The corporate path to green innovation: does the digital economy matter?

Abstract

As China’s digital transformation accelerates, there is growing interest in whether the digital economy can effectively boost green innovation in industrial enterprises and enable China’s development to break through the constraints of resources and environment. Therefore, this study analyzes the data of A-share industrial listed enterprises (2011–2020). Results indicate that the digital economy promotes green innovation. The impact of the digital economy on green innovation varies significantly among different types of enterprises, with stronger effects on state-owned enterprises. Digital economy enhances green innovation via boosting public attention and optimizing energy structure. Therefore, playing the role of monitoring public attention, and optimizing energy use are key strategies to promote corporate green innovation.

Introduction

Nowadays, China’s economy is shifting from the stage of high-speed growth to green development (Lu and Zeng 2022; Qiu et al. 2022). As a result of increased labor costs, gradually rising energy prices, diminishing resource factors, and decreasing marginal efficiency of capital, the pace of economic growth in China is gradually decelerating (Wang et al. 2019; Wang et al. 2023). The mode of relying on investment, factors and trade to promote economic growth can no longer meet the needs of sustained growth, thus the economy must seek new sources of energy to stimulate its economic progress (Jin et al. 2023; Yang et al. 2021; Yao et al. 2021)1. Therefore, the Chinese government has firmly advocated that innovation is key strategic foundation for building a modern economic system (Tu et al. 2023). Technological innovation, as the primary source of economic growth, plays a crucial role in enhancing local productivity, while also acting as the fundamental driving force in the overall progress of national economic development (Wu et al. 2021; Fan et al. 2022; Meng et al. 2022).

Chinese innovation-driven strategy has now entered a comprehensive implementation stage, with increasing investment being made in science and technology innovation. As a result, there has been an exponential growth in scientific and technological (S&T) innovation activities, and a significant increase in national S&T innovation capacity. The 2020 World Intellectual Property Indicators report indicates that in 2019, Chinese invention patent applications reached 1.4 million, accounting for 43% of the global patent applications; this is a noteworthy feat, as China has held the top position in the world’s patent application rankings for 9 consecutive years since 20112. The steady increase patent application number within China serves as a clear indication of the nation’s improving research and development (R&D) and innovation levels. Nevertheless, available statistics reveal that the conversion rate of R&D achievements in China is remarkably low, standing at less than 15%. This figure is notably lower when compared to that of developed countries, where the conversion rate hovers around 40%3. China is facing several challenges in its efforts to achieve technological innovation and enhance R&D achievements. Among these obstacles are issues such as low conversion rates of R&D achievements, a disconnect between the innovation chain and industrial chain and insufficient independent innovation momentum (Xu et al. 2023).

Currently, with the iterative update of digital technologies, socio-economic development has stepped into the digital era (Ran et al. 2023; Razzaq et al. 2023). As the most promising and dynamic area of contemporary economic and social development, the digital economy stimulates consumption, increases investment, and boosts employment (Ren et al. 2022; Patwary 2023). China’s digital economy will exceed 39 trillion RMB in 2020, with a proportion of GDP of about 39%, further underscoring its vital role in economic development4. In light of the escalating international competition, the digital economy has emerged as a pivotal domain for augmenting comprehensive national power and competitiveness (Carlsson 2004; Hao et al. 2023). Unlike the traditional economy, the digital economy has surmounted the geographical constraints of the conventional economy with the aid of digital technology, thereby fundamentally transforming the present economic development paradigm and industrial framework (Luo et al. 2022; Bertani et al. 2021; Patwary 2023). Hence, firms, being the launching point for the digital economy’s advancement, are also the primary agents of China’s innovation-driven strategy. The digital economy endows enterprises with not only a change in the direction of their strategic planning but also presents an opportunity for them to achieve breakthroughs in independent innovation (Vial 2021).

Notably, serious population, resource, and environmental issues have emerged as a significant obstacle to China’s high-quality development (Ge et al. 2023). Nevertheless, resolving this dilemma necessitates focusing on both developing a green economy and guiding firms to initiate technological innovation, while prioritizing sustainable development and conservation (Irfan et al. 2022; Wu et al. 2020; Patwary et al. 2022a). According to the innovation-driven economic growth theory, relying solely on importing and imitating foreign technologies to sustain China’s economic growth are challenging. Instead, by firmly grasping key core technologies and promoting the concept of independent innovation, green growth can be advanced with high efficiency and low cost (Feng et al. 2022b; Fang et al. 2022). Green innovation can minimize the legal cost of production and end-of-production management while reducing the environmental pressure of production to a certain extent. Additionally, green innovation technology can diminish resource usage and pollution emissions at the root, aligning more closely with China’s economic green development reality (Patwary et al. 2022b). Therefore, the present question worth considering is whether the digital economy can spur corporate green innovation, and how the impact varies across property. If the finding is affirmative, what is the underlying mechanism of action?

The marginal contributions of this paper are roughly the following three points. First, this study empirically examines the impact of digital economy on corporate green innovation from a more microscopic perspective. Second, based on the differences with the sample, this study discusses property rights heterogeneous characteristics of the digital economy on corporate green innovation. Third, in order to further clarify the mechanism of the impact of the digital economy on green innovation, this study explores the channel mechanism of its affecting corporate green innovation in terms of public attention and optimizing energy structure.

Review of literature

Research on digital economy

The application of digital technology has spread across different industries, and the scope of the digital economy has continued to expand. However, a complete consensus on its definition and connotation is yet to be reached. Tapscott (1995) was among the first to introduce the term “digital economy,” emphasizing its potential to become the new form of economy in the future. He stated that e-commerce would be its most vital component. Since then, several researchers have proposed different perspectives on the digital economy’s definition. For instance, Kling and Lamb (2000) argued that digital economy counts on digital technologies both for generation, distribution, and supply of goods and services. This viewpoint is supported by the British Computer Society (2014), which considers the digital economy to be a new economic form that results from digital technology. Nathan and Rosso (2013) and Knickrehm et al. (2016) view the digital economy as the part of economic output resulting from digital technology and digital communication devices. The UK House of Commons (2016) defines the digital economy as the trading of goods and services in digital form, whereas Chinese scholar Zhang Peng (2019) considers the digital economy as an economic system that arises from the re-optimization of resource allocation by platform organizations driven by information and communication technology, including trading and data platforms.

With the advent of mobile Internet, the digital economy experienced a significant surge in output and development speed. This prompted scholars to turn their attention toward the study of digital economy (Bertani et al. 2021; Ding et al. 2022). However, the lack of precision in the accounting or statistics of the digital economy has given numerous theoretical contradictions, such as the “digital economy paradox.” Goldfarb and Tucker (2019) argued that the conclusion of the “digital economy paradox”—that the productivity of firms has declined—is a result of arbitrary judgment made after statistical errors. Subsequently, Gopal et al. (2003) developed a welfare measurement model to analyze the benefits of digital products and welfare outcomes. They also conducted a more systematic study of trade characteristics and regulation in the digital economy (Sultana et al. 2021). Brynjolfsson and Oh (2012) furthered the study by adopting the approach of classifying segmented industries into the digital economy accounting category. However, this accounting approach failed to take into account the increase in consumer surplus and efficiency optimization brought about by new business modes in the digital economy. Consequently, it resulted in an underestimation of the effectiveness of the digital economy. The China Academy of Information and Communication Technology (2020), in light of the actual industrial development in China, divided the scale of the digital economy into digital industrialization and industrial digitization. This approach allowed for the stripping out of digital technology to the value added of each industry in the national economy for more accurate measurement.

The current literature examining the effects of the digital economy on other factors can be categorized into the following aspects: in terms of economic effects, the digital economy has the potential to improve income levels, particularly among low-income rural populations, by leveraging digital finance and digital skills, thus fostering inclusive growth (Kapoor 2014; Bauer 2018; Yang et al. 2022a, b, c). However, Gordon et al. (2022) posits that AI technologies alone are insufficient to drive productivity gains, while Acemoglu and Restrepo (2018) contend that excessive digitalization may lead to resource waste and labor mismatch, ultimately inhibiting total factor productivity growth. With regard to employment structure, the digital economy may negatively impact low-skilled labor employment, resulting in structural unemployment issues (Acemoglu and Restrepo 2017). Yet, when the elasticity of substitution of high-medium-skilled labor exceeds that of high-low-skilled labor, the digital economy has the potential to create more knowledge and technology-intensive jobs, expanding the capacity of the job market and optimizing employment structures (Acemoglu and Restrepo 2018; Lordan and Neumark 2018). Concerning environmental effects, the digital economy inhibits pollution by enhancing energy mix and enabling technological advancements (Li et al. 2021). However, Strubell et al. (2019) and Henderson et al. (2020) assert that the use of artificial intelligence within the digital economy may contribute to environmental harm.

Research on digital economy on green innovation

The proliferation, evolution, and convergence of digital technologies have driven digital economy expansion, hastened the digital transformation of economies and societies, and offered novel prospects for technological innovation among small- and medium-sized enterprises (SMEs). The extant literature on digital technologies for enterprise innovation has focused primarily on the Internet, blockchain, and so on. Prior research has noted that the integration of Internet technology with traditional finance effectively stimulates the development of Internet finance (Hou et al. 2016; Qiao et al. 2018). Moreover, scholars have suggested that Internet development facilitates SME technological innovation by mitigating information asymmetry, optimizing resource allocation, and enhancing innovation openness (Fang et al. 2022; Bloom et al. 2012). In addition to the requisite financial support, enterprise innovation depends on the search for and utilization of external knowledge, and big data technology can facilitate the acquisition of extensive and comprehensive data across diverse fields, providing technological support for enterprise innovation (Armstrong 2014; Hou et al. 2016). Relative to big data information retrieval, AI technology enables lower innovation costs and streamlines the technological innovation process with its potent and intelligent features (Cockburn et al. 2018), thus promoting technological innovation among SMEs.

However, scholars have also cautioned that AI technologies, while boosting productivity and technological innovation empowerment, may also generate risks and downsides, such as rising unemployment and increasing wealth inequality (Makridakis 2017; Huang and Rust 2018). Indeed, the digital economy has a profound impact on regional differences in innovation and entrepreneurial activity (Sultana et al. 2021). Bouncken et al. (2020) discovered that the sharing, collaboration, and convergence of the digital economy considerably elevate the level of innovation and entrepreneurial activity across regions, as observed across diverse classes and professionals. The emergence of the digital economy has facilitated the diffusion and adoption of digital technologies along both ends of the industrial and value chains (Guan and Ma 2003), thereby potentially enhancing the innovation capacity of firms. Furthermore, some scholars have demonstrated that a high level of Internet technology support can serve as an effective technological foundation for green technology innovation among enterprises. Grigorescu (2021) has also suggested that digital resources and the industrial Internet sector may stimulate enterprise green technology innovation.

In summary, there is still considerable debate on digital economy measurement, and the varying perspectives on its measurement may lead to biased research results. While research on the economic, social, and environmental impacts of the digital economy has been abundant, its innovation effects have received comparatively less attention, particularly with respect to green technology innovation. Furthermore, only a few scholars have focused on green innovation in enterprises internet technology and digital resources perspectives. Lastly, the depth of the two nexus remains insufficient, and few studies have thoroughly examined the underlying mechanisms through which the digital economy influences green innovation in enterprises. To address these gaps in the literature, this study proposes a digital economy index composed of three dimensions, namely, digital infrastructure, digital technology application development, and digital economy production services. This index more comprehensively reflects digital economy than previous measures. Additionally, this study mines the effect of the digital economy on green innovation and provides insights into the possible mechanisms by which it exerts influence.

Study design

Model setting

This study uses a two-way fixed effects model to investigate the s empirical analysis, which helps to control for model estimation bias due to unobserved heterogeneity and control for the effect of sample differences on the regression results (Xu et al. 2022). Following Xu et al. (2022) the model was constructed as follows:

$${\mathit{Gi}}_{\mathit{it}}=\partial +{\partial }_1{\mathit{De}}_{\mathit{it}}+{\partial }_i{\mathit{Con}}_{\mathit{it}}+v_t+u_i+{\epsilon }_{\mathit{it}}$$ 1

where i is the regional individual and t is the year. Gi is green innovation, De is the core explanatory variable indicating regional digital development, Con is all the control variables set, which are used to prevent possible omitted variables from the model, including book-to-market ratio (Br), net gearing ratio (Ner), asset current ratio (Cf), total asset turnover ratio (Ttr), return on net assets (Roe), and the size of the company’s assets (size). ∂ is the parameter to be estimated. v_t is a time fixed effect, u_i is a regional fixed effect, and ε_it is a random error term.

Variable descriptions and data sources

Explained variables

The green economy is an important driver for the evolution of the economy to high-quality development. Green innovations (Gi) are critically essential, not only to facilitate green economy development but also as an indispensable road to boost economic efficiency (Xu et al. 2022; Liu et al. 2022; Yang et al. 2022a, b, c). Green innovation inputs and outputs are the two main methods of measures of current corporate green innovation (Liu et al. 2022; Sun and Razzaq 2022; Biscione et al. 2021). Through a substantial amount of literature and after a thorough comparison, this study chooses to measure the degree of green innovation of listed industrial enterprises by the number of green patents (Feng et al. 2022a; Wang et al. 2022).

Explanatory variables

The measurement of the digital economy is widely debated. Most of scholars measure the indicators from a single dimension, lacking a comprehensive measurement of the comprehensive index of the digital economy (Ma and Li 2022; Xu and Li 2022). Following Li et al. (2021) and Ding et al. (2022), the informationization index system, represented by the social informatization index issued by the International Telecommunication Union and China’s national informatization index, is fully drawn from the level of digital infrastructure, the dimension of digital technology application. The index system contains three aspects: digital infrastructure level, digital technology application development dimension, and digital economy production service, which makes up for the lack of coverage of digital economy (De). The indicator system is shown in Table 1.

Table 1.

Digital economy index system

Comprehensive indicators	Evaluation dimension	Weights	Indicator interpretation	Weights
Digital economy development level	Digital infrastructure level	0.352	Internet penetration rate	0.083
			Number of Internet port accesses	0.080
			Cell phone penetration rate	0.081
			Number of cell phone users	0.082
	Digital technology application development	0.248	Number of Internet users	0.085
			Number of domain names	0.083
			Number of websites	0.075
	Digital economy production service	0.400	Share of computer and software employees in urban population	0.081
			Total telecom business per capita	0.081
			Software business revenue as a proportion of GDP	0.079
			E-commerce purchases and sales as a proportion of GDP	0.083
			Digital finance development index	0.080

First of all, the data is preprocessed by using the entropy method commonly used in the research. The calculation formula is as follows: a_ij is the single index of the digital economy of area i in year t and x_ij is the true value of the index.

$$a_{\mathit{ij}}=\frac{x_{\mathit{ij}}-min\left(x_{\mathit{ij}}\right)}{max\left(x_{\mathit{ij}}\right)-min\left(x_{\mathit{ij}}\right)}{a}_{\mathit{ij}}=\frac{max\left(x_{\mathit{ij}}\right)-x_{\mathit{ij}}}{max\left(x_{\mathit{ij}}\right)-min\left(x_{\mathit{ij}}\right)}$$ 2

Secondly, calculate the weight (p_ij) and entropy (e_ij) of the j index in province i, in the following form:

$$p_{\mathit{ij}}=\frac{a_{\mathit{ij}}}{{\sum }_{i=1}^n{x}_{\mathit{ij}}}{E}_{\mathit{ij}}=-K\sum _{i=1}^n\left(p_{\mathit{ij}},*,{\mathit{lnp}}_{\mathit{ij}}\right)$$ 3

Third, calculate the redundancy (D_ij) and index weight (w_ij) of j index, in the following form:

$$D_{\mathit{ij}}=1-E_{\mathit{ij}}{w}_{\mathit{ij}}=\frac{D_{\mathit{ij}}}{{\sum }_{j=1}^m{D}_{\mathit{ij}}}$$ 4

Finally, the digital economic index of each province and city over the years is calculated as follows:

$$DE=\sum _{p=1}^n{a}_{\mathit{ij}}*W_i$$ 5

Mechanism variables

Public attention (Pa)

In consideration of their interests and sustainable development, enterprises are likely to become more attentive to protecting the environment and actively enhancing production technology to minimize pollution emissions and environmental damage, when their production behavior is scrutinized by the public. Environmental attentions of the public have been measured primarily through indicators such as environmental letters and complaints, non-governmental organizations (NGOs), and environmental news reports in newspapers, radio, and television. However, the current environmental petition and complaint data are only available at the provincial level due to data limitations, making it relatively macro- and lacking micro-level explanatory power due to the summation fallacy. In China, compared to developed countries, environmental NGOs are developing slowly and the public has difficulty participating effectively in the government’s environmental governance through NGOs. Additionally, news reports such as newspapers, radio, and TV are subject to influence by the government and the market, and may not adequately represent the public’s active concern for the environment. Therefore, this study employs the annual median of the web search volume index (WSVI) for public companies to measure the level of public interest in the firms. Compared to other methods of measuring public attention, web search engines have a broader coverage and better data availability, which provide more significant advantages (Cheng and Liu 2018).

¹ (Es)

China’s industrial development is ruled by coal-based energy consumption structure, which not only consumes a considerable amount of fossil energy but also emits harmful substances into the environment (Ran et al. 2023). It is essential to transform this energy structure to ensure sustainable development (Hao et al. 2023). To achieve this, the present study has adopted a uniform and scientific approach by converting the primary fossil energy used by each province into standard coal. The ratio of this standard coal to the total energy consumption has been used to represent the energy structure.

Control variables

Finally, in order to take into account as many factors as possible that influence corporate green innovation and to avoid the influence of omitted variables, this study includes as many control variables as possible; the following control variables are introduced based on the combination of existing studies: book-to-market ratio (Br), net gearing ratio (Ner), asset current ratio (Cf), total asset turnover ratio (Ttr), return on net assets (Roe), and the size of the company’s assets (size).

Data sources

This study tests the impact of the digital economy on corporate green innovation using data from industrial enterprises listed on the Shanghai and Shenzhen A-share in China from 2011 to 2020. The digital economy data used in this study is sourced from the China Statistical Yearbook of previous years. Additionally, corporate green patent data and financial information for all companies are obtained from CSMAR and ESP databases. Through this process, a final sample size of 2023 industrial enterprises listed on A-shares, consisting of 13,288 observation samples, is obtained. The selected sources of data were chosen for their reliability and consistency, and as such, they provide a robust basis for the empirical model constructed in this study. Descriptive statistics are reported in Table 2.

Table 2.

Descriptive statistics

Variable	Mean	Std. dev.	Min	Max
Gi	0.181	0.531	0.000	5.521
De	0.348	0.159	0.037	0.766
Br	0.892	0.924	0.015	20.965
Ner	0.396	0.193	0.008	1.698
Cf	0.050	0.070	− 1.938	0.664
Ttr	0.657	0.424	0.006	7.871
Roe	0.061	0.568	− 60.15	2.379
Size	22.140	1.237	17.90	28.60
Pa	6.723	0.715	0	10.680
Es	− 1.330	0.684	− 4.947	− 0.376

Empirical analysis

Baseline regression analysis

OLS regression cannot avoid the problem of inter-group or intra-group correlation, which is likely to cause bias in the estimation results. The results of both the OLS regression model and the random effects model are reported in Table 3 and the results are compared with those of the fixed effects model to improve the reliability of the regression results. Among them, columns (1), (3), and (5) of Table 3 display the model performance with no control variables added, while columns (2), (4), and (6) of Table 3 show the result with control variables added. It can be seen that the development of digital economy has a significantly positive impact on the green innovation of companies, the finding similar to that of Litvinenko (2020). The digital development of regional economies leads to improved information technology and improved network infrastructure, and the economies of scale and network economy effects of the digital economy can help enterprises share and exchange, optimization and upgrading of individual product processes at lower costs. Meanwhile, enterprises are able to promote the optimal allocation of production and pollution control production factors, enabling enterprises to obtain the Metcalfe effect, reducing marginal innovation cost of enterprises and realize technology spillover, enhance the return of enterprise innovation level, and provide technological support for green innovation (Li et al. 2021). The networking effect of the digital economy can break the enterprise innovation boundary and reduce transaction costs, which not only helps industrial enterprises to reduce information search costs and the consumption of resources in the industrial chain but also helps enterprises to obtain innovation resources in the value network (Han et al. 2020). As a result, the growth of green innovation can be driven by the evolution of digital economy.

Table 3.

Baseline regression results

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	OLS	OLS	RE	RE	FE	FE
De	0.273***	0.183***	0.334***	0.097***	0.592***	0.520***
	(0.029)	(0.028)	(0.030)	(0.034)	(0.171)	(0.172)
Br		− 0.042***		− 0.014**		0.001
		(0.006)		(0.006)		(0.007)
Ner		0.037		0.010		0.021
		(0.029)		(0.031)		(0.036)
Cf		− 0.227***		− 0.022		0.016
		(0.066)		(0.053)		(0.057)
Ttr		0.001		− 0.014		− 0.037**
		(0.011)		(0.013)		(0.016)
Roe		− 0.006		− 0.004		− 0.002
		(0.008)		(0.006)		(0.006)
Size		0.138***		0.090***		0.036***
		(0.005)		(0.006)		(0.010)
Constant	0.086***	− 2.901***	0.044***	− 1.834***	− 0.001	− 0.738***
	(0.011)	(0.099)	(0.014)	(0.128)	(0.036)	(0.205)
Observations	13,288	13,288	13,288	13,288	13,288	13,288
Year effect	Yes	Yes	Yes	Yes	Yes	Yes
Regional effect	Yes	Yes	Yes	Yes	Yes	Yes

*, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Parentheses indicate standard errors

Heterogeneity analysis

Considering the complexity of the types of listed industrial enterprises, the existing research sample is classified to further explore the impact of the digital economy on the different property rights of green innovation. This study is divide into two parts: state-owned enterprises (SOEs) and non-SOEs (see Table 4). Table 4 reveals that the digital economy has a diametrically opposed impact on enterprises of different natures, with significant contribution to green innovation in SOEs and little impact on green innovation in non-SOEs. This may be caused by the following reasons: firstly, green innovation requires companies to continuously optimize their products, technologies, materials, processes, and other aspects, which leads to larger upfront investment and higher expertise requirements for talents and backup talents, and SOEs have advantages in terms of talents, funds, and policies compared to non-SOEs. Second, SOEs also tend to enjoy many policy preferences and financial subsidies from the state, and financial assistance from the government can increase the level of investment in R&D. As a result, it can promote higher levels of green technology research and facilitate green innovation in enterprises. At as well, Chinese SOEs have a stronger relation with the government and have access to the latest policies to deal with the risks arising from market uncertainties. Meanwhile, the Chinese government has explicitly requested SOEs to take the construction of information technology system as the core, accelerate the construction of data sharing and utilization capacity, and continuously improve the level of digital intelligence, which makes SOEs apply digital technology more widely and is also conducive to improving their own green innovation level (Tian and Liu). Thus, it has a significant impact on Chinese SOEs and benefits their green innovation. In contrast, it has little impact on non-SOEs, which benefit less from digital economy.

Table 4.

Property rights heterogeneity results

Variables	(1)	(2)
	Non-SOEs	SOEs
De	0.178	1.081***
	(0.217)	(0.307)
Br	− 0.000	0.002
	(0.012)	(0.010)
Ner	0.040	0.043
	(0.042)	(0.077)
Cf	0.034	− 0.021
	(0.065)	(0.120)
Ttr	− 0.045**	− 0.001
	(0.020)	(0.031)
Roe	− 0.001	− 0.022
	(0.006)	(0.029)
Size	0.049***	0.034*
	(0.011)	(0.020)
Constant	− 0.962***	− 0.795*
	(0.240)	(0.435)
Observations	9286	4002
Year effect	Yes	Yes
Regional effect	Yes	Yes

*, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Parentheses indicate standard errors

Robustness tests

Due to the problems in model setting and indicator selection, this study uses multiple methods to test previous result robustness. Firstly, the measurement method of the variables is changed. In the benchmark model, the annual amount of green patent applications of industrial enterprises is applied to measure the green innovation. After considering that it is difficult for one method to strongly prove the validity of the measure, this study re-estimated the variables of the original model by replacing the number of green patents obtained (see column (1) of Table 5). Secondly, the period of time span of the research was changed. Considering COVID-19 shock, data for 2020 were excluded and only the study sample from 2011 to 2019 was selected and re-estimated (see column (2) of Table 5). Thirdly, the sample size is changed. Due to the great differences in development between Chinese cities, the business environment of enterprises varies greatly, especially the municipalities directly under the central government and other provinces and regions are still very different in various aspects. In order to prevent the effects caused by the differences in regional development levels, the samples of four municipalities directly under the central government are excluded (see column (3) of Table 5). After re-running the test in a different way, the regression coefficients of the core explanatory factors were not significantly different, proving result reliability. In addition, considering the bias of the results due to endogeneity issues in the empirical process, one lag of the digital economy was used as an instrumental variable and regressed using the 2SLS method (see column (4) of Table 5). Table 5 reveals that the results of different tests justify the instrumental variables, and the coefficients of De are largely consistent with the previous results.

Table 5.

Robustness results

Variables	(1)	(2)	(3)	(4)
	Change variables	Change the research span	Change the study sample	2SLS
De	0.506**	0.434**	0.442**	0.745*
	(0.223)	(0.182)	(0.208)	(0.411)
Br	− 0.005	0.006	0.007	0.004
	(0.009)	(0.007)	(0.007)	(0.010)
Ner	− 0.066	0.031	0.024	0.004
	(0.046)	(0.037)	(0.039)	(0.055)
Cf	− 0.037	− 0.006	0.044	− 0.009
	(0.074)	(0.059)	(0.061)	(0.079)
Ttr	− 0.041*	− 0.024	− 0.046***	− 0.042*
	(0.021)	(0.017)	(0.018)	(0.025)
Roe	0.001	− 0.001	− 0.002	− 0.030*
	(0.008)	(0.006)	(0.006)	(0.017)
Size	0.050***	0.024**	0.042***	0.033*
	(0.012)	(0.010)	(0.010)	(0.017)
Observations	13,288	12,103	11,319	10,726
Year effect	Yes	Yes	Yes	Yes
Regional effect	Yes	Yes	Yes	Yes
Kleibergen-Paap rk LM statistic				432.742 [0.000]
Kleibergen-Paap rk Wald F statistic				6171.891 {16.38}

“()” values are robust standard errors, “[ ]” values are P-values, and “{ }” values are critical values at the 10% level of the Stock-Yogo weak identification test. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Parentheses indicate standard errors

Influence mechanism analysis

Previous research highlights the significant impact of the growth of the digital economy on green innovation. However, the mechanisms of this impact require further investigation. In light of this, this study clarifies the role path of the digital economy’s development to the green innovation of industrial enterprises, with a focus on analyzing two impact mechanisms: public attention and energy structure. The study finds that public attention promotes green innovation. The public, as direct victims of environmental damage, increases monitoring of firms’ production and reduces the possibility of destructive production. This reduction in pollution emissions forces firms to innovate technologically (Zhao et al. 2022). Increased public concern may also force the government to increase its willingness to combat corporate pollution and improve the enforcement of environmental regulations. Porter and Linde (1995), based on a long-term dynamic perspective, argue that environmental regulations force firms to develop green innovations to improve competitiveness, offset additional costs, create an innovation compensation effect, and increase the level of green innovation of firms effectively (Shao et al. 2020). However, the impact of environmental regulations on green innovation can differ based on the heterogeneity of the regulations. Command-based environmental regulations may favor end-of-pipe innovation, while market-based regulations may favor green process and end-of-pipe innovation. Nonetheless, environmental regulation positively contributes to green innovation level in the long run (Fan et al. 2021).

The study also identifies the energy structure of enterprises as a crucial factor that influences its level of green innovation. Energy-consuming sectors can increase the proportion of clean and non-fossil energy sources while reducing the percentage of fossil energy consumption. This shift can force enterprises to make technological innovations, eliminate backward production technology, reduce energy intensity, and reduce pollution emissions, thereby boosting green innovation (Wang 2022). Digital technology can effectively reduce pollution emission levels and production energy consumption indices of energy-intensive industries by monitoring, collecting, and evaluating big data related to their production and manufacturing. This approach provides feasible and learnable paths for corporate green innovation (Xue et al. 2022; Li et al. 2022a, b).

According to the path analysis method used by Ran et al. (2023), and based on Eq. (1), the model was developed as follows:

$${\mathit{Med}}_{\mathit{it}}=\delta +a_1{\mathit{De}}_{\mathit{it}}+{\beta }_i{X}_{\mathit{it}}+{\epsilon }_{\mathit{it}}$$ 6

where Med_it indicates public attention(Pa) and energy structure (Es), respectively, and a₁ indicates the impact of digital economy development on public attention and energy consumption structure.

Columns (1) and (2) of Table 6 reveals that the digital economy can contribute positively to public attention. This indicates that digitalization can not only lead to socio-economic enhancement but also will strongly contribute to the optimization and transformation of social governance and social development methods. Elements of the digital economy system can promote the formation of individual motivation and the reinforcement of external stimuli, thus profoundly influencing the public’s behavioral cognition and living habits. The new technologies and improved facilities will facilitate the formation of individual behavioral motivations consisting of subjective factors such as environmental perceptions and environmental ethics (Ho et al. 2015). Therefore, good behavioral motivation has a positive effect on public concern about corporate behavior and participation in environmental governance. Columns (1) and (3) of Table 6 evident that digital economy inhibits traditional energy use and regional energy structure has been improved.

Table 6.

Mechanism test results

Variables	(1)	(2)	(3)
	Gi	Pa	Es
De	0.520***	0.844***	− 5.416***
	(0.172)	(0.152)	(0.119)
Br	0.001	− 0.116***	− 0.028***
	(0.007)	(0.006)	(0.005)
Ner	0.021	0.193***	0.092***
	(0.036)	(0.031)	(0.025)
Cf	0.016	0.235***	− 0.022
	(0.057)	(0.050)	(0.040)
Ttr	− 0.037**	0.066***	0.035***
	(0.016)	(0.014)	(0.011)
Roe	− 0.002	0.003	0.008**
	(0.006)	(0.005)	(0.004)
Size	0.036***	0.183***	0.013**
	(0.010)	(0.008)	(0.007)
Constant	− 0.738***	2.449***	− 0.327**
	(0.205)	(0.181)	(0.142)
Observations	13,288	13,288	13,288
Year effect	Yes	Yes	Yes
Regional effect	Yes	Yes	Yes

*, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Parentheses indicate standard errors

Conclusion and policy implications

The study of the digital economy on enterprise green innovation can provide a theoretical basis for research related to enterprise green innovation, as well as promote the in-depth development of digital economy research, and provide the corresponding theory for government policy formulation. The studies on the impact of digital economy on green innovation are conducted using a sample of industrial enterprises listed on A-shares from 2011 to 2020. Considering the differences between different types of enterprises, the sample is divided by the perspective of the difference in property rights. Finally, the specific impact path of the digital economy on corporate green innovation is explored from two perspectives: public concern and energy structure. The results are as follows: As the horizontal of digital economy increases, the level of green innovation of enterprises also increases accordingly. Different types of enterprises have different degrees of sensitivity to digital economy, and there are obvious heterogeneous characteristics of enterprises’ green innovation, among which the green innovation of SOEs is more strongly influenced by the developing digital economy. The digital economy enhances green innovation by increasing public attention and optimizing their energy structure. In light of the previous theoretical as well as empirical analysis, the following implications are made to enhance corporate green innovation.

Firstly, the regional digital economy should be developed by increasing the level of digital infrastructure and promoting the deep integration of digitalization and green technology innovation. This can stimulate the industrial growth of the digital economy, thus improving the potential of green innovation efficiency. Policymakers should provide digital technology learning and digital literacy education to cultivate composite talents who can effectively use information technology and help traditional enterprises stay up to date with cutting-edge digital technology.

Secondly, the progress of digital enterprise restructuring should be accelerated, while also reducing the differential policy treatment of enterprises with different ownerships. To achieve this, reasonable guidance should be provided by all levels of government, creating a quality environment for innovation and development. In addition, precise policies should be applied to give biased incentive policies to different types of enterprises and industrial nature. This can continuously enterprise to deal with fluctuations in external environmental policies, thus stimulating the endogenous power of industrial enterprises to develop green and the potential ability to carry out green innovation.

Thirdly, policymakers should strengthen their digital infrastructure and fully utilize information technology in the process of environmental governance by actively disclosing environmental protection information, incorporating public participation, and using informal regulation of public concerns as a supplement to the traditional government-based governance model. Additionally, the development of digital finance should be encouraged, as it can give birth to more new industries and new models, and enrich financing channels. The government should provide financial support for enterprises to develop green technologies and transform innovations. Furthermore, accelerating the integration and intersection of digital technologies with new areas such as new energy development, pollution control, and clean technologies can promote the digital transformation of energy infrastructure, building smart energy, reducing energy consumption intensity, and facilitating the transition of enterprises to green growth.

Although this study mines digital economy on green innovation in industrial enterprises, there are still some limitations. Firstly, this study only examines listed companies, and a large number of SMEs are not included in the scope of examination. Therefore, scholars can further expand the sample of the study in the future research. Secondly, this study discusses the influence of digital economy on corporate green innovation from the perspectives of monitoring public attention and optimizing energy structure, and there may be other influencing factors, such as environmental regulation and R&D input that deserve to be explored.

Author contribution

Yixiang Li: conceptualization, project administration, writing—review and editing, writing—original draft, software, visualization, and formal analysis. Fusheng Wang: formal analysis, methodology, data curation, writing—review and editing, validation, funding acquisition, and supervision.

Funding

This work was supported by the National Natural Science Foundation of China (NSFC) (72172042).

Data availability

Not applicable.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.