Empirical Analysis of Financing Efficiency and Constraints Effects on the Green Innovation of Green Supply Chain Enterprises: A Case Study of China

Abstract

Along with the deterioration of environmental problems, the green supply chain has become an important strategy for Chinese enterprises to improve their competitiveness in the global market. Most enterprises in green supply chains have promoted their green innovation and thus have improved their green performance by managing upstream and downstream enterprises. However, the green innovation capability might be also related to the financing efficiency and financing constraints of enterprises. To reveal the impact of financing efficiency and financing constraints on corporate green innovation, we conducted an empirical study. We considered a sample of 120 listed companies of the green supply chain from 2018 to 2020. The financing efficiency score was calculated using the input–output analysis method of data envelopment analysis, while the financing constraint score was measured using the financing constraints model. Further, multiple linear regression was used to estimate the regression coefficient and investigate the impacts of financing efficiency and financing constraints on corporate green innovation. The results show that a higher financing efficiency can promote green innovation and the financing constraints can limit the green innovation of green supply chain enterprises. Afterward, we provide a summary of innovation embedded in green supply chains.

1. Introduction

The growth of manufacturing makes a significant contribution to the global economy by supporting businesses and providing jobs for local communities. Nevertheless, the industry is also responsible for growing environmental problems such as the depletion of natural resources and environmental imbalances [1,2]. Increasing global warming calls for companies to mitigate their negative impact on the natural environment [3]. It has been established that green innovation has become a modern accepted concept as environmental issues and global warming have become critical issues [4]. Green technology innovations are defined as innovative products and processes that reduce negative environmental impacts [5]. Green innovations contribute to the prevention of environmental pollution, help to recycle waste, and also save non-biodegradable energy [6,7]. The green revolution is a unique strategy that allows organizations to create new technologies and systems to help them to improve their performance. A company’s product development process must be held to a higher environmental standard to improve the sustainability of the company, consuming less energy and other resources. It can then be analyzed to assess whether the resources can be reused and recycled in the manufacturing process [8]. In addition, green technologies can lead to sustainable development [3,9].

Therefore, organizations must adopt green growth strategies because their future survival is based on sustainability, which is considered a fundamental responsibility of organizations competing in a globally competitive environment. There is no doubt that an essential element of green development is the embrace of technological advances. The manufacturing industry is now forced to redesign its organizational policies to mitigate harmful environmental and social impacts, which is essential for the transition to more sustainable forms of development [8,10].

The rapid growth of China into the second largest economy in the world has been facing the problem of obtaining a balance between sustainable resource use and economic development [11,12]. The energy crisis and environmental pollution have led China to face higher production costs and serious environmental problems [13,14,15,16]. Compared with the world’s technologically advanced countries such as the United States, Japan, and Germany, the innovation performance of China’s manufacturing industry is still very low. The existing innovation investment has not yet fully contributed to the improvement of green innovation performance. Innovation capability is an important factor affecting business performance [17].

Given that green technology innovation aims to reduce environmental pollution and improve energy efficiency, it requires sufficient long-term green knowledge learning and technological investment, even generating unpaid knowledge spillovers. Therefore, undercapitalized firms like SMEs may not be able to engage in green R&D [18]. Most of the research on green R&D has focused on large organizations rather than small firms, and these two enterprises differ in terms of resource experience as well as organizational structure. Research on green innovation also tends to be monolithic, mostly in one industry or one sector [19].

Firms in green supply chains satisfy limited financing characteristics considering environmental factors. Therefore, this paper focuses on the two main limiting variables that affect the green R&D performance of firms in green supply chains: financing efficiency and financing constraints, filling in the green supply chain characteristics of such studies.

The remainder of our paper is organized as follows. Section 2 presents a literature review, introducing key constructs of the current research. Section 3 identifies our research data and study path. Section 4 includes the results of our correlation on analysis and the test of multiple linear regression, which verifies the H1 and H2 hypotheses. The paper concludes in Section 5, Section 6, Section 7 and Section 8 with a discussion of the implications and limitations of this study, research directions, and concluding remarks.

2. Literature Review

2.1. Theoretical Basis

The theoretical basis of the study is built on the idea that financing constraints and financing efficiency affect the innovation ability of a firm. The capital structure theory states that the alleviation of financing constraints can help firms to adopt an aggressive competitive strategy and provide continuous financial support for investment projects. It can enhance the value of the firm, which effectively improves the level of firm performance and promotes the long-term development of the firm [19]. The difficulty and high cost of financing for private enterprises, especially SMEs, have become a major obstacle to the sustainable development of Chinese enterprises [20]. The current academic community has conducted more research on “financing constraints”, while “financing efficiency” is equally important in the daily operation and management of enterprises and social and economic development. At the present stage, most of the listed companies in China are facing serious financial inhibition in the process of development. How to effectively improve the current situation of financing for listed companies is an important problem to be solved [21]. A firm’s ability to innovate is limited by financing efficiency and financing constraints, and green innovation capability is a common indicator to measure a firm’s ability to balance environmental friendliness and corporate innovation in a green supply chain. Therefore, the introduction of financing constraints and financing efficiency in the assessment of firms’ green innovation capacity is a way to fully assess the potential of firms’ green innovation capacity and the sustainability of their future development.

2.2. Green Supply Chain Enterprises

A green supply chain is a modern management model that considers environmental factors and resource efficiency. The green supply chain is based on the original supply chain technology, implementing the principle of environmental protection in each manufacturer through which the supply chain passes, and practicing green theory in each link of the supply chain to achieve the purpose of minimal impact on the environment and maximum efficiency in resource utilization.

Firms designing green products that reduce their environmental footprint can make a key contribution to achieving these goals [18,22]. Most of the existing studies on green supply chains have focused on green supply chain coordination and green supply chain optimization and practices. Green supply chain management that focuses on environmental impact can improve competitive advantage [17]. Green supply chain management can also provide companies with strong competitiveness and sustainable development [23]. Ying Luo et al. studied the optimization of financing in green supply chains and explored the optimization analysis of supplier financing or bank financing [24]. Sun et al. studied the green investment strategies of manufacturers and material suppliers in a two-level supply chain and found that government subsidies can reduce free-rider behavior in the market [25]. Wu et al. studied the operational decisions of a green supply chain consisting of manufacturers and financially constrained retailers and showed the optimal order quantity for retailers, the optimal wholesale price for manufacturers, and the optimal carbon emission level financing and credit financing for banks [26]. Yang et al. explored credit strategies for green supply chains and analyzed the impact of credit strategies on green supply chain performance [27]. Hong and Guo studied multiple cooperative contracts in a green product supply chain and investigated their environmental performance [28]. Heydari et al. addressed the optimal coordination decision for a three-tier, two-channel green supply chain [29]. Xu & Fang examined a supply chain financing system that includes a supplier and an emissions-dependent and capital-constrained manufacturer to determine the best ordering decision and the best abatement investment [30]. Wang et al. discussed pricing and service strategies for e-commerce supply chains under capital constraints [31]. However, there are few studies on the financing constraints and financing efficiency of green supply chains, and most of them are broad in supply chain finance without refining the scope of green supply chains.

2.3. The Impact of Financing Constraints on Green Innovation

The research direction of this paper is the impact of financing constraints on green supply chain enterprises, and the scholars’ exploration direction for financing constraints is mostly the effect of financing constraints on supply chain finance. Atkinson argues that firms in the supply chain can reduce their financing costs through the use of financial technology [32]. Fellenz et al. argue that the credit of core firms in the supply chain can help their upstream and downstream firms to alleviate their information asymmetry problems as well as compensate for their credit deficiencies, thus alleviating their financing constraints [33]. Most of the existing literature on supply chain finance is a systematic study of its business model, value creation, risk prevention, and control financing constraints. The impact of financial constraints on firms’ innovation outcomes also falls into two categories; the conventional theory is that financial constraints can harm innovation by reducing firms’ R&D expenditures [34]. However, a large amount of evidence in practice suggests that more financial resources do not necessarily lead to more and better innovation outputs. One explanation is that financial constraints may benefit innovation by increasing the efficiency of innovation activities [35]. The literature has well-documented the disciplinary role of financial constraints in forcing firms to make optimal investment decisions and improve capital efficiency [36]. Some scholars examined the impact of financing channels on green innovation from the empirical level and found that financing has become an important obstacle to green innovation, and external financing has a differentiated impact and dynamic characteristics on green innovation.

For the financing constraint, studies in the existing literature are divided into two directions, among which the financing constraint restricts the innovation ability of enterprises as a common view [34]. However, there are still scholars who believe that the innovation output brought by more financial resources is uncertain and the limitation brought by finance may improve the innovation efficiency of enterprises using limited funds [35]. Because this disagreement exists, we proposed the H1 hypothesis and tested it through our empirical analysis.

Hypothesis H1.

Financing constraints can inhibit green innovation in green supply chain companies.

2.4. The Impact of Financing Efficiency on Green Innovation

The financing efficiency of enterprises is also one of the great internal constraints of corporate innovation financing. Some scholars have studied the technical criteria for measuring financing efficiency: input-oriented and output-oriented techniques. Financing efficiency can fully reflect the financing capacity and effectiveness of SMEs. However, information asymmetry, transaction costs, and unequal matching between the supply side and demand side of financing lead to high financing risk premiums for SMEs. For the assessment method of enterprise financing efficiency, more scholars use DEA data envelopment analysis. Wang Xiuzhen et al. used the DEA method to select a sample of Chinese industrial enterprises SMEs and concluded that the overall financing efficiency of SMEs in China is not high. Some scholars have further extended the DEA method by establishing multi-stage DEA models and DEA-Malmquist dynamic analysis models [37]. In the innovation activities of companies, long-term financial investments are the basis for ensuring innovative R&D, while the uncertainty of innovation activities leads to high upfront investments and risk characteristics. Zhang mentioned that financing efficiency is reflected in the acquisition and use of corporate funds and the overall efficiency improvement contributes to the sustainable development of the firm [38]. Financing efficiency also reflects firms’ willingness and ability to acquire and use innovation resources in an integrated manner [39]. Firms with efficient financing can access more stable funds to provide innovation inputs or enhance their core competitiveness through resource savings. To a certain extent, the improvement of financing efficiency can hedge the impact of financing constraints on corporate innovation and promote corporate investment.

For the H2 hypothesis, the prevailing theory suggests that input-oriented and output-oriented techniques of financing efficiency are conducive to improving the efficiency of enterprise financing. Afterward, various models and research methods have been proposed. However, most of the studies on empirical analysis focused on an industry or geographical enterprise, and no empirical research has been conducted on green innovation of green supply chain enterprises [36,38]. Therefore, the H2 hypothesis focuses on the test of the research sample.

Hypothesis H2.

The improvement of corporate financing efficiency will promote green innovation of listed companies in green supply chains.

To enhance the clarity of our proposed hypotheses H1 and H2, we have developed a conceptual model, as shown in Figure 1. This model serves to illustrate the key components and assumptions of our hypotheses in a visual manner, facilitating the reader’s comprehension of our research.

3. Methodology and Data

3.1. Data Collection

This paper takes listed enterprises with green supply chain nature in China from 2018 to 2020 as the research sample because it allows us to control the impact of COVID-19 on the sample while ensuring that the sample is updated. Subsequently, we took the following approaches to ensure the integrity of the sample data: (1) Exclude enterprises with missing financing data, and (2) Exclude special treatment and delisted enterprises. Finally, we acquired a sample of 120 enterprises in three years, that is, 360 observations. In the above sample, the financing efficiency score was measured by data development analysis, while the financing constraint score was measured by the financing constraint model. Meanwhile, the green innovation data were obtained from the China National Intellectual Property Administration, and the remaining financing data were obtained from the Choice Financial Terminal database. The data processing of this paper was done by SPSS.

3.2. Variable Selection and Interpretation

In this paper, the data of the research samples are classified into an independent variable, a dependent variable, and a control variable. Independent variables are the factors or inputs that we change in our empirical analysis. Dependent variables are the measurements or outputs that we observe as a result of changing the independent variable. Control variables are any other factors that may affect the dependent variable, which we do not want to be changed.

3.2.1. Independent Variable

Financing efficiency and financing constraints are the variables we change in our empirical analysis, which should be classified as the independent variables.

In this paper, financing efficiency is denoted by overall efficiency (STE), which can be measured by data envelopment analysis (DEA). The advantages of the DEA are that this method can quantitatively analyze financing efficiency without setting indicator weights. Referring to the study of Lei and Liu, the DEA–BCC model based on the input perspective can be applied to calculate the STE of each sample, which requires both input indicators and output indicators [40]. We selected total assets, gearing ratio, and total operating cost as input indicators. Meanwhile, we selected return on net assets, total asset turnover, and operating income growth rate as output indicators [40].

Table 1. The variable indicators in the DEA model.

Classification	Variable Name	Variable Description
Input indicator	Input 1: Total asset	Reflect the size of the company’s financing
	Input 2: Gearing ratio	Reflect the company’s capital structure
	Input 3: Total operating cost	Reflect the company’s use of capital
Output indicator	Output 1: Return on net assets	Measure a company’s ability to earn profits from its capital
	Output 2: Total asset turnover	Measure the operational efficiency of the company’s capital utilization
	Output 3: Operating income growth rate	Measure the operational efficiency of the company’s corporate integration capital and its development

presents the above six variables into two categories (input indicator and output indicator) and provides a specific description for each variable.

It is necessary to make the values of both input and output indicators non-negative to meet the requirements of the DEA model. Referring to the method proposed by Xu and Geng, both the input indicators and output indicators were nondimensionalized and all data were processed to the dimensionless interval of [0, 1] [41]. In the DEA model, the validity of the decision unit is not affected by the magnitude of each indicator and the meaning of the data will not be changed by the dimensionless processing. Therefore, the analysis results will not be affected. The transformation function is as follows, where $Y_i$ refers to the indicator and $Y_i^{\ast }$ refers to the indicator being processed:

$$Y_i^{\ast }=0.1+0.9\ast \frac{Y_i-min\left(Y_i\right)}{max\left(Y_i\right)-min\left(Y_i\right)}$$

(1)

In this paper, the financing constraint suffered by the firm can be measured by the FC index according to Gu et al. [39]. It is worth noting that in the FC model presented in this study, the values obtained through logit regression are significant in measuring the financial constraints faced by the firm. Logistic regressions were applied to examine the influences of financing constraint determinants on M&A decisions according to Chen et al. [42]. Specifically, logit regression is a useful technique for modeling binary dependent variables, such as a firm’s financial constraint status, which takes on a value of 1 if the company is financially constrained and 0 if it is not. Moreover, logit regression allows for controlling for other variables, such as industry or macroeconomic conditions, that may affect a firm’s financial constraint status, leading to more accurate estimations of this crucial factor.

The process of calculating the financing constraint variable FC is as follows:

(1) We standardize three variables (enterprise’s size, age, and cash dividend payout) of the research samples by year.

(2) The dummy variables of financing constraints $QUFC$ are determined by ranking listed enterprises according to the mean values of the standardized variables (ascending order). We use the upper and lower quartiles as the cut-off points for financing constraints, respectively. Then, listed enterprises with more than 66.7% quantile are defined as a low financing constraint group with $QUFC=0$; listed enterprises less than 33.3% quantile are defined as high financing constraint group with $QUFC=1$.

(3) We list the influencing factors that may affect an enterprise’s financing constraint $Z_{i,t}$ in

Table 2. The variables in the FC model.

Variables	Symbols	Description
Enterprise’s size	Size	Natural logarithm of total corporate assets
Leverage	Lev	Total liabilities divided by total assets
Cash dividends	CashDiv	Cash dividends paid by the enterprise during the year
Market-to-book ratio	MB	Market value divided by book value
Net working capital	NWC	Total current assets of the enterprise minus all types of current liabilities
Earnings before interest and tax	EBIT	Profit before interest and income taxes are not paid
Total assets	TA	All assets owned or controlled by the enterprise

. The listed variables include the enterprise’s size, leverage, cash dividends, market-to-book ratio, net working capital, earnings before interest and tax, and total assets. The formula for $Z_{i,t}$ is as follows:

$$Z_{i,t}={\beta }_0+{\beta }_{1}Siz{e}_{i,t}+{\beta }_{2}Le{v}_{i,t}+{\beta }_3{(\frac{CashDiv}{TA})}_{i,t}+{\beta }_{4}M{B}_{i,t}+{\beta }_5{(\frac{NWC}{TA})}_{i,t}+{\beta }_6{(\frac{EBIT}{TA})}_{i,t}$$

(2)

(4) We apply logit regression in Formula (2). As a result, the fitted value is the FC index of financing constraint, the larger the FC index, the more serious the financing constraint suffered by the enterprise. The result of the logit regression is as follows:

$$FC=P\left(QUFC=1|Z_{i,t}\right)=\frac{e^{Z_{i,t}}}{1+e^{Z_{i,t}}}$$

(3)

The following Figure 2 is a schematic of the FC model.

3.2.2. Dependent Variable

The number of green patents and green innovation output are classified as the dependent variables, which can observe green patents and green innovation output while changing the independent variable. Green innovation is creating and implementing ideas, processes, products, and services that improve environmental quality [43]. According to Petruzzelli et al., the green innovation capability of enterprises is strongly related to their green patent output [44]. Similarly, Zhai and Liu selected the number of green invention patents granted to measure the green innovation capability of enterprises [45]. Thus, in this paper, we also use the number of green patents obtained by enterprises to measure their green innovation capability.

In addition, according to the study of Zhai and Liu [45], taking the logarithm of the number of green patents can normalize the data and make it easier to compare different values. It also helps to reduce the effect of outliers, as these are less pronounced when using a logarithmic scale. Therefore, this paper defines green innovation output as the logarithm of the number of green patents.

Overall, this paper follows previous studies’ experience and uses the number of green patents of enterprises and green innovation output as the independent variables to represent the green innovation capability of enterprises.

3.2.3. Control Variable

Control variables are any other factors that may affect the dependent variable, which we do not want to be changed. Concerning Zhai and Liu [45], the control variables in this paper include business age, board size, percentage of independent directors, information disclosure assessment, fixed assets, and cash flow. Our selection of variables is not exactly the same as in Zhai and Liu’s study. On the one hand, we focus only on the financing constraints and financing efficiency faced by firms. To ensure the validity of our findings, we exclude the control variables related to the digital economy aspect from Zhai and Liu’s study. In addition, our sample is directly screened from green supply chain firms, while Zhai and Liu’s study classifies the sample using provinces, and to ensure the consistency of the findings, we exclude the control variables related to geographical location in Zhai and Liu’s study. As we expand our analysis of green innovation capacity in firms, we recognize the importance of considering potential confounding variables that may influence our results. In particular, we have identified fixed assets and cash flow as variables that may impact a firm’s overall green innovation, yet also tend to be higher for larger firms. To address this issue, we have chosen to include fixed assets and cash flow as control variables in our analysis. This allows us to account for the influence of these factors on green innovation while focusing specifically on the unique capacity of firms to engage in environmentally sustainable practices. By controlling for firm size in this manner, we can better isolate the impact of other factors on green innovation, such as industry dynamics and technological advances.

Table 3. Variables descriptions.

Variable Types	Symbols	Variables	Description
Independent variables	GN	Number of green patents	Number of green patents obtained by enterprises
	GE	Green innovation output	The number of green patents obtained by enterprises is taken as the logarithm
Dependent variables	STE	Financing efficiency	The combined benefits calculated by the DEA-BCC model
	FC	Financing constraints	Financing constraint index calculated by FC model
Control variables	Age	Business age	Time of business establishment
	DN	Board size	Total number of directors
	IDN	Percentage of independent directors	Number of independent directors/Total number of directors
	Score	Information disclosure assessment	Information disclosure assessment “excellent” is 4 points, “good” is 3 points, “qualified” is 2 points, “unqualified” is 1 point
	FA	Fixed assets	Assets in physical form that are expected to have a useful life of more than one year
	Cash	Cash flow	Ratio of expenditures on various long-term assets to the enterprise’s share of the acquisition cost

lists the independent, dependent, and control variables with their definitions and symbols.

3.3. Regression Model

In this paper, we studied the impact of financing efficiency and financing constraints on the green innovation of listed green supply chain enterprises. To test the hypothesis, we built multiple linear regression models to estimate the relationship between our two independent variables and one dependent variable. The models are as follows:

$$GN=a_0+a_{1}ST{E}_{i,t}+a_{2}F{C}_{i,t}+a_j{{\displaystyle \sum }}^{ }Contro{l}_{i,t}+{\epsilon }_{i,t}$$

(4)

$$GE=a_0+a_{1}ST{E}_{i,t}+a_{2}F{C}_{i,t}+a_j{{\displaystyle \sum }}^{ }Contro{l}_{i,t}+{\epsilon }_{i,t}$$

(5)

The result of the analysis is given in the form of an equation with estimated coefficients for each independent variable that describes the strength of the relationship between that variable and the dependent variable. Specifically, $a_0$ represents the constant term, $a_1$ to $a_j$ represent the regression coefficients, ${\epsilon }_{i,t}$ represents the residual term, and $Contro{l}_{i,t}$ describes the control variables where $i$ and $t$ correspond to different samples and time periods, respectively.

4. Results

4.1. Descriptive Statistics

We first want to have an intuitive understanding of the research sample data. Descriptive statistics are used in empirical analysis to summarize, organize, and present data in a meaningful way. In this paper, we measured the average value, standard deviation, and minimum and maximum values of the variables.

Table 4. Descriptive statistics.

Variables	Observation	Average	Standard Deviation	Minimum	Maximum
GN	360	18.56	6.68	0.00	29.00
GE	360	2.92	1.90	0.00	3.37
STE	360	0.81	0.15	0.18	1.00
FC	360	0.36	0.28	0.0017	0.93
Age	360	21.52	5.45	8.00	41.00
DN	360	5.23	1.39	2.00	10.00
IDN	360	0.50	0.15	0.20	1.00
Score	360	3.20	0.69	1.00	4.00
FA	360	5434.03	16,594.95	6.68	224,866.59
Cash	360	2.27	2.39	0.00	29.47

shows the descriptive statistics of the relevant variables.

For dependent variables, the average values of green patents number and green innovation output are 18.56 and 2.92, respectively. Thereby, green innovation is common in green supply chain enterprises. Besides this, the standard deviations of green patents number and green innovation output are 6.68 and 1.90, respectively, indicating that there are large differences in the level of green innovation among listed enterprises in the green supply chain. In addition, the maximum and minimum values also reflect the characteristics of large inter-firm variations in green innovation.

As for independent variables, the average values of financing efficiency and financing constraints are 0.81 and 0.36, respectively. Therefore, the overall financing efficiency of green supply chain enterprises is high and the overall impact of financing constraints is low. Besides this, the standard deviations of financing efficiency and financing constraints are 0.15 and 0.28, respectively, indicating that the financing efficiency of green supply chain enterprises is stable while the gap in financing constraints they are subject to is large. Additionally, the maximum and minimum values also reflect the characteristics of large inter-firm variation in their financing efficiency and financing constraints.

4.2. Correlation Analysis

Then, we verified the correlation between the independent and dependent variables, which is an important basis for whether we can build a multiple linear regression model. Correlation analysis can also be used to assess the degree of association between variables in order to validate or refute theoretical assumptions.

Table 5. Correlation Analysis.

	GN	STE	FC	FA	Age	DN	Cash	IDN	Score
GN	1 ***
STE	0.192 ***	1 ***
FC	−0.32 ***	0.141 ***	1 ***
FA	0.057	−0.04	−0.055	1 ***
Age	0.053	−0.039	0.272 ***	0.127 **	1 ***
DN	0.021	−0.038	−0.064	0.113 **	−0.023	1 ***
Cash	0.019	0.052	0.085	0.029	0.008	0.158 ***	1 ***
IDN	−0.076	0.127 **	0.07	−0.046	−0.048	0.434 ***	0.104 **	1 ***
Score	0.225 ***	−0.079	−0.42 ***	−0.053	0.14 ***	0.082	0.154 ***	−0.06	1 ***

Note: ***, **, represent 1%, 5%, and 10% significance levels, respectively.

gives the correlation coefficients between the main research variables (independent and dependent variables) and the control variables.

Column (2) of Table 4 shows that green innovation is positively correlated with corporate financing efficiency and negatively correlated with corporate financing constraints. Meanwhile, the absolute values of the correlation coefficients between all variables are lower than 0.5, indicating that there is no cointegration problem between the variables. Therefore, after the correlation analysis, it can be verified that there is a correlation between the independent and dependent variables. Then, the requirements for the use of multiple linear regression models can be met.

4.3. Regression Analysis

Finally, based on the results reflected from both descriptive statistics and correlation analysis, we used a multiple linear regression model to analyze and calculate the correlation coefficients between dependent variables (GN & GE) and independent variables (STE & FC).

Table 6. Multiple linear regression of GN.

	Non-Standardized Coefficient		Standardized Coefficient	t	p	VIF	R2	Adjusted R2	F
	B	Standard Error	Beta
Constant	10.451	3.956	-	2.642	0.009 ***	-	0.187	0.168	F = 10.06 p = 0.000 ***
STE	12.013	2.309	0.255	5.203	0.000 ***	1.038
FC	−7.711	1.36	−0.313	−5.671	0.000 ***	1.311
Score	2.68	2.423	0.055	1.106	0.269	1.048
Age	−0.068	0.064	−0.053	−1.055	0.292	1.101
DN	−0.139	0.272	−0.028	−0.511	0.61	1.284
Cash	0.164	0.143	0.056	1.144	0.253	1.051
IDN	−4.361	2.435	−0.097	−1.791	0.074 *	1.268
FA	1.297	0.537	0.13	2.415	0.016 **	1.248

Note: ***, **, * represent 1%, 5%, and 10% significance levels, respectively.

reflects the regression results of model (4), i.e., the effects of financing efficiency and financing constraints on the number of green patents, the model equation obtained is:

$$\begin{array}{ll}GN=& 10.451+12.013\ast STE-7.711\ast FC+2.68\ast Score-0.068\ast Age-0.139\ast DN\\ & \hspace{1em}\hspace{1em}\hspace{1em}+0.164\ast Cash-4.361\ast IDN+1.297\ast FA\end{array}$$

(6)

The results show that financing efficiency has a positive effect on the number of green patents with a coefficient of 12.013, which is significantly correlated at the 1% level. When the financing efficiency is higher, the cost of financing will be reduced. Therefore, the cost saved can be applied to enterprise’s green innovation. In contrast, financing constraint has a negative effect on the number of green patents with a coefficient of −7.711, which is significantly correlated at the 1% level. If the financing constraint is higher, the financing cost for enterprises can be larger. As a result, fewer funds can be allocated to green innovation activities.

Table 7. Multiple linear regression of GE.

	Non-Standardized Coefficient		Standardized Coefficient	t	p	VIF	R2	Adjusted R2	F
	B	Standard Error	Beta
Constant	2.006	0.328	-	6.121	0.000 ***	-	0.189	0.17	F = 10.222 p = 0.000 ***
STE	1.142	0.191	0.292	5.972	0.000 ***	1.038
FC	−0.625	0.113	−0.305	−5.551	0.000 ***	1.311
Score	0.225	0.201	0.055	1.122	0.263	1.048
Age	−0.004	0.005	−0.041	−0.814	0.416	1.101
DN	−0.007	0.023	−0.017	−0.317	0.752	1.284
Cash	0.007	0.012	0.029	0.59	0.555	1.051
IDN	−0.322	0.202	−0.086	−1.595	0.112	1.268
FA	0.09	0.044	0.109	2.033	0.043 **	1.248

Note: ***, **, represent 1%, 5%, and 10% significance levels, respectively.

reflects the regression results of model (5), i.e., the effects of financing efficiency and financing constraints on the green innovation output, the model equation obtained is:

$$\begin{array}{ll}GE=& 2.006+1.142\ast STE-0.625\ast FC+0.225\ast Score-0.004\ast Age-0.007\\ & \hspace{1em}\hspace{1em}\hspace{1em}\ast DN+0.007\ast Cash-0.322\ast IDN+0.09\ast FA\end{array}$$

(7)

The results show that financing efficiency also has a positive effect on green innovation output with a coefficient of 1.142, which is significantly correlated at the 1% level. When the financing efficiency is higher, the enterprises can manage the use of funds well. Then, the enterprises may produce less waste in the green innovation process, which leads to lower costs of individual patents. Therefore, the green innovation output of the enterprises will be higher. Contrary to financing efficiency, the financing constraint has a negative effect on green innovation efficiency with a coefficient of −0.625, which is significantly correlated at the 1% level. When enterprises suffer more severe financing constraints, they will have fewer disposable funds. Afterward, there will be more bottlenecks in the green innovation process that cannot be improved effectively. When the research period spent on a single green patent is prolonged, the green innovation efficiency of an enterprise will be lower.

It is important to note that while the R2 values in this study may appear low at 0.187 and 0.189, they are acceptable within the field of economics and management. Such values are commonly observed in empirical studies, indicating that the bias may be solely attributed to the model specification [46]. As we included a wide range of control variables for our research sample, such as firmly fixed assets, this led to greater variability between samples and consequently resulted in a lower R2 value.

It should be noted that a low R2 can be problematic when generating predictions that require narrow prediction intervals. However, in the present study, our focus was to examine the effects of financing constraints and financing efficiency on corporate green innovation, rather than generating specific values. Therefore, the lower R2 value does not impact the findings of this research. Our aim was to explore the relationship between the variables of interest and to provide insights for future studies in this field.

In summary, based on the results of the regression analysis, the original hypotheses H1 and H2 can be accepted.

5. Discussion

This study endeavors to shed light on the interplay between financing efficiency and financing constraints and their impact on corporate green innovation within the context of green supply chains. The empirical analysis applies multiple linear regression to investigate the relationship between financing efficiency and financing constraints and the level of green innovation. Additionally, the study employs data envelopment analysis and a financial constraints index to accurately measure financing efficiency and financing constraints, respectively. The findings of this study contribute to the existing literature by providing insights into the direct and indirect effects of financing efficiency and financing constraints on green innovation. Despite the insights gained, it is important to note that there are other factors that may impact green innovation, and future research could explore the interplay between these variables.

Financing efficiency and financing constraints can substantially impact the green innovation of firms in green supply chains. There have been studies related to the financing constraints’ impacts on firm performance, the impact of financing efficiency on green innovation, etc. The present study differs from prior research in several aspects. Firstly, this study focuses on exploring the combined impact of two variables on firms’ green innovation capabilities, providing a deeper understanding of the relationship between multiple independent variables. In comparison, previous studies tend to focus on a single variable’s impact on corporate green innovation [37,38,39]. Secondly, this study specifically examines firms in green supply chains, an understudied area, compared with the conventional focus on the whole listed companies. Thirdly, previous research in the field of green supply chains has primarily remained at a theoretical level [24,25]. The results of this study offer empirical evidence to verify existing theories and contribute to the advancement of knowledge in this area.

After conducting empirical analysis, the study found evidence to support both hypotheses. Specifically, financing constraints were found to have a negative relationship with a firm’s green innovation capability, while financing efficiency was positively related to a firm’s green innovation capability. The results suggest that to enhance the green innovation capacity of firms, it is crucial to continuously explore new financing models, diversify funding sources, expand financing channels, and improve the utilization rate of funds.

6. Practical Implications of the Study

The findings of the empirical study suggest that green supply chain firms in China must address the financing constraints they face and enhance their financing efficiency. Doing so will bring significant benefits to both the firms and society.

For enterprises, addressing financing constraints and improving financing efficiency can reduce financing costs, leading to more disposable funds. This will alleviate bottlenecks in the green innovation process, enabling companies to effectively improve their green innovation efficiency. By reducing the time spent on research for a single green patent, companies can increase their green innovation efficiency.

Furthermore, a company’s increased investment in green innovation will result in a heightened green capacity of society, contributing to the resolution of environmental issues. By addressing these issues, firms will be able to unleash their full potential and drive their development. This complementary relationship between the green capacity of society and the development of enterprises highlights the importance of addressing financing constraints and improving the efficiency of financing for Chinese green supply chain enterprises.

7. Conclusions

As environmental pressures continue to escalate, many green supply chain enterprises have improved their green performance through the use of various financing instruments. However, these companies face the challenge of financing constraints and low financing efficiency. Through empirical analysis, this study verifies the following hypotheses: (1) Financing constraints can inhibit green innovation in green supply chain companies, and (2) The improvement of corporate financing efficiency will promote green innovation of listed companies in green supply chains.

While prior research in the field of green supply chains has explored the relationship between green innovation and financing performance, few studies have conducted an empirical analysis to verify the existing hypotheses. This paper makes a valuable contribution to the field by empirically examining the impact of financing efficiency and constraints on the green innovation of Chinese green supply chain enterprises.

The results of this study indicate that reducing financing constraints and improving financing efficiency can greatly enhance the green innovation of these enterprises. This is due to several factors, including:

• Increased resources for companies to invest in R&D for green technologies and processes, leading to faster innovation.
• Greater access to larger amounts of financing, allowing for the more rapid scaling of operations.
• Lower financing costs, freeing up resources to be devoted to green initiatives.

In conclusion, the findings of this study highlight the importance of financing constraints and financing efficiency in shaping the green innovation of green supply chain enterprises. Further research could explore the role of other factors influencing green innovation in this context.

8. Limitations and Future Studies

It is important to acknowledge the limitations of this study. Firstly, the study has not taken into account other key factors that may influence corporate green innovation, such as organizational culture, long-term strategic goals, industry context, and regulatory framework. Further research is necessary to examine the complex interplay between these variables and financing efficiency and constraints in driving corporate green innovation. Secondly, this study is limited by its sample, as it only includes green supply chain companies from China. As such, the results of this study are only applicable to this region and cannot be generalized to multinational companies or companies based in other nations. To advance our understanding of the topic, future research should consider a more diverse range of companies from various countries.