Green Finance, Economic Policy Uncertainty, and Corporate ESG Performance

Abstract

Given the increasing prevalence of global warming and the frequent occurrence of extreme weather events and other challenges, countries are increasingly recognizing the importance of green and sustainable development. This paper uses the multi-period double difference and PSM-DID method to test the impact of green finance policies on the ESG performance of Chinese listed companies. Research has shown that implementing pilot zone policies can improve corporate ESG performance, especially for enterprises with low business reputations, fierce industry competition, severe information asymmetry, and state-owned attributes. The GFPZ policy drives companies to improve their ESG performance through two paths: promoting environmental innovation and strengthening restrictions on corporate financing. In addition, the increase in economic policy uncertainty hinders the positive impact of GFPZ policies on improving corporate ESG performance. This study enriches the existing micro-research on green finance policies from the perspective of enterprises. It provides empirical evidence and research insights to support the further improvement of pilot zone policies, the promotion of green sustainable development, and the improvement of corporate ESG performance.

1. Introduction

Modern society’s dependence on fossil fuels and uncontrolled emissions of pollutants have caused a series of global and severe climate crises, and the increase in extreme weather has had a significant impact on human well-being and economic activities [1,2,3]. The 2015 Paris Climate Agreement and the subsequent addition of its implementation details at COP26 demonstrate the collective resolve of nations to tackle the frequent extreme weather events, ensure global climate safety, and advance the green development trajectories of all countries [3,4,5].

Green finance may efficiently harmonize the equilibrium between economic growth and environmental preservation, facilitating high-quality economic advancement to balance economic progress and environmental advantages. The advancement of green finance can effectively drive industrial transition and direct the allocation of resources from energy-intensive and polluting sectors to environmentally sustainable and clean manufacturing industries [6,7,8]. The Chinese government has been dedicated to investigating viable avenues for advancing green finance and achieving a holistic transition towards a sustainable and environmentally friendly economy and society. In 2017, the Chinese government designated Guangdong, Guizhou, Jiangxi, Zhejiang, and Xinjiang as the initial pilot zones for GFPZ [9,10,11].

The ESG concept originates from corporate social responsibility investment (SRI), which aims to focus on the comprehensive responsibility value performance of enterprises in non-financial dimensions. The ESG concept is becoming an essential component of organizational decision-making and evaluating potential corporate performance as the green development concept continues to evolve and deepen. The global community becomes more cognizant of the risks associated with environmental and other non-financial factors. To meet the market trend of sustainable investment, companies are under pressure to disclose their ESG performance. The CSRC has amended the code of governance for listed businesses, mandating that listed businesses disclose information about ESG elements in compliance with relevant regulations. Following that, the MOE released the Administrative Measures for the Legal Publication of Corporate Environmental Information, which mandates the compulsory publication of environmental information by relevant listed corporations and other entities that fulfill the specified criteria. Based on stakeholder theory, excellent enterprises can balance the interests of various stakeholders, including shareholder interests and social benefits. Corporate ESG disclosure is committed to the inclusion of multiple stakeholders, and enterprises can utilize sustainability to gain a competitive advantage through disclosure of ESG performance. Under the background of information asymmetry, investors with limited attention will favor related companies with higher ESG scores, especially those who uphold the value investment strategy [12,13]. ESG performance exhibits a positive correlation with company profitability, and a high level of ESG performance leads to increased corporate value [14,15,16]. Enterprises are, therefore, under pressure from stakeholders to focus not only on financial factors but also on social responsibility [17].

Of the current research on economic policy uncertainty, the main focus has been on the macro side, with the existing literature concentrating on the relevance of EPU to economic performance, commodities, and its impact on financial markets, particularly stock market volatility [18,19,20,21,22]. Economic policy uncertainty also has spillover effects and asymmetric implications, in which uncertainty in one country substantially influences other countries [21]. On the micro side, scholars’ focus is mainly on enterprises, and the discussion centers on corporate cash holdings, investment and financing efficiency, and innovation performance [23,24,25]. A direct correlation exists between EPU and the level of cash reserves held by businesses. Elevated levels of economic policy uncertainty impede enterprises’ capacity to invest effectively and efficiently. It increases the risk of either investing too much or too little. It also hinders firms’ innovation in green technology. Furthermore, Xiao Jun observes that when there is a significant increase in EPU, there is a simultaneous decrease in firms’ investment in environmental initiatives and a relaxation of environmental regulations, leading to an increase in pollutant emissions [26]. Just as the coal industry is an important pillar industry for the development of the world economy, the environmental pressure it causes has also attracted much attention. V. I Bondarenko and his team have integrated ESG strategies into coal mining to strengthen the sustainable development and diversity of PJSC DTEK Pavlohraduhillia coal mining enterprise [27]. Fajar Kurniawan and Juniati Gunawan qualitatively analyzed the impact of ESG strategies on four mining companies in Indonesia [28]. They found that ESG strategies improved corporate governance awareness and positively impacted the progress of mining companies. These studies highlight the importance of adopting ESG frameworks to balance economic growth with environmental responsibility and social welfare in the coal mining industry.

In the existing literature on the impact of green finance on enterprises, scholars have extensively examined the influence of green finance on various aspects of corporate operations, including energy transformation, investment and financing mismatches, overcapacity, technological innovation, and financial performance metrics such as stock prices, financing costs, and total factor productivity [6,29,30,31,32]. Scholars have affirmed the positive role of green finance in economic development and environmental protection from their perspective. However, a one-sided evaluation may sever the policy effects of green finance and overlook its comprehensive effects at the micro level. ESG, as a comprehensive evaluation system for enterprises, can more comprehensively reflect the comprehensive effects of green finance policies on micro-enterprises, but only some scholars have studied it. The literature on the relationship between green finance and corporate ESG can be divided into three perspectives: corporate ESG information disclosure, corporate ESG performance, and its impact mechanism [33,34,35]. Liang Zhidong et al. found that green finance and corporate ESG disclosure have a mutually reinforcing effect, and the ESG disclosure level of a single company can drive the improvement of the ESG disclosure level of the entire industry, examining the comprehensive effect of green finance [33]. Ma Dan et al. conducted a quasi-natural experiment using green credit to examine its impact on corporate ESG performance and considered the influence of regional marketization on green credit. Through quantile regression, they found that the impact of green credit policies on the ESG performance of enterprises at different levels varies [36]. ESG can bring more resources to companies, which may lead to companies actively engaging in “greenwashing” behavior to improve their ESG performance. Zhang Dongyang found through research that green finance can suppress the risk of ESG greenwashing behavior, emphasizing that we should not only focus on the ESG information disclosed by companies but also pay attention to their disclosure quality and practical level [37]. In addition, corporate ESG may also appear as an intermediary or moderating variable in other studies [38].

Against the backdrop of a global economic downturn, EPU affects the effectiveness of economic policies and poses risks to business operations [39]. So how will EPU affect the effectiveness of green finance policies? Insufficient existing research underscores the importance of understanding how economic policy uncertainty can influence policy effectiveness, subsequently impacting corporate ESG performance. This study aims to investigate the intricate relationships among these variables. Specifically, it examines the effects of the Green Finance Reform and Innovation Experimental Zone policy, which was enacted in China in 2017, on the ESG performance of Chinese listed companies. Moreover, this research integrates economic policy uncertainty to enhance the understanding of dynamic policy impacts and to advance policy-related inquiries in this field.

(1)

Research on green finance policies mostly focuses on financial factors, but this article introduces the moderating variable economic policy uncertainty (EPU) to explore the impact of green finance policies on corporate ESG performance, taking into account the influence of economic policy instability on green finance policies, making the research more rigorous.

(2)

From the perspectives of financing constraints and green innovation effects, this study examines the impact mechanism of green finance policies on corporate ESG performance, enriching the relevant research on green finance policies and ESG.

(3)

For heterogeneity analysis, in addition to considering common ownership and industry properties, this article innovatively explores the differences in the impact of GFPZ policies on corporate ESG from the perspectives of information asymmetry and business reputation.

(4)

The study not only explores the impact of green finance policies on improving the ESG performance of enterprises but also has certain enlightening significance for guiding enterprise transformation and upgrading through green finance and provides reference for the government to improve the green finance system.

2. Research Hypothesis

2.1. Green Finance Policies and Corporate ESG Performance

The green financial policy is a remarkable activity that combines resource allocation with environmental regulation. Due to its profound significance in financial supply-side reform and promoting green development of the economic system, it has received significant attention from enterprises, financial institutions, and governments. At present, research on the impact of green finance policies has generally confirmed its promoting effect on the macroeconomic environment, financial institutions, and enterprises [40,41,42,43]. Green financial policies have effectively enhanced the financial accessibility for green firms and mitigated their financial burdens by reducing their financing costs. Environmental resources are characterized by the attributes of public resources, which can easily lead to the occurrence of the “tragedy of the commons” and the “free-rider” phenomenon. Hence, enterprises need to take more initiative in terms of environmental resources [44,45,46,47]. Green finance policy, as a financial instrument characterized by market-based environmental regulation, can force enterprises to cater to the policy direction, release green economic signals, build a good social image, and improve their ESG performance [36].

From an environmental point of view, green financial policies can curb the negative external effects of enterprises on the environment and force them to transform and upgrade to green [6,48,49]. When it comes to development, the notion of sustainable development emphasizes the crucial need to satisfy the demands of the present while conserving future generations’ ability to meet their requirements. However, the core objective of enterprises is often to maximize profits, and there needs to be more subjective motivation to improve their polluting behavior towards the environment [50,51]. Green financial policies strengthen green financial incentives and constraints, forcing enterprises to adopt environmentally friendly decision-making, which in turn sends positive signals to financial institutions and investors, suppresses the market behavior of “bad money driving out good money”, and makes enterprises pay more attention to environmental performance in their production and operation processes.

From a social aspect, green financial policies can improve enterprises’ sense of social responsibility. Green financial policies advocate eco-friendly production and operation methods and require financial institutions and enterprises to fulfill their green and low-carbon social responsibilities. Enterprises should conform to policies, laws, and regulations while considering public morality and order in their production and operations [52,53,54]. With the support of green financial policies, outstanding performance in corporate social responsibility can help companies reduce financing costs, ease financing difficulties, lower operational risks, increase customer loyalty, and improve investor confidence, thus enhancing the willingness and ability of companies to be willing to take on social responsibility in order to release more positive market signals.

In terms of governance, implementing green finance policies can enhance the degree of corporate governance in firms. Green financial policies focus on promoting green finance and emphasizing its essential role in shifting towards a green, low-carbon economy and developing an ecological civilization. The initiative encompasses various elements, such as establishing environmentally-friendly financial institutions, setting criteria for certifying sustainable businesses, issuing green bonds, and encouraging the provision of green loans [55,56,57]. These initiatives are designed to establish a robust green financial ecosystem and offer comprehensive assistance for the advancement of the green economy. Green finance policies provide incentives for firms to independently implement and monitor ESG management. Their internal governance systems will be standardized, and their internal governance levels will be improved as they pursue economic development, strengthening their governance capacity.

Hence, Hypothesis 1 is proposed: GFPZ policies can improve corporate ESG performance.

2.2. The Moderating Effect of Economic Policy Uncertainty

Economic policy uncertainty pertains to the intricate and dynamic policy landscape that makes it challenging for economic actors to accurately anticipate the government’s potential alterations to its existing economic policy, as well as the effects of macroeconomic policies on the ongoing business operations and strategic trajectory of enterprises [58]. Uncertainty brought about by policy adjustments often leads to fluctuations in the production and business environments of firms, and external uncertainty leads to shocks in the relationship between green finance policies and ESG performance of companies [21]. The waiting option model proposes that when there is an increase in EPU, the value of the “waiting option” also increases. Consequently, firms choose to decrease and delay their investments during this period to avoid the potential hazards of EPU [59]. Meanwhile, EPU increases the likelihood of business misconduct and loan defaults. Banks and other financial institutions will raise financing thresholds and credit costs for self-protection and risk avoidance, exacerbating the difficulties of corporate financing and aggravating the risk of corporate financial operations, forcing enterprises to slow down the speed of capital structure adjustment. Enterprises’ investment and financing strategies will be selected under careful examination of the current trend of macroeconomic policy changes, and enterprises’ ability to withstand financial risks will be reduced by business disruptions and intensified liquidity pressures. Enterprises will be more prudent in selecting clients and billing periods for accounts receivable issuance, controlling supply chain risks, and reducing the risk of commercial credit supply [60,61]. Finally, the rise in economic policy uncertainty has exposed management to difficulties in identifying risks in decision-making, reduced resources at its disposal, and reduced expected returns on investment, making it necessary for management to make more prudent and robust decisions [62,63].

Hence, Hypothesis 2 is proposed: economic policy uncertainty dampens the effect of GFPZ policies on the improvement of companies’ ESG performance.

2.3. Mechanisms of Green Finance Policies to Improve Corporate ESG Performance

2.3.1. The Mechanism of Technology Innovation

Porter’s hypothesis suggests that appropriate environmental regulation will push firms to innovate [64]. In the absence of mandatory environmental regulation, enterprises often need more internal incentives to divert their energies to environmental and social governance. Environmental policies, as an external driving force, can compensate for the lack of endogenous motivation in enterprises, promote environmental governance and green transformation, and improve the green production efficiency and social responsibility implementation of enterprises [65,66,67]. Green finance policy takes environmental regulation as the core way to realize policy objectives and is a policy tool that combines environmental regulation and financial control. It guides enterprises in carrying out green transformation and upgrading through the resource allocation effect. Financial institutions will facilitate and promote green projects and enterprises by offering simple financial support as well as encouraging them to engage in green activities and innovation. Enterprises will strengthen their investment in green R&D under the guidance of the policy, promoting the transformation and development of enterprises into environmentally friendly high-end technology enterprises. However, financial institutions will restrict the amount of funding provided to highly polluting companies and increase the criteria for obtaining financing, reducing the availability of funding. To mitigate production and operational risks and address financial challenges, environmentally harmful businesses will proactively undertake environmentally friendly transformation and enhance their investment in green R&D [68,69]. Green innovation can promote the iterative upgrading of enterprise products, reduce environmental pollution emissions during the production and operation period, and enhance social, environmental, and corporate financial performance. By increasing green technological innovation, enterprises can reduce energy consumption pollution emissions, lower product costs, and improve production efficiency. While focusing on green innovation, green technological innovation also helps enterprises improve their internal management and monitoring capabilities, reduce financial and operational risks, and improve their core competitiveness and sustainable development capabilities.

Hence, Hypothesis 3 is proposed: GFPZ policy improves corporate ESG performance by increasing the level of green technology innovation.

2.3.2. The Mechanism of Financing Constraints

The punishment and threshold effect of green finance policies have intensified the financing challenges faced by polluting enterprises. Consequently, these enterprises will proactively comply with the policy requirements to alleviate financing difficulties. While reducing existing pollution emissions, enterprises will passively improve their internal management and control capabilities, reduce pollution emissions, improve negative externalities, and enhance their benefits to the entire society, which is in line with the macro vision of green finance policies. The impact of financing constraints on a company’s ability to improve ESG performance is manifested in multiple aspects. In order to improve ESG performance, enterprises not only need to implement the product responsibility system in the whole process of production and operation to ensure employee safety, development, and implementation of a socially friendly corporate image but also need to face the difficulties brought by financing constraints. Increased financing constraints force enterprises to face higher financial risks, formulate more prudent and long-term strategic plans, focus on sustainable operation, and adopt sustainable production modes so as to improve the ESG performance of enterprises [70]. Escalating financial limitations have compelled highly polluting firms to decrease emissions and redirect their investments towards environmentally friendly operations, modernization, and improvement. This behavior not only reflects the company’s control over policy direction, commitment to environmental protection, and social responsibility but also receives positive recognition from stakeholders, institutions, and the government for the company’s strategic development. The emergence of financing difficulties has made enterprises realize that human capital and social capital are equally important. Vital human resources and social relationships can not only reduce external risks but also improve internal operational efficiency [71]. As a result, companies will actively perform high social responsibility performance behaviors to maintain their good social reputation image. Finally, financing constraints will force external stakeholders to increase pressure on firms, which will force firms to strengthen corporate governance by improving resource allocation, increasing capital efficiency, improving internal management mechanisms, and increasing employee satisfaction.

Hence, Hypothesis 4 is proposed: GFPZ policy improves corporate ESG performance by increasing corporate finance constraints.

3. Methodology

3.1. Model Setup

We refer to the practice of Zhang et al. [72], Hou and Shi [73], Lee et al. [74], and Holtmann et al. [75]. This study uses a [76] double difference model to examine the causal analysis of the impact of GFPZ policy on corporate ESG performance. This method can minimize the impact of non-policy factors on the estimation results as much as possible. Furthermore, the subsequent model is established:

$$ES{G}_{it}={\alpha }_0+{\alpha }_1\cdot DI{D}_{it}+\gamma X_{it}+{\omega }_j+{\mu }_i+{\eta }_t+\epsilon$$

(1)

where $ES{G}_{it}$ is the ESG rating performance of firm $i$ in year $t$; $ES{G}_{it}$ is the core explanatory variable and is the treatment group if the location of firm $i$ implemented the pilot zone policy in year $t$. $DI{D}_{it}$ = 1. Otherwise, it is considered as the control group $DI{D}_{it}$ = 0; $X_{it}$, ${\omega }_j$, ${\mu }_i$, and ${\eta }_t$ represent control variables, industry fixed effects, individual fixed effects, and practice fixed effects, respectively, used to exclude the influence of random variables; and $\epsilon$ is the random disturbance term.

In order to deeply test the role of EPU between green finance and firms’ ESG performance, this paper incorporates the interaction term between EPU and GFPZ in model (1), expressed as follows:

$$ES{G}_{it}={\beta }_0+{\beta }_1\cdot DI{D}_{it}+{\beta }_2\cdot EP{U}_t+{\beta }_3\cdot EP{U}_{it}\cdot DI{D}_{it}+\delta X_{it}+{\omega }_j+{\mu }_i+{\eta }_t+\epsilon$$

(2)

3.2. Data Description

The data utilized in this study originate from Chinese publicly traded corporations and cover the timeframe from 2012 to 2022. In order to precisely evaluate the influence of implementing a GFPZ on the ESG performance of businesses in the designated area, as well as the broader macroeconomic mechanism. To achieve this, the research samples in this paper are selected from prefecture-level cities in the pilot provinces. Through this method, the regional spillover effects of GFPZ policies can be comprehensively examined, thereby more accurately calculating the causal relationship between GFPZ and corporate ESG performance. The study sample takes into account the following parameters: The CSMAR database is the source of the economic statistics for listed companies and localities, while Bloomberg provides ESG performance data. This study excluded financial and insurance firms, ST* firms, and firms with substantial missing data from previous studies.

Additionally, outliers with a gearing ratio greater than 1 are removed. We have collected a total of 10,625 sample data points from a pool of 1346 listed organizations. All continuous variables’ top and bottom tails were trimmed by 1% to prevent extreme values from affecting the estimates.

3.3. Variable Selection

Explained variables: At present, relevant government agencies and academia still need to formulate ESG standards for enterprises formally. Existing ESG performance data is mainly released by third-party consulting firms such as Bloomberg, CSI, and Shangdao Ronglu. So chooses the ESG ratings from Bloomberg as the explanatory variables. Bloomberg is a leading global financial information and media company, and its ESG ratings are widely recognized in the international market. Many investors, companies, and stakeholders prefer Bloomberg’s rating criteria. The methodology and criteria of Bloomberg’s ESG ratings are relatively transparent and provide a consistent assessment framework, which makes it easier to compare and contrast between different companies. This consistency and comparability help investors make more accurate decisions and promote market transparency [77,78].

Core Explanatory Variable: Since 2017, the State Council has created GFPZ in six provinces (regions) to promote green finance development actively. The main goal of the pilot zones is to build a highly effective financing structure that supports ecologically sustainable enterprises. This entails the development of diverse financial instruments. In addition, the pilot zones have the objective of broadening the range of financial choices accessible to environmentally friendly enterprises and establishing a comprehensive financial service mechanism to enable the progress and development of these industries. In this paper, the establishment of GFPZ as an exogenous policy shock, if enterprise $i$ location in the year $t$ implementation of GFPZ policy, then for the treatment group, $DI{D}_{it}$ = 1, otherwise regarded as a control group $DI{D}_{it}$ = 0.

Moderating variable: This article utilizes the EPU index, developed by Baker (2016), to gauge the extent of economic policy uncertainty in China [79]. Compared with other measurement methods mentioned in the previous section, this index includes all keywords related to EPU in the search category, characterizes economic policy uncertainty from multiple dimensions, better solves the shortcomings of EPU in terms of continuity and quantification, and has gained wide recognition among scholars.

Control variables: To enhance the persuasiveness of empirical evidence and prevent model estimation bias caused by omitted variables, this paper controls for the following variables: (1) Firm size (Size); (2) Gearing ratio; (3) Return on net assets (Roe); (4) Price-to-book ratio (PB); (5) Quick ratio (Quick); (6) Revenue growth rate (Growth); (7) Board size (Board); (8) Whether the directors and supervisors have a financial background (FinBack) (9); The proportion of shares owned by the largest shareholder (TOP1); and (10) Whether audited by the Big 4 accounting firms (Big4). The precise definitions of the body variables are displayed in

Table 1. List of variables.

Variable Type	Variable Name	Variable Symbol	Description of Variables
explained variable	ESG performance	ESG	Selected ESG scores published by Bloomberg
explanatory variable	Green Finance Pilot Zone	DID	Dummy variable, 1 if the listed company is located in the pilot zone, 0 otherwise
moderator variable	Economic policy uncertainty	EPU	China’s EPU index as Measured by Baker
control variable	Firm size	Size	Ln (total business assets)
	Gearing ratio	Lev	Total liabilities/total assets
	Return on net assets	Roe	Net profit/net assets
	Price-to-book ratio	PB	Share price per share/net assets per share
	Quick ratio	Quick	Quick assets/liquid liabilities
	Revenue growth rate	Growth	Revenue growth/Total revenue of the previous year
	Board size	Board	ln (number of board members + 1)
	Whether the directors and supervisors have financial background	Finback	Dummy variable, 1 for directors and supervisors with financial background, 0 otherwise
	The proportion of shares owned by the largest shareholder	TOP1	The largest shareholder/total number of shares
	Whether audited by the Big 4 accounting firms	Big4	Dummy variable, 1 for Big 4 audit, 0 otherwise

:

4. Results of Empirical Analysis

4.1. Descriptive Statistics

Table 2. Descriptive statistics.

VARIABLES	(1)	(2)	(3)	(4)	(5)
	N	Mean	sd	Min	Max
ESG	10,265	30.02	9.162	12.58	57.63
DID	10,265	0.0521	0.222	0	1
EPU	10,265	418.8	232.4	113.9	791.9
EPUDID	10,265	22.71	109.8	0	747.9
Size	10,265	23.26	1.325	20.54	27.05
Lev	10,265	0.474	0.199	0.0701	0.882
ROE	10,265	0.0875	0.12	−0.489	0.415
PB	10,265	3.318	3.115	0.413	17.83
Quick	10,265	1.51	1.663	0.165	11.22
Growth	10,265	0.166	0.363	−0.492	2.177
Board	10,265	2.174	0.202	1.609	2.708
FinBack	10,265	0.722	0.448	0	1
TOP1	10,265	37.25	16.13	8.716	77.32
Big4	10,265	0.136	0.343	0	1

displays the descriptive statistics of all the variables included in the baseline regression. The ESG performance of different firms varies significantly, with a minimum value of 12.58 and a maximum value of 57.63 out of a sample of 10,265 enterprises from 2012 to 2022. The mean value of the difference-in-differences (DID) is 0.0521, suggesting that the policies implemented in the pilot region directly impact 5.21% of the listed firms. The economic policy uncertainty (EPU) index exhibits a substantial disparity between its most significant and minimum values, with the former being eight times greater than the latter and a standard deviation of 232.4. This indicates a notable variation in the EPU index throughout different periods. All the other control variables fall within an acceptable range.

4.2. Return to Baseline

Table 3. Regression results.

VARIABLES	(1)	(2)
	ESG	ESG
DID	6.535 ***	3.201 ***
	(5.41)	(2.96)
Size		9.345 ***
		(−32.25)
Lev		−17.944 ***
		(−12.38)
ROE		−4.472 ***
		(−4.44)
PB		0.386 ***
		(8.71)
Quick		−0.373 ***
		(−3.35)
Growth		−0.148
		(−0.76)
Board		−6.580 ***
		(−7.59)
FinBack		−0.600 **
		(−2.67)
TOP1		−0.138 ***
		(−7.13)
Big4		3.154 **
		(2.42)
Constant	29.676 ***	−159.932 ***
	(28.74)	(−21.71)
Observations	10,265	10,265
Industry FE	YES	YES
Company FE	YES	YES
Year FE	YES	YES
r2_a	0.215	0.472

*** p < 0.01 and ** p < 0.05.

shows the estimated results. From the first column, which only controls for fixed effects and does not include control variables, GFPZ policy significantly improves corporate ESG performance. In column 2, with added control variables, the effect is still significantly positive with a coefficient of 3.201. The above research results indicate that implementing the pilot zone has improved enterprises’ ESG performance within the pilot zone by at least 3.201 units. In other words, establishing the pilot zone has promoted improving enterprises’ ESG performance within the pilot zone, thereby proving the effectiveness of green finance policies and achieving the original intention and goals of establishing a GFPZ. Therefore, Hypothesis 1 has been confirmed.

The control variables related to ESG performance, except for business growth rate, are all significant. The coefficients of Size, PB, and Big4 are positive, indicating that the expansion of the company’s scale, the improvement of the price-to-book ratio, and the background of the four major audits will help the company improve its ESG performance. The coefficients of Lev, Roe, Quick, Board, Finback, and TOP1 are all negative, indicating that an increase in leverage ratio, operating net profit margin, quick ratio, and the proportion of major shareholders, an expansion of the board of directors, and the financial background of executives will hinder the improvement of corporate ESG. In summary, after adding control variables, the explanatory variable is still significantly positively correlated with the dependent variable, but the regression coefficients have all decreased.

4.3. Parallel Trend Testing

The parallel trend is a necessary prerequisite for the DID method to identify causal relationships accurately. This hypothesis suggests that in the absence of external shocks, the ESG performance of the enterprises should follow the same trend of development with no significant differences.

$$ES{G}_{it}={\sigma }_0+{\displaystyle \sum _{t=-3}^{t=3}{\sigma }_t\cdot DI{D}_{it}}+\vartheta X_{it}+{\omega }_j+{\mu }_i+{\eta }_t+\epsilon$$

(3)

where $DI{D}_{it}$ is a dummy variable for whether firm $i$ was affected by the policy in year $t$; if $t$ takes a negative number, it means year $t$ before the policy. The other variables are defined in a manner identical to that of the regression model (1). The treatment group takes the first year of policy implementation at the firm’s location as the base year, and the control group takes 2017 as the base year. ${\sigma }_t$ reflects the difference between the treatment and control groups of firms in the first year after policy implementation. Other variable settings are consistent with formula (1). This study employs model (3) to do a parallel trend analysis and investigate the dynamic characteristics of the influence of the ESG rating. Figure 1 illustrates the results. Before implementing the GFPZ, companies’ ESG performance in both the treatment and control groups was indistinguishable. Once the pilot zone was established, a clear difference in the performance of the ESG indicators was seen in groups. This difference demonstrates a progressive and continuous trend, as the parallel trend test shows.

4.4. Robustness Test

4.4.1. Placebo Testing

To verify that the observed improvement effect of GFPZ policy on corporate ESG performance in the regression is not accidental, this article uses a placebo test method for robustness testing to enhance the reliability of the benchmark regression. Referring to La Ferrara et al., we generate “pseudo-policy dummy variables” by randomly sampling 1000 samples from the distribution of ESG rating performance in the all sample. Subsequently, we recalibrate model (1) to examine the distribution of its coefficients and p-values. Figure 2 presents the displayed results [80]. The estimated coefficient of corporate ESG performance on the “pseudo policy dummy variable” shows a normal distribution with 0 as the axis, with the majority of p-values greater than 0.1. This means that it is not other random variables that affect a company’s ESG performance, and the above results can be considered reliable.

4.4.2. PSM-DID

The selection of GFPZ is discretionary and not obligatory, as it is determined by government decision-making. Due to the non-random selection of the groups, sample selection bias is possible when comparing green financing policies with company ESG performance. This work utilizes the propensity-to-match score and double difference (PSM-DID) analysis to validate the research hypotheses provided earlier. This approach is chosen to address the constraints of traditional regression models in testing policy effects and to resolve the endogeneity problem.

This study employs the Logit model and a one-to-three nearest-neighbor matching method to match two classes of samples. Additionally, it designates the control variables in the model (1) as covariates. Figure 3 and Figure 4 display the contours of the kernel density function for the propensity score distribution of the final groups, both before and after matching, respectively. After matching, the propensity score values of the two groups of samples show a significant convergence, as indicated by the comparison of these figures. This indicates that the condition of reciprocal assistance is fulfilled. The regression outcomes of the method are displayed in

Table 4. Robust type tests.

VARIABLES	(1)	(2)	(3)
	PSM-DID	Year of Change in Policy	CSI ESG
DID	2.536 ***	0.892	0.157 **
	(6.09)	(1.09)	(2.46)
Constant	−79.310 ***	32.582 ***	50.445 ***
	(−12.58)	(9.82)	(13.95)
control	YES	YES	YES
Industry FE	YES	YES	YES
Company FE	YES	YES	YES
Year FE	YES	YES	YES
Observations	9687	6730	9687
r2_a	0.518	0.368	0.462

*** p < 0.01 and ** p < 0.05.

(1). After implementing propensity score matching treatment, these results suggest that the outcomes remain robust. Specifically, the results show that adopting GFPZ policies has a beneficial impact on companies’ ESG performance.

4.4.3. Year of Change in Policy

By accelerating the adoption of the GFPZ policy to 2014, three years earlier than initially planned, and focusing on the period from 2012 to 2016, before any green finance pilot zones were established (the official policy was implemented in 2017), we will investigate whether the GFPZ has had an impact on the company’s ESG performance. They are assuming that the calculated p-value of the interaction term of the fictional policy dummy variable is not significant. In this case, potential impacts of other variables on the company’s ESG performance can be excluded. The results of item (2) in Table 4 demonstrate the above hypothesis. This means that assuming the impact of the GFPZ policy is not considered and there is no significant difference in the sample, the robustness test has been passed.

4.4.4. Replacement of Explanatory Variables

The Bloomberg ESG is replaced by the ESG rating performance of enterprises issued by CSI. The CSI ESG assessment categorizes corporate performance into nine grades, from “AAA” to “C” [81,82,83]. There is a specific technique of assignment that is as follows: the grades of AAA to C are assigned to the scores that range from 9 to 1, and the scores are assigned to the grades of AAA to C. Then, perform robustness testing. As shown in column 3 of Table 4, the coefficient of DID on CSI ESG performance is 0.157, which still has significant statistical significance and a positive correlation. This result passed the robustness test.

5. Further Analysis

5.1. Mechanism Analysis

5.1.1. Moderating Effect

The results of the experiments conducted to assess the impact of EPU are presented in

Table 5. Moderating and mediating effect.

VARIABLES	(1)	(2)	(3)	(4)
	ESG	ESG	Green Innovation	SA
DID	3.238 ***	2.741 **	0.066 ***	−0.015 **
	(3.19)	(2.36)	(2.95)	(−2.12)
EPU	−0.248 **	−0.097 *
	(−2.37)	(−1.75)
EPUDID	−0.941 ***	−0.523 **
	(−5.33)	(−2.24)
Control	NO	YES	YES	YES
Industry	YES	YES	YES	YES
Company FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observations	10,265	10,265	10,040	10,040
r2_a	0.329	0.525	0.339	0.282

*** p < 0.01, ** p < 0.05, and * p < 0.1.

. There are no control variables in the first column, while the second column indicates that the regression coefficients of the cross-multiplier terms of EPU and DID are statistically significant and negatively associated at a significance level of 5%, and the regression coefficients for DID are significantly positive at the 5% level. Economic policy uncertainty hampers the improvement of corporate ESG performance by GFPZ policy. The reason for this may lie in the fact that a rise in EPU will increase the value of physical options that companies are waiting for, which will affect management decision-making, resist financial and operational risks caused by financing difficulties, converge on current economic behaviors, reduce and postpone investment activities, and adopt conservative operational strategies, thus attenuating the improving effect of the GFPZ policy on ESG performance. Therefore, Hypothesis 2 holds.

The presence of economic policy uncertainty impedes the effectiveness of GFPZ policies in enhancing corporate ESG performance. Economic policy uncertainty can influence companies’ assessments of market expectations, subsequently impacting a range of corporate behaviors. As economic policy uncertainty rises, enterprises tend to slow down their operational efficiency and rein in production costs. They adopt a more cautious approach towards investment activities to prevent overinvestment from exerting pressure on production and operations. Additionally, from a risk-averse perspective, financial institutions may alter their financial supply to businesses, reducing investment or lowering credit limits, thereby limiting a company’s financing capabilities. In such circumstances, it becomes challenging for enterprises to predict the direction and intensity of future economic policy changes based on experience, and determining their business development trajectory becomes difficult. Consequently, companies may opt to scale back on their social responsibility initiatives as a means of mitigating risks and managing earnings volatility.

5.1.2. Mediation Effect

The logarithm of the total number of green patent authorizations for enterprises represents the green innovation index. The regression results are in column (3) of Table 5. At a confidence level of 1%, the impact of GFPZ policy on the green innovation effect of enterprises is significantly positively correlated, indicating that GFPZ policy can improve the green innovation capability of enterprises. Improving green innovation capability helps companies reduce their environmental footprint, develop a circular economy, meet green consumption needs, and enhance their sense of social responsibility, thereby improving their ESG performance. Therefore, Hypothesis 3 has been confirmed.

According to Hadlock (2010), the SA index is used to represent financing restrictions. The formula is $SA=-0.737\cdot Size+0.043\cdot Siz{e}^2-0.04\cdot Age$ [84]. As shown in column 4 of Table 5, at a 5% confidence level, GFPZ policy is negatively correlated with the enterprise SA index, i.e., positively correlated with the absolute value of the SA index. Strengthened financing constraints can force firms to engage in various behaviors such as improving environmental management, adjusting business strategies, increasing information disclosure and transparency, and strengthening cooperation with the government, all of which can help improve corporate ESG performance. The GFPZ policy imposes stricter restrictions on corporate financing, forcing companies to improve their ESG performance. Therefore, assumption 4 holds.

5.2. Heterogeneity Analysis

5.2.1. Ownership

Differences in the ownership structure of enterprises may have different impacts on the ability of GFPZ policies to improve enterprises’ ESG performance. SOEs have more financial resources and stronger social responsibility performance than non-SOEs. They can respond more effectively to the government’s policy call and receive corresponding dividends [85,86]. The explanatory variables for the sample of SOEs in column (1) of

Table 6. Heterogeneity analysis.

VARIABLES	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	State-Owned	Non-State-Owned	Low Market Concentration	High Market Concentration	Low Reputation	High Reputation	Low Information Asymmetry	High Information Asymmetry
DID	4.826 ***	1.842 **	3.830 ***	2.267	3.004 **	2.416 ***	3.641 ***	1.868 **
	(6.09)	(2.14)	(4.33)	(1.26)	(2.25)	(3.77)	(4.16)	(2.070)
Control	YES	YES	YES	YES	YES	YES	YES	YES
Industry	YES	YES	YES	YES	YES	YES	YES	YES
Company FE	YES	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES	YES
Observations	5221	5404	4535	5505	4995	5045	5007	5033
r2_a	0.458	0.432	0.416	0.423	0.415	0.417	0.479	0.325

*** p < 0.01 and ** p < 0.05.

are significant at 4.826. The results of non-SOEs in column (2) show that the explanatory variables are significant at a 10% confidence level. However, the coefficient is 1.842, smaller than that of state-owned enterprises. Therefore, SOEs’ ESG governance performance is significantly improved under the influence of GFPZ policy.

5.2.2. Market Concentration

In the face of policy pressure, different types of enterprises tend to adopt different coping strategies. Enterprises located in the Red Sea market will respond positively to the policy call and follow the development trend to obtain the support of the government, and then maintain their social image to strengthen themselves and maintain or further expand their market share [87,88,89]. However, firms in monopolies have strong bargaining power in the market and can protect their market share even if they react negatively to policy calls. However, enterprises in blue ocean markets have strong pricing power, and even if they respond negatively to policy calls, it is difficult for substantial market threats to emerge, and they are able to maintain their market shares. It is worth testing whether GFPZ policies can improve firms’ ESG performance in monopolized industries.

The Herfindahl index is a type of index that is used to measure industrial concentration. It is primarily used to determine the size of the discrete degree of manufacturers in the market, reflected in the industrial market concentration. The index is calculated by calculating the industry’s total revenue or total assets percentage, which is accounted for by various market competitors. As the Herfindahl index increases, the concentration level in the industrial market also increases [90,91]. This paper’s subsequent analysis categorizes the sample data into two categories based on the median Herfindahl index, which represents industry concentration. These groups are defined as high and low market concentrations. The analysis of Table 6 reveals that implementing GFPZ policies significantly positively impacts the ESG performance of firms with low market concentration, as significantly indicated by the coefficient of 3.830. However, the coefficient of high market concentration industries in column (4) is 2.267, but it is not statistically significant. Enterprises with minimal market concentration typically operate in a highly competitive market setting. Enterprises with high market concentration typically operate in a low-market competitive setting. However, they are highly responsive to policy and market conditions and enhance their ESG performance due to this driving force. Companies with high market concentration are less affected by green financial policies than companies with low market concentration, as they are less responsive to non-regulatory policies. ESG performance. Enterprises with minimal market concentration are more impactful than other enterprises.

5.2.3. Business Reputation

Business reputation can help companies improve the availability of credit funds, alleviate financial pressure, and provide a certain degree of support for their long-term development through reputation effects. Enterprises with a good business reputation are more likely to create a stable and reliable positive and glorious image for the public and gain market recognition. This, in turn, can enhance the enterprise’s ESG performance [92,93]. In order to evaluate the impact of GFPZ policy on companies with diverse business reputations. Table 6 (5) shows that the GFPZ policy significantly affects the ESG performance of firms with a poor reputation (3.004) at the 5% confidence level. Similarly, in column (6), the ESG performance of firms with high business reputations under the influence of the policy is significant at the 1% confidence level of 2.416. Under the influence of the policy, although the promotional effect of ESG performance of firms with low business reputations is greater than that of firms with high business reputations, firms with high business reputations are more significantly influenced by green finance policy. Under the influence of policies, although the promotion effect of ESG performance of low business reputation firms is greater than that of high business reputation firms, high business reputation firms are more significantly affected by green finance policies.

5.2.4. Information Asymmetry

Information asymmetry refers to the fact that in a market economy, buyers and sellers differ in the degree of information they have about the goods they are trading, with the seller usually having more information about the product or service they are offering and the buyer having less information about the good or service they are buying. Asymmetry of information can cause market failure. Firms with low information asymmetry will signal to the outside world that they are more socially responsible and are more likely to be recognized by the market. This paper quantifies the level of information asymmetry by utilizing a daily evaluation of trading data [94,95,96]. According to the data in column (7) of Table 6, enterprises with a low level of information asymmetry are affected by the test area policy, resulting in a significant contribution of 3.641 to their ESG performance. This significance is detected with a statistical confidence level of 1%. Column (8) signifies a notable degree of information imbalance concerning the influence of the company’s environmentally friendly finance policy on its ESG performance. The efficacy of this promotion is quantified as 1.868, indicating statistical significance at a 5% confidence level. Firms with smaller information discrepancies have a greater impact on their GFPZ policies, promoting their ESG performance.

6. Conclusions

In this paper, based on the theoretical analysis of the GFPZ policy as a quasi-natural experiment, Chinese listed companies are selected as the sample data from 2012 to 2022, and the multi-period DID model is used to conduct empirical analyses to study the impact of green financial policies on corporate ESG performance. Research has found that: (1) GFPZ policy has a significant improvement effect on corporate ESG performance, and after robustness tests, the improvement effect persists. (2) The EPU plays a dampening role in the relationship between GFPZ policies improving firms’ ESG performance. (3) GFPZ policies improve firms’ ESG performance by influencing green innovation within firms and increasing firms’ financing constraints. (4) Under different levels of corporate ownership, market concentration, business reputation, and information asymmetry, GFPZ policies exhibit varying policy effects on corporate ESG performance. Building upon the aforementioned findings, this paper proposes the following policy recommendations:

Firstly, the expansion of green financial pilot zones should be accelerated in an orderly manner, taking into full consideration the differences in the level of economic development, resource endowment, and financial emphasis between different regions in China and incorporating more regions with reform initiatives and objective support into the next batch of pilot zone programs. We will fully leverage the radiation-driven function of pilot zone policies in promoting green innovation, industrial transformation, and sustainable development from point to point by expanding the scope of the pilot zones in an orderly manner [97,98].

Secondly, green technology innovation not only plays an important role in enhancing enterprise competitiveness and improving resource utilization but also serves as a key path to promote sustainable development and social civilization construction. We should establish a platform for green technology innovation and financial integration, promote information exchange between enterprises and financial institutions, and improve the efficiency of using green credit. Promote the transformation of green technology innovation achievements, encourage financial institutions to participate in green technology innovation, and form a cooperative mechanism for sharing benefits and risks.

Thirdly, promote the construction of a green credit system dominated by banks, improve the environmental disclosure mechanism of financial institutions, strengthen internal fund control, enhance the accuracy and scientificity of fund supervision, guide enterprises to actively improve the scope and transparency of information disclosure, fully leverage the financing constraint mechanism of the pilot zone policy, force enterprises to adjust their operational strategies, and accelerate industrial transformation.

Fourthly, in order to alleviate the volatility of economic policy uncertainty on the effectiveness of global financial policies, the government should conduct sufficient research and enhance foresight to improve policy stability, strengthen cross-departmental cooperation, and improve policy coordination mechanisms. Enterprises should pay timely attention to policy trends, strengthen internal risk management, optimize internal structure, promote transformation and upgrading, and enhance risk resistance capabilities.

Finally, through the strategic implementation of policies like green finance to bolster support and minimize market noise interference, our objective is to augment the market’s capacity to discern high-quality ESG assets. It guides investors to establish ESG investment concepts, creates and maintains a market atmosphere of rational investment, long-term investment, and value investment, brings the resource allocation function of green finance into full play, guides the flow of capital to key areas, suppresses market speculative sentiments, and maintains the effectiveness of the market. In the context of the GFPZ policy, the power of green finance is used to implant ESG concepts in the real economy deeply.

In summary, the government should enhance policy expectation management, promptly communicate and clarify potential uncertainties with all stakeholders, strengthen cross-departmental coordination, stabilize economic policy uncertainties, systematically expand GFPZ policies, fully leverage regional advantages based on local conditions, achieve the radiation and driving effects of policies, accelerate the enhancement of the green finance system, encourage more enterprises to actively engage in ESG activities, and offer corresponding guidance and assistance for enterprise ESG practices. Financial institutions should fully leverage their intermediary role, serving as a bridge for communication between the government and enterprises, alleviating information asymmetry, providing timely policy micro-feedback, enhancing their risk resistance capabilities, strengthening the stability of the financial system, adjusting the allocation of funds to enterprises under the government’s guidance, actively responding to the call for green finance, and promoting the development of ESG in enterprises. Enterprises should adapt to the development of the times, enhance their ability to grasp policies, accelerate their transformation and upgrading under the GFPZ policy, and seek opportunities amidst challenges. Enterprises need to enhance the flexibility and profitability of funds in production and operation, strengthen their risk identification and assessment capabilities, reduce the negative impact of policy uncertainty on production and operation, actively develop corporate ESG, and enhance their resilience.

The following are the limitations of this paper. When evaluating the policy effects, the data of listed companies will be selected due to the scope of policy implementation and data availability, and future research can consider urban data or provincial data. The pilot policies for green finance reform and innovation exhibit a targeted approach, with reform priorities varying across regions: Zhejiang Province emphasizes green development to propel industrial structural upgrades. Guangdong Province is investigating a development model that harmonizes green finance reform with economic advancement. Guizhou Province is forging an effective pathway to foster the transformation and growth of underdeveloped economies through green finance. Jiangxi Province is establishing a robust green financial system with a diverse range of products and services. Xinjiang Province is concentrating on reinforcing financial backing for modern agriculture and clean energy initiatives. Future research endeavors can delve into these specific areas, leveraging the identified limitations as guiding principles for forthcoming studies.