“Crowding Out” or “Reservoir Effect”? Unraveling the Impact of Financialization on Green Innovation in Heavy Polluting Enterprises: Evidence from China’s Listed Companies

Abstract

In the era of green transformation and sustainable development, green innovation has become a key force driving the greenization of the economy and society, as well as achieving global sustainable development goals. This study aims to investigate the impact of financialization on green innovation in heavy polluting enterprises, a sector where enhancing green innovation capabilities is crucial for both industry transformation and sustained economic growth. As the financialization of enterprises deepens, its effects on green innovation become increasingly significant. Based on data from 667 listed companies in China, we explore the mechanisms through which financialization influences green innovation. Our findings reveal that financialization has both a potential “reservoir” effect, which provides financial support for green innovation, and a significant “crowding out” effect, with the latter outweighing the former, thereby suppressing investment in green innovation. Further analysis indicates that these effects exhibit significant heterogeneity across different property rights, regions, and corporate debt-to-asset ratios. Additionally, the short-term debt-paying ability of enterprises and market competitiveness play important regulatory and threshold roles in this relationship. This study not only provides a new perspective for understanding the relationship between the financialization of heavy polluting enterprises and green innovation but also offers valuable insights for promoting the coordinated development of both and addressing the challenges of green innovation.

1. Introduction

In the wave of rapid global economic development, sustainable development has become a core issue for the international community. To address the challenges in the social, economic, and environmental dimensions, the United Nations officially adopted 17 Sustainable Development Goals on 25 September 2015, leading the world towards a green and low-carbon path of sustainable development. The green economy, as a key engine of this transformation, not only drives economic growth but also safeguards the vitality of our planet. China, as a responsible major country, actively responds to the global call for green development. At the United Nations General Assembly on 22 September 2020, China made a solemn pledge to implement robust measures with the objective of reducing carbon emissions to their peak by 2030 and achieving carbon neutrality by 2060. This commitment not only demonstrates China’s firm determination for green development but also contributes Chinese wisdom and solutions to global green development.

In the journey of green transformation, enterprises, as micro-entities in the market, play a crucial role. The advancement of green innovation represents a pivotal driving force in the promotion of green transformation within the enterprise sector. Such measures not only assist organizations in reducing their energy consumption and emissions, but also facilitate the transition to a more environmentally-conscious production process. This, in turn, can lead to economic benefits for the companies in question, thereby promoting the sustainable development of the industrial economy. However, the high investment, high risk, and long cycle of green innovation have made the path of green innovation challenging for many enterprises. Particularly for heavily polluting enterprises, the significant upfront investment and long payback period make their strides in green innovation even more burdensome.

In recent years, with the rapid development of the Chinese financial market, there has been a notable increase in the phenomenon of enterprise financialization, which has emerged as a new trend. For heavily polluting enterprises, financialization provides a new source of funds, helping to alleviate financial pressure. Nevertheless, it remains an open question as to whether financialization can truly assist heavily polluting enterprises in overcoming the financial challenges associated with green innovation. Therefore, this paper seeks to investigate the impact mechanism of financialization on green innovation within heavily polluting enterprises. The primary objective is to provide both theoretical insights and practical guidance for the coordinated development of financialization and green innovation in these firms. Ultimately, this study aspires to offer valuable decision-making references that can facilitate green development and contribute to the attainment of sustainable development goals.

2. Literature Review

2.1. Green Innovation

Since Schumpeter proposed the “innovation theory”, innovation has become the cornerstone for driving sustained economic growth [1]. In the context of increasingly severe environmental issues, the importance of green innovation, as an extension of traditional innovation theory towards environmental sustainability, has become more prominent. The pioneering work of Fussler et al. and Rennings laid the foundation for green innovation, emphasizing the pursuit of economic benefits while protecting the environment [2,3]. International organizations such as the Organisation for Economic Co-operation and Development (OECD) and the World Intellectual Property Organization (WIPO) have further promoted academic research and practical applications in this field.

In the early 20th century, the economist Schumpeter first proposed the “innovation theory” in “The Theory of Economic Development”, emphasizing innovation as the key force driving economic and social development (Schumpeter) [1]. With the increasingly severe global environmental challenges, green innovation, as an evolution of traditional innovation theory towards environmental sustainability, has become an important approach for driving economic and social sustainability. In this context, Fussler et al. introduced the concept of green innovation, highlighting that green innovation not only involves technological progress but also achieves a win-win situation for environmental protection and economic benefits through technological innovation [2]. Subsequently, Rennings further deepened the connotation of green innovation, defining it as innovation activities oriented towards addressing environmental issues, covering new perspectives, processes, services, products, or management systems [3]. With the continuous deepening of academic research and the sustained promotion of global green development, numerous scholars and international organizations have conducted more comprehensive and cutting-edge discussions on the definition and connotation of green innovation. Cheng and Shiu expanded the connotation of green innovation from the dimensions of ecological organization, processes, and products, proposing a path for green innovation where enterprises seek the optimal production methods to achieve both environmental friendliness and economic benefits [4]. Qi Shaozhou et al. argued that green technology refers to the collective term for technologies, processes, or products that reduce environmental pollution, raw material, and energy use. Innovation activities centered on green technology can be termed as green innovation. With extensive discussions and in-depth research in the academic community, WIPO has the broadest scope in defining green innovation, covering technological innovations closely related to environmental protection and climate change mitigation, providing clear guidance and support for the study and practice of green innovation [5]. Drawing on WIPO’s definition of green innovation, this paper considers green innovation as a means of promoting green technological progress and economic and social sustainability through technological and managerial innovations, emphasizing not only breakthroughs at the technological level but also the harmonious integration of environmental friendliness and economic benefits throughout the entire economic and social process.

With regard to the factors that influence enterprise green innovation, existing studies have constructed analytical frameworks that consider multiple dimensions. Firstly, from the perspective of the external environment of enterprises, environmental regulation is widely regarded as a key variable. Porter’s “Porter Hypothesis” emphasizes that moderate environmental regulation can stimulate the innovative vitality of enterprises, improve production efficiency through technological innovation, and compensate for the increased costs due to environmental regulation, even enhancing the profitability of enterprises [6]. Studies by Berrone et al. and Chen et al. also support this view, finding that government-led environmental regulation policies can significantly drive enterprises to engage in green innovation activities [7,8]. In addition, external factors such as government tax incentive policies (Castellacci) [9], property rights protection for the environment (Lin et al.) [10], financing environment (Silva; Gorodnichenko et al.) [11,12], green credit policies (Liu et al.) [13], and economic policy uncertainty (Cui et al.) [14] also have significant impacts on green innovation. From the internal perspective of enterprises, Yuan et al. explored the influence of corporate social responsibility on green innovation [15]. Fang et al. found that internal characteristics such as the enterprise size and profitability significantly influence green innovation [16]. Oduro et al. demonstrated that the corporate governance level is also an important factor influencing green innovation activities [17]. Furthermore, management characteristics such as the CEO power, equity concentration (Javeed et al.) [18], executive education background (He et al.) [19], and CEO hometown affiliation (Ren et al.) [20] have gradually become new perspectives in green innovation research. A review of the literature reveals that both external and internal factors influence enterprises’ willingness to engage in green innovation and the accumulation of green innovation funds. These factors, in turn, affect enterprises’ green innovation activities. Specifically, factors such as government environmental regulation, property rights protection for the environment, and executive education background enhance enterprises’ willingness for green innovation, thereby stimulating enterprises to engage in green innovation activities. Meanwhile, government tax incentive policies, the financing environment, and the enterprise size provide necessary financial support for enterprise green innovation, promoting the smooth progress of enterprise green innovation activities.

2.2. Financialization of Heavily Polluting Enterprises and Green Innovation

In the existing research, there has been a paucity of discourse regarding the influence of the financialization of enterprises engaged in substantial pollution on the advancement of green innovation. Most studies have focused on the correlation between enterprise financialization and technological innovation, with the motives behind enterprise financialization becoming a hot topic of discussion. Many scholars have discussed the impact of enterprise financialization through the “liquidity” effect and “crowding out” effect on enterprise financialization and its influence on technological innovation.

On one hand, scholars who hold a positive attitude firmly believe that enterprise financialization significantly promotes technological innovation. Bonfiglioli pointed out that enterprises can accumulate returns through financial investment, effectively alleviate financing constraints, and provide stable financial support for innovation activities [21]. Arizala et al. also argued that enterprise financialization can significantly improve financing efficiency, broaden revenue channels, increase capital reserves, meet the high demand for funds in innovation activities, and objectively drive the pace of innovation in enterprises [22]. Gehringer revealed the “liquidity” function of enterprise financialization in a context of limited funds, where enterprises use short-term financial investments as capital reserves to cope with future uncertainties. This not only improves financing efficiency but also promotes capital accumulation, injecting new impetus into enterprise innovation activities [23]. Duchin et al. found that in developed countries, the main motive for enterprise financialization is precautionary savings [24].

On the other hand, some scholars hold a cautious attitude towards enterprise financialization, believing that it may have a suppressive effect on technological innovation. Lazonick used U.S. enterprises as a case study, pointing out that enterprise financialization may lead to a shift of investment from the real sector to the financial sector, resulting in a “crowding out” effect on technological innovation, especially in the manufacturing sector, which may weaken the innovation capability of enterprises [25]. Seo et al. reached a similar conclusion in their study on South Korea, finding that enterprise financialization may squeeze the funds originally allocated for technological innovation, thus not being conducive to the development of enterprise technological innovation activities [26]. Wang Hongjian et al. found a significant “crowding out” effect of financialization on innovation based on their research in China [27]. Tori and Onaran found, in their study of non-financial enterprises in the UK, that as enterprise financial activities increased, physical investment showed a declining trend, indicating a clear “crowding out effect” of financialization [28].

In summary, the academic community holds two drastically different views on the impact of enterprise financialization on technological innovation. Some scholars emphasize the “liquidity” effect of enterprise financialization, believing that by alleviating financing constraints and increasing capital reserves, it provides strong support for enterprise technological innovation activities. Other scholars focus on the “crowding out” effect of enterprise financialization, suggesting that financialization behavior may lead to a shift of enterprise investment from the real sector to the financial market, thereby suppressing enterprise technological innovation activities. This academic controversy provides new perspectives and room for future research.

3. Theoretical Analysis and Research Hypotheses

3.1. The Impact of the Financialization of Heavy Polluting Enterprises on Green Innovation

This paper posits that the motive behind the financialization of heavily polluting enterprises plays a pivotal role in influencing the mechanism between financialization and green innovation. Building upon our earlier analysis of the relationship between financialization and its impacts on innovation, we identify the primary motives for enterprise financialization as capital reserves and market arbitrage. Specifically, when the motive for financialization is driven by the need for capital reserves, it can create a “reservoir” effect that promotes innovation. Conversely, when the motive is primarily market arbitrage, it can lead to a “crowding out” effect that inhibits innovation.

Firstly, from the perspective of precautionary savings theory, in order to address potential cash flow shortages resulting from uncertain risks in the future, enterprises often maintain a certain level of cash reserves. However, given the strong liquidity and high short-term returns of financial assets, an increasing number of enterprises in reality choose to substitute cash with financial assets, leading to the phenomenon of enterprise financialization. This represents the motive of financialization for capital reserves (Duchin et al. [24]; Hu et al. [29]; Yang et al. [30]). Enterprise financialization driven by this motive can alleviate financing constraints (Arizala et al. [22]), increase capital returns (Gehringer) [23], and provide financial security for enterprise technological innovation, thus generating a “reservoir” effect on enterprise technological innovation activities (Hu et al. [29]; Du et al. [31]).

Secondly, based on Adam Smith’s economic man hypothesis, enterprises always seek to maximize their profits when making decisions. When financialization can bring about excess returns for enterprises, leading to profit maximization, enterprises naturally allocate more resources to the financial market, thus giving rise to the phenomenon of enterprise financialization driven by market arbitrage motives (Orhangazi [32]; Demir [33]; Wang et al. [27]). Within the framework of Tobin’s Q theory, there exists a substitution relationship between financial investment and physical investment. Therefore, in situations of limited resources, if enterprises allocate more resources to short-term financial investments, it will inevitably lead to a reduction in resources for physical investment, resulting in the crowding out of funds for technological innovation and thereby inhibiting technological innovation activities, creating a negative “crowding out” effect (Tobin [34]; Wang et al. [27]; Tori et al. [28]). Additionally, the agency conflict problem in the agency theory exacerbates this “crowding out” effect, as corporate agents often make decisions based on personal interests rather than the long-term interests of shareholders, tending to invest funds in the financial market to obtain high profits (Xu et al. [35]; Yang et al. [36]).

For heavily polluting enterprises, the high costs, long cycles, and slow returns in their production and operation make their need for cash flow and financial security more urgent. However, compared to traditional innovation, green innovation entails higher investment costs and greater uncertainty (Xiang et al. [37]). Driven by the economic benefits brought about by financialization, heavily polluting enterprises may reduce their investment in green innovation, thus forming a motive for market arbitrage. In addition to the existing agency conflict problem, the financialization of heavily polluting enterprises may lead to a “crowding out” effect on green innovation.

In summary, this paper explores the dual impact of the financialization of heavily polluting enterprises on green innovation, characterized by both a “reservoir” effect and a “crowding out” effect. Specifically, when the “reservoir” effect is more pronounced, financialization tends to foster green innovation. Conversely, when the “crowding out” effect dominates, financialization may hinder green innovation. Based on this analysis, we propose the following hypotheses:

Hypothesis 1.1 (H1.1).

In contexts where the “reservoir” effect is prevalent, the financialization of heavily polluting enterprises will promote green innovation.

Hypothesis 1.2 (H1.2).

In contexts where the “crowding out” effect is dominant, the financialization of heavily polluting enterprises will inhibit green innovation.

3.2. Moderating Effect of Short-Term Debt Repayment Ability

When exploring the relationship between the financialization of heavily polluting enterprises and green innovation, we need to consider the potential impact of short-term debt repayment ability on this relationship. Based on the existing research (Duchin et al. [24]; Hu et al. [29]; Yang et al. [30]), heavily polluting enterprises tend to use financialization as a means to reserve funds, particularly to mitigate future uncertainty and cash flow shortages. These enterprises often face high costs, long operating cycles, and slow returns, making the repayment of short-term liabilities a significant risk factor for their cash flow shortages. Therefore, when the short-term debt repayment ability of heavily polluting enterprises is weak, their motivation to reserve funds through financialization becomes more pronounced, and the positive effect of the “reservoir” effect will be more significant. As the short-term debt repayment ability of enterprises gradually improves, the pressure on their cash flow shortages will gradually ease, and the motivation for financialization to reserve funds will weaken, thereby weakening the positive effect of the “reservoir” effect and enhancing the inhibitory effect of financialization on green innovation. Combining the above analysis, we can reasonably infer that the enhancement of the short-term debt repayment ability will further strengthen this inhibitory effect. Therefore, we propose the following hypothesis:

Hypothesis 2 (H2).

The enhancement of the short-term debt repayment ability will strengthen the inhibitory effect of financialization on green innovation in heavily polluting enterprises.

3.3. Threshold Effect of Market Competitiveness

When exploring the impact of the financialization of heavily polluting enterprises on green innovation, in addition to analyzing the role of internal factors within companies, it is also necessary to consider the external environment in which companies operate, especially the potential influence of market competitiveness on the relationship between the two.

Firstly, from the perspective of product market competition theory, the information hypothesis suggests that intense product market competition is conducive to enhancing the transparency of information within companies. In such a market environment, shareholders can more accurately evaluate the operational performance of company agents based on public information and industry comparisons, thereby more effectively implementing monitoring and constraints on agents, reducing their short-term behavior (Wang et al. [38]). This mechanism helps to alleviate agency conflicts in heavily polluting enterprises, reducing the “crowding out” effect caused by agency conflicts in the principal –agent theory. Therefore, when market competitiveness is high, the inhibitory effect of heavily polluting enterprises on green innovation will be weakened.

Secondly, from the perspective of industrial organization theory, market competition has two opposing effects on corporate innovation: first, the escape effect of competition, where companies engage in technological innovation activities to escape pressure from other competitors, thus promoting innovation (Dinopoulos [39]); second, the Schumpeterian effect, where intense market competition may lead to competition between companies, resulting in the easy imitation and substitution of innovative results, thereby reducing the benefits of technological innovation and ultimately inhibiting corporate innovation activities. Aghion et al. [40] and Hashmi [41] concluded that the combination of these two effects will result in an inverted U-shaped relationship between market competition and technological innovation, and this trend is significantly influenced by market structure and environmental differences. In the context of China’s actual situation, due to its imperfect industrial structure and insufficient market competition, the enhancement of market competitiveness will significantly promote technological innovation with the increasingly sound patent protection system (Zhang et al. [42]; Xu and Zhu [43]). Green innovation, as a crucial component of technological innovation, will also be subject to similar impacts. Therefore, in the Chinese market, high market competitiveness weakens the inhibitory effect of the financialization of heavily polluting enterprises on green innovation through its positive promotion of technological innovation. Conversely, in a low market competitiveness scenario, the inhibitory effect of market competition on technological innovation will further strengthen the negative impact of the financialization of heavily polluting enterprises on green innovation.

Based on the above theoretical analysis, this study proposes the following hypothesis:

Hypothesis 3 (H3).

The impact of the financialization of heavily polluting enterprises on green innovation exhibits a threshold effect based on market competition. Specifically, when market competitiveness is high, the inhibitory effect of the financialization of heavily polluting enterprises on green innovation is weak; whereas when market competitiveness is low, this inhibitory effect is relatively strong.

4. Research Design

4.1. Sample Selection and Data Sources

This study selects heavily polluting enterprises listed on the A-share market in China from 2012 to 2021 as the research sample, utilizing foundational data sourced from the CSMAR database.

First, concerning the selection criteria for heavily polluting enterprises, we adhere to the definitions established by the Ministry of Ecology and Environment of China in the 2008 publication titled “Environmental Protection Verification Industry Classification Management Directory for Listed Companies.” Accordingly, we categorize heavily polluting enterprises into 16 specific industries, including thermal power, steel, cement, and electrolytic aluminum. To ensure the accuracy of our sample selection, we conduct a meticulous comparison of these industry classifications with the “Guidelines for the Classification of Listed Companies” published by the China Securities Regulatory Commission in 2012. This process enables us to definitively identify the specific codes for heavily polluting industries and subsequently recognize the corresponding enterprises.

Second, regarding the selection of the sample period, we note that the “Guidelines for the Classification of Listed Companies”, which serve as a critical reference for defining heavily polluting enterprises, were published in 2012. Additionally, due to significant gaps in green patent data following 2021, we have opted for the period from 2012 to 2021 to ensure the validity and reliability of our data.

Finally, to ensure the randomness and representativeness of our sample, we implement several data processing steps: first, we exclude samples with missing or anomalous values for key variables; second, we remove companies classified as ST, *ST, or those facing insolvency issues, as these entities often exhibit a poor operational performance that could undermine the representativeness of the sample; third, to mitigate the influence of outliers, we apply a 1% and 99% winsorization to continuous variables. As a result, our final sample comprises 6670 observations.

4.2. Variable Selection and Definition

4.2.1. Dependent Variable: Green Innovation

The dependent variable in this paper is green innovation. In the existing literature, there is still debate about the measurement methods for green innovation, primarily including measurement methods based on the input perspective (Shen et al. [44]; Wang et al. [27]) and those based on the output perspective (Popp [45]; Li et al. [46]; Bai et al. [47]).

The input-based method refers to measuring the level of green innovation using input elements such as research and development investment. This method presents two main issues: firstly, whether the input is entirely effective for the complete research and development innovation process, and secondly, whether this innovation process entirely possesses green attributes. Therefore, the measurement of green innovation obtained from the input perspective often fails to fully reflect the true level of green innovation. The output-based method mainly measures the level of green innovation using the quantity of green patents. Not only is this method more direct, precise, and objective in terms of data, but it can also effectively avoid the problems faced by input-based measurement methods. Moreover, using the quantity of green patents as a measure of green innovation also helps to reveal differences in innovation among enterprises (Xu et al. [48]; Li et al. [49]), aligning more closely with the purpose of this study. Additionally, considering that the number of granted green patents more accurately reflects the actual level of a company’s green innovation compared to the number of applied green patents (Qi et al. [5]), this paper, drawing on the research of Qi et al. [5] and Li et al. [49], uses the number of granted green patents to measure green innovation. It then takes the natural logarithm of the quantity of granted green patents plus one to alleviate the heteroscedasticity and collinearity issues of the variable.

4.2.2. Core Explanatory Variable: Financialization of Heavily Polluting Enterprises

The financialization of heavily polluting enterprises is the core explanatory variable in this study. Drawing on the research of Stockhammer [50], Krippner [51], and Demir [33], we define it as the behavior of heavily polluting enterprises transferring reserves and investments from the real sector to the financial sector to gain more economic benefits, thereby changing their operational focus and capital accumulation mode.

Regarding the measurement of the financialization of heavily polluting enterprises, the existing literature mainly utilizes two types of measurement methods: those based on the asset perspective (Demir [33]; Song [52]; Zhang [53]) and those based on the income perspective (Krippner [51]; Zhang [54]). The former emphasizes reflecting the financial investment intentions of enterprises, while the latter focuses on depicting the economic consequences of enterprise financialization. As this study primarily aims to examine the decision-making intent of the financialization of heavily polluting enterprises, we choose to measure enterprise financialization from the asset perspective.

The determination of the financial asset calculation method is crucial for measuring enterprise financialization. The existing literature does not reach a consensus on whether cash, investment properties, and long-term equity investments should be included in the statistical scope of financial assets. Our approach is as follows: firstly, the primary purpose of enterprises holding cash is for daily operational needs rather than investment for profit (Song [52]; Du [31]); therefore, we do not include cash in the statistical scope of enterprise financial assets. Secondly, as real estate increasingly exhibits financial and speculative attributes, investment properties can create significant capital appreciation and rental income for enterprises (Du [31]); hence, we include them in the statistical scope of financial assets. Lastly, although the distinction between the operational and investment purposes of long-term equity investments may not be clear, as a fundamental financial asset, enterprises can indirectly engage in investment and speculative activities through long-term equity investments, reflecting their investment intentions (Huang [55]). Therefore, we include long-term equity investments in the statistical scope of financial assets.

Based on the above analysis and drawing on the research of Demir [33], Song [52], Du [31], and Huang [55], we choose to include seven types of assets—trading financial assets, derivative financial assets, net loans and advances, net available-for-sale financial assets, net held-to-maturity investments, net investment properties, and net long-term equity investments—in the measurement scope of financial assets. The proportion of the sum of these assets to the total assets of the enterprise serves as the quantitative indicator of enterprise financialization. Additionally, considering the modification of enterprise accounting standards in China in 2018, to ensure data accuracy, we refer to the study of Zhang [56] and adjust the financial asset calculation method post-2018 by excluding “net available-for-sale financial assets” and “net held-to-maturity investments”, while including four types of assets—debt investments, other debt investments, other equity instrument investments, and other non-current financial assets—in the statistical scope of financial assets.

4.2.3. Moderating Variable: Short-Term Debt Repayment Ability

The quick ratio is the ratio of a company’s quick assets to its current liabilities, and we use the quick ratio to characterize a company’s short-term debt repayment ability. The quick ratio not only considers the immediate liquidity of current assets but also combines it with current liabilities to reflect the ability of the liquid portion of current assets to cover debts in proportionate form when facing short-term debt pressures. This indicator overcomes the comparison difficulties brought about by differences in company size and can intuitively reflect a company’s risk-management strategy. Based on the above considerations, we choose the quick ratio as the measure of a company’s short-term debt repayment ability.

4.2.4. Threshold Variable: Market Competitiveness

Existing literature offers two primary indicators for measuring market competition, namely the Herfindahl–Hirschman index (HHI) and the Lerner index. The HHI is primarily used to assess industry concentration, while the Lerner index measures the market monopoly based on a company’s profitability within the industry. In line with the research purpose of this study, we select the Lerner index as the key indicator for measuring market competitiveness. Proposed by the economist Lerner, the Lerner index reflects the degree of market monopoly by the deviation rate between price and marginal cost. This index ranges from 0 to 1, where a higher index value indicates stronger monopolistic power and lower market competitiveness, and vice versa.

4.2.5. Control Variables

Drawing on the research of Wang [27] and Duan [57], we select the enterprise size, enterprise age, enterprise growth, ownership concentration, independent director ratio, return on assets, financing constraints, and board size as control variables, with their measurement methods outlined in

Table 1. Control variables and their measurement methods.

Names of Control Variables	Symbols of Control Variables	Measurement Methods
Enterprise Size	lnsize	Natural logarithm of total assets at the end of the period
Enterprise Age	lnage	Natural logarithm of the difference between the observation year and the year of establishment plus one
Enterprise Growth	growth	Revenue growth rate of the enterprise
Ownership Concentration	TOPten	Percentage of ownership held by the top ten shareholders of the enterprise
Independent Director Ratio	indep	Ratio of the number of independent directors to the total number of directors
Return on Assets	Roaa	Ratio of net profit at the end of the year to total assets at the end of the period
Financing Constraints	SA	SA index
Board Size	lnBoardsize	Natural logarithm of the number of board members

. Specifically, the measure of financing constraints, using the SA index, follows the approach of Hadlock and Pierce [58] and Ju et al. [59]. A negative SA index indicates the existence of financing constraints, and the larger the absolute value, the more severe the financing constraints.

4.3. Model Specification

To explore the impact mechanism of the financialization of heavily polluting enterprises on green innovation and to verify the theoretical hypotheses proposed earlier, we establish the baseline empirical model as follows:

$$\ln {\mathrm{green}}_{\mathrm{it}}={\beta }_0+{\beta }_1\mathrm{fin}{1}_{\mathrm{it}}+Control{s^{\prime }}_{\mathrm{it}}{\beta }_2+\sum Year+\sum Ind+{\epsilon }_{\mathrm{it}}$$

(1)

where the subscript i represents the enterprise, t represents the year, $\ln \mathrm{green}$ represents green innovation, $\mathrm{fin}1$ represents the financialization of heavily polluting enterprises, $Controls$ represents a column vector consisting of all control variables, $\sum Year$ represents year dummy variables, $\sum Ind$ represents industry dummy variables, and ${\epsilon }_{\mathrm{it}}$ represents the error term. When the coefficient ${\beta }_1$ is positive, it indicates that the “reservoir” effect of the financialization of heavily polluting enterprises is greater than the “crowding out” effect, implying that the financialization of heavily polluting enterprises can promote green innovation, thus supporting Hypothesis H1.1. When the coefficient ${\beta }_1$ is negative, it suggests that the “reservoir” effect of the financialization of heavily polluting enterprises is smaller than the “crowding out” effect, indicating that the financialization of heavily polluting enterprises can inhibit green innovation, thus supporting Hypothesis H1.2.

We introduce the company’s short-term debt repayment ability as a moderating variable based on the baseline regression model (1) to test H2 and further explore its underlying logic and reasons. The specific testing model is as follows:

$$\ln {\mathrm{green}}_{\mathrm{it}}={\beta }_0+{\beta }_1\mathrm{fin}{1}_{\mathrm{it}}+{\beta }_2{\mathrm{quick}}_{\mathrm{it}}+{\beta }_3{\mathrm{quick}1}_{\mathrm{it}}+Control{s^{\prime }}_{\mathrm{it}}{\beta }_4+\sum Year+\sum Ind+{\epsilon }_{\mathrm{it}}$$

(2)

where $\mathrm{quick}$ represents the quick ratio, $\mathrm{quick}1$ represents the interaction term between the quick ratio and the financialization of heavily polluting enterprises, and the meanings of the other variables are the same as in Model (1).

Based on the previous analysis, we proceed to construct a threshold regression model based on Hansen [60] to test the potential threshold effect of market competitiveness between the financialization of heavily polluting enterprises and green innovation. The specific testing model is as follows:

$$\ln {\mathrm{green}}_{\mathrm{it}}={\alpha }_0+{\alpha }_1\mathrm{fin}{1}_{\mathrm{it}}I(LEN{A}_{\mathrm{it}}\le \gamma )+{\alpha }_2\mathrm{fin}{1}_{\mathrm{it}}I(LEN{A}_{\mathrm{it}}>\gamma )+Control{s^{\prime }}_{\mathrm{it}}{\alpha }_3+{\epsilon }_{\mathrm{it}}$$

(3)

where $LENA$ represents market competitiveness, $\gamma$ represents the threshold value, and $I(\cdot )$ is the indicator function.

5. Empirical Analysis

5.1. Baseline Regression Results

The baseline regression results are presented in

Table 2. Baseline regression results.

	(1)	(2)	(3)	(4)
	lngreen	lngreen	lngreen	lngreen
fin1	−0.252 *	−0.326 ***	−0.236 **	−0.284 ***
	(0.130)	(0.107)	(0.105)	(0.094)
lnsize		0.380 ***		0.365 ***
		(0.010)		(0.010)
lnage		0.745 ***		0.634 ***
		(0.064)		(0.069)
growth		0.000		0.000 ***
		(0.000)		(0.000)
TOPten		−0.240 ***		−0.073
		(0.078)		(0.079)
indep		−0.174		0.108
		(0.238)		(0.250)
Roaa		−0.158 **		−0.047
		(0.065)		(0.049)
SA		1.556 ***		1.458 ***
		(0.082)		(0.093)
lnBoardsize		−0.018		0.261 ***
		(0.066)		(0.067)
_cons	0.836 ***	1.569 ***	0.652 ***	0.920 ***
	(0.016)	(0.303)	(0.042)	(0.302)
YearFE	no	no	yes	yes
IndustryFE	no	no	yes	yes
N	6670.000	6670.000	6670.000	6670.000

Standard errors in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

. The first column shows the regression results considering only the core explanatory variable “financialization of heavily polluting enterprises,” the second column shows the model estimates after including control variables, and the third and fourth columns present the model estimates further controlling for time and industry fixed effects based on the first two columns. According to Table 2, regardless of whether factors affecting green innovation at the enterprise level are controlled for, the estimated coefficients of the core variable, the financialization of heavily polluting enterprises, are significantly negative. This indicates that the “crowding out” effect of the financialization of heavily polluting enterprises on green innovation is greater than the “reservoir” effect, significantly inhibiting green innovation in enterprises, thus confirming Hypothesis H1.2. Furthermore, the estimation results of the control variables indicate that companies with a larger asset size, older age, and larger board size tend to have more active green innovation activities, whereas financing constraints inhibit green innovation activities within enterprises.

5.2. Robustness Tests

To ensure the robustness of the research conclusions, this study proceeds to re-estimate the baseline regression model using two methods: an instrumental variable approach and variable replacement. Firstly, to address potential endogeneity issues arising from reverse causality, omitted variables, and other factors, this study adopts the approach of Yu et al. [61] by using the lagged one-period value of the financialization of heavily polluting enterprises (fin3) and the average value of the financialization of heavily polluting enterprises in different years, provinces, and industries (fin4) as instrumental variables. The two-stage least squares method (2SLS) is employed to re-estimate the baseline regression model. Secondly, to avoid inconsistent results due to measurement errors in the variables, this study excludes long-term equity investments from broad financial assets to obtain narrow financial assets and uses the ratio of narrow financial assets to total assets (fin2) as the measure of the financialization of heavily polluting enterprises to re-estimate the baseline regression model. The estimation results are shown in

Table 3. Results of robustness testing.

	2SLS Estimation		Core Variable: fin2
	Instrumental Variable: fin3	Instrumental Variable: fin4
fin1	−0.222 **	−0.336 **
	(0.105)	(0.163)
fin2			−0.377 ***
			(0.139)
lnsize	0.365 ***	0.365 ***	0.363 ***
	(0.010)	(0.010)	(0.010)
lnage	0.634 ***	0.635 ***	0.631 ***
	(0.069)	(0.069)	(0.069)
growth	0.000 ***	0.000 ***	0.000 ***
	(0.000)	(0.000)	(0.000)
TOPten	−0.071	−0.075	−0.071
	(0.079)	(0.079)	(0.079)
indep	0.108	0.107	0.112
	(0.249)	(0.249)	(0.250)
Roaa	−0.047	−0.047	−0.046
	(0.049)	(0.049)	(0.049)
SA	1.460 ***	1.457 ***	1.458 ***
	(0.092)	(0.092)	(0.093)
lnBoardsize	0.262 ***	0.260 ***	0.262 ***
	(0.067)	(0.067)	(0.067)
_cons	0.922 ***	0.917 ***	0.928 ***
	(0.301)	(0.301)	(0.302)
YearFE	yes	yes	yes
IndustryFE	yes	yes	yes
N	6670.000	6670.000	6670.000

Standard errors in parentheses, ** p < 0.05, *** p < 0.01.

.

The 2SLS estimation results in Table 3 show that the estimated coefficients of financialization of heavily polluting enterprises are −0.222 and −0.336, significant at the 5% level, consistent with the previous conclusions, indicating no apparent endogeneity issues in the baseline regression results. Additionally, according to the results in the third column of Table 3, the estimated coefficient of the new core variable is −0.377, significant at the 1% level, indicating that the selection of variable measurement does not affect the estimation results of the baseline regression. These test results demonstrate that the estimation results of the baseline regression model in the previous section are robust.

5.3. Heterogeneity Tests

The next step of this study involves examining the heterogeneity of the impact of the financialization of heavily polluting enterprises on green innovation in three aspects: enterprise property rights, regional differences, and the corporate debt-to-asset ratio.

Firstly, the property rights of enterprises have a significant influence on their decision-making processes, resource allocation, and innovation incentives. State-owned and non-state-owned enterprises exhibit distinct differences in resource acquisition, policy responsiveness, and innovation incentives. For instance, state-owned enterprises may have easier access to government support and resources but may also face more administrative intervention and constraints. On the other hand, the decision-making processes of non-state-owned enterprises may be more flexible, but they may also encounter greater market pressure and financing constraints. Therefore, examining the heterogeneity of the impact of the financialization of heavily polluting enterprises on green innovation under different property rights can help us to understand the innovation dynamics and resource utilization efficiency of enterprises with different property rights.

Secondly, China’s vast geographical diversity results in significant disparities in economic development, industrial structure, environmental regulations, and policy environments across different regions. These differences may affect the impact of financialization of heavily polluting enterprises on green innovation. For example, regions with higher levels of economic development may place greater emphasis on environmental protection and green development, thus prioritizing the promotion of green innovation in enterprises. Conversely, regions with lower levels of economic development may prioritize economic growth and employment, resulting in a relatively lower emphasis on environmental protection. Therefore, examining the heterogeneity of the impact of the financialization of heavily polluting enterprises on green innovation across different regions can help us to understand the innovation strategies and environments of enterprises in different regions.

Lastly, the debt-to-asset ratio reflects a company’s financial structure and solvency, serving as an important indicator of its financial condition. Companies with different debt-to-asset ratios exhibit differences in financing capabilities, funding costs, and risk-bearing capacities, which may affect their attitudes towards financialization and their investments in green innovation. For instance, companies with lower debt-to-asset ratios may find it easier to obtain external financing, leading to more funds being allocated to green innovation. Conversely, companies with higher debt-to-asset ratios may face greater repayment pressure and financing constraints, making it difficult for them to make substantial investments in innovation. Therefore, examining the heterogeneity of the impact of financialization of heavily polluting enterprises on green innovation under different debt-to-asset ratios can help us to understand the innovation decisions and capabilities of enterprises in different financial conditions.

As shown in

Table 4. Results of heterogeneity tests.

	(1) Non-State-Owned Enterprises	(2) State-Owned Enterprises	(3) Enterprises in Eastern and Central Regions	(4) Enterprises in the Western Region	(5) Enterprises with Low Debt-to-Asset Ratios	(6) Enterprises with High Debt-to-Asset Ratios
fin1	−0.274 **	−0.075	−0.324 ***	−0.267	−0.320 ***	−0.149
	(0.112)	(0.163)	(0.115)	(0.170)	(0.110)	(0.191)
lnsize	0.293 ***	0.372 ***	0.371 ***	0.326 ***	0.327 ***	0.368 ***
	(0.015)	(0.015)	(0.012)	(0.019)	(0.017)	(0.014)
lnage	0.193 **	0.688 ***	0.758 ***	−0.150	0.406 ***	0.738 ***
	(0.091)	(0.132)	(0.077)	(0.164)	(0.112)	(0.103)
growth	0.000 **	0.000 ***	−0.000	0.000 ***	−0.001	0.000 ***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.006)	(0.000)
TOPten	−0.125	−0.037	−0.188 **	−0.025	−0.178 *	0.149
	(0.101)	(0.124)	(0.094)	(0.151)	(0.105)	(0.121)
indep	0.060	−0.390	0.553 *	−1.503 ***	−0.458	0.576 *
	(0.357)	(0.340)	(0.285)	(0.463)	(0.362)	(0.336)
Roaa	−0.004	−0.057	−0.087	−0.024	−0.053	−0.021
	(0.081)	(0.069)	(0.108)	(0.049)	(0.120)	(0.044)
SA	0.608 ***	1.599 ***	1.549 ***	0.817 ***	1.127 ***	1.525 ***
	(0.137)	(0.141)	(0.103)	(0.202)	(0.176)	(0.118)
lnBoardsize	0.064	0.345 ***	0.253 ***	0.229*	0.118	0.361 ***
	(0.096)	(0.095)	(0.078)	(0.134)	(0.093)	(0.098)
_cons	−0.033	1.216 ***	0.828 **	1.527 **	1.163 **	0.353
	(0.399)	(0.469)	(0.344)	(0.610)	(0.480)	(0.428)
YearFE	yes	yes	yes	yes	yes	yes
IndustryFE	yes	yes	yes	yes	yes	yes
N	3571.000	3099.000	5246.000	1424.000	3484.000	3186.000

Standard errors in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

, the test results indicate that the impact of the financialization of heavily polluting enterprises on green innovation exhibits heterogeneity based on enterprise property rights, regional differences, and corporate debt-to-asset ratios.

Firstly, the results of the heterogeneity test for enterprise property rights in Columns (1) and (2) show that the financialization of non-state-owned heavily polluting enterprises has a significantly negative impact on green innovation, while the impact of financialization on green innovation in state-owned heavily polluting enterprises is not significant. This suggests significant heterogeneity in the impact of financialization on green innovation for heavily polluting enterprises with different property rights. The possible reason is that compared to non-state-owned enterprises, state-owned enterprises have certain advantages in obtaining policy support, credit funds, and often exhibit stronger social responsibility awareness. Faced with the higher investment costs and greater uncertainty of green innovation, non-state-owned heavily polluting enterprises may be more inclined to pursue market arbitrage when presented with the excess returns brought about by financialization, potentially leading to crowding out effects and negative impacts on green innovation. In contrast, the influence of the social responsibility awareness of state-owned heavily polluting enterprises may lead them to engage in green innovation activities, thereby mitigating the negative impact of financialization on green innovation to a certain extent.

Secondly, the results of the regional heterogeneity test in Columns (3) and (4) indicate that in the eastern and central regions of China, the financialization of heavily polluting enterprises has a significantly negative impact on green innovation, whereas this impact is not significant in the western region. This suggests significant regional differences in the impact of the financialization of heavily polluting enterprises on green innovation. The reason behind this is that the eastern and central regions of China have a solid economic foundation and a higher level of financial development, providing abundant financial investment resources for enterprises. This makes it easier for enterprises to obtain high profits in the financial market, resulting in a more significant negative impact of financialization on green innovation for heavily polluting enterprises in these regions. In contrast, the lower level of financial development in the western region weakens the potential impact of the financialization of heavily polluting enterprises on green innovation due to the relatively weaker financial foundation for enterprises.

Lastly, the results of the debt-to-asset ratio heterogeneity test in Columns (5) and (6) indicate that for heavily polluting enterprises with low debt-to-asset ratios, financialization has a significantly negative impact on green innovation, whereas this impact is no longer significant for heavily polluting enterprises with high debt-to-asset ratios. This suggests heterogeneity in the impact of financialization of heavily polluting enterprises on green innovation based on the debt-to-asset ratio. The primary reason may be that heavily polluting enterprises with low debt-to-asset ratios have more abundant funds for financial investment and, due to lower debt risks and sufficient liquidity, they are more inclined to pursue excess returns in the financial market. As a result, the financialization behavior of these enterprises is more likely to lead to market arbitrage motives, resulting in crowding out effects on green innovation. Conversely, heavily polluting enterprises with high debt-to-asset ratios face constraints due to financial tightness and higher debt risks, which, under the counterbalance of the reservoir effect, weaken the negative impact of financialization on green innovation.

These findings indicate that the impact of the financialization of heavily polluting enterprises on green innovation exhibits significant heterogeneity based on enterprise property rights, regional differences, and corporate debt-to-asset ratios.

5.4. Empirical Test of Moderating Effect

According to the results in

Table 5. Test results of the moderating effect of short-term debt repayment ability.

Variables	Coefficients
fin1	−0.183 *
	(0.109)
quick	−0.011 ***
	(0.003)
quick1	−0.042 **
	(0.018)
lnsize	0.357 ***
	(0.010)
lnage	0.631 ***
	(0.069)
growth	0.000 ***
	(0.000)
TOPten	−0.041
	(0.080)
indep	0.095
	(0.250)
Roaa	−0.036
	(0.046)
SA	1.465 ***
	(0.093)
lnBoardsize	0.249 ***
	(0.068)
_cons	1.049 ***
	(0.304)
YearFE	yes
IndustryFE	yes
N	6670

Standard errors in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

, the coefficient of the interaction term between the quick ratio and the financialization of heavily polluting enterprises is −0.042, and this result is statistically significant at the 5% level. This indicates that an increase in the quick ratio strengthens the inhibitory effect of the financialization of heavily polluting enterprises on green innovation. Therefore, Hypothesis H2 is validated, suggesting that when companies have a higher short-term debt repayment ability, the potential inhibitory effect of financialization on green innovation will be strengthened. The test results are shown in Table 5.

5.5. Empirical Test of the Threshold Effect

We conducted tests for single-threshold, double-threshold, and triple-threshold settings. The test results, as shown in

Table 6. Bootstrap test for threshold effect (p values obtained from 500 times of bootstrap resampling).

Number of Thresholds	F Values	p Values
Test for single threshold	16.48	0.036
Test for double thresholds	10.28	0.130
Test for triple thresholds	3.76	0.890

, indicate that the single-threshold model should be adopted.

Table 7. Estimation of threshold value.

Threshold Value	95% Confidence Interval
0.2278	(0.2191, 0.2293)

presents the estimation results of the optimal threshold value and its 95% confidence interval for the single-threshold model. Figure 1 depicts the estimated value of the threshold variable and its 95% confidence interval, where the estimated value of the threshold parameter is the value of $\gamma$ when the likelihood ratio test statistic LR equals zero, and the 95% confidence interval is the interval where the LR value is less than the critical value of the 95% significance level (indicated by the intersection of the dashed line and the curve in the figure).

Table 8. Threshold effect of market competitiveness.

Variables	Coefficients
lnsize	0.253 ***
	(0.023)
lnage	0.609 ***
	(0.101)
growth	−0.000
	(0.000)
TOPten	0.234 **
	(0.118)
indep	0.481 *
	(0.275)
Roaa	−0.073
	(0.053)
SA	0.554 ***
	(0.146)
lnBoardsize	−0.011
	(0.094)
fin1A(LENA ≤ 0.2278)	−0.001
	(0.164)
fin1B(LENA > 0.2278)	−0.928 ***
	(0.231)
_cons	−1.299 ***
	(0.448)
YearFE	yes
IndustryFE	yes
N	6670.000

Standard errors in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

presents the regression results of the threshold model.

In Table 8, $fin1A$ and $fin1B$ represent the financialization of heavily polluting enterprises when $LENA\le \gamma$ and $LENA>\gamma$, respectively. As shown in the table, when the market competitiveness exceeds the threshold value of 0.2278, the coefficient for the financialization of heavily polluting enterprises is −0.928, significant at the 1% level. This indicates that when market competitiveness is low, the financialization of heavily polluting enterprises has a strong inhibitory effect on green innovation. When the market competitiveness is less than 0.2278, the coefficient of financialization in heavy polluting enterprises is −0.001. This indicates that when market competitiveness is high, the alleviation of agency conflicts and the impact of intense market competition on green innovation will weaken the inhibitory effect of financialization on green innovation in heavy polluting enterprises. These results confirm Hypothesis H3.

6. Further Discussion

The research conclusions from the preceding sections hold significant theoretical implications and practical significance.

Firstly, in terms of financialization, existing research has conflicting views on the impact of financialization. This study, from the perspective of the financialization of heavily polluting enterprises and green innovation, further validates the potential negative effects that corporate financialization may bring. This highlights the potential impact of financialization on the real economy in the context of imperfect financial market mechanisms and underdeveloped development. Financialization acts as a “double-edged sword” that can provide financing support to the real economy, broaden revenue channels, and drive the development of the real economy with abundant financial resources. However, when financial market mechanisms are inadequate, it may excessively attract resources from the real economy due to disproportionately high profits, thereby inhibiting its long-term development. Therefore, “improving financial sector development and actively leveraging financial services for the real economy” has become a major issue that society urgently needs to address.

Secondly, in terms of green innovation, sustainable development is a crucial topic in today’s society. The green economy serves as a significant driver of sustainable development, playing an irreplaceable role in promoting economic growth and protecting the ecological environment. Promoting the green transformation of the economy and society requires not only the external regulation of environmental pollution and resource waste but also, more importantly, raising awareness of sustainable development across society. The “crowding out” effect triggered by the financialization of heavily polluting enterprises actually reflects the current inadequate emphasis on green innovation by companies and weak environmental awareness. It also reveals that the concept of sustainable development still needs to be further ingrained in society. While external regulations can temporarily delay environmental degradation, without the support of intrinsic awareness, it will be challenging to elevate human civilization to higher levels. Only when a widespread consciousness of sustainable development is established across society can genuine progress towards sustainable goals be achieved, leaving a greener, more harmonious world for future generations.

7. Conclusions and Policy Recommendations

7.1. Conclusions

This study investigates the impact mechanism of the financialization of heavily polluting enterprises on its green innovation based on the A-share market in China from 2012 to 2021, yielding several significant contributions to the field.

Firstly, we advance the theoretical understanding of the dual mechanisms through which the financialization of heavily polluting enterprises influences green innovation. Our findings indicate that while the “reservoir effect”, driven by capital reserves, has the potential to foster green innovation, the “crowding out effect”, arising from market arbitrage motivations, predominantly exerts an inhibitory influence. Empirical evidence confirms that the crowding out effect is more pronounced, resulting in an overall negative impact of financialization on green innovation.

Secondly, our research highlights the heterogeneous effects of financialization on green innovation, shaped by factors such as corporate ownership, regional disparities, and the corporate debt-to-asset ratio. Specifically, we demonstrate that non-state-owned enterprises, those located in the eastern and central regions, and firms with lower debt-to-asset ratios experience a more significant crowding out effect. This finding underscores the importance of contextual factors in elucidating the relationship between financialization and innovation.

Thirdly, we examine the moderating role of corporate short-term solvency within this impact mechanism. Our results reveal that higher short-term solvency amplifies the inhibitory effect of financialization on green innovation, suggesting that firms with greater liquidity may be less inclined to invest in innovative green technologies under financialization pressures.

Lastly, we explore the role of market competition as an external factor influencing the relationship between financialization and green innovation. Our research identifies a threshold effect of market competitiveness: when competitiveness is high, the negative impact of financialization on green innovation diminishes, whereas it intensifies under conditions of low competitiveness. This finding highlights the critical role that market dynamics play in shaping the outcomes of financialization.

In summary, this study not only elucidates the complex interactions between financialization and green innovation but also provides empirical evidence that can inform policymakers and industry stakeholders about the nuanced effects of financialization on sustainable practices within heavily polluting sectors. Our contributions pave the way for further research into strategies that can mitigate the adverse effects of financialization on green innovation.

7.2. Policy Recommendations

In advancing the path of green development, heavily polluting enterprises represent a significant challenge. Green innovation serves as a crucial internal driving force for facilitating the green transformation of the economy and achieving global sustainable development. Therefore, guiding heavily polluting enterprises towards green transformation through innovative practices is of paramount importance. Our study reveals that the financialization of these enterprises may negatively impact their green innovation activities.

To promote the healthy development of the financial market, alleviate the inhibitory effects of financialization on green innovation, and enhance the positive role of green innovation in this transformation, we propose the following policy recommendations:

Enhance Financial Market Mechanisms: It is essential to actively improve the mechanisms within the financial market to support its healthy development and promote financial services that align with the real economy. The current tendency for companies to favor financial investments, driven by excess profits and uncertainty associated with green innovation, often squeezes the investment available for green initiatives. To counteract this, market-based reforms should be implemented to break the monopoly of the financial industry, reducing its exploitation of the real economy. This will ensure that financial services better meet the needs of green innovation, fostering its vigorous development.

Strengthen Corporate Environmental Awareness: Reinforcing the promotion of green development concepts is vital for enhancing the environmental consciousness of heavily polluting enterprises. As these companies cultivate a stronger awareness of environmental issues, they are likely to recognize the significance of green innovation and become more inclined to invest in such initiatives. This shift will help to mitigate the inhibitory effects of financialization on green innovation, encouraging these enterprises to actively engage in sustainable practices and contribute to societal green transformation.

Encourage Healthy Market Competition: Fostering healthy competition among industries can help to alleviate the negative impact of financialization on green innovation. Our findings indicate that in competitive market environments, the inhibitory effects of financialization are less pronounced. By promoting market competition, we can drive innovation-led economic development and effectively reduce the constraints imposed by financialization on green initiatives, achieving a synergistic relationship between green development and economic growth.

7.3. Research Limitations and Future Directions

While this study provides valuable insights, it is important to acknowledge its limitations.

Firstly, the focus is primarily on heavily polluting enterprises within the A-share market in China, which may limit the generalizability of the findings to other contexts or industries. Future research could expand this analysis to include a broader range of sectors and geographical regions to validate and enhance the robustness of the conclusions drawn here.

Secondly, while the study effectively examines the direct impacts of financialization on green innovation and explores the moderating effects of short-term debt capacity and market competition, it could benefit from a deeper investigation into the underlying internal mechanisms at the enterprise level that may mediate this relationship. Future research could further explore how factors such as corporate governance, organizational culture, and stakeholder engagement interact with financialization to influence green innovation outcomes. By examining these internal dynamics, researchers can gain a more comprehensive understanding of how financialization shapes corporate strategies toward sustainability.