Introduction

Innovation is a critical way for enterprises to obtain product competitiveness and is also a particularly important driving force for economic development. Along with the rapid rise of China’s economy, the traditional labor-driven and factor-driven model of economic development has induced serious pollution and frequent environmental emergencies (Zhao & Qian, 2023). These environmental emergencies have become a major threat to human health and high-quality, sustainable economic development. Therefore, as the main force of innovation, many Chinese companies are actively engaging in green innovation and making it as key strategy to achieve environmental protection and improve their own competitiveness.

Green innovation, also known as eco-innovation and low-carbon innovation, aims to reduce carbon emissions (Zhao et al., 2023) and environmental risks (Castellacci & Lie, 2017). Enterprises that actively engage in green innovation can benefit themselves by achieving higher environmental performance, realizing corporate social responsibility (Wu et al., 2022a, b) and gaining an environmental reputation (Dangelico, 2017; Hsu et al., 2011). Green innovation incorporates green into the framework of enterprise output, enabling enterprises to have technological advantages in line with future development trends, produce green products and services, and gain green competitive advantages (Caplan & Oladi, 2018), thus maximizing their own interests (Lu, 2021).

However, corporate green innovation is typically an activity with high information asymmetry, uncertain returns, and high capital requirements, which often suffers from higher financial constrain and agency costs (Zhang, 2023). Therefore, corporate green innovation requires a large amount of cash, but relying on the internal profit accumulation of enterprise alone does not meet the cash demand for green innovation (Xiang et al., 2022), which determines it needs the external financial sector’s support (Cao et al., 2021; Feng et al., 2022a, b). However, from the actual situation of China, China’s diversified capital market is not well established, and there’s a long disconnect between the financial system and the technology system (George & Prabhu, 2003; Sheng et al., 2021). These problems narrow enterprises’ financing channels, and bank credit becomes the main way for enterprises to obtain R&D start-up capital (Giebel & Kraft, 2020). But the “institutional discrimination” and “size discrimination” in traditional bank credit have compromised the ability and the motivation of SMEs (Wei, 2019), which possess greater innovation potential (e.g., technology SMEs), to engage in green innovation (Bertrand & Murro, 2022). The existing financial modal is not enough to meet enterprises’ production capacity and innovation capacity, and there is a very urgent need for new forms of finance to cover it.

SCF mainly based on the actual transaction behaviors occurring in upstream and downstream enterprises in the supply chain and is supported by the credit of the core enterprise (Moretto & Caniato, 2021a). Although there are various definitions of SCF, Sang (2021) define it as “a set of financial financing solutions for financial institutions that focus on core enterprises, based on credit assessment and commercial transaction supervision of the entire supply chain, for capital management between core supply chain enterprises and node enterprises.” To date, with the continuous improvement of China’s modern flow system construction and the accelerated deep integration of digital technologies with economy, such as financial technology, supply chain finance (SCF) has become a more efficient and convenient way of financial support to break the dilemma of enterprise finance constraints and then promotes the circulation of innovation factors (Wang et al., 2020), which provide various financing channels for enterprises to alleviate financial constraint and bring in more monitoring agents to reduce agency costs. Therefore, the financing and governance effects of SCF are beneficial for enterprises to enhance green innovation.

However, on the one hand, existing studies have mainly focused on analytical modeling or case study approaches (Gelsomino et al., 2016); on the other hand, there are literatures which analyzed the impact of SCF on corporate green innovation in terms of a single effect: the financing effect of SCF (Gu et al., 2023). Less literature analyzed the effect of SCF on enterprises’ green innovation using empirical analysis methods and dual perspectives of financing and governance. To bridge this research gap, we utilize Chinese listed companies in Shanghai and Shenzhen A-shares from 2010 to 2020 as the research sample to examine the actual impact of SCF on corporate green innovation. Then, we performed robustness tests and endogeneity tests to verify the robustness of the results. Finally, we test the company’s mechanism and internal and external heterogeneity about the effect of SCF on green innovation.

By doing these, the margin contributions of this paper are as follows: (1) We constructed a scientific indicator system for SCF by combining hand searching and machine learning text mining to further enrich the measurement of SCF. (2) We explored the impact of SCF on corporate green innovation with the help of an empirical research approach, which further enriches the research on SCF in terms of the empirical field and provides a reference for subsequent empirical research on SCF. (3) We analyze the mediating effects of financing constraints and agency costs in SCF’s impact on corporate green innovation from the financing perspective and governance perspective of SCF, which helps to deeply analyze the transmission mechanism of SCF on corporate green innovation. (4) We further analyze the different strengths of SCF’s impact on firms’ green innovation from three perspectives, i.e., enterprises’ ownership attributes, enterprises analysts’ attention, and enterprises’ level of financial development in their regions, which helps to further analyze the heterogeneity of SCF’s impact on firms’ green innovation in a precise manner.

The arrangements of this paper are as follows: “Literature Review” presents the literature review; “Theoretical Analysis and Research Hypotheses” denotes the theoretical analysis and research hypotheses; “Methodology and Data” reports methodology and data; “Empirical Results and Discussion” presents empirical results and discussion. “Further Test” presents the further test. “Conclusion and Policy Implication” shows the conclusions and policy implications. “Limitations and Future Research” presents the limitations and future research.

Literature Review

SCF

With the development of SCF, it produces varieties of SCF models such as accounts receivable financing, prepaid accounts financing, reverse factoring, order cycle financing, and inventory financing. SCF minimizes the risk of supply chain disruption and enhances corporate performance, cash holdings, reducing supply chain costs, and firm value (Pan et al., 2020; Pfohl & Gomm, 2009; Sung & Ho, 2020; Lu Wang et al., 2021; Wetzel & Hofmann, 2019). There is a growing body of research on SCF, and previous studies have been categorized into three groups based on research perspectives:

  1. 1.

    Financing effect of SCF: Compared with traditional bank credit, SCF establishes a network connection between enterprises in the chain, which can realize interoperability and sharing of resources among enterprises (Caniato et al., 2016; Gilsing & Duysters, 2008) and then effectively alleviate the financing constraints of enterprises (Wetzel & Hofmann, 2019). At the same time, with the network was established, SCF could effectively reduce the information asymmetry between small and medium enterprises (SMEs) and external investors (Kajjoune et al., 2023), which will produce a variety of financial channels for SEMs (Albertazzi et al., 2021; Lin & Lin, 2016). Song et al. (2020) found that traditional finance is gradually replaced by SCF; SCF can effectively alleviate information asymmetry and increase access to financing for SMEs.

  2. 2.

    Risk offsets for SCF. The outbreak of the COVID-19 pandemic produces a huge toll on the healthy operation of the business, but some scholars found the emergence and development of SCF effectively reduced this loss. For example, Moretto and Caniato (2021b) found SCF plays a significant role in recovering and responding to the financial disruptions and the financial disruptions caused to SMEs caused by COVID-19. At the same time, Paul et al. (2022) found the application of blockchain technology has strengthened the response capability of SCF in the face of unexpected risk events, such as COVID-19. After research, Wei (2019) found the combination of blockchain and SCF had a deleterious effect on reducing the valuation losses and trading volatility associated with COVID-19.

  3. 3.

    SCF model innovation. Financial technology produces a huge impact on the SCF model, and there are some researchers had noticed this issue (Sang, 2021). For example, Zhang et al. (2021) noticed that the continuous in-depth implementation of blockchain on SCF motivates the financial effect of SCF on SEMs. Yu et al. (2021) found the development of big data analytics is gradually integrating with SCF, and the big data analytics capacity is beneficial to the internal integration of SCF.

Corporate Green Innovation

Green innovation is a general term for “pollution-free” or “less polluting” technologies, processes, and products (Bai et al., 2019; Carrión-Flores & Innes, 2010), which follow the laws of ecology and keep the economic growth to achieve a friendly relationship with the ecological environment (Wang & Jiang, 2021). It can achieve sustainable economic development by saving resources, avoiding excessive consumption of energy, and reducing the excessive destruction of the environment in the process of economic development (Dahesh et al., 2020; Luo et al., 2019; Sarikaya & Güllü, 2015). With the rising concern for sustainable economic development, many scholars have conducted extensive research on the internal and external factors that determine corporate green innovation.

Firstly, regarding the external factors influencing corporate green innovation, previous studies have been conducted mainly in terms of capital market opening, government subsidies, stakeholder pressure, and environmental regimes. (1) Capital market opening: Capital market opening is a significant decision made by the Chinese government to build a diversified capital market system and ease restrictions on foreign investors (Moshirian et al., 2021; Zhen, 2013). Studies have confirmed the positive effects of capital market opening on corporate green innovation by effectively alleviating corporate information asymmetries, reducing agency costs, easing financing constraints, and raising corporate environmental awareness (Chari & Blair Henry, 2008; Feng et al., 2022a, b; Henry, 2000; Sha et al., 2022; Xiong et al., 2021). (2) Government subsidies: Government subsidies are a common fiscal tool used by the Chinese government to regulate economic operations (Wang et al., 2022a, b). It can increase the additional income of enterprises and thus effectively alleviate their financing constraints, which has a significant impact on their green innovation performance (Aerts & Schmidt, 2008; Foreman-Peck & Zhou, 2022; Xia et al., 2022). Due to differences in industry attributes, there has been much debate in previous studies as to whether government subsidies are a “trap” or a “pie” for firms in the innovation process. For example, Dai and Cheng (2015) found that public subsidies have an inverted U-shaped relationship with R&D investment in the manufacturing industry. Klette and Møen (2012) found a non-significant correlation between government subsidies and innovation activities of high-tech firms, and Link and Scott (2009) found that government subsidies squeeze the innovation capacity of small firms by examining them. (3) Stakeholder pressure. Pressure from other stakeholders, such as the media and consumers, can motivate companies to focus more on environmental benefits, and they actively take steps to improve their environmental performance and take responsibility for the environment (Berrone et al., 2013; Dangelico & Pujari, 2010; Rennings et al., 2004). Stakeholders can influence corporate green innovation through various means, such as environmental regulations, social media monitoring, or boycotting non-green innovative products (Wagner, 2007). Legitimacy pressure from stakeholders has a significant impact on corporate green innovation, and the higher the stakeholder pressure, the higher the likelihood that a company will adopt green innovation strategy. Li et al. (2017) found that stakeholder legitimacy pressure has a positive impact on process innovation and process innovation of green products in firms. (4) Institutional environment. China has introduced a series of green development policies after setting the “double carbon” target, and academics have also studied the actual effectiveness of these policies in generating green innovation in enterprises. For example, Wang et al. (2022a, b) studied the effect of green credit policy on the quality of green innovation in heavy polluting firms and found a significant positive relationship between the two. Ren et al. (2022) found a significant positive effect on green innovation from the enactment of a pollution permit system. Zhou and Wang (2022) conducted a quasi-natural experiment using the carbon emission trading scheme, and the final results found a positive and positive association between the carbon emission trading scheme and the green innovation of firms.

Regarding the internal factors that influence corporate green innovation, they mainly include social responsibility, innovation resources, and corporate governance.

  1. 1.

    Corporate social responsibility (CSR). Wu and Yu (2023) found CSR have a positive impact on company’s processes and product innovation. Firstly, according to the information effect theory, CSR information, as a kind of non-financial supplementary information, is a useful supplement to the financial information of enterprises that can effectively reduce information asymmetry and enhance information transparency (Svensson et al., 2018). Secondly, according to stakeholder theory, stakeholders utilize CSR information to help company establish a good stakeholder relationship (Thijssens et al., 2015; Wu & Yu, 2023), which will form a good network of cooperation and is beneficial to improving the performance of green innovation (Hao & He, 2022). Thirdly, CSR effectively enhances corporate social reputation, establishes a good corporate image, and makes employees accept the organization’s green philosophy (Hao & He, 2022; Mbanyele et al., 2022; Ioannou & Serafeim, 2012; LINS et al., 2017; Wu & Yu, 2023), which in turn can promote green innovation capacity (Achi et al., 2022).

  2. 2.

    Corporate innovation resources. In the previous literature on corporate technology innovation, it was found that corporate technology innovation resources largely determine the success chances of green innovation. Aragon-Correa and Leyva-de la Hiz (2016) found that the process of corporate green innovation is also a process of systematic integration of internal corporate resources, which include factors such as knowledge, capital, and materials. Lin et al. (2013) found that capital accumulation resulting from the economic performance of a firm is an important prerequisite for a corporate’s green innovation capability. Abbas and Sağsan (2019) found that the acquisition, sharing, and creation of a firm’s knowledge has a significant impact on a corporate’s green innovation performance.

  3. 3.

    Corporate governance. Enterprises with higher quality of management have more green innovations (Xia et al., 2022); enterprises with poorer governance have fewer green innovations (Amore & Bennedsen, 2016). Amore and Bennedsen (2016) stated that the green innovation performance of firms with higher governance is higher than that of firms with low governance. Wang et al. (2015) found after a study that board governance and the green innovation capability of firms have a significant positive correlation.

Literature Gap

To summarize, firstly, we find most of the previous literature on SCF focuses on the financing effect of SCF, risk resistance, and SCF mode innovation. As for the research methodology, most of them use theoretical models (Ali et al., 2020; Fan et al., 2020; Gelsomino et al., 2016; Lee & Rhee, 2011; Pfohl & Gomm, 2009; Tsao, 2019), case studies (John Mathis & Cavinato, 2010; Nienhuis et al., 2013) or survey data to carry out the analysis (Ali et al., 2019), and the impact of SCF on enterprise is more important to be empirically researched from the data of large samples of micro-enterprises, and how to scientifically measure SCF is still relatively weak. Secondly, combing through the literature on corporate green innovation reveals that there are various factors affecting corporate green innovation decisions. Although some studies have analyzed the effect of SCF on corporate green innovation, they have only focused on the financing effect of SCF and ignored the governance effectiveness of the SCF network. Finally, the existing literature on SCF focuses on the single supply chain and financing attributes of SCF, which to some extent severs the unity of these two attributes of SCF.

Theoretical Analysis and Research Hypotheses

Financing Effects of SCF

External investors are often cautious to invest in green innovation activities because of the long R&D cycle, the uncertainty of returns, and the high failure rate of innovation activities, which leads to the deeper financing constraint (Jiao et al., 2020; Martínez-Ros & Kunapatarawong, 2019). The financing constraint results in the reduction of the scale of corporate green innovation, such as abandoning innovative activities with good development prospects, which becomes a major obstacle to the growth of green innovation output (Corradini et al., 2014; Ding et al., 2022). However, the financing effect generated by SCF can effectively resolve this dilemma.

Firstly, SCF enhances the partnership between banks and enterprises (Gornall & Strebulaev, 2018). Under the model of SCF, banks utilize the credit and actual operating conditions of the whole supply chain as the basis for credit assessment (Wetzel & Hofmann, 2019) and then compensate for relatively weak enterprises in the supply chain, which lack of credit through the energy diffusion of large enterprises (Medina et al., 2023; Qiao & Zhao, 2023). By this way, SCF weakens enterprises’ restrictions in granting loans, lower the entry threshold for weak enterprises, and enable banks to expand the scope of profitability based on controlling their financial risks (Jena et al., 2023). At the same time, enterprises will form a digital SCF platform with banks through blockchain and digital information technology (Moretto & Caniato, 2021b). By establishing the SCF platform, banks are able to judge the current operation status and future development trends of enterprises in more detail (Shiralkar et al., 2023), which is beneficial to narrowing the information asymmetry between banks and enterprises and reducing the default risk of lending enterprises and transaction costs of banks (Bai et al., 2022; Jiang et al., 2022; Natanelov et al., 2022; Xiao et al., 2023). Therefore, under the SCF model, enterprises and banks establish a mutually beneficial and symbiotic partnership. A good relationship between banks and enterprises can improve the financing efficiency of enterprises, reduce the cost of debt financing, enhance the accessibility of enterprise financing, and create a relaxed financing environment for enterprises to conduct green R&D activities (Wang et al., 2018).

Secondly, SCF realizes the circulation of resources within the supply chain. Through the catalyst of SCF, the strong linkages and dense social networks were established between core enterprises and upstream and downstream enterprises (Bai et al., 2022; Natanelov et al., 2022). It is beneficial to optimize the flow of capital and other factors at the organizational level and promote the integration of supply chain logistics, information flow, and capital flow (Ali et al., 2019; Pfohl & Gomm, 2009). Therefore, SCF realizes the expansion of the financing ability and the financing efficiency for enterprises with capital shortages through the spillover effect of innovation resources among enterprises (Hofmann, 2021). In summary, SCF can effectively reduce the shortage of investment in green innovation by strengthening the partnership between banks and enterprises and promoting the circulation of resources within the supply chain to solve the dilemma of enterprise innovation financing, which will produce more and more benefits to corporate green innovation. Accordingly, this paper defines it as the financing effect hypothesis of SCF.

H1: SCF has a positive impact on corporate green innovation.

H2: SCF has a positive impact on corporate green innovation by increasing corporate investment in R&D.

Governance Effects of SCF

Due to the high information asymmetry of innovation activities, it is difficult for shareholders and external stakeholders to implement effective supervision, which makes it easy to generate irrational behaviors such as the malicious appropriation of R&D funds by management (Zhang & Zhou, 2022). However, the governance effectiveness of SCF can effectively solve the lack of green innovation capacity caused by agency problems.

Firstly, SCF enhances the supervisory ability of core enterprises. The commercial credit arising from business transactions leads to the interest bundling between core enterprises and upstream and downstream enterprises (Guo et al., 2022; Silvestre, 2015). To minimize their own guaranteed risk and the high cost brought by supply chain rupture, the core enterprises are more inclined to conclude long-term trade strategic alliances and obtain more information about the internal transaction activities, investment activities, and financial status of the upstream and downstream enterprises by utilizing their dominant position. Core enterprises punish defaulting enterprises through strong relationship-based governance such as taking possession of R&D results and eliminating legal positions in the supply chain (Wiengarten et al., 2016), which will form a constraint and deterrence for the rest of the enterprises and force non-core-enterprises to improve their corporate governance and standardize their R&D investment decision-making behavior.

Secondly, SCF strengthens the external supervision function of banks. According to the financial function theory, banks have the function of supervising and managing the internal transaction behavior and financial status of financing enterprises. SCF embeds banks into the “supplier-enterprise-customer” framework (Sudusinghe & Seuring, 2022); banks can utilize the private information of enterprises held by core enterprises as the basis of supervisory data. Together with the empowerment of information technology such as financial technology and blockchain, which makes banks’ supervisory function more effective in the middle and late stages of lending to enterprises’ capital and internal investment decisions. Therefore, SCF realizes the two-way cooperation and supervision between the core companies in the supply chain and the bank, it will force the enterprise managers to make more rigorous and scientific R&D investment decisions, and then effectively prevent the occurrence of opportunistic behaviors such as misappropriation of green R&D funds. Accordingly, this paper defines it as the supervision effect hypothesis of supply chain finance (Fig. 1).

Fig. 1
figure 1

The arrangement of the research

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H3: SCF has a positive impact on corporate green innovation by cutting corporate’s agency costs.

Methodology and Data

Sample Selection and Data Sources

In order to minimize the impact of the new crown epidemic on business, this paper takes Chinese listed enterprise in Shanghai and Shenzhen A-shares from 2010 to 2020 as the research sample. The sample is screened to (1) exclude the financial industry; (2) exclude the enterprise in ST, * ST and PT; (3) exclude the asset-liability ratio greater than 1 and related data with missing financial data. Finally, the overall sample of our study is 25,924. The selected continuous variables are winsorized at the 1 and 99% levels to eliminate the effect of extreme values. Among them, corporate green patent data are obtained from CNRDS; corporate financial data are collected from CSMAR; and the data SCF are obtained from corporate annual reports.

Variable Selection

Dependent variable: Corporate green innovation. Existing studies adopted green patent applications to measure corporate green technology innovation level (Junaid et al., 2022; Rennings, 2000). Because there is a significant right-hand bias in the green patent data, we take ln (the total number of green patent applications + 1) to measure corporate green innovation. Considering that it takes 1 or 2 years from R&D decision to the actual output of patents, drawing on the study of (He et al., 2022; Wang, 2023; Wu et al., 2022a, b), we use ln (the total number of green patent applications + 1) in period t + 1 to measure the firm’s green innovation performance in period t.

Independent variable: SCF. This paper measures supply chain finance (SCF) with the help of text analysis techniques. The specific steps are as follows: Firstly, the SCF lexicon is constructed by drawing on the studies of Liu et al. (2022) and Pan et al. (2020) (as shown in Fig. 2). Secondly, the annual reports of the annual reports of sample enterprises from 2010 to 2020 were obtained from Juchao Information Network (http://www.cninfo.com.cn/new/index) and then converted them into text documents. Thirdly, we extend SCF lexicon to Jieba Chinese word separation database in Python. Finally the frequency of each keyword appearing in the annual reports was counted based on machine learning. Finally, we take ln(keywords frequencies of SCF + 1) to measure the development level of SCF. The larger the index indicates the higher the degree of supply chain finance development.

Fig. 2
figure 2

Supply chain finance lexicon

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Control variables: Referring to the studies of He et al. (2022), Junaid et al. (2022), Rennings (2000), Wang (2023), and Zhang (2023), we added firm-level relevant variables to control for endogeneity problems arising from omitted variables. The control variables are as follows: return on assets (ROA), asset-liability ratio (LEV), enterprise age (FirmAge), enterprise size (Size), percentage of shareholding of the largest shareholder (Top1), operating income growth rate (Growth), the value of TobinQ (TobinQ), nature of ownership (SOE), dual role of the board chairman (Dual), and the fixed effects of year and industry. Table 1 shows the definition of all the above variables.

Table 1 The definition of variables
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Empirical Model

We establish a two-way fixed-effects OLS model to explore the marginal effect and mechanism of SCF on green innovation; the models are shown in Eqs. (1), (2), and (3). Among the above models, Eq. (1) is used to study the effect of SCF on green innovation, which forms the mediating effects model with Eqs. (2) and (3). In Eq. (1) the i denotes the ith sample; t denotes time; ε denotes the residual, and control denotes the control variable. In Eqs. (2) and (3), M denotes the intermediate variables.

$${Gre}_{it}={\beta }_{1}{SCF}_{it}+{\beta }_{i}{control}_{it}+{Year}_{t}+{Industry}_{i}+{\varepsilon }_{it}$$
(1)
$${M}_{it}={\beta }_{1}{SCF}_{it}+{\beta }_{i}{control}_{it}+{Year}_{t}+{Industry}_{i}+{\varepsilon }_{it}$$
(2)
$${Gre}_{it}={\beta }_{1}{SCF}_{it}+{M}_{it}+{\beta }_{i}{control}_{it}+{Year}_{t}+{Industry}_{i}+{\varepsilon }_{it}$$
(3)

Empirical Results and Discussion

Statistical Summary and Mean Difference Test

Table 2 represents the statistical summary and mean difference test. We can learn that the mean of Gre is 0.933 and the median is 0.000, which means that most companies’ green innovation ability exceeds the median level. The maximum and minimum of Gre are 7.386 and 0.007, and the standard error is 1.229, which means that the green technology innovation capabilities of different enterprises are quite different. The mean of SCF is 0.340 and the median is 0.010, which means that the development level of SCF exceeds the medium level.

Table 2 Statistical summary and mean difference test
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We divide all the samples into SCF group and Comparison group according to whether there are SCF keywords in the sample annual report. Then, we conduct the sample mean T-test, and the result is shown in Table 5. We can know that the sample size of the SCF group, which contains SCF keywords, is 6715, and the sample size of the Comparison group, which does not contain SCF keywords, is 19,209. The mean of Gre in the Comparison group is 0.839; in the SCF group, it is 1.200, and the result of the T-test is−0.361 and passes the 1% significant test, which is preliminary proof SCF can prompt the improvement of corporate green innovation.

Primary Test

Table 3 denotes the primary test results from Eq. (1). Column (1) is the result that does not add the control variables, and column (2) is the result of adding the control variables. The coefficient of SCF is 0.161 and 0.066 in columns (1) and (2), and they all pass the 1% significant test, which means that SCF can prompt corporate green innovation; this finding is consistent with the study of Gu et al. (2023) and supports the hypothesis H1.

Table 3 Primary test results
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The coefficient of Size in column (2) is positive at 1% significance level, which means that larger firms may have more improvement in Gre. The coefficient of ROA is 0.215 and passes the 10% significant test, which represents that corporate profitability is beneficent to the growth of corporate green technology innovation. The coefficient of Firmage and Top1 is significantly negative at 1% significant level, which means that the older firm and the more concentrated the equity is harmful to corporate Gre. The coefficient of SOE and TobinQ is significantly positive at 1% significant level, which means that the enterprise of state-owned possesses huge development prospects is beneficial to the green technology innovation.

Robustness Test

To verify the robustness of the above findings, we conducted the robustness test such as replacing independent variable, eliminating the influence of special time, and replacing the regression model. The results of robustness tests are shown in Table 4.

Table 4 Robustness test
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Replacing the Dependent Variable

Panel A is the result of replacing the independent variable. According to “whether the sample contains SCF keywords,” the SCF is replaced by dummy variable (0/1), which is 1 if the sample contains SCF keywords, 0 otherwise. We can learn that the coefficient of SCF is positive at 1% significant level from columns (1) and (2). It proves that SCF is beneficial to the increase of corporate green technology innovation, which is same as the previous findings.

Excluding Part of the Sample

The stock market crash that China experienced in 2015 had a huge negative impact on companies. Therefore, we shorten the research period, which begins in 2010 and ends in 2015, to eliminate its effect of it, and the result is shown in Panel B of Table 4. From columns (3) and (4), we can learn that the coefficient of SCF is positive at the 1% level, which reveals SCF prompts the growth of corporate green technology innovation. Therefore, we can prove that the result is robust when we eliminate the effect of the stock market crash.

Replacing the Regression Model

We also test the robustness of the finding by replacing the regression model; Panel C is the quantile regression results at the 50, 75, and 90% quartiles. The coefficient of SCF is 0.061, 0.074, and 0.094 at the 50, 75, and 90% quartiles, and they all pass the 1% significant test. We can know that the effect of SCF on Gre is gradually growth as the growth of SCF, it also proves robust of our findings.

Endogeneity Test

PSM

In this paper, we adopt the control variable as the covariate to do the propensity matching score (PSM) to solve the problem caused by sample self-selection. The result of it is shown in Panel A of Table 5; the coefficient of SCF is 0.073 and 0.051, and all pass the 1% significant test. It means that the result after PSM is similar to our findings, which proves our result is robust.

Table 5 Endogeneity test
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Instrumental Variable Method

We use the annual urban SCF mean with one period lag as the instrumental variable (IV) for SCF and then performed the 2SLS test to eliminate the endogeneity problem caused by two-way causality and omitted variables. The final results are shown in Panel B.

The coefficient of IV in column (3) is positive at 1% significant level, which means that the IV satisfies the relevance requirements. The coefficient of SCF in column (4) is 0.404, which passes the 1% significant test. It is a resemblance to our finding, which means that our results are still significantly positive after excluding endogenous problems caused by omitted variables and bidirectional causality.

Further Test

Mechanism Test

To further clarify the mechanism of SCF’s role in corporate green innovation, we select R&D and total asset turnover (Turn) as mediating variables, and Eqs. (1), (2), and (3) are conducted to the mediation test model. The final results are shown in Table 6.

Table 6 Mechanism test
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The coefficient of SCF in column (2) is positive at a 1% significant level, which means that SCF is beneficial to increasing the total asset turnover, which is negative to agency cost. Column (3) is the result of Eq. (3), and the coefficient of SCF and Turn is positive at a 1% significant level; and the result satisfies the mediation test criterion. Therefore, the above results verify SCF can prompt corporate Gre by reducing agency costs, which supports hypothesis H3.

Similarly, the coefficient of SCF in column (4) is positive at a 1% significant level, which reveals SCF could prompt the increase of R&D. The coefficient of SCF and RD in column (5) is positive and they all pass the 1% significant test, which means SCF can prompt corporate Gre by increasing R&D, which supports hypothesis H2. This result is consistent with the study of Jia et al. (2023), which concluded that the development of green finance can effectively alleviate corporate financing constraints and thus enhance corporate green innovation.

Heterogeneity Test

Property Rights

In order to analyze whether the effect of SCF on corporate green innovation is different due to the ownership attributes of enterprises, this paper sets the dummy variable SOE, which is state-owned enterprises when SOE = 1 and non-state-owned enterprises otherwise. Columns (1) to (2) are the result of SCF in non-SOEs and SOEs, respectively, which show that the coefficient of SCF is 0.085 in SOE = 0 and is 0.055 in SOE = 1, they all pass the 1% significant test, and the Empirical P-value is 0.073. It means the effect of SCF on Gre in non-SOEs is greater than in SOEs. Therefore, the above findings prove that the effect of SCF in non-SOEs is stronger than in SOEs. And this result is consistent with the findings of Gu et al. (2023), both of which concluded that SCF achieved a more significant facilitating effect in non-state-owned firms.

The main reason for this difference is the level of financial constraints. State-owned enterprises (SOEs) are generally large in China, backed by government credit guarantees, and generally have “soft budget constraints,” so banks often show a clear “preference” when making credit decisions. Therefore, the SOEs face less constraint in financing (Zhang, 2023). However, the non-SOEs often suffer from institutional discrimination compared to SOEs, so banks show “lending shame” and “lending caution” to non-SOEs (Wang, 2023), which led to narrow its access to financing, higher financing difficulty, and stronger financing constraints. The difference in financing constraints between SOEs and non-SOEs causes different sensitivities to SCF in their Gre activities.

The Development Level of the Urban Financial Market

We also utilize the loan amount of financial institutions/regional GDP in each city as a proxy variableFootnote 1 for the level of financial market development and divide the high financial market and low financial market by the median of each year. The coefficient of SCF in column (3) is 0.054 and in column (4) is 0.082, which are all significant at a 1% level, and the Empirical P-value is 0.090. It means the impact of SCF is more obvious in higher financial market regions (HFM > LFM). In summary, SCF has a more obvious boosting impact on the regions with the higher development levels of financial development.

The reasons for this difference are as follows: firstly, regions with higher financial markets possess a larger number of financial intermediaries, an adequate amount of financial capital, and higher efficiency of financial capital allocation (Ali et al., 2019; Amore & Bennedsen, 2016), which is beneficial to promote the development and innovation of SCF. It can help SCF to more effectively alleviate the corporate financial constraint and then prompt the growth of Gre more effectively. Secondly, with the deep integration of financial markets and digital technology, a series of financial technologies have emerged, making the regulatory effectiveness of financial markets more effective and providing technical support for SCF to reduce enterprises’ agency costs (Gornall & Strebulaev, 2018; He et al., 2022; Medina et al., 2023).

Analyst Focus

To test the different effect of SCF on Gre in different analyst focus (AF), this paper measures the AF of enterprises by the logarithm of the number of analysts plus one and divides them into high AF (HAF) and low AF (LAF) by the median. The results are shown in columns (5) to (6) of Table 7. The coefficient of SCF in column (5) is 0.078 and in column (6) is 0.039, which are all significant at a 1% level, and the Empirical P-value is 0.026. It means the impact of SCF is more obvious in HAF(HAF > LAF), which means the growth of AF strengthens the positive effect of SCF on Gre. In summary, SCF has a more obvious boosting impact on HAF.

Table 7 Heterogeneity test
Full size table

The reasons for this difference are as follows: on the one hand, analysts are an important part of the capital market, they could collect high-quality information about enterprises through professional information networks such as long-term tracking and regular visits, which effectively reduces the degree of information asymmetry between external investors and enterprises themselves (Wang, 2023). On the other hand, a higher analyst focus means enterprises have a higher degree of information disclosure, SCF participants utilize the information disclosed by analysts to implement effective supervision of enterprises to better restrain the irrational investment behavior of management (He et al., 2022). Therefore, an enterprise with higher analyst focus is beneficial to not only enhancing the financing effectiveness of banks and other SCF entities but also enhancing the supervision effectiveness of the capital market.

Conclusion and Policy Implication

This article utilizes China’s 2010–2020 Shanghai and Shenzhen A-listed companies as the research object, based on SCF’s financing effect hypothesis and governance effect hypothesis, to test the impact of SCF on corporate Gre from both theoretical and empirical perspectives. The final research results found that: (1) SCF can effectively improve enterprise Gre, and this result is still stable after a series of robustness tests and endogeneity tests. (2) The mechanism test found that SCF can effectively reduce agency costs and increase corporate R&D investment to increase the level of Gre, which fully proves the existence of the SCF financing effect hypothesis and governance effect hypothesis. (3) The internal and external heterogeneity tests of enterprises found that SCF has a stronger effect on enterprise Gre that are non-state-owned, located in regions with a high level of financial development and high analyst attention.

Based on the research conclusions, the following policy implications are proposed to give full play to the effect of SCF in promoting corporate green innovation:

Firstly, our paper found that SCF can effectively promote green innovation in enterprises, so the government should focus on promoting the development of SCF in the future. To promote the development of SCF, SCF platforms and strategic alliances should be built by combing digital technologies such as financial technology to enhance SCF capabilities and release financing effects of SCF. At the same time, the corporate should do a good job of risk prevention and early warning beforehand, as well as implementation supervision during the event, to prevent core enterprises from using SCF to carry out the excessive financial transformation and to prevent the spread of financial risks of a single enterprise along the supply chain.

Secondly, stick to the basic principle of SCF serving the real economy, and innovate and develop a variety of supply chain finance products and service solutions. Our study finds that SCF has a heterogeneous impact on different types of firms, therefore, in this process, it is necessary to avoid a one-size-fits-all service model and to provide differentiated and high-quality supply chain financial services based on understanding the heterogeneity of enterprises.

Limitations and Future Research

This paper studies the effect of SCF on corporate Gre, the mechanism of SCF impact on Corporate Gre, and its internal and external hypotheses of the enterprise. However, it inevitably has the following limitations: firstly, we investigate the effect of SCF on corporate Gre, but the structural characteristics of the supply chain such as customer concentration and supplier concentration are not taken into account, so the future research will take the structural characteristics of the supply chain into our research. Secondly, there are many attributes of enterprise such as the relationship between banks and enterprises are also a significant factor that influences the actual effect of SCF on corporate Gre. Therefore, we hope future studies could explore its effect. Thirdly, we set our research in the Chinese situation, as we all know, the most obvious factor of China is high collectivism, so the study conclusion may not be suitable for other counties. Future research could establish research relying on their actual situation. Finally, there are potential biases (e.g., the effect of special values) in the use of data from Chinese A-share listed companies as the research sample, and methodological limitations (e.g., potential endogeneity issues that cannot be effectively dealt with, the reliability of the mediation testing procedure in the sample, or the omission or failure to explore some of the more interesting questions) that often arise when using existing econometric models in the analysis.