Introduction

Along with accelerated industrialization and modernization, China has rapidly become the world’s second-largest economy. China’s economic growth accompanying concerns about energy consumption and environmental pollution have also increased dramatically. According to the UN’s 2020 Emissions Gap Report, China’s GDP accounts for 16.36% of the world’s total, but its greenhouse gas emissions account for 26.7%. More than 80% of pollution in China is caused by the production and operation of enterprises (Wei and Zhou 2021; Huang and Lei 2021). Enterprises are the subject of driving economic growth, but they are also the main culprits of environmental pollution. Firms play a vital role in environmental protection by undertaking environmental responsibility. Therefore, under the situation of rapid economic growth and increasingly serious environmental problems, how to effectively motivate enterprises to actively undertake environmental responsibility to achieve sustainable development has become an important research topic in China.

Previous studies on the determinants of corporate environmental responsibility (CER) focus on firms’ internal and external factors, and it has been established that institutional environment and financial resources are important factors affecting CER (Falavigna and Ippoliti 2022; Huang and Lei 2021). Therefore, green finance, a new policy tool with environmental regulatory and financial constraints, is proposed by the Chinese government to solve environmental problems. Green finance is a new financial pattern that combines environmental protection with economic benefits through green financial products and services such as green credit, green securities, carbon trading market, and green insurance to realize a win-win between environment and economy (Lee and Lee 2022). Green finance can guide capital and social resources to green industries, while restricting the expansion of polluting industries by cutting off capital sources (Nawaz et al. 2021; Wang et al. 2020). Under the call of the government, China is forming a strong and coordinated development trend of green finance. By the end of 2021, China had issued 754 green bonds with a total volume of CNY 801.438 billion. The green credit balance of 21 major banking financial institutions had reached CNY 15.1 trillion. The cumulative volume of carbon emission allowances traded in the national carbon market reached 179 million tons, with a turnover of CNY 7.661 billion.

As an attempt of China’s environmental regulation system, does the rapid development of green finance can produce green effects? The research field of green finance has grabbed a lot of attention recently. Xiong and Sun (2022) studied the relationship between green finance and carbon emissions through qualitative comparative analysis of fuzzy sets based on panel data of various provinces in China and found that green finance reduces carbon emissions. A study by Zhang et al. (2021) also came up with similar results. Chen and Chen (2021) found that green finance can improve environmental quality by minimizing carbon dioxide emissions. Despite the above studies have confirmed the contribution of green finance to environmental quality, there are still obvious deficiencies in these studies. First, it is very rough to measure only the impact of green finance on regional pollutant emissions. The main organizational level directly affected by green finance is micro-firms, whether green finance can produce a green effect largely depends on the environmental behavior of firms. However, the impact of green finance on environmental quality is rarely discussed from the perspective of firms. Second, pollution emissions are not a good proxy variable for environmental quality. Some scholars have pointed out that firms can reduce pollution emissions by diminishing production scale or suspending production (Cao et al. 2021; Fan et al. 2021; Lin and Xu 2022), which is not brought by green technological progress and is harmful to sustainable economic development. Using regional emission reduction data is difficult to measure the real green effect of green finance. Third, whether green finance has raised the environmental awareness of firms and prompted them to undertake environmental responsibility? What are the inherent mechanisms that play roles in these effects? Existing studies do not provide a specific explanation.

To fill the gaps mentioned above, heavily polluting firms should be taken as the object of analysis to explore whether green finance has a green effect on firms from the perspective of CER. Heavily polluting enterprises are selected because they are the key objects to undertake environmental responsibility and the individuals directly affected by green finance. Specifically, the objectives of this study include the following: (1) to explore the impact of green finance on CER; (2) to test the heterogeneous impact of green finance on CER from the aspects of corporate property rights and external environment regulation; (3) and to investigate the channels that contribute to the changes of CER under the influence of green finance. Theoretical exploration and empirical research on these problems have implications for improving the green finance system and realizing sustainable development in many developing countries.

The main contributions of this paper are as follows. First, this study broadens the literature on CER. Existing studies have examined the factors influencing CER from internal corporate structures and external regulatory instruments. However, green finance, as a new financial regulation tool with both financial and regulatory attributes, is rarely explored in the literature. This paper expands the research literature on CER from the background of green finance. Second, the functions of green finance need to be transmitted through micro-firms. By investigating the impact of green finance on CER, it is found that green finance works on enterprises through financing and investment channels. Our findings provide strong support for a better understanding of the transmission mechanism of green finance on corporate environmental behavior. Third, based on China’s special institutional background, our results reveal the asymmetric impact of green finance on enterprises under different property rights and different institutional environments. By doing so, we can further provide evidence of heterogeneity at the firm level and offer a new perspective for studying the differential effects of green finance. It can also provide experience and guidance for developing countries and emerging market countries to design better green finance policies.

The remainder of this paper is structured as follows. The “Literature review and hypotheses development” section presents the literature review and proposes some hypotheses. The “Sample and empirical methodology” section describes the data, variables, and methodology. The “Empirical results” section analyzes the empirical results. The “Mechanism analysis” section further discusses the transmission mechanism of the impact of green finance on CER. The “Conclusions and recommendations” section concludes this study and proposes some policy implications.

Literature review and hypotheses development

Green finance

Green finance originated in developed western countries in the 1970s. In 1995, the People’s Bank of China promulgated the “Circular on Implementing Credit Policies and Strengthening Environmental Protection,” which was China’s first attempt to develop green finance. Since then, the government and financial institutions have launched a series of financial policies to promote the development of green finance. Green finance is the embodiment of financial innovation in the field of environmental protection and is considered to be a bridge between the economy and the environment. Compared with traditional finance, green finance more emphasizes the ecological environment benefit. Its main goal is to allocate financial resources to environmental-friendly industries and limit resources supply to polluting industries.

The practical effects of green finance have been explored by academic circles from different aspects, mainly focusing on environmental and economic impacts. First, for economic impact, most literature measures green finance by green credit or green bond, and studies its impact on firm financing (Fan et al. 2021; Ji et al. 2022; Peng et al. 2022), investment (Wang et al. 2020), total factor productivity (Feng and Liang 2022), and financial performance (Yao et al. 2021). These studies generally conclude that green finance has a punishment effect on the financial behavior of polluting firms. Second, for environmental impact, researchers are focusing on the impact of green finance on regional environmental quality from a macro perspective. Wang et al. (2021b) found that green finance policies can promote regional green development through industrial structure upgrading and technological innovation. Khan et al. (2021), Meo and Karim (2022), and Xiong and Sun (2022) investigated the impact of green finance on pollutant emissions in different countries, and all agreed that green finance reduces regional pollutant emissions. Conversely, Sinha et al. (2021) tested the impact of green bond on environmental and social sustainability and found that green finance has gradual negative transformational impacts on environmental and social responsibility. Ren et al. (2020) confirmed that China’s green finance policy strongly affected carbon mitigation, but its effect always fell short and lacked continuity. From a micro perspective, empirical research on green finance is limited, and most studies concentrate on examining the policy effects of green credit on firms. For instance, Liu et al. (2021) and Hu et al. (2021) concluded that the environmental benefits of the green credit policy are reflected in promoting green innovation of polluting firms. However, some scholars such as Zhang et al. (2022) asserted that China’s green credit policy has not yet worked.

Through the above analysis, we can find that, first, the environmental impact of green finance has received attention. However, studies on the impact of green finance on environmental quality mostly used regional-level data, while the use of firm-level data is limited. Second, existing studies mainly use single indicators such as green credit or green bond as a proxy variable for green finance. Green credit or green bond is only one kind of green finance tool, and it is difficult to reveal the overall picture of green finance. A comprehensive evaluation indicator for the development level of green finance should be established. Third, existing research has not reached a unanimous conclusion on the effectiveness of green finance. Green finance aims to promote firms to assume environmental responsibility, improve environmental quality, and then promote sustainable economic development. Therefore, it is necessary to construct a more comprehensive green finance indicator to study its impact on corporate environmental behavior. This, in turn, can provide new evidence on the effectiveness of green finance at the micro-firm level.

CER

Corporate environmental responsibility refers to the responsibility of firms to minimize the negative impact of their business activities on the environment through using environmentally friendly products, recycling, and other means (Chen et al. 2021; Dummett 2006). The study of the antecedents of CER is conducted from three aspects: individual level, organization level, and external environment level. At the individual level, it mainly includes management and director characteristics (Bhuiyan et al. 2021; Xu and Ma 2021). The influencing factors at the organization level mainly focus on corporate governance (Li et al. 2020), ownership structure (Chen et al. 2021; Wei and Zhou 2021), and financial condition (Falavigna and Ippoliti 2022; Testa and D’Amato 2017). In terms of external factors, it includes media attention (Aerts and Cormier 2009), public appeal (Liao and Shi 2018), government subsidies (Wang and Zhang 2020), environmental regulation policy (Huang and Lei 2021), and market competition (Tsendsuren et al. 2021).

Despite an extensive literature on the economic consequences of green finance and a large literature on the determinants of CER, few studies have investigated the relationship between green finance and CER. As a special financial tool, green finance has both the power of regulatory instruments and the flexibility of the market economy. Although the promoting effect of green finance on improving regional environmental quality has been confirmed, its impact on CER is worth further examination. Moreover, the increase or decrease in the level of environmental responsibility of heavily polluting firms, which are the main producer of environmental problems and the target of green finance, can reflect whether the green incentive effect of green finance has been fully played to a certain extent. Therefore, this paper is the first attempt to use empirical data to test the impact of green finance on the environmental responsibility-bearing capacity of heavily polluting firms. The results of this paper can both reflect the environmental effect of green finance and expand the literature on the influencing factors of CER.

The impact of green finance on CER

Compared with other social responsibilities, environmental responsibilities are characterized by high investment, high risk, long cycle, and strong positive externality, which makes enterprises need long-term and stable financial support when carrying out environmental governance activities. Under the financial structure of limited internal financing and indirect financing, external financing has increasingly become the main source of capital for heavily polluting enterprises (Wang et al. 2020; Yao et al. 2021). Research has pointed out that the change of the external financing environment will have a significant impact on the ability of firms to undertake environmental responsibility. Green finance allocates financial resources through financial instruments such as green stock, green funds, and green credit. Under the influence of green finance, the external financing environment of heavily polluting enterprises will change correspondingly with the development of green finance, which may affect CER. Specific analysis is as follows.

On the one hand, green finance policy requires commercial institutions to fully consider the environmental risks of financing targets when lending, and the environmental status of firms will be the main threshold for credit financing. However, due to information asymmetry, insufficient disclosure of corporate environmental information, and the existence of greenwashing phenomenon, commercial institutions cannot accurately judge the environmental status of enterprises, leading them to allocate financial resources mainly according to the industry attributes of enterprises. This indicates that the unique financing criteria of green finance will significantly increase the accessibility of external financing for green firms because they have lower environmental risks and lower probability of environmental violations. In contrast, heavily polluting enterprises are labeled as “pollution” and have greater environmental risks, so it is not easy for them to obtain financing support from commercial institutions. Peng et al. (2022) and Ji et al. (2022) revealed that after the implementation of green finance, commercial institutions will require heavily polluting enterprises to pay higher credit rates, which will significantly increase corporate financing costs and reduce corporate financing scale. On the other hand, green finance will send negative signals to the capital market about heavily polluting enterprises. Heavily polluting enterprises will face greater public pressure and may even face environmental litigation, which will significantly reduce the willingness of external creditors to provide debt capital for these enterprises. In addition, with the continuous development of green finance instruments such as green bonds, green stock, and green insurance, investors are provided with more investment choices, which may make investors reduce or even withdraw their investment in heavily polluting enterprises. In other words, green finance will prompt commercial institutions, creditors, and investors to give negative evaluations to heavily polluting enterprises and reduce financial support to these enterprises.

Green finance strengthens the financing constraints of heavily polluting enterprises. Capital constraints make heavily polluting enterprises pay more attention to business performance. CER is not conducive to corporate development because it increases firm costs and reduces firm profitability (Chen et al. 2021; Darnall and Edwards 2006; Li et al. 2017). In addition, CER needs a large amount of funds, resulting in the reduction of investment volume to the core business. This may prompt enterprises to divert funds from environmental governance investment to productive investment. The low level of environmental investment limits the possibility of increasing environmental equipment and developing or introducing environment-friendly technologies, which ultimately leads to the decline of corporate environmental responsibility performance. To sum up, we argue that green finance will significantly increase the financing constraints of heavily polluting enterprises, and insufficient capital supply will lead to the lack of motivation for enterprises to invest in environmental protection and green technology innovation, thus reducing CER performance. We propose the following hypothesis:

  • H1: Green finance has a significant negative effect on CER of heavily polluting firms.

Moderating effect of property rights

The dual nature of corporate property rights must be considered in the context of China. Different firms are affected by green finance differently due to their different attributes and resources, and they attach different emphasis and enthusiasm to undertake environmental responsibility. The behavior of state-owned enterprises usually reflects the wishes of local governments because their actual controller is the government. The government will require state-owned enterprises to take more environmental responsibility (Zeng et al. 2012). The main goal of private enterprises is to maximize profits. Attention to CER generally increases costs and reduces profits, which is the opposite of the primary purpose of private enterprises (Wang et al. 2021a). Compared with environmental protection investment and green innovation, which have high input costs and long payback periods, private enterprises pay more attention to improving economic efficiency in allocating resources. Most of the credit resources of China’s commercial institutions are captured by state-owned enterprises, resulting in financial discrimination against private enterprises (Yao et al. 2021). With the continuous development of green finance, private heavily polluting enterprises with narrow financing channels will inevitably suffer from increasingly serious financing discrimination. Changes in the external financing environment make enterprises more sensitive to the cost of fulfilling environmental responsibility, which leads to a lack of motivation for firms to participate in CER. State-owned heavily polluting enterprises have more financing channels due to their political attributes, which implies that after the implementation of green finance, compared with private enterprises, state-owned heavy-pollution enterprises face fewer financing constraints. And state-owned heavily polluting enterprises have always been required to fulfill their environmental responsibility. Regardless of whether the green finance policy is implemented or not, its impact on the environmental responsibility-bearing capacity of heavily polluting enterprises is insignificant. According to the above analysis, we believe that the relationship between green finance and CER will be affected by the property rights of firms. Green finance has a significant negative impact on the environmental responsibility performance of private heavily polluting enterprises. We propose the following hypothesis:

  • H2: Compared with state-owned heavily polluting firms, the negative impact of green finance on CER is stronger in privates heavily polluting firms.

Moderating effect of environmental regulation

Firms are embedded in various political and economic environments that influence their behavior. Duan and Niu (2011) pointed out that the level of regional law will affect the effectiveness of green finance implementation, and the development of green finance requires a mandatory legal environment guarantee. There are significant differences in the legal environment between different regions, which may lead to large differences in the impact of green finance on CER. In areas with high environmental regulation, the level of legalization is relatively high. Environmental regulation policies will regulate and guide commercial institutions to actively implement green finance policies. Firms in these areas, especially heavily polluting firms, must improve their organizational environment if they want to survive and develop. Meanwhile, the higher the degree of local environmental regulation, the smoother the information transmission path. Heavily polluting enterprises can achieve a good reputation by delivering positive signals to the market and stakeholders by fulfilling their environmental responsibilities. Environmental pressure and economic pressure force heavily polluting enterprises to take responsible green initiatives, such as actively participating in environmental investment and implementing green technology innovation, which will help improve the score of CER. However, in areas with weak environmental regulation, economic development is often backward and the government’s primary goal is to pursue economic benefits. Heavy polluting enterprises are the main contributors to the regional economy, which makes local governments intervene in the capital allocation of commercial institutions to pursue economic growth, resulting in the ineffective implementation of green finance. Moreover, in regions with low levels of environmental regulation, where information is not transparent, the incentive for firms in the region to gain a competitive advantage by signaling their environmental responsibility to stakeholders will be weakened. Therefore, we believe that environmental regulation plays a key role in understanding the relationship between green finance and the CER of heavily polluting firms. Green finance can be synergistic and complementary with environmental regulation. Strengthening environmental regulation can effectively improve the environmental governance effect of green finance and ultimately increase the environmental responsibility level of heavily polluting firms. Loose environmental regulation leads to low environmental compliance standards and insufficient incentives for firms to assume environmental responsibility. The following hypothesis is proposed.

  • H3: Environmental regulation has a positive moderating effect on the nexus between green finance and CER.

To improve clarity and readability, the framework of hypothesized relationships is depicted in Fig. 1.

Fig. 1
figure 1

Framework for hypothesized relationships

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Sample and empirical methodology

Sample selection

We choose to start our sample from 2010 because the CER data from the Hexun website is published since 2010. CER data is obtained from the corporate social responsibility rating agency Hexun. Other financial data are obtained from the database of the China Stock Market and Accounting Research (CSMAR) database and the China National Research Data Service (CNRDS) platform. Following the industry classification of the China Securities Regulatory Commission (CSRC), we define the following industries as heavily polluting industries: thermal power, steel, cement, electrolytic aluminum, coal, metallurgical, chemical, petrochemical, building materials, papermaking, brewing, pharmaceutical, fermentation, textile, leather, and mining. In other words, we classify firms whose industry codes are B06, B07, B08, B09, C17, C19, C22, C25, C26, C28, C29, C30, C31, C32, and D44 as heavily polluting firms. We exclude firms with special treatment, firms that belong to financial industries, and firms with missing values. All continuous variables are winsorized at the 1% and 99% levels to avoid the influences of outliers and bad data points. The final sample comprised a total of 7593 observations representing 940 heavily polluting firms.

Definition of variables

Independent variable

Referencing Ren et al. (2020) and Lee and Lee (2022), this paper uses four indicators of green credit, green securities, green insurance, and green investment to construct a comprehensive index of green finance. These data were sourced from the China Insurance Yearbook, China Environment Yearbook, and China Statistical Yearbook. The relevant indicators in the evaluation system of green finance are described in Table 1.

Table 1 Green finance indicators
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As there are dimensional differences among different indicators, it is necessary to preprocess the original data to eliminate the differences between features. Meanwhile, the influence of positive and negative indicators on data summation should also be eliminated.

$$\mathrm{Positive}\ \mathrm{indicator}:{Z}_{ij}=\frac{X_{ij}-\mathit{\min}\left({X}_i\right)}{\mathit{\max}\left({X}_i\right)-\mathit{\min}\left({X}_i\right)}$$
$$\mathrm{Negative}\ \mathrm{indicator}:{Z}_{ij}=\frac{\mathit{\max}\left({X}_i\right)-{X}_{ij}}{\mathit{\max}\left({X}_i\right)-\mathit{\min}\left({X}_i\right)}$$

Here, Xij represents the original value of the i indicator in the j province; Zij denotes the standardized value of the i indicator in the j province; and max(Xi) and min(Xi) are the maximum and minimum values of the i indicator for all provinces, respectively. j=1, 2, …, m; m is the number of evaluation provinces; i = 1, 2, …, n; and n is the number of evaluation indicators.

The key to calculating a composite index lies in the selection of weights. We use the entropy quotient method to give some weight to each index according to the amount of valid information provided by each index data. The calculation process of the entropy method is as follows.

First, calculate the information entropy value of each indicator Ei:

$${E}_i=-\mathit{\ln}{(m)}^{-1}{\sum}_{j=1}^m{P}_{ij},{P}_{ij}={Z}_{ij}/{\sum}_{j=1}^m{Z}_{ij}$$

Then, determine the weight of each index:

$${W}_i=\frac{1-{E}_i}{n-\sum_{i=1}^n{E}_i}$$

where Wi is the weight of the i indicator.

Finally, using the standardized value and the weight of each indicator, the comprehensive index of green finance in each province is then obtained by:

$${GF}_j={\sum}_{i=1}^n{W}_i\times {X}_{ij}$$

Here, GFj is the comprehensive index of green finance in the j province.

Dependent variable

The dependent variable is corporate environmental responsibility (CER). CER data can be obtained in two main ways: questionnaire surveys and ratings from independent rating agencies. Compared with questionnaire survey, the rating data can reduce small sample problem and improve the objectivity and repeatability of research. The most popular rating databases are KLD, RNS, and Hexun. KLD database is for firms in the USA and lacks data for Chinese firms. RKS only uses social responsibility reports to evaluate firms and only gives the rating of CSR without giving the rating for CER. Based on public information, financial reports, social responsibility reports, and sustainability reports, Hexun evaluates all listed firms in China and provides a rating for each indicator level including CER. CER is a composite measure of five sub-indicators, including corporate environmental awareness, environmental management system certification, environmental protection investment, number of pollutants, and number of energy-saving categories. The Hexun rating is increasingly used and recognized as the most authoritative indicator of CER performance of Chinese listed firms (Chen et al. 2021; Han et al. 2019). Hence, to analyze the CER performance of listed firms and avoid sample selection bias problems, the CER data are obtained from Hexun. It is a score that reflects how much environmental responsibility a firm undertakes, with higher scores indicating better CER performance. The score ranges from 0 to 25.

Moderating variables

Corporate property rights (state) is a dummy variable, which we code as 0 if the controlling shareholders of the firm are state-owned, and 1 otherwise.

Based on the discharge of industrial wastewater, industrial smoke, and industrial sulfur dioxide, we calculated the environmental regulation intensity (ER) in different provinces using the entropy method. This index has been widely used in related empirical research (Feng and Liang 2022; Liao and Shi 2018).

Control variables

We control for several well-known determinants of CER based on previous research (Chen et al. 2021; Han et al. 2019; Liao and Shi 2018).

  1. (1)

    Firm size (Size)—the logarithm of total assets

  2. (2)

    Ownership concentration (Top)—the shareholding percentage of the largest shareholder

  3. (3)

    Firm performance (Roa)—the return on assets measures the firm’s profitability

  4. (4)

    Financial leverage (Lev)—the ratio of liabilities to assets

  5. (5)

    Capital intensity (Tangible)—the ratio of tangible assets to total assets

  6. (6)

    Investment opportunities (TQ)—the ratio of the market value of the firm to total assets

  7. (7)

    Cash flow (Cash)—the ratio of net cash flow from operating activities to total assets

  8. (8)

    Integration of two positions (Dual)—a dummy variable that equals 1 if a firm’s chairman and CEO are the same people and 0 otherwise

  9. (9)

    Size of the supervisory board (Supn)—the logarithm of the number of supervisors

  10. (10)

     Firm age (Age)—the logarithm of the years of establishment of a firm

  11. (11)

     Degree of regional financial development (FD)—dummy variable that equals 1 if the firm is located in a province with financial development higher than the sample median, and 0 otherwise

Empirical model

We use the following fixed effect model to examine the effects of green finance on CER of heavily polluting firms after controlling other factors that have been documented to affect CER:

$${CER}_{i,t+1}={\upalpha}_0+{\upalpha}_1{GF}_{tj}+{\upalpha X}_{i,t}+{\upsigma}_t+{\uplambda}_i+{\varepsilon}_{i,t}$$
(1)

where subscripts i, t, and j refer to firm, year, and province, respectively. Variable CER represents the CER scores from Hexun. To deal with endogeneity and ensure the reliability of the research conclusions, the lagged one-period CER score is used as the dependent variable. The independent variable GF is constructed by the development degree of green finance in each province. X denotes a set of characteristic variables of firms that have potential influence on CER. σ and λ denote time-fixed effects and firm-fixed effects, respectively. In other words, this study uses the two-way fixed effect panel model to exclude the interference of other exogenous factors and individual heterogeneity issues during the study period. ε is the error term. The standard errors are corrected for heteroskedasticity and clustered at the firm level.

Based on Equation (1), we added the interaction term between green finance and corporate property rights, as well as the interaction term between green finance and environmental regulation. The following model is employed to verify the moderating effect:

$${CER}_{i,t+1}={\upalpha}_0+{\upalpha}_1{GF}_{tj}+{\upalpha}_2{GF}_{tj} \times {MD}_{i,t}+{\upalpha}_3{MD}_{i,t}+{\upalpha X}_{i,t}+{\upsigma}_t+{\uplambda}_i+{\varepsilon}_{i,t}$$
(2)

Among them, MD is the moderating variable, which represents property rights and environmental regulation, respectively. We use the interaction coefficients to examine H2 and H3.

Descriptive statistics

Table 2 provides the descriptive statistics of the key variables for listed firms in the sample. It can be observed that the average value of CER is 2.019, and the median value of CER is 0, which are far below the maximum, indicating that CER of heavily polluting firms is relatively low.

Table 2 Descriptive statistics
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Empirical results

In this section, we first confirm the relationship between green finance and CER. Then, we examine the heterogeneity of the impact of green finance on CER. Finally, we use a series of methods for robustness analysis.

Baseline results

This part aims to give an exploratory analysis on the relationship between green finance and CER. A two-way fixed effect model is used for regression analysis. The results are shown in Table 3. The coefficient of GF captures the effect of green finance on CER. Column (1) does not include control variables, and only includes time-fixed effects and firm-fixed effects. The coefficient of GF is −9.621, which is significant at the level of 1%. Column (2) includes all control variables, and the results show that the coefficient of GF is negative and significant at the level of 1%. The above regression results imply that whether control variables are added, green finance directly and effectively reduces the environmental responsibility performance of heavily polluting firms, which is consistent with the theoretical expectations of this study, and Hypothesis 1 is supported.

Table 3 Baseline results
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Moderating effects

To test the micro effects of green finance under different property rights, we add the interaction term between green finance and corporate property rights to Eq. (2). The results are shown in column (1) of Table 4. The interaction coefficient between green finance and property rights is significant and negative. Besides, we have divided the full sample into state-owned firms sample and private firms sample according to property rights. The results in columns (2) and (3) in Table 4 show that in the subsample regression of private firms and state-owned firms, the coefficients of GF are −13.956 and −3.924, respectively, and the latter fails the significance test. The above results indicate that green finance has a significant inhibitory effect on the level of environmental responsibility of heavily polluting firms in the samples of private firms, but there is no significant inhibitory effect in the samples of state-owned firms. That is to say, private firms strengthen the negative relationship between green finance and CER, which validates Hypothesis 2.

Table 4 The results for the moderating effect of property rights and environmental regulation
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According to Eq. (2), we further explore the moderating role of environmental regulation. The results are presented in column (4) of Table 4. The interaction term between green finance and environmental regulation is significant and positive, indicating that green finance significantly improves the environmental responsibility performance of heavily polluting firms in areas with high environmental regulation intensity, so Hypothesis 3 is supported. Meanwhile, the median value of the environmental regulation index is used as the criterion; we divide the sample into high environmental regulation and low environmental regulation subsamples. Columns (5) and (6) report the subsample analysis of environmental regulation. In column (5), with the high ER subsample, the coefficient of GF is significantly and positively correlated with CER. Conversely, in column (6) with the low ER subsample, the coefficient of GF is significant and negative. The above results indicate that environmental regulation will increase corporate environmental efforts in the face of green finance.

Robustness test

To verify the robustness of the baseline regression results, the following tests are conducted.

Dealing with endogeneity

Although the fixed effect model applied in this paper can alleviate endogeneity to a certain extent, there may still be problems such as the omission of key variables and sample selection bias, which may lead to endogeneity problems. We employ two-stage least square (IV-2SLS) and propensity score matching (PSM) methods to solve these problems.

For the propensity score matching method, we use the heavily polluting firms as the experimental group and non-heavily polluting firms as the control group. The first step is to use the Probit model to estimate the propensity scores. The second step is to use the nearest neighbor matching method according to the propensity score to find the most similar controls for each heavily polluting firm. The final step is to estimate Eq. (1) only using paired sample. The results are shown in column (1) of Table 5. The coefficient of GF is significant and negative at the level of 1%, which further verifies the reliability of the conclusion in this paper.

Table 5 Robustness results
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For the two-stage least square method, we use the province level of Internet development (IV) as the instrumental variable, which is measured by the logarithm of the number of broadband Internet access ports and the Internet penetration in each province. Regional Internet development can increase information transparency and reduce the cost for commercial institutions to obtain corporate environmental information. The higher the level of Internet development, the faster the development of green finance. There is no evidence showing that the level of Internet development will directly affect corporate environmental responsibility. The first-stage results are shown in columns (2) and (4) of Table 5, which shows that the instrumental variables are significantly positively associated with the independent variable. And the second-stage results in columns (3) and (5) indicate that the coefficients of GF are negative and significant at the level of 1%. Moreover, the results of LM statistics, F statistics, and Sargan test show that there is no weak instrumental variable problem. Hence, the instrument selection in this paper is appropriate. Overall, any potential endogeneity problems are not pronounced enough to influence the conclusions of this paper.

Tobit

Considering that more than half of firms in our sample have zero CER value, the Tobit model is adopted. The results of Tobit regression are reported in column (6) of Table 5. The coefficient of GF is significant and negative. We can still conclude that the better the development of regional green finance, the lower the willingness of heavily polluting firms to assume environmental responsibility. The results are stable and support the research hypotheses.

Alternative indicator

Following Yao et al. (2021), the pollution attributes of firms are identified based on the pollution emission intensity of the industry. Specifically, we calculated the emission intensity of four pollutants for all industries (sulfur dioxide, industrial dust, industrial solid waste, and industrial wastewater) and divided the industries into heavily polluting industries and non-heavily polluting industries according to the median. Pollutant emission data come from China Environmental Statistics Yearbook. Then, the environmental responsibility score of firms in the reclassified heavily polluting industries is used as the new dependent variable to perform regression on Eq. (1). The regression results are shown in column (7) of Table 5. On the whole, the new results are consistent with our previous conclusions, which support the hypotheses in this study once again.

Mechanism analysis

Mediating effect model

According to the above analysis, it can be found that green finance does not improve the environmental responsibility level of heavily polluting firms, but reduces the environmental responsibility score of firms. In this context, to explore the specific reasons why green finance fails to meet expectations, this paper further examines the mechanism of the role of green finance in inhibiting CER. The previous theoretical analysis mentioned that firms need long-term and stable financial support to carry out environmental protection activities, while the implementation of green finance would form excessive financing constraints for heavily polluting firms, which may cause firms to reduce environmental investment and technological innovation activities due to lack of funds. In other words, financing constraints, environmental investment, and technological innovation may play a mediating effect on undertaking CER of heavily polluting firms inhibited by green finance. According to the stepwise regression method proposed by Baron and Kenny (1986), the following model is constructed to test the channel:

$${M}_{i,t}={\gamma}_0+{\gamma}_1{GF}_{t,j}+{\gamma X}_{i,t}+{\upsigma}_t+{\uplambda}_i+{\varepsilon}_{i,t}$$
(3)
$${CER}_{i,t+1}={\delta}_0+{\delta}_1{GF}_{t,j}+{\delta}_2{M}_{i,t}+{\delta X}_{i,t}+{\upsigma}_t+{\uplambda}_i+{\varepsilon}_{i,t}$$
(4)

Among them, M is the intermediate variable, representing financing constraints, environmental investment, and technological innovation. The intermediate effect is tested in three steps. First, we examine the total effect of green finance on CER with Equation (1), which does not include intermediary variables. Second, we identify the impact of green finance on the mediating variable by setting the mediating variable as the dependent variable and green finance as the independent variable, as seen in Equation (3). Finally, we determine the mediating effect between green finance and CER by adding the mediating variable to Equation (4). If the coefficients of γ1 and δ2 are both significant, an intermediate effect exists. If δ1 is not significant, there is a complete intermediate effect. If δ1 is significant and the absolute value is smaller than α1, it indicates that there is only partial intermediate effect. Moreover, the bootstrap method is introduced to ensure that the mechanism test results are more reliable (Zhao et al. 2010).

Financial constraints

Existing research shows that firms with large resources tend to make large and frequent contributions to society. Managers consider adding CER programs only when the firm has sufficient resources (Testa and D’Amato 2017). For example, Ortas et al. (2015) claimed that firms with poor financial status and economic environment are less likely to participate in environmental responsibility activities. Similarly, Falavigna and Ippoliti (2022) pointed out that firms with higher financial constraints spend less money on CER activities. In this context, the financing discrimination of green finance makes heavily polluting firms more restricted in obtaining external funds, reduces the scale of financing, increases the cost of financing, and further inhibits the motivation of firms to undertake environmental responsibility. Therefore, we predict that green finance can reduce CER performance by increasing the financial constraints of heavily polluting firms.

According to Kaplan and Zingales (1997), the Kaplan-Zingales (KZ) index is constructed to represent the degree of corporate financing constraints. Columns (1)–(3) of Table 6 report the parameter estimates from the stepwise regression model. The results in column (1) of Table 6 show that the coefficient of GF is significant, indicating that we can further examine the mediating effect. In column (2), the coefficient of GF is significant and positive, which indicates that green finance increases heavily polluting firms’ financing constraints. In column (3), the coefficient of KZ is significantly negative correlated with CER. When firms face financing constraints, heavily polluting firms will invest more funds and energy to maintain their production and operation activities and lack motivation to participate in environmental protection activities, thereby reducing the level of CER. Using the Bootstrap method, it is found that the 95% confidence interval of the mediation effect test does not contain 0, which implies the existence of a partial mediating effect of financial constraints between green finance and CER.

Table 6 Mechanism test results
Full size table

Environmental investment

From the close connection between financing and investment, financing is an important source of investment capital supply. Once firms are constrained by the financing of green finance, they will immediately adjust investment strategies (Ji et al. 2022; Wang et al. 2020). This may prompt firms to reduce non-productive investments and transfer funds used for environmental governance investments to productive investments. Many studies have demonstrated the positive impact of environmental investment on CER. A high level of environmental investment can encourage firms to increase more environmental protection equipment, which is conducive to improving corporate environmental performance. Thus, we predict that green finance reduces CER performance by reducing the enthusiasm of environmental investment of heavily polluting firms.

To explore this underlying mechanism by which green finance impacts CER performance through environmental investment, the ratio of environmental protection investment to total revenues is taken as the measurement index of environmental investment (EPI). It can be seen from columns (4) and (5) of Table 6 that environmental investment plays a mediating role in the influence mechanism of green finance on CER. Specifically, column (4) confirms that green finance significantly reduces the level of environmental investment of heavily polluting firms. Column (5) indicates that the regression coefficient of EPI on CER is significantly positive. Meanwhile, the coefficient of GF in columns (5) is smaller than that in column (1), and the Bootstrap test is used to verify that the mediating effect is robust. Hence, environmental investment is one of the channels for green finance to reduce the CER performance of heavily polluting firms. This result indicates that green finance prompts firms to change investment behavior and reduce the environmental investment level, thus affecting their CER performance.

Technological innovation

A large number of studies believed that technological innovation is an important driving force to improve corporate environmental performance. For instance, Levinson (2009) calculated the environmental impact of technological progress in the US manufacturing industry and found that technological progress reduces sulfur dioxide emissions in the USA. Alam et al. (2019) agreed that corporate R&D investment improves firms’ environmental performance. Technological innovation can improve CER scores by reducing green costs and pollution emissions. However, the implementation of the green finance policy leads to the reduction of capital supply of heavily polluting firms. Under various pressures, firms are unable to upgrade technology in the short term, which may weaken the efficiency of resource utilization and increase pollution emissions, ultimately reducing the environmental responsibility score of firms.

R&D investment is an important prerequisite to realizing technological innovation. Following Alam et al. (2019), we define a mediator variable, technical innovation (RD), which is measured by the ratio of corporate R&D expenditure to total revenues. Columns (6) and (7) of Table 6 show the results. It can be seen that the coefficients of GF and RD are both significant, so the intermediary variables are path-dependent. Therefore, technological innovation is one of the channels that green finance influences CER.

Conclusions and recommendations

Conclusions

Corporate initiatives to take responsibility for environmental governance are critical to achieving China’s dual carbon goals of peak carbon by 2030 and carbon neutrality by 2060. This paper uses a sample of 7593 observations from A-share listed firms in the heavy-pollution industries to explore the impact of green finance on CER, and further examines the moderating effect of property rights and environmental regulation on the nexus between them. The results show that (1) green finance has a significant negative effect on the CER of heavily polluting firms. The higher the development of regional green finance, the lower the enthusiasm of heavily polluting firms to undertake environmental responsibility. The finding is robust when tested with a number of alternative estimation approaches and instrumental variables. (2) Property rights and environmental regulation have moderating effects on the relationship between green finance and CER. The negative relation between green finance and CER is concentrated among private firms and firms in provinces with low environmental regulation intensity. (3) Mechanism analysis indicates that financial constraints, environmental investment, and technical innovation play a crucial linkage role between green finance and CER.

Recommendations

There are several practical recommendations from our findings, as shown below. First, the government and commercial institutions need to improve the implementation process of green finance policy. Results of this paper show that green finance does not promote heavily polluting firms to undertake environmental responsibility. At present, in the process of implementing the green finance policy, corporate financing thresholds are set according to industry attributes, which leads to a veto attitude of commercial institutions to heavily polluting firms, and prompts heavily polluting firms have a negative attitude toward environmental governance. Therefore, in the follow-up implementation of the green finance policy, the government needs to improve the operability of green finance. In addition to giving play to the restraining effect of green finance on polluting industries, an incentive mechanism should be established to support polluting firms to participate in environmental governance. And the environmental information and environmental improvement status of firms should be taken as the basis of credit financing instead of industry attributes. Meanwhile, commercial institutions should combine the specific characteristics of the industry to develop personalized green products and services for different types of firms. Second, in the process of implementing green finance policy, the actual situation of regions and firms should be considered for a reasonable layout, and the asymmetry of policy effects should be eliminated as soon as possible. Our results show that green finance is only binding for private firms and firms with low levels of external environmental regulation. Thus, the government should consider the characteristics of corporate differences and formulate specific environmental control measures and evaluation indicators for private firms. Meanwhile, it is also necessary to optimize the construction of environmental laws and regulations related to green finance to promote the synergistic effect of green finance and environmental regulation, and ultimately promote the sustainable development of society. Third, our research finds that financing and investment are key channels for green finance to work. When formulating and implementing green finance, governments and commercial institutions should fully consider the role of financing and investment, and vigorously guide and standardize corporate green behavior through these two paths. Finally, CER is an effective way for firms to transmit green information to relevant stakeholders. Firms undertaking environmental responsibility can win public recognition and effectively improve market competitiveness. Therefore, corporate management should not unilaterally regard environmental responsibility as a burden of the firm. Instead, they should establish a correct view of environmental responsibility and incorporate environmental governance thinking into corporate management culture and business decisions.

Limitations and future studies

The limitations of this paper mainly include the following aspects. First, green finance includes many aspects, such as green credit, green bonds, and green insurance. Due to the incomplete information disclosure, it is difficult to collect the above-mentioned information. Our study is unable to identify the effects of different green finance tools on corporate environmental responsibility. With the advancement of academic research, relevant data can be more easily obtained, and the impact of different green finance tools on environmental responsibility can be concerned in future research. Second, sustainable development is a comprehensive term encompassing economic, environmental, and social circles. This paper mainly focuses on corporate environmental responsibility performance, and future studies can further consider the impact of green finance on corporate environmental behavior in other dimensions to enrich research results. Finally, since we focus on Chinese firms, the generalizability of our results to other contexts is quite difficult because cross-cultural differences might have a significant impact on environmental awareness and behavior. Therefore, it is necessary to involve more firms from different countries in future research.