqueness and independence of competitive resources and effectively consolidating the leading position of enterprises in the industry and safeguarding their monopoly profits. In such contexts, where market concentration is relatively high, even if enterprises undertake digital transformation, they may fear the leakage of internal information, thereby reducing openness to external entities. If firms are unwilling to share technology and resources with external parties, the degree of digital transformation may decrease accordingly. Therefore, this paper proposes the following hypothesis:

H4. 

Market concentration negatively moderates the relationship between digital transformation and green technological innovation.

3. Research Design

3.1. Model Setup

To test the hypothesis, this paper constructs the following regression equation for examination:

$$G{I}_{it}={\alpha }_0+{\alpha }_{1}DC{G}_{it}+\omega Contro{l}_{it}+{\mu }_i+{\epsilon }_{it}$$

(1)

$$G{I}_{it}={\beta }_0+{\beta }_{1}Fissu{b}_{it}+\phi Contro{l}_{it}+{\mu }_i+{\epsilon }_{it}$$

(2)

$$DC{G}_{it}={\gamma }_0+{\gamma }_{1}Fissu{b}_{it}+\tau Contro{l}_{it}+{\mu }_i+{\epsilon }_{it}$$

(3)

$$G{I}_{it}={\delta }_0+{\delta }_{1}DC{G}_{it}+{\delta }_{2}Fissu{b}_{it}+\sigma Contro{l}_{it}+{\mu }_i+{\epsilon }_{it}$$

(4)

$$G{I}_{it}={\epsilon }_0+{\epsilon }_{1}DC{G}_{it}+{\epsilon }_{2}HH{I}_{it}+{\epsilon }_{3}DC{G}_{it}\ast HH{I}_{it}+\rho Contro{l}_{it}+{\mu }_i+{\epsilon }_{it}$$

(5)

In the equation, “$i$” and “$t$” represent enterprises and years, respectively. $G{I}_{it}$ represents the level of green technological innovation in the enterprise, $DC{G}_{it}$ represents the level of enterprise digital transformation, $Fissu{b}_{it }$ represents the scale of enterprise fiscal subsidies, $Contro{l}_{it}$ represents control variables selected for the article, including enterprise size(Size), listing years(Age), asset-liability ratio(Lev), growth(Growth), independent director ratio(Dire), return on equity(Roe), number of employees(Numb), and enterprise nature(Poll), ${\mu }_i$ represents fixed effects, including time fixed effects(Year) and industry fixed effects(Industry), and ${\epsilon }_{it}$ represents the random error term.

In Model (1), when the coefficient ${\alpha }_1$ for digital transformation is positive and statistically significant, it indicates that digital transformation can facilitate green technological innovation within firms. In Model (2), when the coefficient ${\beta }_1$ for fiscal subsidies is positive and statistically significant, it suggests that fiscal subsidies are conducive to green technological innovation within firms. In model (3), when the coefficient ${\gamma }_1$ of financial subsidies is positive and significant, it implies that financial subsidies can promote firms to adopt digital transformation. Therefore, in Model (4), when the coefficient ${\delta }_1$ for digital transformation is positive and significant, it indicates that fiscal subsidies facilitate green technological innovation by driving digital transformation within firms. In Model (5), which incorporates the interaction between market concentration (HHI) and digital transformation (DCG), if the coefficient ${\epsilon }_3$ for the interaction term is negative and statistically significant, it suggests that market concentration diminishes the facilitating effect of digital transformation on green technological innovation within firms.

3.2. Variable Selection

3.2.1. The Dependent Variable

The level of green innovation within firms (GI) refers to the development of new scientific technologies that adhere to ecological principles and contribute to reducing resource consumption and pollution. Green patents not only reflect the innovation and application of green technologies such as sustainable development, energy saving, and emission reduction but also serve as a concrete indicator of the level and scale of green innovation of enterprises. Following the approach of Li, X et al., in this study, use the number of green patent applications as a measure of the level of green technological innovation of enterprises. Specifically, the total green innovation (TGI) is represented by the natural logarithm of the sum of green invention patent applications, green utility model patents plus one. As an important carrier of innovative knowledge with significant economic value, the complexity of the knowledge contained in a patent for an invention will inevitably affect the quality of the patent. The quality of green innovation (QGI) is represented by the natural logarithm of the number of green invention patent applications plus one; the quantity of green innovation (NGI) is represented by the natural logarithm of the number of green utility model patents plus one [48].

3.2.2. Core Explanatory Variables

Government subsidies (Fissub): Based on the details of government subsidy projects in annual reports, keywords closely related to low-carbon environmental protection, such as “green”, “environmental subsidy”, “sustainable development”, “clean”, and “energy saving”, are identified. Then, the amount of environmental subsidies received by enterprises each year is compiled. Due to the relatively small scale of the data, and for the purpose of comparison and analysis, the adjusted relative level of environmental subsidies is used as a measurement indicator, expressed in a percentage [49].

Enterprise Digital Transformation (DCG): Current research on enterprise digital transformation focuses mainly on qualitative analysis, with relatively few quantitative studies. In the empirical testing process, the target variable needs to be highly condensed. Since digital transformation is a core strategic direction to promote high-quality development in enterprises and frequently appears in the summary annual reports of enterprises, tracking the keywords and frequency of occurrence in enterprise annual reports can reflect the focus of enterprise development and future planning. This study is based on Wu Fei’s research, which collected and organized the annual reports of all A-share listed companies through the Python web crawling function. Then, the keywords and their frequency counts in the annual reports were extracted, and the word frequency of key technical directions was classified and aggregated, thereby constructing an indicator system for enterprise digital transformation [29].

3.2.3. Control Variable

In order to enhance the precision of the research, for example, factors such as the basic characteristics of the enterprise and its resources, this study adopts the following variables as control variables [32], drawing on existing research, due to the numerous factors influencing enterprise green technological innovation, such as Enterprise Size (Size): Measured by the natural logarithm of total assets; Growth (Growth): Measured by the growth rate of operating income; Listing Age (Age): Measured by the number of years since the enterprise started filing annual reports; Debt-to-Equity Ratio (Lev); and Proportion of Independent Directors (Dire): The proportion of independent directors on the board is an important indicator of corporate governance, as it provides insight into the extent to which the interests of shareholders are represented on the board. This study employs a ratio measurement of independent directors relative to the total number of board members; Return on Equity (ROE): ROE represents the development capability of the enterprise. This study measures it by the ratio of net profit to average shareholder equity; Number of Employees (Numb): The natural logarithm of total number of employees in the enterprise; and Nature of Enterprise (Poll): Assigned as 1 for heavy-polluting industries and 0 otherwise [50].

3.2.4. Mechanism Variables

To test the moderating effect of market concentration on the relationship between enterprise digital transformation and green technology innovation, this study refers to the research of Agostino et al. [51] and sets up the variable HHI (Herfindahl–Hirschman Index). The Herfindahl–Hirschman Index quantifies the degree of market concentration in which enterprises operate. The calculation method of this index is based on the sum of squares of the proportions of the assets of each firm in the industry to the total assets of the industry, which reflects the degree of dispersion of firm sizes in the market. The higher the market concentration, the higher the corresponding HHI value and the more obvious the monopoly phenomenon.

3.3. Data Sources and Descriptive Statistics

Our sample is drawn from Chinese A-share companies listed on the Shanghai and Shenzhen stock exchanges from 2015 to 2022. The objective is to guarantee the precision and dependability of the data analysis; we conducted the following screening and processing of the initial sample. We excluded companies listed after 2015 and excluded companies with significant missing data to eliminate the impact of data gaps on the study; in order to circumvent the potential influence of outliers in continuous variables on the outcomes of the analysis, all continuous variables underwent a 1% shrinkage prior to and following the analysis. Essential company information, financial data, and details of government subsidies were mainly obtained from the Guotai An database and the Wind database. We manually collated the patent information of companies by consulting the official website of the China Intellectual Property Office, and we learned about the data of company annual reports from the official websites of the Shenzhen Stock Exchange and the Shanghai Stock Exchange. Following the completion of the preceding processing stages, 1,746 companies were identified as having valid data, representing a total of 13,968 sample observations.

Table 1. The descriptive statistical analysis of the variables.

Type	Variable	Obs	Mean	Std. Dev.	Min	Max
Explained Variable	TGI	13,968	0.28391	0.821113	0	5.560682
	QGI	13,968	0.21283	0.726841	0	5.036952
	NGI	13,968	0.15822	0.50357	0	4.672829
Core explanatory variables	Fissub	13,968	0.010049	0.040966	0	0.332638
	DCG	13,968	1.409368	1.322941	0	5.010635
control variable	Size	13,968	23.00377	1.380198	19.63002	26.45228
	Lev	13,968	0.503586	0.191826	0.055024	0.894361
	Roe	13,968	0.06257	0.117105	−0.82073	0.407163
	Growth	13,968	0.16047	0.48728	−0.65756	4.024214
	Dire	13,968	37.24051	5.633085	30.77	60
	Age	13,968	3.139994	0.157017	2.079442	3.401197
	Numb	13,968	8.026447	1.360823	4.26268	11.18075
	Poll	13,968	0.260906	0.439313	0	1
Mechanism variables	HHI	13,968	0.07	0.063	0.011	0.572

presents the descriptive statistics for the primary variables. Data statistics and processing were conducted using the software Stata17.0.

4. Empirical Analysis

4.1. Digital Transformation Benchmarks Return

In this section, we primarily use model (1) to examine the impact of firms’ digital transformation on green technology innovation.

Table 2. Digital Transformation Benchmarks Return.

Variable	TGI		QGI		NGI
	(1)	(2)	(3)	(4)	(5)	(6)
DCG	0.1256 ***	0.0503 ***	0.146 ***	0.054 ***	0.0169 ***	0.0177 ***
	(7.1312)	(2.9217)	(4.2247)	(2.629)	(7.5087)	(3.7921)
Size		0.1285 ***		0.0575 ***		0.1288 ***
		(5.4931)		(3.8175)		(6.2359)
Lev		−0.2639 **		−0.2196 **		−0.1875 *
		(−2.0723)		(−2.5684)		(−1.6669)
Roe		−0.5958 ***		−0.2755 **		−0.4702 ***
		(−3.1211)		(−2.2419)		(−2.7895)
Growth		−0.0624		−0.0401		−0.0405
		(−1.4272)		(−1.4249)		(−1.0491)
Dire		0.0011		0.0024		0.003
		(0.3017)		(0.9899)		(0.9139)
Age		1.2831 ***		0.6875 ***		1.2259 ***
		(9.5762)		(7.9694)		(10.3621)
Numb		0.1346 ***		0.0798 ***		0.0915 ***
		(5.9596)		(5.4876)		(4.5872)
Poll		−0.1782 ***		−0.1451 ***		−0.1317 ***
		(−3.5925)		(−4.5421)		(−3.0071)
_cons	0.1069 ***	0.3918	0.093 ***	0.4352	0.0481	0.3381
	(3.1378)	(0.6441)	(4.3937)	(1.1113)	(1.6007)	(0.6294)
Year	Yes	Yes	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes	Yes	Yes
N	13,968	13,968	13,968	13,968	13,968	13,968
Adj.R2	0.141	0.244	0.182	0.267	0.233	0.248

Note: ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. T-values in parentheses, Same table below.

presents the regression results. Columns (1) to (6) show that the coefficient of digital transformation is consistently positive and statistically significant, regardless of the inclusion of control variables, years, or firm fixed effects. This suggests that digital transformation significantly increases firms’ investment in research and development, which in turn increases the level of green technological innovation within firms, confirming Hypothesis 1. Comparatively, the impact of digital transformation on the quantity of green technology innovation is significantly smaller than its impact on the total quantity and quality. This could be attributed to the high-quality development strategy implemented in China in recent years, which guides digital transformation in enterprises to achieve the policy goals of “effective qualitative improvement and reasonable quantitative growth”.

Among the control variables, Size, Age, and Numb significantly affect firms’ green technological innovation. Specifically, firm size has a significant positive impact on the total amount, quality, and quantity of green technology innovation. This is because large enterprises usually have stronger financial strength and are able to bear the high R&D costs and potential risks required for green technology innovation. Moreover, large enterprises often accumulate a large amount of technological experience and patent reserves in the course of long-term development, which provides a solid foundation for green technology innovation. They are more likely to combine their existing technological advantages with environmental protection concepts to develop new technologies that are more efficient and environmentally friendly. Finally, large enterprises usually have a more complete and efficient management system, which can better coordinate internal resources, optimize the innovation process, and improve innovation efficiency.

Age at listing is significantly and positively related to the total amount, quality, and quantity of green technology innovation. This suggests that as the length of time on the market increases, the green innovation capacity of firms also increases. This is mainly due to the fact that with the increase in listing time, firms are able to alleviate financing constraints, thus promoting green innovation activities.

Furthermore, the number of employees has a considerable positive impact on the overall amount, quality, and quantity of green technology innovation. As the number of employees increases, the scale of the enterprise expands, business activities expand, and the enterprise’s ability to withstand risks increases, thus promoting green technology innovation within the enterprise.

4.2. Benchmark Return of Government Subsidies

Table 3. Benchmark Return of Government Subsidies.

Variable	TGI				QGI				NGI
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
Fissub	−0.2209 ***	−0.2415 ***	2.3229 ***	0.7788 ***	−0.0451	−0.0606	1.477 ***	0.4136 ***	−0.3246 ***	−0.3471 ***	1.8077 ***	0.8783 ***
	(−2.5803)	(−2.5753)	(6.2303)	(5.4479)	(−0.1266)	(−0.186)	(5.2761)	(5.3706)	(−2.6312)	(−2.7721)	(6.0815)	(5.5708)
Fissub2			−7.4996 ***	−3.6185 ***			−5.4319 ***	−4.2508 ***			−5.2917 ***	−4.3429 ***
			(−7.17)	(−5.6135)			(−6.3821)	(−5.3772)			(−6.9326)	(−5.3827)
Size		0.129 ***		0.1299 **		−0.0575		−0.050		0.1294		0.1304
		(5.4964)		(5.5222)		(−3.8201)		(−3.38317)		(6.2285)		(6.2070)
Lev		−0.2899 **		−0.292 **		−0.2209 *		−0.2217 *		−0.2171 *		−0.2197 *
		(−2.2737)		(−2.289)		(−1.7007)		(−1.7077)		(−1.9245)		(−1.9462)
Roe		−0.5993		−0.6029		−0.2757		−0.2770		−0.4741 *		−0.4785 *
		(−1.1285)		(−1.1451)		(−1.2435)		(−1.2518)		(−1.7969)		(−1.8210)
Growth		−0.0654		−0.0644		−0.0402		−0.0399		−0.044		−0.0428
		(−1.4905)		(−1.4674)		(−1.4279)		(−1.4147)		(−1.1329)		(−1.1025)
Dire		0.0012		0.0012		0.0024		0.0024		0.0031		0.003
		(0.3143)		(0.3097)		(0.9895)		(0.9867)		(0.9273)		(0.9210)
Age		1.2419 ***		1.2418 ***		0.685 ***		0.685 ***		1.1787 ***		1.1786 ***
		(9.288)		(9.2849)		(7.9915)		(7.7898)		(9.9611)		(9.9591)
Numb		0.1525 ***		0.1523 ***		0.081 ***		0.0805 ***		0.112 ***		0.1118 ***
		(6.987)		(6.9738)		(5.7543)		(5.7457)		(5.8006)		(5.7849)
Poll		−0.2104		−0.22		−0.147 *		−0.151		−0.1682 *		−0.1797 *
		(−1.3361)		(−1.3148)		(−1.7232)		(−1.5962)		(−1.9185)		(−1.9823)
_cons	0.2861 ***	−0.1999	0.2939 ***	0.1817	0.1578 ***	−0.4257	0.1634 ***	0.4194	0.2161 ***	0.1187	0.2216 ***	0.956
	(11.68)	(−0.3295)	(11.58)	(0.2989)	(10.5003)	(−1.0934)	(10.499)	(1.0755)	(9.9663)	(0.2211)	(9.8614)	(0.879)
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	13,968	13,968	13,968	13,968	13,968	13,968	13,968	13,968	13,968	13,968	13,968	13,968
Adg.R2	0.202	0.239	0.205	0.241	0.185	0.267	0.185	0.266	0.214	0.339	0.224	0.339

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

presents the regression results pertaining to the impact of government subsidies on green technology innovation. Columns (1) and (2) show the regression results of government subsidies (Fissub) on the total amount of green technological innovation. The regression results demonstrate that, irrespective of the inclusion of control variables, years, or firm fixed effects, Fissub consistently have a significant negative effect on green technological innovation. Columns (5) and (6) present the regression results of Fissub on the quality of green technological innovation. It can be seen that the coefficient of Fissub is negative but insignificant, indicating that there is no significant relationship between Fissub and the quality of green technological innovation. Finally, columns (9) and (10) show that Fissub negatively affects the quantity of green technological innovation within firms. Overall, these results contradict Hypothesis 2. In order to delve deeper into the underlying mechanisms, this study conducted regression analysis by adding the quadratic term of fiscal subsidies to Model (2) and constructing the regression model as shown in Model (6).

$$G{I}_{it}={\beta }_0+{\beta }_{1}Fissu{b}_{it}+{\beta }_{2}Fissu{b}_{it}^2+\phi Contro{l}_{it}+{\mu }_i+{\epsilon }_{it}$$

(6)

The regression results are reported in Table 3. From the regression results in columns (3) and (4), it can be seen that regardless of the inclusion of control variables, years, or firm fixed effects, the coefficient of government subsidies is positive and significant, while the coefficient of the squared term of government subsidies is negative and significant. This indicates the existence of an inverted U-shaped relationship between government subsidies and the total amount of green technological innovation within firms. Similarly, the results of columns (7) and (8) show the same pattern for the quality of green technological innovation. Finally, the results of columns (11) and (12) show a similar structure for the quantity of green technological innovation. This confirms the existence of an inverted “U”-shaped relationship between government subsidies and green technological innovation, which, although contrary to the hypothesis of this study, is consistent with the conclusions of scholars such as Zou Ganna et al. [52].

According to the regression results in column (4) of Table 3, controlling for relevant variables, the coefficient of the Fissub²-related term is −3.6185. This gives the inflection point (in percentage form) of government subsidies as −[0.7788/(−3.6185 × 2)] = 0.1076. Further conversion of this value gives 0.1076 × 100 = 10.76. Considering that this study processed the original data in units of ten thousand CNY, from an economic point of view, the inflection point for the impact of fiscal subsidies on the total amount of green technology innovation in enterprises is 1,076,000 CNY. When fiscal subsidies exceed this inflection point value, it will hinder the growth of the total amount of green technological innovation within enterprises. Conversely, when fiscal subsidies are below this inflection point value, they significantly stimulate green technological innovation within enterprises positively. Similarly, it can be deduced that the inflection point values for the impact of Fissub on innovation quality and quantity are 1,737,000 CNY and 1,011,200 CNY, respectively.

In terms of the control variables, gearing ratio, time to market, and number of employees all have a significant impact on green technology innovation. As can be seen from Table 3, the gearing ratio (Lev) has a significant negative impact on green technology innovation. This is because the higher the gearing ratio, the higher the financial risk of the firm, and the more reluctant the firm is to engage in long-term activities such as green technological innovation and focus on debt repayment. The time (age) of a firm’s listing has a positive effect on innovation. The longer listing time means the stronger the company’s foundation and the more risk-resistant it is, which is conducive to green technology innovation. The number of employees (Numb) also has a positive impact on innovation. Although the number of employees is not linearly related to corporate green innovation, the active participation and contribution of employees are crucial for improving the efficiency of corporate green innovation. Employees are the implementation body of enterprise technology innovation activities, and their participation, creativity, and execution directly affect the implementation effect of green technology innovation projects.

As for the inverted “U” relationship between fiscal subsidies and green technology innovation in enterprises, the reason is the invisible bond formed between enterprises and the government when receiving fiscal subsidies. As the amount of subsidies increases, firms may engage in rent-seeking behavior, spending considerable resources to build relationships with government officials in order to secure more subsidies. This situation tightens the bond between companies and the government, squeezing the funds originally allocated for green technology innovation. Moreover, when firms are successful in securing larger fiscal subsidies through rent-seeking, their enthusiasm for investing in risky ventures such as green technological innovation diminishes, and their motivation shifts to profit-making. To further validate the research conclusions, this study randomly selected and plotted two companies listed on the Shanghai and Shenzhen Stock Exchanges, with the time frame divided into the years 2010 to 2021. Figure 1 and Figure 2 show that there is, in fact, an inverted U-shaped relationship between Fissub and the degree of GI. This further demonstrates that the research findings in this study are valid from a scientific standpoint.

4.3. Intermediary Mechanisms

This study used digital transformation as a mediating variable to further explore the inherent relationship between government subsidies (Fissub), digital transformation (DCG), and green technological innovation (GI). The relevant regression findings are shown in

Table 4. Mediation Mechanism Test Table.

Variable	(1)	(2)	(3)	(4)
	DCG	TGI	QGI	NGI
Fissub	5.2386 ***	1.0422 ***	0.4278 ***	1.1787 ***
	(2.8025)	(3.5198)	(3.8285)	(4.823)
Fissub2	−6.467	−3.9409 ***	−1.2684 ***	−4.7137 ***
	(−0.6565)	(−4.5689)	(−3.256)	(−5.3546)
DCG		0.0503 ***	0.0527 ***	0.0573 ***
		(2.600)	(2.738)	(3.528)
Size	0.0091 **	0.1294	0.0578	0.1299
	(0.232)	(0.989)	(0.979)	(1.183)
Lev	−0.5132 **	−0.007	0.106	−0.112
	(−2.4072)	(−0.052)	(0.884)	(−0.936)
Roe	−0.0634 *	−0.5997 ***	−0.2768 **	−0.4749 ***
	(−0.1979)	(−3.3182)	(−2.2495)	(−2.8148)
Growth	−0.0599	−0.0614	−0.0397	−0.0394
	(−0.8156)	(−0.903)	(−0.313)	(−1.172)
Dire	0.001	0.003	0.005	0.002
	(0.1573)	(0.743)	(1.591)	(0.625)
Age	0.8205 ***	0.4123 ***	0.442 ***	0.083
	(3.6708)	(3.010)	(3.841)	(0.725)
Numb	0.3556 ***	0.038	0.019	−0.020
	(9.7446)	(1.099)	(0.664)	(−0.675)
Poll	−0.6316 ***	−0.110	−0.125 *	−0.056
	(−7.4110)	(−1.293)	(−1.742)	(−0.789)
_cons	−3.7893 ***	0.3721	0.4296	0.3140
	(−3.731)	(0.6107)	(1.0949)	(0.5838)
Year	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes
N	13,968	13,968	13,968	13,968
Adj.R2	0.281	0.343	0.266	0.247

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

. The columns (1) in Table 4 show the effect of government subsidies on digital transformation after controlling for variables such as year and firm fixed effects and reveal a significant positive effect of government subsidies on digital transformation. The columns (2) examine whether DCG mediates the relationship between Fissub and TGI. The results indicate that the impact of Fissub on the TGI still significantly shows an inverted “U” shaped relationship. Similarly, the results in columns (3) and (4) show that government subsidies also exhibit the same inverted “U”-shaped relationship with the quality and quantity of green technological innovation, consistent with the baseline regression results mentioned earlier. From the second to the fourth columns, it can be seen that digital transformation has a significant positive effect on the total amount, quality, and quantity of green technological innovation. This implies that digital transformation serves as a mediator between fiscal subsidies and the level of green technological innovation, thus confirming Hypothesis 3. This suggests that moderate government subsidies can stimulate enterprises to accelerate the process of digital transformation, thereby promoting green technological innovation. However, when the amount of government subsidies exceeds a certain critical threshold, firms’ enthusiasm for digital transformation may decrease, leading to a reduction in green innovation output.

4.4. Robustness Check

4.4.1. Substitution of Explanatory Variables

Green technological innovation in the firm is measured in this paper using the natural logarithm of the sum of green invention patent applications and green utility model patent applications in the baseline regression analysis. The quantity of green technological innovation is represented by the natural logarithm of green utility model patent applications, while the quality of green technological innovation is reflected by the natural logarithm of green invention patent applications. The ratio of green patent applications to total patent applications in the current year (RatioTGI), the ratio of green invention patent applications to total patent applications in the current year (RatioQGI), and the ratio of green utility patent applications to total patent applications in the current year (RatioNGI) are used in this study as proxy variables for TGI, QGI, and NGI, respectively, to further test the robustness of the findings. The regression results are displayed in

Table 5. Robustness Test Table.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	RatioTGI			RatioQGI			RatioNGI
DCG	0.041 ***		0.0411 **	0.0501 ***		0.0499 ***	0.012 *		0.014 *
	(2.716)		(2.1651)	(3.0318)		(2.9967)	(1.883)		(1.914)
Fissub		3.2131 ***	3.3041 ***		2.809 ***	2.9192 ***		1.3371 ***	1.3306 ***
		(3.6236)	(3.6724)		(3.6142)	(3.6842)		(3.923)	(3.837)
Fissub2		−11.0874 ***	−10.9591 ***		−10.2407 ***	−12.920 ***		−4.0276 ***	−4.0369 ***
		(−3.6791)	(−3.6628)		(−3.362)	(−3.256)		(−5.23)	(−5.49)
Size	0.1002 ***	0.105 ***	0.1035 ***	0.1035 ***	0.1084 ***	0.1065 ***	0.0433 ***	0.0444 ***	0.0445 ***
	(3.9957)	(4.171)	(4.1142)	(4.7024)	(4.8945)	(4.8254)	(2.622)	(2.6812)	(2.6856)
Lev	−0.1909	−0.2403 *	−0.207	−0.1721	−0.2275 *	−0.1871	−0.1195	−0.1229	−0.1253
	(−1.3425)	(−1.6932)	(−1.4529)	(−1.3797)	(−1.8236)	(−1.4969)	(−1.2763)	(−1.3171)	(−1.3343)
Roe	−0.765	−0.7883	−0.7782	−0.5801	−0.6046	−0.5923	−0.345	−0.3491	−0.3498
	(−0.087)	(−0.082)	(−0.078)	(−0.062)	(−0.071)	(−0.049)	(−0.054)	(−0.057)	(−0.057)
Growth	−0.0518	−0.0523	−0.0499	−0.031	−0.0322	−0.0293	−0.022	−0.024	−0.023
	(−1.1526)	(−1.1612)	(−1.11)	(−0.786)	(−0.8144)	(−0.7441)	(−1.085)	(−1.228)	(−1.191)
Dire	−0.0042	−0.004	−0.0041	−0.0019	−0.0017	−0.0017	−0.002	−0.003	−0.003
	(−1.05)	(−1.0109)	(−1.0238)	(−0.5318)	(−0.4859)	(−0.5031)	(−0.765)	(−1.011)	(−0.964)
Age	1.3426 ***	1.3276 ***	1.354 ***	1.2633 ***	1.2411 ***	1.2731 ***	0.7473 ***	0.754 ***	0.7521 ***
	(9.7598)	(9.662)	(9.834)	(10.4665)	(10.2727)	(10.5396)	(8.2485)	(8.3448)	(8.2871)
Numb	0.1491 ***	0.164 ***	0.1491 ***	0.1077 ***	0.126 ***	0.1079 ***	0.0802 ***	0.0789 ***	0.08 ***
	(6.3307)	(7.2603)	(6.3269)	(5.2111)	(6.344)	(5.2198)	(5.169)	(5.3115)	(5.1471)
Poll	−0.1921 ***	−0.2531 ***	−0.2206 ***	−0.1348 ***	−0.2003 ***	−0.1608 ***	−0.1407 ***	−0.1491 ***	−0.1515 ***
	(−3.2673)	(−4.2586)	(−3.6063)	(−2.6141)	(−3.8323)	(−2.9967)	(−3.6335)	(−3.8157)	(−3.7554)
_cons	1.2778 *	1.0829 *	1.2387 *	1.0795 *	−0.461	−0.029	1.0546 **	1.0531 **	1.0419 **
	(1.9767)	(1.6818)	(1.9154)	(1.9033)	(−0.899)	(−0.054)	(2.4773)	(2.4873)	(2.4443)
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	13,968	13,968	13,968	13,968	13,968	13,968	13,968	13,968	13,968
Adj.R2	0.259	0.257	0.260	0.0263	0.258	0.264	0.266	0.266	0.265

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

. Columns (1), (4), and (7) of Table 5 show that when the variables are replaced as dependent variables, digital transformation of enterprises (DCG) still has a significant positive impact on them; whereas, columns (2), (5), and (8) show that Fissub still has a significant inverted “U”-shaped impact on the level of GI in enterprises, which is consistent with the results above. Columns (3), (6), and (9) of Table 5 test the robustness of the mediation effect, demonstrating that digital transformation continues to have a significant positive impact on the total amount, quality, and quantity of green technological innovation, implying that DCG acts as a mediator between Fissub and GI. After substituting the dependent variables, the results remain similar to the prior ones, demonstrating that this study’s baseline regression results are robust.

4.4.2. Replacement of Sample Data

The Shanghai and Shenzhen A-share companies listed after 2015 are excluded from the regression analysis in order to ensure the accuracy of the results. To strengthen the results, 50 companies—23 of which were listed after 2015—are added for regression analysis once more. The results are shown in

Table 6. Robustness test table for replacement sample data.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
	TGI		QGI		NGI
DCG	0.0483 ***		0.055 ***		0.042 *
	(2.6987)		(3.5079)		(1.76)
Fissub		1.7881 **		1.65 ***		1.8535 ***
		(2.166)		(3.184)		(3.238)
Fissub2		−6.7346 ***		−6.7205 ***		−5.5532 ***
		(−3.748)		(−3.956)		(−4.631)
Size	0.1165 ***	0.1202 ***	0.1175 ***	0.1215 ***	0.062 *	0.062 *
	(4.8202)	(4.9549)	(5.5086)	(5.653)	(1.753)	(1.794)
Lev	−0.045	−0.123	−0.1464	−0.1887	−0.155	−0.174
	(−0.267)	(−0.741)	(−1.2265)	(−1.5857)	(−1.229)	(−1.411)
Roe	−0.604 ***	−0.616 ***	−0.4723 ***	−0.4863 ***	−0.2601 **	−0.2625 **
	(−3.035)	(−3.089)	(−2.6862)	(−2.7496)	(−2.0405)	(−2.058)
Growth	−0.015	−0.020	−0.009	−0.012	−0.023	−0.026
	(−0.616)	(−0.833)	(−0.451)	(−0.638)	(−1.152)	(−1.321)
Dire	0.002	0.001	0.002	0.002	−0.002	−0.003
	(0.595)	(0.330)	(0.732)	(0.509)	(−0.724)	(−0.980)
Age	1.3381 ***	1.3065 ***	1.2739 ***	1.2365 ***	0.7218 ***	0.7339 ***
	(9.79)	(9.57)	(10.54)	(10.22)	(8.2395)	(8.2953)
Numb	0.1288 ***	0.1455 ***	0.0869 ***	0.1061 ***	0.0754 ***	0.0755 ***
	(5.5997)	(6.5404)	(4.2723)	(5.3843)	(5.1121)	(5.3078)
Poll	−0.056	−0.047	−0.1072 **	−0.1529 ***	−0.1319 ***	−0.1382 ***
	(0.575)	(0.486)	(−2.283)	(−3.1826)	(−3.8738)	(−3.9874)
_cons	1.0952 *	0.8748	0.977 *	0.7268	0.8788 **	0.869 **
	(1.738)	(1.3923)	(1.7533)	(1.3054)	(2.1751)	(2.1636)
Year	Yes	Yes	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes	Yes	Yes
N	14,368	14,368	14,368	14,368	14,368	14,368
Adj.R2	0.34	0.41	0.242	0.273	0.216	0.22

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

. The conclusions of this article are quite robust since, as Table 6 shows, neither the regression results nor the mediation effect have altered.

4.5. Endogeneity Test

The results of the previous regression analyses indicate that digital transformation (DCG) has a significant contribution to green technological innovation (GI), and there is an inverted U-shaped relationship between government subsidies (Fissub) and the green technological innovation of enterprises. Although some of the endogeneity problems can be mitigated by controlling for fixed effects, scientifically, there is still the effect of omitted variables that lead to endogeneity problems. In order to try to solve this problem, this paper uses instrumental variable hair to conduct regression analysis on these two parts.

To deal with the endogeneity of enterprises’ digital transformation on green technology innovation, this paper draws on the research of Ruiz-Rodríguez et al. [53] and takes “the average of the degree of digital transformation of other enterprises in the enterprise’s industry” as an instrumental variable. On the one hand, enterprises in the same industry have a certain degree of similarity in digital transformation, which is caused by the general characteristics of the industry, but there are big differences in the digital transformation of different industries, for example, when digital transformation is carried out in the retail industry and the energy industry, the retail industry tends to digital improvement in marketing, while the energy industry focuses on production and other aspects. On the other hand, due to the competition among enterprises, the enterprises that have not yet succeeded in transformation will imitate the enterprises that have already succeeded, which is also a manifestation of the “cohort effect”, in which the digitalization level of other enterprises has a linear relationship with the digitalization level of their own enterprises. Therefore, it is scientific to choose the average value of the digital transformation level of other enterprises in the enterprise’s industry.

For the endogeneity problem arising from financial subsidies affecting the green technology innovation of enterprises, this paper adopts the research method of Tong Jinzhi et al. [54] and chooses “the natural logarithm of the average value of financial subsidies of enterprises in the province where the enterprise is located, excluding this enterprise, after adding 1” as the instrumental variable. Due to the macro-control of national policies, enterprises in the same region are consistent in the institutional environment, and at the same time, the financial subsidies they receive also show a certain regularity. On the whole, the financial subsidies received by other enterprises have no relationship with the green technology innovation of this enterprise, so it is feasible to use this variable for regression.

The endogeneity test findings employing the instrumental factors are shown in

Table 7. Endogeneity test.

Variable	TGI		QGI		NGI
	(1)	(2)	(3)	(4)	(5)	(6)
DCG	1.345 **		1.373 **		0.896 *
	(2.176)		(2.424)		(1.732)
Fissub		0.2763 *		0.393 **		0.468 *
		(1.7902)		(1.735)		(1.791)
Fissub2		−0.0074 *		−0.0138 *		−0.0279 *
		(−1.88)		(−1.74)		(−1.78)
Cragg—Donald Wald F	574.525	589.348	584.733	590.1746	614.735	588.472
	(16.380)	(19.430)	(17.624)	(20.636)	(23.233)	(18.563)
K—Paap L M	297.683	367.1746	303.744	387.475	329.667	318.640
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
Observations	13,968	13,968	13,968	13,968	13,968	13,968
Adj.R2	0.245	0.330	0.142	0.370	0.185	0.179
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes	Yes	Yes

Note: ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

. At the 10% level, all of the Cragg–Donald Wald F-values are higher than the Stock–Yogo critical value. Even after addressing the endogeneity issue, firms’ DCG continues to have a positive impact on GI, as shown by the positive and significant coefficients of digital transformation in Table 7’s columns (1), (3), and (5). According to the findings in columns (2), (4), and (6), there is still an inverted “U” relationship between Fissub and firms’ GI, which is in line with the findings of the earlier study. Therefore, the estimation results are robust, i.e., the conclusions of this study are further supported.

4.6. Placebo Test

To address the effects of omitted variables and unobservable, we conduct a placebo test, cf. Feng, Chen, and Nie (2023) [55], Specifically, we randomly disrupt DCG levels across firms and run the baseline regression again. The process is repeated 500 times, and if the baseline regression results are robust to unobservable or omitted variables, most of the “dummy” coefficients will be insignificant and their kernel density distributions will be close to normal. As shown in Figure 3, the regression coefficients are clustered around 0 and approximately positively distributed, so it can be inferred that the main results are not significantly affected by omitted and unobservable variables.

4.7. Mechanism Testing

Based on the level of market concentration, we empirically test the strong and weak changes in the impact of DCG on GI by entering the cross-multiplier term of DCG and HHI variables into the regression model; the results of the regression are presented in

Table 8. Moderating effects test table.

Variable	TGI		QGI		NGI
	(1)	(2)	(3)	(4)	(5)	(6)
DCG	0.0501 ***	0.0804 ***	0.0463 ***	0.0713 ***	0.0575 ***	0.0829 ***
	(2.9133)	(3.875)	(3.011)	(3.2166)	(3.7864)	(4.5222)
HHI	−0.6545 *	0.8505	−0.0002	0.5564	−0.4995	0.8490 *
	(−1.6703)	(0.5776)	(0.2305)	(0.3606)	(0.3272)	(1.7103)
DCG*HHI		−0.429 ***		−0.36 **		−0.3595 **
		(−2.586)		(−2.1795)		(−2.4533)
_cons	0.4788	0.0442	0.4252	0.441	0.4064	0.3801
	(0.7847)	(0.728)	(1.0832)	(1.1252)	(0.7541)	(0.7089)
Control	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes	Yes	Yes
N	13,968	13,968	13,968	13,968	13,968	13,968
adj. R2	0.254	0.248	0.266	0.266	0.249	0.251

Note: ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

. From column (2) of Table 8, we can see that the coefficient of the cross-multiplier term of DCG and HHI is −0.429 and is significant at the 1% level, indicating that market concentration negatively moderates the promoting effect of digital transformation on the overall green technological innovation of enterprises. As can be seen in columns (4) and (6), market concentration is negative and significant. It indicates that when the market concentration of the enterprise’s industry is higher, it will reduce the information flow between the enterprise and the outside, which will reduce the degree of digital transformation of the enterprise, and then reduce the positive effect of DCG on the GI of the enterprise. Through the previous analysis, hypothesis 4 is verified.

4.8. Expanded Analysis

In order to explore the role of enterprise digital transformation and government subsidies on the impact of green technology innovation in depth, this paper divides the selected 1746 sample enterprises according to the nature of the enterprise (Poll), which is 1 if it is a heavily polluting industry and 0 if it is not, and regresses the total amount of green technology innovation as the dependent variable. The regression results are reported in

Table 9. Analytical table.

Variable	TGI
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Non-heavily polluting industries				Heavily polluting industries
DCG	0.074 ***		0.075 ***	0.244 ***	0.006 *		0.0058 *	0.0088 **
	(4.120)		(4.269)	(3.635)	(1.707)		(1.896)	(2.335)
Fissub		4.281 **	4.144 **			5.66 ***	5.67 ***
		(2.484)	(2.432)			(2.706)	(2.698)
Fissub2		−35.23 ***	−33.93 ***			−37.67 ***	−37.62 ***
		(−4.080)	(−4.077)			(−2.914)	(−2.905)
HHI				1.3341				0.0508
				(0.825)				(0.5692)
DCG*HHI				−1.31 ***				−0.2511 *
				(−3.382)				(−1.8018)
_cons	0.220	0.350	0.233	0.0028	0.14	0.183	0.179	0.144
	(1.545)	(1.602)	(1.617)	(1.574)	(1.266)	(1.583)	(1.482)	(1.252)
Control	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	7120	7120	7120	7120	6848	6848	6848	6848
Adj.R2	0.24	0.22	0.25	0.22	0.31	0.28	0.28	0.32

Note: ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

. Among them, columns (1) and (5) show that the digital transformation of enterprises promotes the total amount of green technology innovation of both heavily polluting enterprises and non-heavily polluting enterprises, but the regression coefficients of heavily polluting enterprises are significantly smaller than the coefficients of non-heavily polluting enterprises. From the results in columns (2) and (6), it can be seen that government subsidies have an inverted “U” shaped relationship on the total green technological innovation of both heavy polluting enterprises and non-heavy polluting enterprises but the inflection point of heavy polluting enterprises of 751,261 CNY has enlarged the impact of financial subsidies compared with the inflection point of non-heavy polluting enterprises of 607,579 CNY. The reason may be that with the implementation of the dual-carbon policy, heavily polluting industries are subject to more government regulation and subsidies, which enables them to obtain additional funds from government subsidies and tax breaks and enables enterprises to break through the financing constraints and fully play the promoting role of government subsidies so that appropriate government subsidies play a more important role in promoting the green technological innovation of heavily polluting enterprises. However, due to the targeting effect of government subsidy issuance, it makes enterprises spend more funds on green innovation and cannot take into account the digital transformation of enterprises, which is the reason why the regression coefficients of heavily polluting enterprises are significantly smaller than those of non-heavily polluting enterprises, as well as the fact that the mediating effect of digital transformation of heavily polluting industries is weaker than that of non-heavily polluting enterprises from column (3) and column (7). Finally, column (4) and column (8) of the table verify the moderating effect of market concentration in the two industries, and it can be seen that the moderating effect of market concentration is stronger for non-heavy polluting enterprises, which is because the market concentration of heavily polluting enterprises in China is not very high, and thus, there is very intense competition among enterprises. Compared with industries with low market concentration, those with high market concentration tend to be more able to encourage enterprises to innovate, so the moderating effect of market concentration in the heavily polluting industry is even weaker.

5. Conclusions and Recommendations

5.1. Conclusions

Digital transformation significantly contributes to the level of green technological innovation of enterprises as a whole, but the decomposition study finds that the impact of digital transformation on the quantity of green technological innovation of enterprises is weaker than that on the total quantity and quality of green technological innovation. Furthermore, the regression results show that government subsidies have an inverted U-shaped relationship with the level of green technology innovation of enterprises from a macro perspective, while digital transformation has a mediating effect in the relationship. In the moderating effect analysis, it is found that market concentration inhibits the facilitating effect of digital transformation on enterprise green technology innovation. Finally, in the extended analysis, it was found that the promotion of digital transformation on the total green technological innovation of enterprises in heavily polluted enterprises is smaller than that in non-heavily polluted enterprises, while the moderate space for government subsidies to act on the total green technological innovation of enterprises in heavily polluted enterprises is larger, and at the same time, in the analysis of the moderating effect of market concentration, it is learned that the relationship between digital transformation and the total green innovation of enterprises is smaller than that in non-polluting enterprises.

5.2. Recommendations

(1) Enterprises should fully utilize digital technology to inject new momentum into innovation activities, as digital transformation has a significant positive effect on enterprise green technology innovation. More and more enterprises have begun to pay attention to digital transformation under the guidance of ‘Digital China’, which has provided a more solid technical foundation and a larger innovation platform for green technology innovation. Enterprises are utilizing digital transformation to optimize resource allocation, establish partnerships and innovation networks, and improve innovation efficiency to gain a competitive advantage in the industry;

(2) Government subsidies have a mixed impact on green technology innovation in enterprises, initially promoting it but later inhibiting it. Therefore, the government should consider the moderate space for enterprises in macro-control. Because digital transformation plays a mediating role between government subsidies and green technology innovation, a portion of the subsidies will be allocated to support enterprises in their digital transformation efforts. It is important to note that the relationship between subsidies and innovation cannot be studied in isolation when considering the moderating effect of digital transformation;

(3) The state should establish targeted policies to enhance the impact of the digital economy on green development. Additionally, government subsidies should be expanded to promote green technological innovation, and policy should play a guiding role;

(4) In conclusion, the state should consider the synergistic effect between the development of the digital economy and policy subsidies as an important means and key driving force for green development. It is necessary to optimize the market system on the macro level, construct and improve the market-oriented green innovation system, gradually adjust the rationality of the relevant economic subsidy policies, steadily promote the green transformation of enterprises, and construct a complete green technological innovation and intellectual property protection policy to promote the green development of enterprises. The promotion of enterprise enthusiasm for green initiatives is a fundamental means of solidifying their competitive position and complying with the trends of the times. The promotion of enterprise enthusiasm for green initiatives is a fundamental means of solidifying their competitive position and complying with the trends of the times. Strengthening their own digital and green transformation is crucial for enterprises. This will enhance corporate governance and talent cultivation, eliminate waste of resources caused by inefficient environmental protection, and improve the efficiency of enterprise innovation and rationality of investment. It will ultimately lead to the goal of green digital transformation and intellectual property protection.

5.3. Shortcomings and Prospects

Our research process has certain flaws and potential for improvement. First, in the selection of indicators, due to the factors of problem orientation and data accessibility, we selected the more widely recognized ‘TGI’, ‘QGI’, and ‘NGI’ to represent the green technological innovation of enterprises. However, the majority of green patents chosen by previous research cannot cover the specifics of green technological innovation in an entire business. Second, the variations resulting from the nature of various firms are only partially analyzed in the research on the expansion of enterprise nature; no in-depth investigation is conducted. The differences in policy direction between substantially polluting and non-heavily polluting businesses can be thoroughly examined in future research [56]; how the government may encourage highly polluting businesses to implement green transformation can offer more detailed policy suggestions for businesses’ sustainable growth [57,58].