Green Innovation and Supply Chain Financing–Evidence from China

. Based on the stakeholder theory, this study investigates the relationship between green innovation and supply chain ﬁnancing using the data of 3490 Chinese listed ﬁrms from 2012 to 2019. The results show that green innovation of ﬁrms could promote their supply chain ﬁnancing. And the channel mechanism test indicates that green innovation could improve the peer recognition gained by ﬁrms from the industry, thus it would be more convenient for green-oriented ﬁrms to obtain ﬁnancing along the supply chain, especially SOEs. Moreover, after launch of the Green Credit Guideline, the positive relationship between green innovation and supply chain ﬁnancing became more signiﬁcant. These ﬁndings remain consistent after robustness tests including instrumental variables (IV), propensity score matching (PSM) and replacing variable metrics. Further results present that green innovation in non-heavy pollution ﬁrms and ﬁrms with high-level environmental disclosure can signiﬁ-cantly beneﬁt supply chain ﬁnancing. Our ﬁndings have important implications on how ﬁrms’ green e ﬀ orts a ﬀ ect their short-time ﬁnancing ability through the supply chain. Keywords: Green


Introduction and Literature Review
Nowadays many countries are trying to seek green development [1] to achieve peak greenhouse gas emissions and net zero greenhouse gas emissions which they reached consensus in the Paris Agreement. China is no exception as a major emitter and major carbon sink in the world [2]. In 2021, Xi Jinping stressed the goal of reaching CO emissions peak before 2030 and achieving carbon neutrality before 2060, which is included in the "Government Work Report". Therefore, China's green transition brings fundamental changes in carbon emissions and carbon sinks which require the efforts from all walks of life in the society.
Because firms are the main participants of economic activities, their green transformation is an inevitable choice to achieve sustainable development. Chinese firms have already been actively realizing the green transformation in pursuit of better environmental performances [3]. In this process, green innovation plays an important role in firms' green transformation. The concept of green innovation was first raised in 1994 [4]. Although not consistent, main explanations related to green innovation agree that it is the hardware or software innovation related to green products or processes, which includes new technologies in energy-saving and waste recycling [5]. A lot of studies have shown that green innovation could reduce internal operating costs and enhance firms' image and financial performances [6]. Based on the stakeholder theory, some scholars point out that green innovation is an important strategy of firms to enhance their own legitimacy and meet the environmental requirements of stakeholders [7,8]. With stronger green innovation abilities, firms are more likely to cause less pollution in the production process and to meet the environmental regulations formulated by the government [9] and the demands of suppliers for green products. At the same time, the living quality of consumers will also be improved. Consequently, green innovation can improve firms' legitimacy and meet the environmental requirements of stakeholders [10,11], so that green-oriented firms could establish long-term and stable relationship with stakeholders, which benefits firms in gaining valuable resources through stakeholders [8], thus enhancing the competitiveness of firms and finally enabling green firms to obtain more advantageous financing along the supply chain. And some scholars point out that green innovation can attract more investment from external stakeholders [12][13][14][15][16]. Although the existing theories have proved that green innovation benefits firms and their stakeholders, problems and challenges still exist on the way of Chinese enterprises seeking green innovation.
An obstacle is the financing constraints for investing in green innovation of Chinese firms [17], especially for private firms [18]. The reasons have been explained by substantial literatures including information asymmetry between external investors and firms, higher cost and uncertain returns for investing in innovating firms [19]. In order to achieve the dual-carbon goals and promote green innovation and green transformation, the Chinese government has issued a series of green credit policies including the Green Credit Guideline to guide the funds within the financial system to green areas since 2012 [20]. With the guidance of the green credit policies, the financing channels from supply chain act as a bridge between firms and banks, and these channels mainly contain short-term loans provided by banks and suppliers along the supply chain for green-oriented firms. Specially, supply chain financing mainly includes accounts receivable financing, accounts payable financing and inventory financing [21,22]. In practice, trade credit, factoring and reverse factoring are the most commonly used solutions [23]. There are two main reasons for banks and suppliers along the supply chain to provide funding. On the one hand, if firms focus on green innovation, it can improve firms' environmental legitimacy, images [24], and financial performances [6], thus attracting suppliers with the same identity and improving supplier relationships. From the perspective of the stakeholder theory, other firms along the supply chain might benefit from green firms, including reducing the production cost, carrying out product innovation [25] and alleviating the external environmental pollution pressure [26], so firms along the supply chain will believe that these green firms are in line with themselves [27], thus providing more advantageous financing conditions; On the other hand, there is an extremely strong relationship between the Chinese government and commercial banks [28], and lending to green-oriented firms helps banks cater to the government's environmental requirements. Based on the stakeholder theory, we put forward our main hypothesis that green innovation of firms promotes supply chain financing.
To more fully understand how green innovation promotes supply chain financing, we also examine the mediating effect of supplier-base concentration that might transmit the impact of green innovation onto supply chain financing, which may be explained by peer recognition. If firms focus on green innovation, they are more recognized by suppliers due to their stronger sustainable development ability [26], which increases the number of alternative suppliers, so as to lower the supplier-base concentration. Consequently, green innovation firms' dependence on suppliers and switching cost will be reduced, which will strengthen the ability for green innovation enterprises to gain supply chain financing. In this paper, the Herfindahl In-dex of supplier concentration is used to find out whether the green innovation of firms affects supply chain financing through supplier-base concentration.
Following analysis is conducted to find out whether the Green Credit Guideline and firms' heterogeneity will play the moderating role between green innovation and supply chain financing. In order to mitigate the endogenous problems, this paper employs a series of robustness tests including instrumental variable analysis (IV), propensity score matching (PSM) and replacing methods of variable measuring. The results are still consistent and robust.
The contribution of this paper is as follows. First, our results confirm how firms' green efforts affect their short-time financing abilities along supply chain; Second, we provide a new insight on green innovation and supply chain financing by analyzing moderating effect of the Green Credit Guideline and firms' heterogeneity. Third, we get some implications that green innovation can lower the supplier-base concentration, and further affect the supply chain financing. This paper is organized as follows. Section 1 introduces the topic and reviews relevant literature. Section 2 describes data and reports descriptive statistics. Section 3 presents and discusses the empirical results and the last section concludes the findings.

Data
This paper selects the data of A-share listed firms on Shenzhen Stock Exchange and Shanghai Stock Exchange as samples. In 2012, the Construction of Ecological Civilization was proposed and the Environmental Protection Law of China was amended. From then on, firms attach great importance to green innovation. Consequently, this paper chooses 2012-2019 as the sample period. The following samples are excluded: 1) ST, * ST and PT companies, 2) financial industry companies, 3) the samples lacking key variables data. This paper also carries out the tailing treatment on the 1st percentile of continuous variables. The main source of the data involved in this paper is CSMAR database.

Variables
(1) Explained variable Referring to existing researches [29,30], this paper uses the ratio of short-term bank loans and bills payable to total assets (SCF) to measure the firms' supply chain financing, and the data is collected from CSMAR database.
(2) Explanatory variable Green innovation (Green_inno). Since green patent applications could be regarded as the most direct reflection of green behaviors, they are commonly used to measure firms' green innovation capabilities [18,[31][32][33]. Considering the lagged effect of patents granted in measuring firm's green innovation ability and being in line with the existing literature, this paper treats the sum of the number of green inventions and utility patents applied independently or jointly as firms' green innovation. We don't include the green appearance patents for their limited innovation. This paper calculates the number of green patent applications filed by firms according to the IPC code in the Green list of International Patent Classification issued by the World intellectual property Organization [34]. The data sources are the "Green Patent List" issued by the World Intellectual Property Organization (WIPO) in 2010 and the National Bureau of Statistics. Considering the lagged effect of green innovation and the endogeneity problem, Green_inno is lagged by one year [15,16].
(3) Control variables Following the existing researches [16,33,[35][36][37][38], this paper adds the following ten variables to control for their potential effects on supply chain financing: firm size(Ln_Firmsize), the debt-to-asset ratio(Leverage), the net cash flow (Cash f low), the shareholding ratio of the largest shareholder(S hare_Largest), the integration of two positions(Dual), the size of the board of directors(Board_S ize), the number of independent directors(Board Indir), and the rate of return on assets(ROA), Tobin's Q(T obinQ), and state ownership (S OE), and the data are derived from the CSMAR database. See the Appendix 4 for specific variable descriptions and Appendix 4 for correlation coefficient of variables.

Descriptive Analysis of Main Variables
From the summary statistics in table 1, it can be seen that the mean value of green innovation is 0.360, and the median is 0, which indicates that more than half of listed firms have not carried out green innovation; The maximum value and minimum value of green innovation are 3.761 and 0, respectively, reflecting that there are significant differences in firms' green innovation willingness and capabilities. Moreover, the mean value of supply chain financing is 0.131, while the median is 0.100, illustrating that the development degree of supply chain financing of more than half of firms is lower than the average level.

Model Design
To explore the relationship between green innovative capability and supply chain financing, the following regression model is estimated based on the Hausman test and the fixed-effects method.
Where S CF i,t is the supply chain financing of firm i in year t, Green i nno i,t is the green innovation performed by firm i in year t, and Control i,t are all the control variables aforementioned. And we control both the year fixed effect (YearFE) and firm fixed effect (CompanyFE) in the model. Variables are winsorized by 1%.

Analysis of Whole Sample Regression Results
Results are shown in Column (1) and (2) in table 2. It's found that the coefficients of Green_inno are both significantly positive at the level of 1%, with β= 0.0119, p < 0.01 and = 0.0086, p < 0.01, respectively, which indicates that higher level of green innovative capability contributes to a higher level of supply chain financing. Our finding supports our main assumption put forward by the stakeholder theory [27], suggesting that firms that perform better in green innovation are strengthening their environmental legitimacy and greening their images [15,24], thus attracting suppliers with the same identity and improving supplier relationships. Consequently, firms with stronger green innovation abilities are like to gain more supply chain financing. Although we adopt the one-year-lag (t − 1) independent variable to mitigate the reverse causality issue, empirical studies on supply chain financing may still suffer from other endogeneity problems. Therefore, we control both firm fixed effects (Firm FE) and industry fixed effects (Industry FE) respectively to the baseline regression to control for unobservable characteristics of firms. The results are exhibited in columns (3) and (4), which are consistent with the baseline finding, preliminarily showing that the positive relationship between green innovation and supply chain financing is valid and reliable. Supply chain financing is a short-term financing behavior, usually less than one year, which has limited impact on firms' long-term behaviors. However, some short-term factors may still exist leading to the reverse causality issue. To further mitigate these endogeneity problems, this paper employs a series of robustness tests including IV analysis, PSM and replacing methods of variable measuring in the next section.

Robustness Test
(1) Endogeneity issue To mitigate the endogenous problem and be in line with the existing researches [15], this paper uses the proportion of firms applying for green patents in the industry (Indus_green) as the instrumental variable of green innovation and the reasons are as follows: 1) If a firm applies for more green patents, other firms in the industry will also probably apply for green patents to keep up with the green transformation; 2) the proportion of firms applying for green patents in the industry will not directly affect supply chain financing. And the instrumental variable lagged by one stage due to the lag of the independent variable.
Column (5) in table 2 shows the results of the first-stage regression. As we can see, the coefficient of Indus_green is significant at 1% level and the Cragg-Donald statistic of weak instrumental variable test is 177.091, much greater than the critical value 16.38 under 10% bias. This means the hypothesis of weak instrumental variable is rejected, proving that the instrumental variable is effective. In the regression of the second stage in column (6), Green_inno plays a significant positive role in supply chain financing at the level of 1%, which indicates the endogeneity issue caused by reverse causality does not affect the findings of this research.
To further mitigate the endogeneity concern caused by sample self-selection that firms are states-owned, with better financial performances or larger-scale tend to have more resources to manage their green images and to also invest in green innovation. As a result, this paper employs PSM to mitigate this endogeneity problem. Specifically, this paper uses the crosssectional data of 2011 to perform PSM and the matching method is as follows: If a firm applied for one or more green patents from 2012 to 2019, the grouping variable is set to 1, otherwise 0. The firm size, the proportion of shares held by the largest shareholder, the debt-to-asset ratio, return on assets, and the numbers of independent directors are used as covariates. And then we use the Probit model and 1-1 nearest neighbor matching method. Figure 1 shows the result of PSM. The regression results after PSM in column (7) and (8) of table 2 show that green innovation still has a positive impact on supply chain financing after matching, indicating that the robustness of conclusions from aforementioned regressions.
(2) Other robustness tests Change the number of patents into dummy variable This paper changes the number of green patent applications to dummy variable T iny ([15] to further examine the impact of whether to apply for green patents on supply chain financing. If the firm applied for green patent last year, T iny is set to 1, otherwise 0. Column (1) and (2) of table 3 show the regression results. The coefficient of T iny is significantly positive which still indicates that green innovation can significantly promote supply chain financing.
Divide the number of patents by the intensity of R&D investment In order to measure the enthusiasm of green innovation more accurately, this paper divides the independent variable green patent by the intensity of R&D investment (Ln_ginno_rd) and puts it into the model. The R&D investment intensity = the total amount of R&D investment assets, and the regression result is shown in column (2) of table 3.
Narrowing the range of green patent selected Some studies believe that the patent of green invention is more groundbreaking and can better reflect the ability and willingness of firms to innovate green [39]. This paper uses the sum of the number of green invention patents applied by firms independently (Ln_lag_dep_inv) and the number of green invention patents applied by firms jointly or independently (Ln_lag_indep_inv) as proxy variables of green innovation for a robustness test. The results are shown in column (3) to (4) in table 3. The coefficients of Ln_lag_dep_inv and Ln_lag_indep_inv are significantly positive, indicating that firms' joint or independent green invention can also significantly promote the supply chain financing.
Replace green innovation with the change of green innovation To find whether the changes of green innovation patents have an impact on supply chain financing, we replace the independent variable with Delta_green_innovation, which is the logarithm of (green i nno t − green i nno t−1 ). Table 3 shows the regression results that changes of green innovation patents have a positive impact on supply chain financing.

Mediating effect of supplier-base concentration in the supply chain
To further understand how corporate green innovation increases supply chain financing, we also examine the mediating effect of supplier-base concentration (SC) that might transmit the impact of green innovation on supply chain financing. Supplier-base concentration is defined as the concentration degree to a firm's purchases from its major suppliers accounting for a significant proportion of the firms' purchases. Referring to the existing literature, we measure it with the Herfindahl-Hirschman Index [40,41]: Where share i, j,t is the share of a firm's (firm i) purchase from its main supplier j (j ≤ 1, 2, . . ., 5) in year t standardized by the firm's total purchase (Purchase i,t ) in year t, and (j ≤ 1, 2, 418 Standard errors in parentheses *** p < 0. 01, ** p < 0. 05, * p < 0.1. In column (1), we replace Green_inno witha dummy variable T iny; In column (2), we adjust Green_inno with R D; In column (3), we change the scope of thegreen patent; In column (4), we replace green innovation with delta green innovation.
. . ., 5) means we include the top five suppliers; Purchase i, j,t is the purchase of firm i from its main supplier j in year t; S C i,t is the supplier-base concentration of firm i in year t.
We conjecture that the firm with stronger green innovation ability will gain more peer recognition from suppliers due to their stronger sustainable development abilities [26], so as to attract more alternative suppliers, which will lower supplier-base concentration (SC). As a result, firms will have higher negotiating power along the supply chain [42,43], which leads to stronger financing capabilities. In line with existing researches [44], this paper constructs models as follows: Notes: Standard errors in parentheses *** p < 0. 01, ** p < 0. 05, * p < 0. 1 Where ln_supa i,t is the logarithm of the purchase of top three suppliers in firm i in year t, and the definitions of other variables are aforementioned. The results presented in column (2) of table 4 show that green innovation is significantly and negatively related with the suppliers-base concentration (δ=-0.178, p < 0.05), indicating a positive role of green innovation in attracting suppliers. Moreover, the results in column (3) suggest that the coefficient of Green_inno is significantly positive and the coefficient of SC is significantly negative, with θ= 0.00408, p < 0.01 and θ=-0.00029, p < 0.05, respectively. It shows that the decrease of supplier-base concentration imposes a significant impact on supply chain financing. To conclude, the results for our channel mechanism tests confirm the mediating effects of supplier-base concentration, and support the fact that firms with stronger green innovation abilities will gain higher peer recognition from suppliers, leading to a stronger capability of supply chain financing.
In addition, we explore the difference in mediating effect of supplier-base concentration between SOEs and non-SOEs. The results in column (4) to (9) of table 4 show that only in SOEs, supplier-base concentration works as an intermediary between green innovation and supply chain financing. Since SOEs are an important channel for the Chinese government to play a macro-control role, they naturally have more access to enter the industry, get credit and obtain other resources than non-SOEs [45], which makes it easier for SOEs to make use of their suppliers to transfer the risks of green innovation. However, non-SOEs facing more discriminatory policies in credit and industry accesses are difficult to transfer their risks through supplier relationships. Notes: Standard errors in parentheses *** p < 0. 01, ** p < 0. 05, * p < 0. 1

Moderating effect of the Green Credit Guideline
The Green Credit Guideline issued in 2012 establishes the core framework of Chinese green credit systems [18], and it promotes the financing of green innovation firms. In order to test the promoting effect, this paper constructed two intersections. When it is after 2012, the year_dummy is set to 1, otherwise 0. Column (1) to (4) in table 5 indicate that the role of green innovation in promoting supply chain financing has become significant after the implementation of the Green Credit Guideline.
Moderating effect of firms' heterogeneity We further explore the heterogeneous effects of pollution degree (Non-heavy pollution vs. Heavy pollution) and environmental disclosure (low-level environmental disclosure vs. High-level environmental disclosure) on the relationship between green innovation and supply chain financing. Table 6 presents the moderation results for pollution degree in column (1) and (2) and environmental disclosure in column (3) and (4).
To test the moderating effect of pollution degree, we divide the whole sample into heavy pollution and non-heavy pollution 1 group. The moderating analysis of pollution degree indicates that the impact of green innovation on increasing supply chain financing is more pronounced in non-heavy pollution enterprises, while not observed in heavy pollution enter- prises. It confirms that the Green Credit Guideline issued by the Chinese government aims to guide the funds to environmental-friendly sectors, making firms with heavy pollution face stronger financing constraints. To test the moderating effect of environmental disclosure, we divide the whole sample into low-level environmental disclosure and high-level environmental disclosure group. The moderating analysis of environmental disclosure indicates that the impact of green innovation on increasing supply chain financing is more pronounced in firms with high-level environmental disclosure. It indicates that enhancing information transparency could deliver effective information to external investors and reduce information asymmetry, thus bringing a significant impact on financing [46].

Conclusion
This paper discusses the relationship between green innovation and supply chain financing, and proposes peer recognition effect to explain why supplier-base concentration is part of the intermediary. Moreover, we also find that the Green Credit Guideline generates a significant green synergistic effect as the moderator. Specifically, this paper proposes relevant research hypotheses through literature review and theoretical derivation, then establishes an empirical model to verify the hypotheses based on the data of 3492 firms from 2012 to 2019. The results show that the enhancement of green innovation level has significant positive influence on supply chain financing, and supplierbase concentration is an important channel. Moreover, the Green Credit Guideline shows a significant positive moderating effect in the process. We also find that green innovation in non-heavy pollution enterprises and those with high-level environmental disclosure can significantly promote supply chain financing. Our findings have important implications on how enterprise's green efforts affect its short-term financing ability through supply chain.