Fiscal and Tax Policies, Access to External Financing and Green Innovation Efficiency: An Evaluation of Chinese Listed Firms

Abstract

China has placed significant importance on the development of a circular economy and achievement of sustainable prosperity. It employs multiple fiscal and tax policies to facilitate clean production and improve resource efficiency by fostering corporate green innovation. Policy signalling boosts companies’ external funding, including debt and equity. As such, this research focuses on how government subsidies and tax incentives influence corporate green innovation efficiency, accounting for the mediating roles of debt financing and equity financing. Under the SBM model, we utilise the Luenberger index to quantify green innovation efficiency. In addition, the fixed-effect regression with 19,228 firm-year observations from 3549 firms between 2015 and 2021 is used. Based on empirical findings, government subsidies reduce green innovation efficiency, while tax incentives increase it. In addition, debt financing mediates the association between tax incentives and corporate green innovation efficiency. Furthermore, government subsidies and tax incentives play more significant roles in non-state-owned enterprises (non-SOEs) and for businesses in growing and mature stages than other listed firms. To improve access to external financing and green innovation efficiency, it is suggested that the government implement various government subsidies or tax incentives according to business characteristics, with each company applying policies customised to its specific circumstances.

1. Introduction

In recent decades, most countries have prioritised sustainable expansion. This requires the transformation of economic activities that previously relied on environmentally harmful energy sources by applying technologies with a less adverse environmental impact [1]. Green innovation is one of the critical mechanisms for clean production and the circular economy [2], with the circular economy serving as a fundamental mechanism for enhancing resource efficiency, minimising resource inputs, and reducing waste and emissions generation [3]. Innovation-driven development is an essential strategy in China for achieving sustainable economic prosperity [4,5]. Companies play a critical role in innovation-related endeavours [6,7]. Previous studies have showcased how technological innovation can mitigate environmental pollution and ecological damage, as well as positively influencing firm performance and promoting green development [8]. Firms invest in numerous activities, such as engaging in research and development (R&D), recruiting highly qualified individuals, and reshuffling business operations for innovation purposes [9]. Still, activities intended for innovation incur substantial costs and lead to financial unsustainability; thus, innovation is considered a complex process instead of a singular output action [10]. It is appropriate to situate corporate green innovation within the broader context of efficiency.

The Chinese government has exercised its financial and regulatory authority when allocating resources such as government grants, tax relief, innovation funds and license issues, actively encouraging innovation [11,12]. Among these government interventions, subsidies and tax incentives are the most significant for the state to foster domestic innovation and guide the economy’s sustainable growth [13,14]. As fiscal and tax policies, government subsidies effectively promote company-level innovation, lowering business invention costs [15]. Nevertheless, deviations can arise in how government subsidies are implemented due to information asymmetry, causing a disparity between the outcome of government subsidies and expectations [16,17]. Moreover, it is a common practice for governments to stimulate corporate investment behaviour through tax incentives [12]. Through improving the funds retained within a company and the cash flow levels of businesses, tax incentives can heighten the realistic returns on inventions [12,18]. The United States, Europe and other developed nations are the primary subjects of timely research. However, the effects of tax incentives prove to be far more pronounced in China due to its unique economic structure and size, as the most extensive developing nation [19,20]. Given the abovementioned problems, the question is how government subsidies and tax incentives influence corporate green innovation efficiency in China.

Compared to the capital demand for innovation projects, the funds offered by fiscal and tax policies are relatively limited [12]. More importantly, policies support businesses by releasing signals to companies’ internal and external parties. Fiscal and tax policies increase the attraction of external financing, including debt financing and equity financing, by offering external fund providers positive signs regarding the quality and sustainability of companies [21]. Bank loans are the primary form of debt financing. Banks realise their earnings by charging interest when offering businesses short- and long-term loans. Considering equity financing, new shareholders are introduced to access long-term funding without repaying any principal or interest. Both generate virtual avenues for companies to access external funding [22]. Debt and equity financing can promote innovation by alleviating the constraints of financing [22,23]. As such, our research questions cover how debt and equity financing mediate the relationships between government subsidies, tax incentives and green innovation efficiency.

We make the following three contributions: First, we explore the function of external financing in mediating the relationship between the efficiency of green business innovation and fiscal and tax policies, while the previous literature focuses primarily on the direct effects of fiscal and tax policies or external financing, e.g., [12,22,24,25]. Secondly, this research represents one of the pioneering empirical studies using the SBM–Luenberger index for dynamic green innovation efficiency calculations according to the input and output indicators suggested by Zhong et al. [26], Wang et al. [27] and Liang et al. [1]. The previous literature employed the index to calculate agricultural total factor productivity, carbon productivity, green productivity, and energy efficiency, e.g., [27,28,29,30]. Lastly, the study empirically investigates the heterogeneity of fiscal and tax policies on ownership and life cycle through sub-sample regression, in line with the work of Li et al. [20] and Ryu and Won [31], as firm-related characteristics reshape policy instruments’ effect on innovation efficiency. Subsequently, we explore how government subsidies and tax incentives influence firms with diverse ownership and life cycles, which could be beneficial for companies to enhance their green innovation efficiency by suitably utilising fiscal and tax policies. Hence, given these findings, we shed light on the underlying mechanism by which fiscal and tax policies affect corporate green innovation efficiency and offer recommendations to governmental entities and businesses.

The remaining portions of the study have been separated into sections, with Section 2 overviewing the research scope and conducting a literature review. Section 3 is dedicated to describing the methodology in more detail. Section 4 empirically analyzes the data to present the results. Finally, in Section 5, the study is concluded.

2. Literature Review and Research Hypothesis

2.1. Government Subsidies and Green Innovation Efficiency

Government subsidies for innovative activities are commonly practised in most industrialised countries, with their justification stemming from market failures [32]. Numerous studies have been conducted on government subsidies. For example, a study conducted in Germany uncovers a positive relationship between government subsidies and patent applications [15]. Likewise, in Sweden, government subsidies have positively affected total innovative investments among small manufacturing and services companies [33]. The study by Boeing et al. [34] demonstrates that government subsidies foster innovation inputs while promoting advanced technologies, thereby stimulating sustainable economic growth in China. Furthermore, Meng et al. [35] find that government subsidies for manufacturers encourage the supply chain to practice green innovation.

Nevertheless, two main categories in the present study centre on the effect that government subsidies may have on companies’ innovation ability: either stimulating or hindering (see

Table 1. The literature about government subsidies and corporate innovation.

Year	Authors	Scope	Findings	Sources
2022	Wang et al.	China	positive	[32]
2022	Boeing et al.	China	positive	[34]
2021	Meng et al.	China	positive	[35]
2014	Czarnitzki & Lopes-Bento	Germany	positive	[15]
2005	Heshmati & Loof	Sweden	positive	[33]
2022	Qiao & Fei	China	negative	[37]
2022	Zhou & Zhao	China	negative	[38]
2016	Dimos & Pugh	Cross-country	negative	[16]
2016	Hong et al.	China	negative	[39]

). Both policymakers and academicians have expressed doubts about the efficiency of subsidies, with many innovative enterprises having failed despite funding [36]. Moral hazards and financial instability undermine the policy’s effect on improving efficiency and profits [37]. Moreover, the information asymmetry generated by government subsidies harms corporate investment efficiency, as businesses tend to conceal sensitive information, leading to the suboptimal allocation of public funds [16,38]. As argued by Hong et al. [39], government subsidies discourage companies from investing in innovation, hindering technological advancement.

Since the majority of the literature on China suggests that government subsidies harm green innovation efficiency, Hypothesis 1 is put forward:

Hypothesis 1.

Government subsidies significantly impact green innovation efficiency.

2.2. Tax Incentives and Green Innovation Efficiency

Tax incentives play a notable role in influencing a company’s internal resource allocation [40]. By directly compensating a portion of innovation expenses and minimising the tax burden, these incentives can push businesses to scale up their innovation investment [41]. Considering an intangible mark recognised by the government, Kleer [42] finds that tax incentives effectively promote the product and enhance the market of inventions for companies. Therefore, allocating more resources to innovative activities promotes corporate innovation. Sun [12] also highlights that companies prefer sustainable internal financing as the primary means of funding innovation to lower their capital adjustment costs. Through strengthening the relationship between green innovation and enterprise performance, preferential tax treatment generates a more pronounced incentive than government subsidies [1]. As such, tax incentives contribute to the sustainable supply of funds for green innovation by retaining a more significant portion of earnings within a company. Thus, Hypothesis 2 is proposed:

Hypothesis 2.

Tax incentives significantly positively impact green innovation efficiency.

2.3. Fiscal and Tax Policies, Debt Financing and Green Innovation Efficiency

The inherent unpredictability and risks associated with innovative activities pose formidable challenges for businesses aiming to secure external market funding, and the information asymmetry further exacerbates financing constraints [43]. Fiscal and tax policies, acting as certification, play a critical part in alleviating these difficulties by signalling to external fund providers and facilitating access to financing from outside sources for businesses [21]. Additionally, sponsors and investors tend to be more inclined towards supporting sustainable companies with a higher likelihood of successful commercialisation than ventures pursuing uncertain initiatives [44]. Against the Chinese institutional background, Han and Gu [22] found that fiscal and tax policies increase companies’ market value, signalling to the marketplace that the firm has prospects and promoting outside funding, advancing its progress into the commercialisation stage.

Debt financing creates a channel for government subsidies to improve the degree of green innovation for publicly listed companies [45]. Czarnitzki and Kraft [46] proclaim that debt financing lessens the agency issue arising from senior executives’ discretionary behaviour by committing to loan repayment. Failure to fulfil loan obligations can lead to severe financial consequences, ensuring that funds are allocated to profitable investments instead of serving the private interests of executives. By securing adequate cash flow through debt financing, companies can guarantee smooth daily operations and production activities, setting the foundation for managers to invest in R&D innovation [47]. Thus, Hypothesis 3and Hypothesis 4 are suggested as follows:

Hypothesis 3.

Debt financing significantly mediates government subsidies and green innovation efficiency.

Hypothesis 4.

Debt financing significantly mediates tax incentives and green innovation efficiency.

2.4. Fiscal and Tax Policies, Equity Financing and Green Innovation Efficiency

In addition to debt financing, equity financing represents another category by which companies can raise funds from external sources [22]. Scholars have observed that fiscal and tax policies serve as a signalling mechanism in equity financing. For instance, Islam et al. [48] surveyed green power start-ups in the United States and discovered that those receiving government support had a 12% higher likelihood of obtaining further venture capital funding than those without aid. This can be attributed to the fact that venture capitalists recognise the signals embedded in various government grants, which indicates a start-up’s potential for expansion. Moreover, Meuleman and Maeseneire [49] claim that fiscal and tax policies help small and medium-sized enterprises to activate their funding by addressing the information asymmetry between investors and firms.

Equity financing has been found to have a more pronounced positive impact on innovation than debt financing. The reason for this is attributed to the flexibility of equity financing, as it does not require strict repayment schedules, making it better-suited to the longer cycles associated with corporate innovation [23]. Start-up firms receiving venture capital funding experience significant improvements in diverse aspects, including human resources policies, marketisation, sales and stock options [50]. Research by Casamatta [51] outlines that enterprises supported by venture capital notably outperform those without such support in terms of their innovation performance, making venture capital an indicator of enhanced profitability. Xiang et al. [45] also prove that government subsidies encourage firms’ green innovation through equity financing. Hence, fiscal and tax policies indirectly influence corporate green innovation efficiency through equity financing, leading to Hypothesis 5 and Hypothesis 6:

Hypothesis 5.

Equity financing significantly mediates government subsidies and green innovation efficiency.

Hypothesis 6.

Equity financing significantly mediates tax incentives and green innovation efficiency.

Table 2. Summary of expected regression coefficient sign.

Hypothesis	Independent Variable	Dependent Variable	Mediator	Expected Results
Direct effects
H1	Government subsidies	Green innovation efficiency		negative
H2	Tax incentives	Green innovation efficiency		positive
Indirect effects
H3	Government subsidies	Green innovation efficiency	Debt financing	mediation
H4	Tax incentives	Green innovation efficiency	Debt financing	mediation
H5	Government subsidies	Green innovation efficiency	Equity financing	mediation
H6	Tax incentives	Green innovation efficiency	Equity financing	mediation

provides a summary of the expected regression coefficient sign in each hypothesis.

Based on these six hypotheses, the conceptual framework for this research is constructed and illustrated in Figure 1.

The following deficiencies exist in current research on fiscal and tax policy instruments, external financing, and corporate green innovation efficiency. Firstly, the previous literature primarily concentrates on the impact of government subsidies, paying less attention to tax incentives. Secondly, despite the relatively abundant studies on innovation investment, the influencing factors of green innovation efficiency still need to be explored. Thirdly, most researchers are restricted to investigating the direct impacts of fiscal and tax policy instruments and external financing on innovative performance, while more research is needed into the mediating effects of external funding on government subsidies, tax incentives and green innovation efficiency. Finally, due to firm heterogeneity, it is necessary to justify how fiscal and tax policies affect companies with distinct ownership or at different life-cycle phases. Our research compensates for the above deficiencies by extending the correlation among fiscal and tax policy instruments, firms’ external financing and corporate green innovation efficiency.

3. Methodology

3.1. Data and Sample

All Chinese firms with data from 2015 to 2021 took part in this study. MaxDEA8.0 and STATA15.0 software were utilised to measure corporate green innovation efficiency and conduct further analyses. The input and output indicators of green innovation efficiency were obtained from the Chinese Research Data Service platform (CNRDS). Subsequently, the China Stock Market and Accounting Research Database (CSMAR) originated from other financial data sources.

Considering the long span of the original data related to numerous companies, interpolation was performed to compensate for any missing data. For example, if X_t, representing the variable X of a company in year t, was missing, we replaced it with the average of the previous year’s data and the following year’s data [26]. That is to say, X_t = (X_t−1 + X_t+1)/2. Addressing the impact of outliers, we followed the examples Cook et al. [52] and Hassanein et al. [53] to witness the increase in variables at the 1% and 99% levels. Finally, from 2015 to 2021, our final data sample entailed 19,228 firm-year observations attributed to 3549 companies.

3.2. Variables

3.2.1. Dependent Variable

Within the framework provided by the DEA efficiency measurement approach, the SBM efficiency measurement, outlined by Tone [54], functions as a non-radial efficiency indicator. It directly measures the distance between input or output to the front of production, with the input excess and output shortfall being called slacks. However, the model cannot indicate the change in efficiency from year to year [55]. Many scholars employ the Malmquist, Luenberger or the Malmquist–Luenberger indexes when analysing dynamic productivity, that is, a decision unit’s productivity change from period t to t + 1 [29]. The Luenberger index developed by Chambers et al. [56] is the most general one and is used in this paper [28].

The calculation of the SBM–Luenberger index requires both input and output indicators. Employing indicators from the research by Zhong et al. [26], Wang et al. [27] and Liang et al. [1], input indicators include the number and percentage of innovation staff and the amount and rate of innovation expenditure; the number of green patent applications and revenue from upcoming products are viewed as output indicators (see

Table 3. Indicators of innovation efficiency.

Layer	Indicator
Input	Number of innovation staff
	Percentage of innovation staff
	Amount of innovation expenditure
	Innovative expenditure as a proportion of operating income
Output	Number of green patent applications
	Revenue from new products

).

3.2.2. Independent Variables

This research considered two types of fiscal and tax policies: government subsidies and tax incentives. The former were divided by total assets to eliminate the influence of firm size [57,58]. In line with the studies of Sun [12] and Feng et al. [58], the effective tax rate was utilised to measure tax incentives, reflecting the total tax burden level. A business enjoys more tax incentives with a lower effective tax rate.

3.2.3. Mediating Variables

Companies can choose between two exterior financing options: debt and equity. The proportion of short- and long-term loans to total assets indicates debt financing, while fluctuations in capital and capital reserve as a percentage of total assets serve as the proxies of equity financing [22,45].

3.2.4. Control Variables

We included four representative control variables in this study: size, financial leverage, ownership concentration and patent. Each variable’s measurement is outlined in

Table 4. Definition of variables.

Variable	Symbols	Description	Sources
Dependent variable
Green Innovation efficiency	GIE	The Luenberger index under the SBM model. Input indicators include the number of innovation staff, the percentage of innovation staff, the amount of innovation expenditure and the percentage of innovation expenditure. Output indicators include the number of green patent applications and the revenue from new products.	[1,26,27]
Independent variables
Government subsidies	SUB	Government subsidies/total assets.	[57,58]
Tax incentives	TAX	Income tax expenses/earnings before interest and tax	[12,58]
Mediating variables
Debt financing	DEB	(Short-term loans + long-term loans)/total assets.	[22,45]
Equity financing	EQU	(Changes in capital + changes in capital reserve)/total assets.	[22,45]
Control variables
Size	SIZ	The natural logarithm of total assets.	[59]
Financial leverage	LEV	Total liabilities/total assets.	[58]
Ownership concentration	CON	Largest shareholder’s shareholding ratio.	[59]
Patent	PAT	The natural logarithm of (1+ patents).	[60]

.

3.3. Model Specification

Herein, five regression models are developed to verify the above-proposed hypotheses. A one-year lag is used for government subsidies, tax incentives, debt financing and equity financing to capture their effects on green innovation [61]. Equation (1) is established to evaluate the impacts of government subsidies and tax incentive instruments on green innovation efficiency.

$$GI{E}_{i,t}={\beta }_0+{\beta }_{1}SU{B}_{i,t-1}+{\beta }_{2}TA{X}_{i,t-1}+{{\displaystyle \sum }}_{i,t}control+{\epsilon }_0$$

(1)

Moreover, Equations (2) and (3) are constructed to determine how government subsidies and tax incentives affect debt financing and equity financing.

$$DE{B}_{i,t-1}={\beta }_0+{\beta }_{1}SU{B}_{i-1}+{\beta }_{2}TA{X}_{i-1}+{{\displaystyle \sum }}_{i,t}control+{\epsilon }_0$$

(2)

$$EQ{U}_{i,t-1}={\beta }_0+{\beta }_{1}SU{B}_{i-1}+{\beta }_{2}TA{X}_{i-1}+{{\displaystyle \sum }}_{i,t}control+{\epsilon }_0$$

(3)

Next, government subsidies, tax incentives and debt financing are factored into Equation (4). Equations (1) and (2), and (4) are utilised in causal stepwise regression to determine whether debt financing mediates the association between fiscal and tax policies and corporate green innovation efficiency.

$$GI{E}_{i,t}={\beta }_0+{\beta }_{1}SU{B}_{i,t-1}+{\beta }_{2}TA{X}_{i,t-1}+DE{B}_{i,t-1}+{{\displaystyle \sum }}_{i,t}control+{\epsilon }_0$$

(4)

Finally, we merge equity financing with government subsidies and tax incentives within Equation (5). Along with Equations (1) and (3), Equation (5) is utilised to verify if equity financing mediates the effect of fiscal and tax policies on corporate green innovation efficiency.

$$GI{E}_{i,t}={\beta }_0+{\beta }_{1}SU{B}_{i,t-1}+{\beta }_{2}TA{X}_{i,t-1}+EQ{U}_{i,t-1}+{{\displaystyle \sum }}_{i,t}control+{\epsilon }_0$$

(5)

The green innovation efficiency of company i in year t is outlined by $EF{F}_{i,t}$. The amount of government subsidies company i received in year t is represented by $SU{B}_{i,t}$. The tax incentives for the company i in year t are indicated by $TA{X}_{i,t}$. $DE{B}_{i,t}$ and $EQ{U}_{i,t}$ refer to the debt and equity financing levels, respectively. Size, financial leverage, ownership consolidation, and patent are included in the control variables denoted by ${\displaystyle \sum }_{i,t}control$. ${\beta }_0$ represents the constant, and ${\epsilon }_0$ refers to an error term.

4. Results and Discussion

4.1. Descriptive Statistics

Table 5. Descriptive statistics.

Variable	Obs	Mean	Median	Std.Dev.	Min	Max	Range
GIE	19,228	0.952	0.943	0.294	0.001	1.579	1.579
SUB	19,228	0.006	0.003	0.011	−0.015	0.774	0.789
TAX	19,228	0.016	0.013	0.022	−0.189	0.101	0.290
DEB	19,228	0.271	0.208	0.245	0.001	1.064	1.063
EQU	19,228	0.387	0.359	0.213	0.048	1.318	1.270
SIZ	19,228	22.27	22.09	1.286	18.35	28.64	10.29
CON	19,228	33.15	30.77	14.47	2.430	99	96.57
LEV	19,228	0.424	0.412	0.233	0.008	11.39	11.38
PAT	19,228	1.456	0.693	1.426	0	3.932	3.932

outlines the descriptive statistics for variables. The green innovation efficiency (GIE) varies from 1.579 to 0.001, indicating a substantial difference in green innovation efficiency between businesses. After dividing total assets, there is a wide range (0.789) of government subsidies (SUB) for different firms, suggesting that the government grants firms differently. On average, the tax burden placed on Chinese firms proves relatively low in light of the tax incentives’ mean value (0.016) (TAX). The means of debt financing (DEB) and equity financing (EQU) are 0.271 and 0.387, exceeding the medians of 0.208 and 0.359. This indicates that most businesses have below-average access to external funding. In addition, differences in the sample are reflected in control variables, including size (SIZ), financial leverage (LEV), ownership concentration (CON) and patent (PAT), which better suit this study’s empirical analysis.

4.2. Correlation Analysis

Through Spearman correlation analysis, the coefficients in

Table 6. Spearman correlation matrix.

	GIE	SUB	TAX	DEB	EQU	SIZ	CON	LEV	PAT
GIE	1
SUB	−0.0154 *	1
TAX	−0.0566 *	−0.0234 *	1
DEB	0.0493 *	−0.1563 *	−0.2082 *	1
EQU	−0.0140	0.1977 *	−0.0149 *	−0.3213 *	1
SIZ	0.0324 *	−0.3436 *	0.00550	0.3087 *	−0.5460 *	1
CON	−0.0186 *	−0.0325 *	0.1000 *	−0.0411 *	−0.2064 *	0.1088 *	1
LEV	0.0501 *	−0.2027 *	−0.2942 *	0.6558 *	−0.6231 *	0.4844 *	0.0291 *	1
PAT	0.0910 *	−0.0158 *	−0.0183 *	0.0083	0.0151 *	0.0172 *	0	−0.0011	1

The symbol * indicates statistical significance at 5%.

demonstrate all connections between sample variables. The green innovation efficiency (GIE) of companies points to a significant correlation with government subsidies (SUB), tax incentives (TAX), debt financing (DEB), size (SIZ), ownership concentration (CON), financial leverage (LEV) and patent (PAT), with r = −0.0154, r = −0.0566, r = 0.0493, r = 0.0324, r = −0.0186, r = 0.0501 and r = 0.0910, respectively. Both debt financing (DEB) and equity financing (EQU) prove closely related to government subsidies (r = −0.1563 and r = 0.1977). Subsequently, debt financing (DEB) and equity financing (EQU) correlate with tax incentives to a significant extent (r = −0.2082 and r = −0.0149). Thus, the paper’s hypotheses are confirmed. The coefficient correlations of all independent variables range between −0.8 and 0.8, indicating weak multicollinearity between variables.

4.3. Fixed-Effect Regression Analysis

We utilised the Hausman test to choose from fixed-effect and random-effect models. The results indicate that model estimation must be performed under the fixed-effect model since the null hypotheses for all models are rejected at the 1% significance level. In addition, the macroeconomic conditions shared by all firms during the analysis period, industry differentiation and firm differences are controlled by factoring year, industry and firm fixed effects in the regression model [58].

Model 1 in

Table 7. Fixed-effect regression.

Variable	Model 1	Model 2	Model 3	Model 4	Model 5
	GIE	L.DEB	L.EQU	GIE	GIE
L.SUB	−0.595 **	0.0485	0.454 ***	−0.599 **	−0.573 **
	(−2.21)	(0.48)	(5.88)	(−2.22)	(−2.13)
L.TAX	−0.779 ***	−0.785 ***	−0.201 ***	−0.726 ***	−0.788 ***
	(−4.34)	(−11.58)	(−3.90)	(−4.02)	(−4.39)
L.DEB				0.067 ***
				(2.78)
L.EQU					−0.049
					(−1.55)
SIZ	−0.064 ***	−0.00484	−0.124 ***	−0.064 ***	−0.070 ***
	(−6.26)	(−1.25)	(−41.77)	(−6.23)	(−6.40)
CON	−0.0009	−0.00128 ***	0.00000867	−0.0008	−0.0009
	(−1.23)	(−4.83)	(0.05)	(−1.10)	(−1.29)
LEV	0.074 **	0.268 ***	−0.0863 ***	0.056 *	0.070 **
	(2.46)	(23.61)	(−9.94)	(1.81)	(2.30)
PAT	0.080 ***	0.00162	0.000505	0.080 ***	0.080 ***
	(29.52)	(1.59)	(0.65)	(29.48)	(29.53)
Firm	Control	Control	Control	Control	Control
Year	Control	Control	Control	Control	Control
Industry	Control	Control	Control	Control	Control
Constant	1.879 ***	0.244 **	3.207 ***	1.863 ***	2.038 ***
	(6.95)	(2.39)	(41.44)	(6.89)	(7.04)
R2	0. 0868	0. 1125	0.1873	0. 0874	0.0870
F	21.10 ***	28.13 ***	51.15 ***	20.87***	20.77 ***
N	15,515	15,515	15,515	15,515	15,515

t statistics in parentheses. The symbols *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

outlines a significant negative effect of government subsidies on green innovation efficiency (β = 0.595, p < 0.05). Thus, H1 is supported. An increase in government subsidies lowers green innovation efficiency. There are several reasons for this phenomenon. First, the Chinese government is discreet in providing grants, often breeding rent-seeking activities through the unreasonable allocation of resources [62]. Furthermore, most government subsidies are offered once before innovation, while the performance of innovation activities is a long cycle. The financing constraints during the enterprise’s innovation process remain ignored [63].

Additionally, tax incentives display a significantly negative coefficient with β = −0.779 (p < 0.01). As tax incentives are inversely measured from the tax burden perspective, a higher green innovation efficiency is indicated among companies with a higher tax incentive. Hence, H2 is confirmed. Tax incentives, which show governmental support for innovations, push businesses to instruct and inspire innovation, promote the cash flow anticipation of decision-makers, and stimulate enterprises’ comprehensive innovation behaviour to a specific degree [12].

According to Model 2 in Table 7, government subsidies insignificantly influence debt financing. In contrast, Model 3 indicates a positive correlation between government subsidies and equity financing (β = 0.454, p < 0.01). Representing direct support to companies, government subsidies alleviate the financial pressures of businesses to an extent. This dependence on the government makes companies insensitive to debt and interest rates [37], leading to an insignificant association between debt financing and government subsidies. Hence, H3 is rejected. Additionally, tax incentives (TAX) make for a strong indicator of debt and equity financing (β = −0.711, p < 0.01 and β = −0.785, p < 0.01, respectively). Unlike government subsidies, which provide direct grants, tax incentives minimise financing constraints by curbing business spending [41]. As such, a low tax burden proves conducive to improving corporate cash flow, signalling a bright future for companies [12] and encouraging them to seek sustainable growth through external financing.

Model 4, combined with Models 1 and 2, is applied to assess the mediating role of debt financing in the association between two forms of fiscal and tax policy instruments and green innovation efficiency. Models 1 and 2 have significant tax incentive coefficients (β = −0.779, p < 0.01 and β = −0.711, p < 0.01, respectively), as does debt financing in Model 4 (β = 0.067, p < 0.01). Therefore, tax incentives improve corporate green innovation efficiency by debt financing, confirming H4. However, the coefficient of equity financing proves insignificant in Model 5, verifying the failed mediation of equity financing for the association between green innovation efficiency and fiscal and tax policies. Thus, H5 and H6 are disproved.

Myers and Majluf [64] claim that companies are inclined to seek external financing when the internal sources of funds are inadequate to support their innovation investment. Under these circumstances, companies undertaking innovative projects are in demand in the financing market, making firms more vulnerable to financing constraints [65]. As such, the likelihood of companies carrying out innovation activities positively correlates with their access to debt financing [47]. Additionally, external funds are more inclined to flow into companies enjoying tax incentives [12]. Thus, tax incentives enhance green innovation efficiency by increasing debt financing.

4.4. Robustness Tests

This study employs instrumental variable (IV) estimation and two-stage least squares (2SLS) regression to address potential endogeneity issues between government subsidies, tax incentives, and corporate green innovation efficiency. The instrumental variables that are employed are the median industry level of government subsidies and tax incentives in the firm’s region, as suggested by prior research [66,67]. Firms in regions with higher industry-level government support are more likely to enjoy fiscal and tax policies.

Table 8. Robustness tests—fixed-effect instrumental variable estimations.

First-Stage Regressions			IV (2SLS) Estimation
Variable	L.SUB	L.TAX	Variable	GIE
Instrumental variable			Independent variable
L.IVSUB	0.2438 ***	−0.1658 ***	L.SUB	−8.1676***
	(32.72)	(−11.23)		(−3.96)
L.IVTAX	0.0155 ***	0.0542 ***	L.TAX	−8.5363 ***
	(3.12)	(5.52)		(−3.13)
SIZ	0.0072 ***	−0.0056	SIZ	−0.0441
	(25.31)	(−9.97)		(−3.62)
CON	−0.0001 ***	−0.0001	CON	−0.0026
	(−3.92)	(−3.75)		(−3.02)
LEV	0.0001	0.0215	LEV	0.2478
	(0.12)	(13.02)		(3.93)
PAT	0.0001	0.0002	PAT	0.0608
	(1.02)	(1.53)		(22.08)
Firm	Control	Control	Firm	Control
Year	Control	Control	Year	Control
Industry	Control	Control	Industry	Control
N	15,084	15,084	N	15,084
Anderson canon. corr. LM statistic	38.922
Chi-sq (1) p-value	0.0000
Cragg–Donald Wald F statistic	19.44
Stock–Yogo critical value at 10%	7.03

t statistics in parentheses. The symbols *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

outlines the results of fixed-effect IV regression. These findings reaffirm the negative impact of government subsidies and the positive influence of tax incentives on green innovation efficiency. In short, the obtained results validate the leading conclusions, enhancing the consistency of the estimates. Statistical tests have ensured the selected instrumental variables’ validity. The Cragg–Donald Wald F-statistics surpass the critical value at 10% significance levels, highlighting that weak instrument problems fail to affect the models. Moreover, the p-values of the Anderson LM statistic equal 0, demonstrating statistical significance at the 1% level.

Considering the potential impact of the COVID-19 pandemic on government subsidies and tax incentives, we excluded the data from this period and performed a robustness test utilising data from 2015 to 2019. Even though there are variations in the significance levels of specific coefficients,

Table 9. Robustness tests—exclusion of samples during the COVID-19 pandemic period.

Variable	Model 1	Model 2	Model 3	Model 4	Model 5
	GIE	L.DEB	L.EQU	GIE	GIE
L.SUB	−1.061 ***	0.213 *	0.338 ***	−1.029 ***	−1.021 ***
	(−3.40)	(1.73)	(4.08)	(−3.30)	(−3.27)
L.TAX	−0.702 ***	−0.297 ***	−0.0377	−0.688 ***	−0.707 ***
	(−2.73)	(−4.99)	(−0.55)	(−2.68)	(−2.75)
L.DEB				0.128 ***
				(3.20)
L.EQU					−0.119 **
					(−2.54)
SIZ	−0.108 ***	−0.0202 ***	−0.0760 ***	−0.106 ***	−0.117 ***
	(−6.86)	(−3.26)	(−18.20)	(−6.72)	(−7.25)
CON	−0.000172	−0.000247	−0.000947 ***	−0.000154	−0.000284
	(−0.15)	(−0.56)	(−3.21)	(−0.14)	(−0.26)
LEV	0.107 ***	0.208 ***	−0.0454 ***	0.0404	0.101 **
	(2.64)	(12.99)	(−4.23)	(0.89)	(2.50)
PAT	0.0878 ***	0.0000615	0.00109	0.0876 ***	0.0879 ***
	(23.68)	(0.44)	(1.10)	(23.64)	(23.72)
Firm	Control	Control	Control	Control	Control
Year	Control	Control	Control	Control	Control
Industry	Control	Control	Control	Control	Control
Constant	2.926 ***	0.448 ***	2.120 ***	2.852 ***	3.178 ***
	(7.43)	(2.88)	(20.27)	(7.23)	(7.83)
R2	0.1017	0.0991	0.1082	0.1031	0.1026
F	15.03 ***	14.61 ***	16.12 ***	14.96 ***	14.87 ***
N	9388	9388	9388	9388	9388

t statistics in parentheses. The symbols *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

confirms our findings’ robustness. Furthermore, according to the analysis, equity financing mediates between government subsidies and green innovation efficiency when excluding the samples taken during the pandemic.

Referring to Wei and Xiao [1], the variable of tax incentives was reconstructed as the number of tax incentives, equalling the total profits multiplied by the difference between nominal and effective income tax rates. The nominal income tax rate is published in financial statements, whereas the effective income tax rate is determined by dividing income tax expenses by earnings before interest and tax. The regression results from

Table 10. * Robustness tests—substitution variables.

Variable	Model 1	Model 2	Model 3	Model 4	Model 5
	GIE	L.DEB	L.EQU	GIE	GIE
L.SUB	−0.702 **	0.107	0.0104	−0.709 ***	−0.683 **
	(−2.56)	(1.06)	(0.13)	(−2.59)	(−2.49)
L.TAX	0.121 ***	0.0734 ***	0.0636 ***	0.115 ***	0.123 ***
	(4.05)	(8.30)	(9.25)	(3.84)	(4.12)
L.DEB				0.0697 ***
				(2.85)
L.EQU					−0.0398
					(−1.22)
SIZ	−0.0622 ***	0.0226 ***	−0.159 ***	−0.0621 ***	−0.0669 ***
	(−5.90)	(7.18)	(−64.91)	(−5.89)	(−5.96)
CON	0.0673 **	0.292 ***	−0.0838 ***	0.0484	0.0636 **
	(2.19)	(45.56)	(−16.80)	(1.54)	(2.06)
LEV	−0.00106	−0.000734 ***	−0.00349 ***	−0.000973	−0.00109
	(−1.50)	(−3.41)	(−20.83)	(−1.37)	(−1.53)
PAT	0.0795 ***	0.00244 ***	0.00268 ***	0.0794 ***	0.0795 ***
	(29.35)	(2.72)	(3.85)	(29.31)	(29.35)
Firm	Control	Control	Control	Control	Control
Year	Control	Control	Control	Control	Control
Industry	Control	Control	Control	Control	Control
Constant	1.867 ***	−0.301 ***	3.930 ***	1.854 ***	1.990 ***
	(6.79)	(−3.61)	(60.73)	(6.74)	(6.80)
R2	0.0871	0.1826	0.2812	0.0878	0.0872
F	20.91 ***	62.89 ***	110.13 ***	20.69 ***	20.56 ***
N	15,347	15,347	15,347	15,347	15,347

t statistics in parentheses. The symbols *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

are robust, given the above findings.

Another further robustness test uses government subsidies, tax incentives, and debt and equity financing with a two-period lag. Despite slight differences in the significance levels of some coefficients,

Table 11. Robustness tests—two-period lag in independent variables.

Variable	Model 1	Model 2	Model 3	Model 4	Model 5
	GIE	L2.DEB	L2.EQU	GIE	GIE
L2.SUB	−0.811 ***	0.0572	0.750 ***	−0.765 ***	−0.687 **
	(−3.02)	(0.54)	(9.27)	(−2.85)	(−2.55)
L2.TAX	−0.488 **	−1.081 ***	−0.00610	−0.425 *	−0.489 **
	(−2.05)	(−11.49)	(−0.09)	(−1.78)	(−2.06)
L2.DEB				0.130 ***
				(3.92)
L2.EQU					−0.166 ***
					(−4.74)
SIZ	−0.0540 ***	−0.0303 ***	−0.0742 ***	−0.0542 ***	−0.0663 ***
	(−4.46)	(−6.34)	(−20.34)	(−4.48)	(−5.36)
CON	−0.00116	−0.00152 ***	0.000675 ***	−0.000953	−0.00104
	(−1.39)	(−4.63)	(2.70)	(−1.15)	(−1.26)
LEV	0.0338	0.0237 ***	0.00431	0.0215	0.0345
	(1.48)	(2.62)	(0.63)	(0.93)	(1.51)
PAT	0.0840 ***	0.00105	−0.00131	0.0840 ***	0.0838 ***
	(27.62)	(0.87)	(−1.43)	(27.63)	(27.58)
Firm	Control	Control	Control	Control	Control
Year	Control	Control	Control	Control	Control
Industry	Control	Control	Control	Control	Control
Constant	1.651 ***	1.267 ***	2.040 ***	1.633 ***	1.990 ***
	(5.22)	(10.13)	(21.41)	(5.17)	(6.14)
R2	0.0951	0.0833	0.0939	0.0966	0.0973
F	18.21 ***	15.74 ***	17.96 ***	18.19 ***	18.34 ***
N	12,125	12,125	12,125	12,125	12,125

t statistics in parentheses. The symbols *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

verifies the robust conclusions. In addition, equity financing mediates government subsidies and green innovation efficiency when utilising a two-period lag.

4.5. Sub-Sample Analysis

This study conducts a sub-sample analysis to verify whether the impacts of fiscal and tax policy instruments on the efficiency of green innovation can be reshaped by firm-related characteristics. Li et al. [20] suggest variations in the performance of SOEs and non-SOEs, and

Table 12. Distribution by ownership.

Group	Frequency	Percentage
SOEs	5448	28.33
non-SOEs	13,780	71.67
Total	19,228	100

highlights that SOEs account for nearly 30% of the sample.

Companies in different life-cycle stages also exhibit a dissimilar performance due to their distinct targets [31]. This paper is initiated with Dickinson’s [68] life cycle: introduction, growth, maturity, shake-out, and decline. Cash flow patterns determine a company’s life-cycle stage. For example, an introductory firm has positive financing cash flow and negative operating and investing cash flows.

Nevertheless, some researchers suggest dividing the life cycle into four stages [69], with Faff et al. [70] combining the shake-out stage and decline phase. Subsequently, the four steps are finalised as an introduction, growth, maturity and decline in this paper.

Table 13. Cash flow as a firm life-cycle indicator.

Cash Flow Category	Introduction	Growth	Mature	Decline
Operating cash flow	−	+	+	−	+	−
Investing cash flow	−	−	−	−	+	+
Financing cash flow	+	+	−	−	−/+	−/+

The symbols − and + indicate negative and positive cash flow, respectively.

offers a concise summary of cash flow patterns for operating, investing, and financing activities.

Next,

Table 14. Distribution by life-cycle.

Group	Frequency	Percentage
Introduction	2255	11.73
Growth	6825	35.50
Mature	6445	33.52
Decline	3703	19.25
Total	19,228	100

lists the sample’s distribution by life-cycle stages.

The results of sub-sample regression following ownership and life-cycle heterogeneity are presented in

Table 15. Fixed-effect regression for sub-samples.

Variable	Ownership Heterogeneity		Life-Cycle Heterogeneity
	SOE	Non-SOE	Introduction	Growth	Mature	Decline
L.SUB	0.303	−0.653 **	2.578	−0.765 **	−0.350	0.496
	(0.41)	(−2.38)	(1.34)	(−2.22)	(−0.39)	(0.53)
L.TAX	−1.185 **	−0.567 ***	−0.611	−1.415 ***	−0.823 **	−0.819 *
	(−2.55)	(−2.93)	(−0.91)	(−2.75)	(−2.08)	(−1.95)
SIZ	−0.049 **	−0.067 ***	−0.118 ***	−0.059 **	−0.004	−0.112 ***
	(−2.00)	(−5.82)	(−2.68)	(−2.41)	(−0.14)	(−3.19)
CON	0.001	−0.001	−0.004	−0.002	0.002 *	0.001
	(0.80)	(−1.11)	(−1.10)	(−1.27)	(1.66)	(0.33)
LEV	0.139 *	0.052	0.089	−0.015	0.053	−0.061
	(1.72)	(1.58)	(0.72)	(−0.19)	(0.63)	(−0.88)
PAT	0.059 ***	0.090 ***	0.085 ***	0.079 ***	0.081 ***	0.105 ***
	(11.26)	(28.38)	(6.87)	(14.61)	(15.22)	(11.84)
Firm	Control	Control	Control	Control	Control	Control
Year	Control	Control	Control	Control	Control	Control
Industry	Control	Control	Control	Control	Control	Control
Constant	1.803 ***	2.129 ***	2.859 ***	1.861 ***	0.779	3.524 ***
	(3.14)	(7.53)	(2.73)	(2.84)	(1.28)	(4.27)
R2	0. 0518	0. 1053	0.1530	0.0957	0.0852	0.1145
F	7.53 ***	36.53 ***	5 ***	10.58 ***	11.50 ***	6.94 ***
N	4482	11,033	1714	5096	5522	3183

t statistics in parentheses. The symbols *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

. Since tax incentives are inversely measured from the tax burden, the findings indicate that both SOEs and non-SOEs benefit from tax incentives (β = −1.185, p < 0.05 and β = −0.567, p < 0.01, respectively), while government subsidies only significantly influence non-SOEs (β = −0.653, p < 0.05) concerning green innovation efficiency.

More unevenness is required when distributing government subsidies on the state-private axis. The government tends to offer more significant policy resources to SOEs [71], and their development significantly depends on state intervention and assistance. According to the efficiency hypothesis and the signal transmission theory, the incentive for SOEs to improve the efficiency of government subsidies is limited [72].

In contrast, non-SOEs are not supported by the state-owned background [73]. Due to the strict loan applicant, non-SOEs face more significant financing constraints than SOEs [74]. Some non-SEOs with innovation strength are hindered from innovative activities in light of the necessity for further funds. Thus, they tend to improve green innovation efficiency through the efficient utilization of available funds for surviving the fierce market competition [75]. The significantly limited tax burden is beneficial to improving the green innovation efficiency of non-SOEs. Moreover, non-SOEs are keener to receive subsidies from government officials through rent-seeking activities than SOEs, distorting organisational resource allocation. Ultimately, this deviates from the priorities related to value creation [76]. Due to the unproductive rent-seeking activities, companies cannot sensibly allocate resources [69]. Consequently, non-SOEs are more negatively influenced by government subsidies than SOEs regarding green innovation efficiency.

As Table 15 indicates, neither government subsidies nor tax incentives significantly influence the green innovation efficiency of firms during their introduction and decline. Initial-stage businesses display idiosyncratic volatility, excessive financial expenditures, significant capital holdings, less efficient processes, and unpredictable cash inflow [77]. Hence, given their high demands, fiscal and tax policies provide limited funds for introductory firms. Decline-stage companies face declining product competitiveness, a shrinking market share and challenges in achieving profitability. In this phase, firms seek short-term objectives or rearrange their businesses [31]. Following the stimulation of fiscal and tax policies, they are less likely to invest in R&D activities than to survive or return to earlier stages.

In line with the total sample results, growing firms’ government subsidies and tax incentives significantly influence green innovation efficiency (β = −0.765, p < 0.05 and β = −1.415, p < 0.05, respectively). Drobetz et al. [78] discover that expanding businesses require every dollar in the bank to fund investment and innovation, given the lack of internal capital.

In contrast, only tax incentives significantly affect the efficiency of green innovation in mature firms (β = −0.823, p < 0.05). These businesses possess significant scale advantages, larger market shares, and firm profitability [79]. Benefiting from market competitiveness, they gain access to plenty of innovation resources. Thanks to their productive activities, the excess earnings generated by innovation create a virtuous circle, significantly improving productivity [69]. Hence, mature companies are less likely to pursue government subsidies than those in the growth phase.

5. Conclusions

In this research, we conducted a comprehensive examination of how government subsidies and tax incentives affect corporate green innovation efficiency, based on the mediating roles of debt financing and equity financing. The SBM–Luenberger index is used to measure green innovation efficiency.

A sample of Chinese listed companies and a dynamic green innovation efficiency metric generate various findings. First, the results substantiate our hypothesis on the relationship between fiscal and tax policy instruments and green innovation efficiency: government subsidies decrease corporate green innovation efficiency while tax incentives promote it. Second, debt financing mediates the relationship between tax incentives and green innovation efficiency. Third, fiscal and tax policies significantly affect non-SOE green innovation efficiency, moreso than SOEs. Finally, fiscal and tax policies were found to significantly impact the green innovation efficiency of firms in growing and mature stages rather than in the introductory or declining phases. This leads to the following implications for the government and businesses.

Policymakers can utilise fiscal and tax policies guiding businesses in allocating R&D resources wisely, introducing external financing, and improving green innovation efficiency. Moreover, while supporting SOEs, more fiscal and tax policies must be implemented to assist non-SOEs. In addition, the government can provide subsidies and tax incentives for growing and mature businesses and devise other policies to improve the green innovation efficiency of introductory and declining firms.

Companies must actively pursue fiscal and tax policies and make adequate use of external financing. Furthermore, they must establish close ties with the government, adopting a diversified ownership structure to minimise risks in green innovation. Notably, growing and mature businesses should prioritise government subsidies and tax incentives for innovation, while start-ups and declining firms should conduct green innovation activities based on operation maintenance.

Our findings have specific limitations. First, this research is limited to listed firms, although non-listed firms are also critical to economic sustainability. Thus, it is necessary to further discuss whether our conclusions can be extended to non-listed companies. Second, the sample is limited to companies based in China, whereas the government has a worldwide influence on corporate operations and green innovation. Therefore, the sample size could be expanded globally, including other nations from diverse institutional backgrounds, generalising the research findings by replicating the research framework within those nations.