Can Green Credit Policies Promote Fund Investment? Evidence from China

Abstract

Fund investment, as a type of financial investment in the capital market, is designed to attract more social capital towards the green environmental protection sector and foster a harmonious relationship between economic development, social advancement, and ecological conservation. Therefore, as a significant policy instrument, will implementing the green credit policy impact the investment preferences of fund investors? How does it influence their participation in the market? This study utilizes microdata from Chinese Shanghai and Shenzhen A-share-listed companies from 2004 to 2020 to establish a DID model based on the Green Credit Guidelines introduced in 2012. The research delves into the effects of the green credit policy on fund investment and its underlying mechanisms. The green credit policy was found to favor the entry of fund investment, and the results are still valid after a series of robustness tests. The attraction effect of the green credit policy on investors is more evident in non-state-owned enterprises, small and micro enterprises, and non-green industries. Green credit policy can positively influence investor entry through the financing constraint effect and productivity effect. The study theoretically supplements the literature in the field of evaluating the effect of the green credit policy, and practically provides practical guidance and inspiration for strengthening the synergy of the government, banks, and enterprises in implementing green credit policy, promoting industrial transformation, and upgrading, and realizing high-quality economic development.

1. Introduction

Presently, China’s economic growth entered the stage of high-quality development. Given the urgent need for the coordinated advancement of China’s economy and environmental protection, green financing emerged as a significant driver in promoting sustainable economic development. In the national macro-control and economic development under the new regular, enterprise capital demand is also increasingly monumental. Financial means to achieve resource allocation became an indispensable part of guiding the flow of social capital to the sustainable development of enterprises, which became the government’s core objective. Green credit, a result of the advancement of green finance, seeks to direct credit resources towards green allocation. Its purpose is to assist enterprises in obtaining investment support from the capital market and accessing innovative capital. This, in turn, enables the establishment of low-carbon, recycling, and sustainable production structures and development models for enterprises. China’s fund investment has seen substantial expansion and is becoming a vital investment channel due to the rapid development of the financial market. Based on the most recent data published by the China Securities Investment Funds Association (CSIFA), as of the end of March 2024, China, as the world’s fourth-largest fund market, exceeded the 29 trillion Chinese Yuan (CNY) mark in terms of the size of its public funds. However, investors usually make capital market investments with a rational perspective. So, is the green credit policy effectively directing capital towards pertinent enterprises, and how are enterprises attracting investors’ attention and obtaining financial support through green finance?

The financial policy has long been an essential means of macro-control in China and an indispensable tool for fueling lasting business development [1]. Strategically allocating financial resources [2], it can facilitate the redirection of capital from environmentally harmful enterprises to sustainable, green enterprises [3]. In February 2012, the former China Banking Regulatory Commission (CBRC) officially published the Guidelines for Green Credit (referred to as the “Guidelines” hereafter), indicating that China’s green credit program is progressing towards a standardized phase. The rules mandate that banking and financial institutions proactively advocate for environmentally friendly credit, enhance assistance for initiatives related to green, low-carbon, and circular economy, and reduce environmental and social risks. The goal is to optimize the credit system and drive industries’ eco-friendly transformation and progress. The Guidelines require banking and other financial institutions to establish the concept of green credit that is economical, environmentally friendly, and sustainable, and to pay attention to the role played by banking and other financial institutions in comprehensive, coordinated, and sustainable development [4]. Implementing measures to regulate capital flow from financial institutions and optimize resource allocation for non-environmentally friendly businesses [5] aims to facilitate their transition towards sustainable practices, promoting environmentally responsible and enduring societal progress. Green credit is a crucial tool in environmental regulation that helps promote green finance by directing the actions of the banking sector and supporting eco-friendly and sustainable social development [6]. For non-green enterprises, enterprises through the green credit policy may subject them to various banking industry loan restrictions, making it challenging for enterprises to develop. However, by embracing green finance, enterprises can alleviate financial constraints, reduce resistance to enterprise transformation, and ultimately achieve long-term development [7,8]. The green credit policy plays a crucial role in directing and supporting the transition and improvement of firms’ environmentally friendly infrastructure. Can this policy attract support from investors? What are the specific differences in industry divisions? Through what mechanism of action? To address these issues, this study utilizes the introduction of the 2012 Green Credit Guidelines as a quasi-natural experiment. It selects Chinese A-share-listed enterprises as the research subjects and utilizes a difference-in-differences (DID) model to evaluate the impact of this green credit policy on fund investment.

Green credit policies attracted fund investors to enterprises by directing capital policy bias. Funds became an important force in the rise and fall of the capital market. Their investment conduct exerted a substantial influence on business development, receiving both positive and negative evaluations. On the one hand, fund investment follows the internationalised value assessment system, which is conducive to listed companies’ performance and competitiveness, as well as their longevity and market capitalization. On the other hand, the media repeatedly exposed incidents such as transfer of benefits and “rat trading”, which led to a negative impression of market speculators. So, are funds investing in the pursuit of value? Has the adoption of green credit policies enhanced the attractiveness of non-green credit-constrained sectors to investors?

In consideration of the aforementioned, this paper explores the significant influence of the green credit policies on investor participation. The primary contributions of this paper can be summarized as follows: (1) This paper analyzes the allure of the green credit policies for fund investors, shedding light on how enterprises can facilitate their transformation and upgrading within the framework of “dual-carbon”. While previous studies predominantly focused on the innovative effects of green credit, this analysis emphasizes the attractiveness of the green credit policies for fund investors. (2) By examining the impact mechanism of green credit on investment, this article argues that green credit alleviates enterprise financing constraints and enhances productivity, thereby promoting investment. It also suggests a development direction for further mitigating enterprise financing constraints. (3) The heterogeneity section emphasizes the promotional impact of the green credit policies on non-green industries, underscoring the crucial role that such policies play in nurturing long-term enterprise development.

The remaining structure of this paper is organized as follows: the second section presents the theoretical analysis and research hypotheses while also examining the current status and limitations of existing research; the third section outlines the establishment of econometric models and data sources; the fourth section conducts empirical tests to explore the relationship between green credit and investment, incorporating analysis from both heterogeneous perspectives and mechanisms; finally, the fifth section of the paper offers conclusions and presents policy recommendations.

2. Theoretical Analysis and Research Hypothesis

The green credit policy is crucial in directing the most efficient distribution of money for businesses to attain sustainable growth, a matter of great importance for the country’s future strategic posture. The investment of funds is closely linked to the future fluctuations of the capital market and will also significantly influence the future growth and progress of businesses. This paper aims to investigate the correlation between these two variables: whether the implementation of the green credit policy will garner support from fund investors and in what manner it will attract fund investment.

In terms of the implementation impact of the green credit policy, scholars primarily conduct research at two main levels. The first level is at the bank level, examining the influence of the green credit policy on its business performance. On one hand, Hu and Li [9] argue that integrating green credit into the business strategy of commercial banks is beneficial for serving as a “loan supervisor”, enhancing banks’ competitiveness, and improving their profitability. Commercial banks promoting green credit will also enhance their social reputation, fostering more prudent decision-making behavior and bolstering their ability to withstand risks and maintain market competitiveness. On the other hand, certain scholars argue that there are notable disparities in the financial requirements of businesses, and that the market’s regulatory mechanism can effectively facilitate the allocation of credit resources to various industries and sectors. Therefore, just concentrating on the allocation of environmentally friendly loans would lead to an increase in banks’ operational costs, leading to a decline in operational effectiveness [10].

Second, at the enterprise level, the impact of the green credit policy on enterprises is examined. Some studies focus on the impact of the green credit policy on corporate credit financing, as bank credit dominates the financing channels of Chinese firms. Thus bank credit also determines the intensity of corporate financing constraints. Green credit will raise the financing expenses for highly polluting companies, leading to a considerable decrease in the amount of funding they receive from banks. This reduction will particularly affect their ability to secure new bank loans and long-term debt financing, especially for those resource-based industries with high pollution and high energy consumption and industries with excess capacity. In addition, the green credit policy also contributes to the advancement of technological innovation in environmentally friendly enterprises [11] and discourages technological innovation in high-pollution enterprises. Regarding the impact of the green credit policy on the green technological innovation of high-pollution enterprises, most scholars believe that it played a positive role in promoting such innovation [12]. He and Tian [13] found that implementing green credit policies can provide incentives for enterprises to participate in green technological innovation based on their research of Chinese enterprises.

Currently, there is yet to be a consensus on the conclusions of studies on investor behavior. Fund investment, which takes fund operation as its practice, pursues soundness and value investment by virtue of its information, capital, and professional advantages. Fund investors tend to have more information channels as well as professional analysis ability. Some current research on fund investment focuses on how to make fund choices based on corporate development. Based on the noise trader risk hypothesis [14] and the consistency risk hypothesis [15], fund investors reflect the rational demand of investors and the pursuit of long-term corporate value enhancement. However, some scholars believe that fund investors pursue short-term gains too much. Dupuy et al. [16] showed that fund investors lack the patience to develop with listed companies and usually sell their shares before the value of the stock increases to develop the carry. Wiseman [17] argued that fund investors, as mature investors, use the available information to predict the future growth of the enterprise. Xiang et al. [18] find that funds have an information advantage and invest aggressively based on it. However, in the capital market, information asymmetry exists objectively, and fund investors are unable to grasp the real internal information of enterprises to make predictions [19], and the national policy guidance becomes an important basis for fund investors’ investment decisions. However, there is no doubt that fund investors are considered to be high-end investors with stronger information purchasing and analyzing abilities than individual investors. Gong et al. [20] argued that funds, as informed traders, can predict the future value of stocks to a certain extent. Fund investors may prefer companies with higher social responsibility performance.

In addition, some researchers and scholars also point out that fund investors can improve corporate social performance. Wu et al. [21] show that in different economic environments, fund investment is likely to focus on using time-timing and stock-picking abilities, and investment strategies will change with economic fluctuations. As a result of transformation and upgrading, firms’ efforts in sustainable development will be exposed, recognized by society at large, and build up a good image [22], making fund investors likely to prefer firms that transformed and upgraded under green credits.

In conclusion, comprehensive analysis is absent on the multifaceted impact of the green credit policies, particularly concerning fund investments. Commercial banks fulfill a pivotal function in optimizing the allocation of corporate resources under green credit, thereby alleviating enterprises’ financing difficulties. Fund investors in the capital market will also benefit from this opportunity. Consequently, we need to carefully examine and explore the crucial function of the green credit policy in the overall capital market. Practicing green credit policy can provide firms with excess returns from a long-term perspective [23,24]. Green finance will establish a beneficial external context for the growth of companies. The entry of investors can not only provide social capital for enterprises but also bring various invisible benefits, such as human connections and driving the improvement of total factor productivity. This ultimately culminates in an augmentation of the enterprise’s valuation [25]. Furthermore, optimizing and upgrading of industrial structures can position enterprises as industry leaders over the long run. The financing situation of firms that engage in green development under the support of the green credit policy also reflects their operating conditions and capital strength [26]. Enterprises with solid capital will attract fund investors’ attention, increasing investment preference for green credit enterprises. Based on this, this paper proposes the following hypothesis:

Hypothesis 1.

Green credit policies help to attract fund investors into the business.

In China, during the transition period from 2003 to 2012, the banking system held a dominant position in China’s financial system, with bank credit being the primary source of funding for enterprises [27], which determines the strength of enterprise financing constraints [28]. Through the implementation of the green credit policy, enterprises can more efficiently access capital support. This is evident in the fact that corporate green credit not only reduces the information imbalance between enterprises and fund investors but also improves their ability to obtain capital, lowers the risk of default, and increases the willingness of fund investors to invest. The risk reduction hypothesis suggests that responsible behaviors such as corporate green credit can improve risk management and risk avoidance capabilities of enterprises and reduce the probability of corporate risks and that green credit policies can also significantly enhance the management of enterprise debt financing in both the short and long term [29]. In addition, the green credit policy will create good external development conditions for enterprises and enhance their risk response ability. Cultivating a conducive external environment for enterprise development mitigates potential risks enterprises face, discourages short-term thinking, and strengthens enterprises’ resilience in risk management [30]. However, the risk-increasing hypothesis suggests that corporate social responsibility deviates from the objective of maximizing shareholder value, and under the condition of limited corporate resources, excessive use of capital leads to insufficient investment in research and development, which reduces the competitiveness of the enterprise [31], thus increasing the risk of the enterprise. Practicing green credit may lose the opportunity cost of economic effect enhancement in the short term due to increased social responsibility expenditure because of the transition. However, from a long-term perspective, it will allow the growth of corporate business credit, which not only broadens the channels of corporate financing, but also reduces the cost of corporate financing.

The entry of investors into the enterprise not only reduces the financing constraints for the enterprise, but also brings a series of invisible wealth for the enterprise, including human connections, and drives the improvement of total factor productivity, which ultimately manifests itself in the increase in enterprise value [25]. Furthermore, the enhancement of the enterprise’s total factor productivity can lead to the optimization and upgrading of its industrial structure. Enterprises that carry out green development under the guidance of the green credit policy usually show better social responsibility, and enterprises with good social responsibility performance also reflect their business conditions and capital strength to a certain extent [32], and enterprises with capital strength will be more willing to show their social responsibility, which may lead to an increase in investment preferences of investment institutions for green credit enterprises [33]. Some researchers argued that enterprises’ strong development of green finance could effectively solve the problem of moral hazard and enhance the effectiveness of business administration while maximising the economic worth of firms. Some researchers and scholars also point out that institutional investors can enhance the performance of corporations in terms of social responsibility, Dhaliwal et al. [34] highlighted that institutional investors holding a significant percentage of shares can compel companies to disclose comprehensive corporate social responsibility information and enhance their performance in social responsibility. Similarly, some researchers and scholars argue that corporate social responsibility may reduce the value of institutional investors and affect their returns, making such investors to motivate firms to perform less social responsibility and reduce social performance. In fact, the productivity effects of the green credit policies and the alleviation of financing constraints are mutually reinforcing [35]. The implementation of the green credit policy will ease financing constraints for enterprises, allowing them to allocate sufficient capital to enhance productivity development further. As a result, the improved business performance and increased productivity achieved through green credit will be recognized by external media and the capital market, leading to more significant investment in enterprises and ultimately reducing their financing constraints.

Hypothesis 2.

Green credit attracts fund investors into firms by alleviating financing constraints and improving business performance.

3. Empirical Design

3.1. Data Sources

This study investigates the influence of the green credit policy on the participation of institutional investors, utilizing panel data from Chinese A-share-listed companies in Shanghai and Shenzhen between 2004 and 2020. The fund investor data come from China Stock Market & Accounting Research (CSMAR) databases, which matches the “fund subject information table” with the “stock investment detail table” in the fund market series to obtain the detailed table of funds investing in listed companies, and based on which the market value and the number of shares held by the enterprise contained by fund institutional investors are counted. On the basis of this, the market value and number of shares held by fund institutional investors in the enterprise in the same year are counted. The remaining variables in this paper are sourced from the CSMAR database. In order to ensure the robustness of the empirical results, this paper treats the relevant data as follows: (1) excluding the samples of financial listed companies; (2) excluding the samples of property listed companies; (3) excluding the samples of special treatment (ST and *ST)-listed companies. Enterprises with special treatment are those that are inconsistent with the actual situation of the enterprise and have abnormal financial systems (continuous losses and financial abnormalities).

3.2. Modelling

Based on the double difference method, this paper constructs the following regression model to test the attraction effect of green credit on fund investors:

$$Y_{it}={\beta }_0+{\beta }_1{policy}_t+{\beta }_2{did}_{it}+{\beta }_3{gcres}_i+\gamma X_{it}+{\mu }_i+{\sigma }_t+{\epsilon }_{it}$$

(1)

where the subscripts $i$ and $t$ represent firms and years, respectively; the explanatory variables $Y_{it}$ denote fund investment, using the presence or absence of fund investor shareholding market value ($GImv$) and the number of shares held by fund investors ($GIsnum$); the core explanatory variable ${did}_{it}$ represents green credit policy;

Where ${policy}_t$ is the green credit policy time dummy variable, this paper takes the Green Credit Guidelines formulated by the former CBRC in 2012 as the entry point, and takes the value of 0 before the implementation of the policy in 2012, and takes the value of 1 after the implementation of the policy in 2012.

Where ${gcres}_i$ is the industry dummy variables, according to the Guidelines on Industry Classification of Listed Companies (revised in 2012) issued by the China Securities Regulatory Commission and the Guidelines on Environmental Information Disclosure of Listed Companies (Draft for Public Comments) issued by the Ministry of Environmental Protection of the People’s Republic of China in September 2010, it defines thermal power, iron and steel, cement, electrolytic aluminium, coal, metallurgy, chemical industry, petrochemical industry, building materials, paper manufacturing, brewing, pharmaceutical industry, fermentation industry, and textile industry. The paper selects the above industries as the heavy polluting industries. In this paper, the above heavy polluting industries are selected as green credit-restricted industries, and non-heavy polluting industries are non-green credit-restricted industries; meanwhile, green credit-restricted industries are set as the control group and non-green credit-restricted industries are set as the experimental group.

The interaction term ${did}_{it}$ examined is the effect on the entry of fund investors in green credit-restricted industries and non-green credit-restricted industries before and after the implementation of the Guidelines. If ${\beta }_2$ is significantly greater than 0, it indicates that the Guidelines significantly promote investor entry in non-green credit-restricted sectors, and vice versa, it indicates no significant promotion.

$X_{it}$ is a set of control variables; ${\mu }_i$ are individual fixed effects, the ${\sigma }_t$ are year fixed effects, and ${\epsilon }_{it}$ is a random error term. In addition, this paper provides a cluster of robust standard errors in the industry year dimension.

3.3. Data Characteristic

The model variables are set as follows:

(1)

Explained variable: fund investment. This paper uses the market value of fund investors’ shareholding (GImv) and the number of fund investors’ shareholding (GIsnum) in the current year of the firm to characterize them.

(2)

Core explanatory variables: ${did}_{it}$. This paper explores the attraction effect on fund investors using the Green Credit Guidelines formulated by the former CBRC in 2012 as the policy point in time.

(3)

Other control variables: In this paper, asset–liability ratio (Lev), total asset turnover (ATO), proportion of independent directors (Indep), proportion of institutional investors’ shareholding (INST), size of supervisory board (JP), retained earnings (Rep), undistributed profit per share (Cpp), proportion of other receivables to total assets (Occupy), and degree of equity checks and balances (Balance), etc., are used as control variables.

Table 1. Key variable description.

Variable Category	Variable Name	Symbol	Variable Description
Explained variables	the market value of fund investors’ shareholding	GImv	The market value of fund investors’ holdings is taken as the logarithm plus one.
	Number of shares held by fund investors	GIsnum	The number of shares held by the fund’s investors is taken as the logarithm plus one.
Explanatory variable	Interaction term	did	the interaction item between gcres and policy.
	Group dummy variable	gcres	When an enterprise belongs to non-heavy-polluting firms, the value ofTreat is 1; otherwise, it is 0.
	Time dummy variable	policy	If the sample year is 2012 and later, post = 1, otherwise post = 0.
Control variables	Asset–liability ratio	Lev	Total liabilities divided by total assets at year-end.
	Total Assets Turnover	ATO	Operating income divided by average total assets.
	Proportion of independent directors	Indep	Independent directors divided by number of directors.
	Proportion of institutional investors’ shareholding	INST	Total number of shares held by institutional investors divided by outstanding capital.
	Size of supervisory board	Jp	Size of the supervisory board.
	retained earnings	Rep	Retained earnings per share taken as logarithmic plus 1.
	Undistributed profit per share	Cpp	Undistributed earnings per share taken as logarithmic plus 1.
	Proportion of other receivables to total assets	Occupy	Other receivables divided by total assets.
	Degree of equity checks and balances	Balance	Sum of the shareholdings of the second to fifth largest shareholder divided by the shareholding of the first largest shareholder.

and

Table 2. Descriptive statistics.

Variable	N	Min	Mean	Sd	Max
GImv	32,519	2.091	18.55	3.144	27.27
GIsnum	32,519	0.693	16.04	2.905	23.36
did	32,519	0	0.415	0.493	1
Lev	32,519	0.007	0.421	0.207	3.625
ATO	32,517	−0.0480	0.707	0.567	12.37
Indep	32,441	0	0.371	0.0550	0.800
INST	32,519	0	0.349	0.247	3.267
Jp	32,516	0	3.647	1.179	13
Rep	31,569	−6.628	0.830	0.621	4.841
Cpp	31,501	−8.004	0.727	0.631	4.705
Occupy	32,506	0	0.0190	0.0410	1.852
Balance	32,519	0	0.707	0.609	4

show the definitions of the main variables and their descriptive statistics, respectively.

4. Empirical Results and Analysis

4.1. Baseline Model Result

Table 3. Benchmark test of the impact of the green credit policies on investor entry.

	(1)	(2)	(3)	(4)
	GImv	Gisnum	Gimv	Gisnum
did	0.482 ***	0.329 ***	0.447 ***	0.332 ***
	(0.060)	(0.058)	(0.096)	(0.085)
Lev	0.958 ***	1.573 ***	0.196	0.478 ***
	(0.082)	(0.080)	(0.154)	(0.144)
ATO	0.165 ***	0.074 **	0.543 ***	0.373 ***
	(0.028)	(0.027)	(0.058)	(0.055)
Indep	1.650 ***	1.514 ***	0.043	−0.027
	(0.259)	(0.251)	(0.305)	(0.294)
INST	4.510 ***	4.477 ***	3.560 ***	3.225 ***
	(0.064)	(0.062)	(0.105)	(0.097)
Jp	0.030 **	0.072 ***	0.004	0.008
	(0.013)	(0.013)	(0.025)	(0.024)
Rep	1.407 ***	0.545 ***	0.557 **	−0.499 **
	(0.106)	(0.103)	(0.171)	(0.202)
Cpp	−0.012	0.245 **	0.306 **	0.799 ***
	(0.098)	(0.095)	(0.145)	(0.176)
Occupy	−5.248 ***	−4.868 ***	−4.886 ***	−4.298 ***
	(0.434)	(0.421)	(0.573)	(0.538)
Balance	0.219 ***	0.115 ***	0.105 **	−0.011
	(0.024)	(0.023)	(0.046)	(0.044)
_cons	14.374 ***	12.237 ***	16.031 ***	14.303 ***
	(0.122)	(0.118)	(0.192)	(0.183)
Industry FE	YES	YES	NO	NO
Firm FE	NO	NO	YES	YES
Year FE	YES	YES	YES	YES
Observations	31,344	31,344	30,962	30,962
R-squared	0.391	0.334	0.671	0.658

Note: *, ** and *** denote significance at the 10%, 5% and 1%, respectively, and the values in parentheses are robust standard errors of clustering in the industry-time dimension. This also apply for following tables.

presents the results of the baseline test of the impact of the green credit policies on the size of fund investors’ entry. In particular, columns (1) and (3) present the effect of the green credit policy on the market value of fund investors’ holdings; columns (2) and (4) present the effect of the green credit policy on the number of fund investors’ holdings; columns (1) and (2) control for industry and year, clustered at the industry year level, and columns (3) and (4) control for firms and year, clustered at the industry year dimension. From the results in columns (1) and (2), the regression results of the green credit policy on the market value of investor holdings and the number of holdings are both significantly positive at the 1% level controlling for industry, year-fixed effects only, suggesting that green credit policy enhances the attraction effect of non-green credit-restricted industries on fund investors. From the results in columns (3) and (4), it can be seen that the regression coefficients of the green credit policy on the market value of investor holdings and the number of investor holdings are 0.447 and 0.332, respectively, after further controlling the firm and year dimensions, and they are still significantly positive at the 1% level, indicating that the green credit policy helps to enhance the scale of investor’s investment in the non-green credit-restricted industries. Taken together, it can be seen that green credit policy helps to enhance the attraction effect of non-green credit-restricted industries on investors.

4.2. Parallel Trend Test

The parallel trend test is an essential prerequisite for using the DID method; that is, the treatment group and the control group have the same trend of change before and after the implementation of the policy. Based on this, this paper, in order to verify the effectiveness of the green credit policy and test the parallel trend, refers to the research method of Deschenes et al. [36] to carry out the parallel trend test and construct the following econometric regression model:

$$y_{\mathrm{it}}={\alpha }_0+{{\sum }_{z=-7}^7\rho }_{t}gcres\times {\delta }_t+\beta tControl+ui+\lambda t+{\epsilon }_{it}$$

(2)

where ${\delta }_t$ is a time dummy variable ${\rho }_t$. It is the coefficient estimate for 2004–2020, which describes the difference in changes between the treatment and control group provinces after the promulgation of the Green Credit Guidelines. Meanwhile, the previous period of the promulgation of the Green Credit Guidelines is selected as the base period in this paper, and the subscript “z” denotes the count of periods in the year “t” that differ from the base period, and the definitions of the rest of the variables are consistent with model (1).

Figure 1 presents the dynamic effect of the market value of fund investors’ shareholdings, and Figure 2 presents the dynamic effect of the number of fund investors’ shareholdings, and the results show that before the promulgation of the Green Credit Guidelines, its effect on the number of investors’ shareholdings and the market value of their shareholdings is insignificant; while after the issuance of the Green Credit Guidelines, it significantly increases the number of capitalists’ shareholdings and the market value of their shareholdings, and it passes the parallel trend test.

4.3. Robust Tests

4.3.1. PSM-DID

Propensity score matching (PSM) is a statistical method for processing observational study data. In observational studies, due to various reasons, there are many data biases and confounding variables. Propensity score matching is designed to reduce the influence of these biases and confounding variables to make a more reasonable comparison between the experimental and control groups. This section uses PSM-DID to match green credit policy unrestricted industries with green credit policy-restricted industries to control for differences in control variables between the treatment and control groups. Specifically, this paper uses radius matching and kernel matching to test the effect of the green credit policy on the market value of fund investors’ holdings and the number of holdings.

The results of propensity score matching are shown in

Table 4. Robustness test: propensity score matching.

	(1)	(2)	(3)	(4)
	Radius Matching		Kernel Matching
	GImv1	GIsnum1	GImv1	GIsnum1
did	0.445 ***	0.328 ***	0.446 ***	0.330 ***
	(0.096)	(0.085)	(0.096)	(0.085)
_cons	16.073 ***	14.387 ***	16.065 ***	14.377 ***
	(0.193)	(0.184)	(0.192)	(0.183)
control variable	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observations	30,945	30,945	30,947	30,947
R-squared	0.671	0.659	0.671	0.659

. From columns (1) and (2), it can be seen that whether the market value of investor holdings or the number of investor holdings is used as the explanatory variable, the regression results of the green credit policy on both are significantly positive at the 1% level; from columns (3) and (4), it can be seen that using the kernel matching method, the regression results of the green credit policy on the market value of fund investor holdings and the number of fund holdings are both significantly positive at the 1% level. Results are all significantly positive at the 1% level. This indicates that the investment attraction effect of the green credit policy on unrestricted sectors remains significant after excluding the interference of sample selection bias and green credit policy effectively enhances the attraction effect on fund investors relative to green credit-restricted sectors.

4.3.2. Change the Time Window

In this paper, we examine additional factors impacting firms, leading to variations in the time trend of certain indicators across firms and potentially influencing the empirical findings [37,38]. Therefore, in order to control the time trend of other factors affecting investors’ decision-making, referring to the study of Moser and Voena, the third-order polynomials of control variables and time trend and the interaction terms between control variables and time dummy variables are constructed. These two types of interaction terms are added to the benchmark model, respectively, to control the time trend of many influencing factors of investors’ entry. The specific models are constructed as follows:

$$Y_{it}={\beta }_0+{\beta }_1{did}_{it}+{\beta }_x{X}_{it}+D_i\left(X_{it}\times f\left(T\right)\right)+{\mu }_i+{\sigma }_t+{\epsilon }_{it}$$

(3)

$$Y_{it}={\beta }_0+{\beta }_1{did}_{it}+{\beta }_x{X}_{it}+U_i\left(X_{it}\times {\sigma }_t\right)+{\mu }_i+{\sigma }_t+{\epsilon }_{it}$$

(4)

where $f\left(T\right)$ is a third-order polynomial of the time trend, using the first–third-order representation of the time trend, and ${\sigma }_{\mathrm{t}}$ is a time dummy variable. To control the time trend, multiply $f\left(T\right)$ and ${\sigma }_t$ by the control variables. The design of other variables is kept consistent with model (1), and the regression results of controlling the time trend are shown in Table 3. Among them, (1) and (2) columns are the effects of the green credit policy on the market value of the shares held by the corporate investors after adding the third-order polynomials of the control variables and the time trend; (3) and (4) columns are the effects of the green credit policy on the effect of corporate investors’ shareholding quantity.

The results in

Table 5. Robustness tests: controlling for time trends.

	(1)	(2)	(3)	(4)
	Time Trend Third Order Polynomial		Time Dummy Variable
	GImv	GIsnum	GImv	GIsnum
did	0.308 ***	0.206 **	0.272 **	0.172 **
	(0.091)	(0.080)	(0.088)	(0.078)
_cons	16.296 ***	14.572 ***	16.240 ***	14.529 ***
	(0.187)	(0.179)	(0.183)	(0.175)
control variable	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observations	30,962	30,962	30,962	30,962
R-squared	0.693	0.679	0.694	0.680

show that the regression coefficients of the green credit policy on both the market value of investor holdings and the number of holdings are all significantly positive, at least at the 5% level after the inclusion of the control variables with time trend third-order polynomials and the interaction terms of the control variables with time dummy variables, i.e., the green credit policy contributes to the enhancement of the attractiveness effect of the non-green credit-restricted industries on investors and the baseline conclusion holds.

4.3.3. Replacement of Variable Setting Method

In this section, fund investor shareholding ratio (GIration) is used as a replacement indicator for the explanatory variables, and further clustered into the province time dimension to test the impact of the green credit policy on investors’ investment decisions. The related regression results are presented in

Table 6. Robustness tests: replacing the way the variables are set, clustering to province time.

	(1)	(2)	(3)
	GIration	GImv1	GIsnum1
did	0.070 **	0.430 ***	0.319 ***
	(0.024)	(0.066)	(0.060)
_cons	0.260 ***	16.027 ***	14.290 ***
	(0.051)	(0.199)	(0.194)
control variable	YES	YES	YES
Firm FE	YES	YES	YES
Year FE	YES	YES	YES
Observations	30,962	29,170	29,170
R-squared	0.444	0.671	0.657

.

Among them, column (1) displays the regression outcomes that demonstrate the influence of the green credit policy on the proportion of corporate investors’ holdings. Columns (2) and (3) show the regression results of the green credit policy on the market value of shareholding and the number of shareholdings of corporate investors after clustering into the province–time dimension.

The results in column (1) show that the regression coefficients of the effect of the implementation of the green credit policy on the proportion of investor shareholdings are significantly positive at the 5% level; the results in columns (2) and (3) show that after controlling for the individual fixed effects, the time-fixed effects, and clustering the standard errors into the province time dimension, the effect of the green credit policy on the market value of investor shareholdings and the number of shareholdings is still significantly positive at the 1% level, which means that green credit policy can effectively increase the scale of investor investment in non-credit-constrained industries.

4.3.4. Other Robustness Tests

Other national credit and financial policies may influence the expansion of investor holdings to varying degrees during the same period. During the sample period in 2016, the People’s Bank of China and seven other ministries and commissions jointly issued the Guiding Opinions on Building a Green Financial System (hereinafter referred to as the “Opinions”). This marked the first time that green finance was officially defined at the national policy level, leading to establishing a more comprehensive green financial system. Based on this, the paper excludes the cities in the green financial pilot zones to examine the impact of green credit policies on investors’ market value of shareholding and number of shareholdings. The analysis is restricted to the data of enterprises in the three years preceding and following the implementation of the green credit policy, aiming to mitigate potential effects from other policies or issues related to long-term serial correlation. Essentially, the sample data are constrained within the time window interval of 2009–2015. Columns (1) and (2) exclude green finance pilot cities; columns (3) and (4) shorten the sample interval.

The results in

Table 7. Other robustness tests.

	(1)	(2)	(3)	(4)
	Excluding Green Finance Pilot Cities		Shorter Sample Intervals
	GImv1	GIsnum1	GImv1	GIsnum1
did	0.433 ***	0.310 ***	0.595 ***	0.432 ***
	(0.105)	(0.092)	(0.127)	(0.108)
_cons	15.957 ***	14.252 ***	16.558 ***	14.886 ***
	(0.223)	(0.212)	(0.292)	(0.277)
control variable	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observations	22,020	22,020	12,074	12,074
R-squared	0.672	0.659	0.708	0.702

show that the regression results of excluding green finance pilot cities in columns (1) and (2), the effect of the green credit policy on the market value of investors’ stock holdings and the number of stock holdings, is still significantly positive at the 1% level; and the regression results of shortening the sample intervals in columns (3) and (4), the effect of the green credit policy on the market value of investors’ stock holdings and the number of stock holdings, is still significantly positive.

4.4. Heterogeneity Analysis

4.4.1. Enterprise Size

The size of the enterprise will affect the implementation of the green credit policy, the size of the enterprise, the effect may be very different, and the objective of the green credit policy is to alleviate the financial challenges encountered by micro and small enterprises. For micro and small enterprises, the promotion, display, and growth of the enterprise is its primary task. For this reason, this paper examines the ramifications of the green credit policy on the market valuation and shareholding volume for investors across enterprises of diverse scales.

Table 8. Heterogeneity (firm size).

	(1)	(2)	(3)	(4)
	Big Enterprise		Small Enterprise
	GImv1	GIsnum1	GImv1	GIsnum1
did	0.095	0.021	0.413 ***	0.285 **
	(0.111)	(0.092)	(0.113)	(0.108)
_cons	18.154 ***	16.738 ***	15.076 ***	13.245 ***
	(0.250)	(0.229)	(0.281)	(0.276)
control variable	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observations	13,714	13,714	16,946	16,946
R-squared	0.719	0.692	0.666	0.621

shows the heterogeneity analysis under the influence of enterprises of different sizes. We divide the sample into large and small enterprises according to the median enterprise asset size, columns (1) and (2) show the regression results of the green credit policy in large enterprises for fund investment, and columns (3) and (4) show the regression results of the green credit policy in small enterprises for fund investment.

The regression coefficient of green credit policy on fund investment is insignificant in large enterprises, indicating that the impact of green credit policy on the market value and the number of shares held by investors in large enterprises is not significant. However, the regression coefficients of the green credit policy of small enterprises on the market value and the number of shares held by investors are significantly positive at the level of 1%, indicating that the green credit policy is more helpful for small enterprises to attract investors. This may be because small enterprises, due to production and scale restrictions, and hindering the further development of enterprises, enterprises lack transformation basis and the green credit policy to alleviate the small enterprises in the credit financing of all kinds of constraints to optimize the configuration of the enterprise industrial structure to participate in a role in promoting the attraction of the fund to further rise in investment. Large enterprises are in large platforms, have the technology, human resources, capital, and other natural conditions for green transformation and upgrading, and their capital volume is not sensitive to general investment. The green credit policy for its policy strength is not significant, the green credit policy’s impact on attracting fund investment is also not substantial.

4.4.2. Nature of Property Rights

State-owned enterprises and non-state-owned enterprises in China have different impacts on investor attraction. In general, state-owned enterprises have strong financial strength and a large scale of personnel, while non-state-owned enterprises have more flexible corporate structures that are more adaptable to the policy direction, due to the stability of state-owned enterprises. People’s investments are more inclined to state-owned enterprises. In recent years. The private economy burst out of great vitality, investors are more and more inclined to be full of vitality of the enterprise. Based on this, this paper is grouped according to the nature of property rights. The results are shown in

Table 9. Heterogeneity (nature of property rights).

	(1)	(2)	(3)	(4)
	State Enterprise		Non-State Enterprise
	GImv1	GIsnum1	GImv1	GIsnum1
did	0.301 **	0.206 **	0.496 ***	0.357 ***
	(0.113)	(0.096)	(0.103)	(0.095)
_cons	16.087 ***	14.528 ***	16.240 ***	14.556 ***
	(0.261)	(0.244)	(0.282)	(0.273)
control variable	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observations	12,700	12,700	18,162	18,162
R-squared	0.703	0.687	0.678	0.664

. Columns (1) and (2) for the regression results of the green credit policy on fund investment in state-owned enterprises, columns (3) and (4) for the regression results of the green credit policy on fund investment in non-state-owned enterprises.

It is evident that the regression coefficient of the green credit policy on the market value and number of investors’ holdings in state-owned enterprises shows a significantly positive effect at the 5% significance level. Similarly, for non-state-owned enterprises, the regression coefficients of their green credit policies on market value and number of investors’ holdings exhibit a significantly positive impact at the 1% significance level. The test results show that whether it is a state-owned enterprise or a non-state-owned enterprise, the green credit policy for the fund to increases the investment in the fund to show the difference in the growth trend; compared to state-owned enterprises, the enterprise green credit policy on the attraction of the fund investment effect is more significant in the non-state-owned enterprises. On the one hand, non-state-owned enterprises do not have the capital advantages owned by state-owned enterprises, financing costs are small, and they are not sensitive to fund investment; on the other hand, when it comes to non-state-owned enterprises relative to state-owned enterprises, with relatively fewer rules and regulations, investors will be more inclined to flexible forms of enterprise investment, and green credit policy allows non-state-owned enterprises to be bolder in the transformation of the activities as well as alleviates the pain of the process of enterprise transformation, making non-state-owned enterprises attractive to investment.

4.4.3. Green Industry

Green industry refers to the industry that actively adopts cleaner production technology, adopts non-hazardous or low-hazardous new processes and new technology, vigorously reduces raw material and energy consumption, achieves less input, high output, low pollution, and reduces the release of harmful substances into the environment in the production process as far as possible. We distinguish between green and non-green industries according to conceptual stocks and classify stocks with keywords such as new energy vehicles, combustible ice, hydrogen energy, beautiful China, photovoltaic roofs, waste power generation, sewage treatment, energy conservation and environmental protection, exhaust gas treatment, atmospheric treatment, energy-saving lighting, energy-saving buildings, carbon neutrality, solid waste treatment, medical waste treatment, and new energy as green industries, and classify the rest of the industries as non-green industries.

The benchmark regression results for green and non-green industries are presented in

Table 10. Heterogeneity (green businesses).

	(1)	(2)	(3)	(4)
	Green Enterprise		Non-Green Enterprise
	GImv1	GIsnum1	GImv1	GIsnum1
did	0.438	0.447	0.448 ***	0.322 ***
	(0.307)	(0.293)	(0.102)	(0.090)
_cons	15.778 ***	14.141 ***	15.870 ***	14.152 ***
	(0.717)	(0.713)	(0.199)	(0.190)
control variable	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observations	2208	2208	28,661	28,661
R-squared	0.733	0.715	0.676	0.663

, columns (1) and (2) are the regression results of green enterprises, the regression results of the green credit policy in the green industry for the market value of investor holdings and the number of holdings are not significant. It suggests that the green industry is not responsive to the green credit strategy aimed at encouraging investment in the sector, which may be due to the fact that the enterprises in the green industry enjoy the benefits brought about by the policy from multiple sources, compared with the other industries. Compared with other policies, the driving ability of the green credit policy for investment attraction is insufficient; columns (3) and (4) are the benchmark regression results of the non-green industry. The regression analysis indicates that in non-green enterprises, the regression coefficients of the green credit policy on the market value of investors’ shareholding and the number of shareholdings is all significantly positive at the 1% level, indicating that the non-green enterprises are significantly attracted to fund investment through green credit policy fund investment. Green firms are less sensitive to fund investment due to the nature of their industry and their ability to attract many green investments on their own. Non-green enterprises cannot easily keep up with the pace of the times due to the lack of their own green technology and green innovation level, while the green credit policy builds a good platform for non-green enterprises to display, provides better policy support for non-green enterprises, and helps non-green enterprises to obtain better financial capital support [39].

4.5. Analysis of Mechanisms

It was argued above that green credit exerts a significant attraction effect on the entry of fund investors and contributes to the promotion of green credit policies for micro and small enterprises as well as non-green sectors from a perspective of heterogeneity. What are the pathways through which green credit policies enhance fund investment? This paper argues that green credit policy helps enterprises to consolidate the relationship with various stakeholders, helps enterprises to obtain competitive advantages in the market, reduces the cost of financing and external risks, and then enhances the profitability of enterprises and realizes the enhancement of enterprise value. In consideration of this, this paper analyzes the mechanism from the financing mechanism effect, productivity effect, and other paths.

4.5.1. Financing Constraints

Green credit policy aims to guide the market investors’ willingness to invest to realize investor support, which conveys the signal of risk taking to enterprises and makes market investors confident in enterprises. Consequently, this paper initially delves into the formation mechanism of the investor attraction effect of the green credit policy from the perspectives of debt financing cost and default risk, followed by an analysis from the vantage point of financing constraints. Referring to [40] and other scholars, we constructed a data index to measure the financing constraints of listed companies, and the larger the calculated financing constraint WW index is, the higher the degree of financing constraints is. We calculate the ratio of corporate finance expenses to total liabilities at the end of the period to measure the cost associated with debt financing. The Z-score [41] indicator is mainly chosen to measure in the corporate risk taking literature. Altman [42] constructed a multivariate Z-score model to assess the magnitude of corporate risk based on five major indicators: liquidity, profitability, financial leverage, solvency, and asset turnover speed.

Table 11. Analysis of mechanisms.

	(1)	(2)	(1)
	WW Index	lncost1	Z-Score
did	−0.003 **	−0.087 **	0.629 **
	(0.002)	(0.038)	(0.282)
_cons	−1.010 ***	−4.419 ***	15.797 ***
	(0.005)	(0.108)	(0.514)
control variable	YES	YES	YES
Firm FE	YES	YES	YES
Year FE	YES	YES	YES
Observations	17,680	14,263	30,961
R-squared	0.725	0.514	0.732

exhibits the findings from the benchmark regression analysis and the results in column (1) show that the regression coefficient of listed companies’ green credit policy on the cost of debt financing is significantly negative at the 5% level, indicating that the green credit policy reduces the cost of corporate debt. The results in column (2) show that the regression coefficient of listed companies’ green credit policy on equity capital is significantly negative at the 5% level. The results in column (3) show that the regression coefficient of listed companies’ green credit policy on default risk is significantly positive at the 5% level, indicating that green credit provides guarantees of risk taking ability for non-green credit enterprises, thus enabling them to obtain investments from investors further. The above findings demonstrate that the implementation of the green credit policy resulted in a notable augmentation of enterprises’ commercial credit, thus further reducing the debt financing cost as well as the default risk of enterprises, alleviating the difficulties of enterprise financing, and reducing the obstacles of enterprise financing, which increase the investment willingness of the fund investment.

4.5.2. Business Performance

Public dissemination of the enterprise’s operational performance will bolster investor confidence, fostering increased investment. The influx of investors will not compromise the enterprise’s development; instead, it will contribute to a stable and sustainable growth trajectory, ultimately fostering positive enterprise development in a conducive manner. To a certain extent, enterprises with good social responsibility performance also reflect good business conditions and strength.

In many studies, Tobin’s Q generally refers to the proportion between the market value of an enterprise and its replacement cost. The greater the value, the higher the industrial return of the enterprise. Therefore, Tobin’s Q is often used as a market-based performance indicator. This paper employs the methodology proposed by Guiso et al. [43] to evaluate enterprise performance based on Tobin’s Q value. The present study further leverages the findings of Levinsohn and Petrin [44] and applies the LP method to assess the total factor productivity of enterprises. In order to ensure the robustness of the results, this paper uses the study of Olley and Pakes [45] for reference and also adopts the OP method to measure the total factor productivity of enterprises. It is a crucial index to measure the production efficiency and technological progress of a company. It reflects the productivity improvement brought about by technological progress, management innovation, and organizational change in addition to labor and capital input in the production process of an enterprise.

The results are shown in

Table 12. Analysis of mechanisms (operating performance, total factor productivity).

	(1)	(2)	(3)
	TobinQ	Tfp(lp)	Tfp(op)
did	0.213 ***	0.116 *	0.124 *
	(0.045)	(0.068)	(0.067)
_cons	1.559 ***	5.467 ***	5.478 ***
	(0.085)	(0.147)	(0.144)
control variable	YES	YES	YES
Firm FE	YES	YES	YES
Year FE	YES	YES	YES
Observations	30,473	27,762	27,762
R-squared	0.624	0.695	0.696

: (1) The column results show that the coefficient of the impact of the green credit policy on the profitability of the enterprise is significantly positive at the 1% level, which indicates that the fund investors into the enterprise significantly improve the performance of the enterprise. The results in columns (2) and (3) show that the regression coefficient of the green credit policy on corporate profitability is still significantly positive with a level of 10%, which suggests that the green credit policy drives the increase in corporate profitability. The regression results show that under the guidance of the green credit, enterprises attract fund investment through business performance and total factor productivity improvement. The reason may be that enterprises under the guidance of the green credit policy, easing the financing constraints on enterprise credit not only gives the enterprise sufficient funds for better allocation of resources, but also allows the enterprise to bring more intangible assets, leading to the optimization of the allocation of enterprise factor resources and the improvement of production and operation efficiency, which is reflected in the increase in enterprise profitability.

5. Conclusions and Enlightenment

Investing in funds is a crucial component of the capital market that aids in the long-term growth and stability of the economy; how to guide the injection of social capital into environmental protection enterprises and build China’s green financial system is of great significance. As a significant environmental regulatory tool, the green credit policy aims to steer the transformation of economic development patterns and enterprise restructuring through the efficient allocation of credit resources, thereby better serving the real economy. Enterprises, as the microscopic subjects carrying economic development, how to carry out transformation and upgrading in the current environmental protection background not only exerts a significant influence on the future growth and advancement of businesses but also contributes to the high-quality development of the national economy. So, what is the impact of corporate green credit policy on fund investors’ investment behavior? What are the channels through which the influence of the green credit policy on fund investors’ investment behavior can be achieved? Based on the panel data of Chinese Shanghai and Shenzhen A-share-listed companies from 2004 to 2020, this paper examines the impact of the green credit policy on the entry of fund institutional investors. It is found that green credit policy significantly promotes the increase in fund investment, and green credit policy in non-state-owned enterprises, small and micro-enterprises, and non-green enterprises, the policy is more significant for the increase in investment, and the green credit policy attracts investors to enter through the financing constraints effect as well as the productivity effect path.

This paper holds significant theoretical implications. A comprehensive review and analysis of the existing literature reveal that the majority of scholarly research on green credit demonstrates a certain level of concentration, primarily focusing on endogenous factors within enterprises while lacking in-depth exploration of exogenous factors influenced by green credit policy. This paper takes the policy time period as an entry point to further study the impact of green credit policy on the entry of fund institutional investors, which shows that green credit policy helps to attract fund investors to enter the enterprise. This enriches and deepens findings in green innovation research, expands the factors influencing fund investment and the scope of research on green credit policies, and contributes to the integration of green resources, promotion of enterprise’s green transformation, and realization of harmony between humanity and nature.

This paper holds significant practical implications, as the study’s findings can offer valuable insights and recommendations for advancing green credit at both macroeconomic and microeconomic levels in the country. The green credit policy, an integral component of sustainable finance, plays a pivotal role in the nation’s environmental conservation efforts. This paper is instrumental in providing guidance for the country to implement the green transformation of enterprises. The investment in funds plays a crucial role in the capital market, contributing to the sustainable development of the economy. This paper serves as a valuable guide for directing fund investments towards environmental protection enterprises. The research in this paper advocates for the promotion of green transformation and upgrading of enterprises, aiming to achieve a more efficient and environmentally friendly mode of economic development, thereby realizing the goal of sustainable development.

The research findings have substantial theoretical and practical consequences for implementing green finance regulations, ensuring high-quality business growth, and the sustainable progress of society. Based on the above findings, this paper’s conclusions can provide important insights.

First, the guiding role of the green credit policies needs to be further strengthened. The government should persist in implementing and enhancing the green credit policy, ensuring its sustained effectiveness in supporting the continued development of enterprises. In addition, the government should further increase green credit subsidies for enterprises and strengthen information sharing between banks and enterprises so that enterprises have sufficient capital to achieve transformation and upgrading. Furthermore, it is imperative to enhance the capital market system, proactively regulate and direct investors’ investing behavior, and channel investments towards firms that urgently require financial support. As the main body of the implementation of the green credit policy, the effective implementation of the banking industry is related to the effectiveness of the green credit policy. In the study of this paper, green credit policy can alleviate enterprise financing constraints. We should pay attention to the credit support role of the banking sector in green credit policy and improve the support and guidance role of the banking sector to non-green enterprises in green credit so that the banking sector can provide financing support in the green transformation of enterprises and expand the policy effect of green credit.

Second, differentiated policies are based on the heterogeneity of enterprises. Focus on the development status of non-state-owned enterprises, small and microenterprises, and non-green industries in the course of undergoing transformation. During the process of enterprise transformation and upgrading, non-state-owned enterprises, compared with state-owned enterprises, due to the different nature of the property rights of capital, the investment is a more robust component, their transformation and upgrading will be more prudent, itself due to the fact that the difference in the amount of capital will have a variety of problems; for small and micro-enterprises themselves, in the scale of difference with the large enterprises, the financial troubles significantly impede the reform and upgrading of the company, hence hindering its development; for non-green enterprises, along with the intensification of the pollution problem, under the pressure of the country to increase efforts for environmental remediation, the survival of non-green enterprises is a predicament. In the future, it is necessary to provide targeted green credit policies for non-state-owned enterprises, small and micro-enterprises, and non-green industries. This could include government subsidies for the enterprises to support their green transformation, as well as strengthened management guarantees. It is recommended that the enterprises seek financing guarantees from government guarantee companies. Risk mitigation measures such as local government credit guarantee funds, bank–government guarantee cooperation, and credit insurance should be actively explored.

Thirdly, enterprises should establish a sense of sustainable development and formulate reasonable policies to facilitate the advancement and enhancement of businesses. Small and medium-sized enterprises, especially those in non-green industries, should use the policy advantages brought about by the green credit policy to force enterprises to transform and upgrade themselves, take on social responsibility, and realize the development of economic and social benefits in a unified manner. Enterprises should focus on their low-carbon energy-saving transformation and upgrading, use government policies to achieve industrial greening and energy saving, improve corporate reputation, release positive signals to the outside world, guide all sectors of society to focus on green governance, green investment, improve the environmental performance of investment activities, enhance the enterprise’s innovation vitality, and improve business performance in order to maximize the attractiveness of green credit to investors and enable enterprises to dominate in the increasingly fierce competition, thus realizing the long-term development of enterprises.

This study has certain limitations and requires further investigation in the future. This paper utilizes the Green Credit Guidelines, widely adopted in academic circles, as a starting point to examine the impact of green credit policies on fund investment. However, besides green credit, the government also introduced other relevant policies. In future research, a continuous double difference model can be constructed to analyze the effects of green credit policy, and the influence on fund investor participation can be further explored in light of multiple policy shocks.