The impact of green credit policies on debt financing for heavily polluting enterprises

: On October 16, 2022, in the report of the 20th National Congress of the Communist Party of China, China's dual carbon goal was once again emphasized, and China would actively and steadily promote carbon peaking and carbon neutrality. This paper selects the heavy polluting enterprises of A-share listed companies(2007-2021) as the research samples, using the non-heavy polluting enterprises as the control group, and applying the double difference model to explore the effect of green credit policy on the debt financing scale and debt financing cost of heavy polluting enterprises. Empirical tests show that, on the one hand, the promulgation of the Green Credit Guidelines significantly reduces the financing scale of heavily polluting enterprises and increases the financing cost of their liabilities. On the other hand, compared with non-state-owned enterprises, the green credit policy has a greater impact on the financing cost of state-owned enterprises' liabilities.


Research Background
The report of the 20th National Congress of the Communist Party of China proposed to "actively and steadily promote carbon peaking and carbon neutrality". "Green credit" provides a guarantee for the construction of a "two-type" society and the strategy of sustainable development.

Research Methods
Firstly, the evaluation index is established, and the analytic hierarchy method is used to analyze the weight of each evaluation factor. Secondly, a set of evaluation system combining internal control and external control was constructed based on China's national conditions; Finally, STATA15 is used for descriptive statistics, correlation tests and model tests.

Research hypotheses on the impact of green credit policies on debt financing
Compared with non-heavy polluting enterprises, heavy polluting enterprises are more likely to be focused by all sectors of society, resulting in more serious results and greater negative impacts. Based on the above analysis, these hypotheses is proposed: H1: After the promulgation of the Guidelines, the scale of debt financing for heavy polluting enterprises would decrease significantly compared to non-heavy polluting enterprises. H2: After the promulgation of the Guidelines, the debt financing costs of heavy polluting enterprises would increase significantly compared with non-heavy polluting enterprises.
H3: After the promulgation of the Guidelines, it is more obvious that state-owned enterprises are more constrained by debt financing than non-state-owned enterprises.

Model Building
In this study, heavy polluting enterprises are used as the experimental group and non-heavy polluting enterprises as the control group. Treated=1 represents heavy polluting enterprises, and Treated=0 represents non-heavy polluting enterprises. Time=1 represents the year after the implementation of the green credit policy, that is, 2012-2021, and Time=0 represents the year before the implementation of the green credit, that is, before 2012. This paper draws on the research methods of Zhou Li'an and Chen Ye (2005) [1] to set up the following model. Among them, Loan it and Cost ij are the t-year debt financing scale and debt financing cost of the ith enterprise; Treated i is a grouped dummy variable; Time t is a time dummy variable; Treated i Time t is a double differential variable; X i , t−1 are the firm control variables (considering the lag effect), and λ i is the individual fixed effect; v t is the time-fixed effect; ε it is a random perturbation term.

3.2.1, Sample data
In this paper, the data was taken from the CSMAR and Wind databases in which the heavily polluting listed enterprises in A-shares from 2007 to 2021 were selected as the the experimental group, and the rest as the control group1 .
Finally, 7790 listed companies were obtained from databases and acted as the total observation samples, including 3490 in the experimental group and 4300 data in the control group. In order to remove outlier interference, continuous variables are tailed by Winsorize in both the upper and lower layers. Data processing is carried out using STATA15.0 software.

, indicator selection
The selected explanatory, explanatory, and control variables are shown in the following table The value of state-owned enterprises is 1, otherwise it is 0  Table 4-2 shows that the average debt financing scale and asset-liability ratio are 0.5413 and 0.8952, respectively, indicating that debt financing is still the main way for enterprises to raise money. The maximum value of debt financing scale is 3.7762 and the minimum value is 0.0321, which indicates the difference is significant, and the standard deviation is 0.5800, showing that the significant difference in debt financing scale between enterprises. It can be seen from Figure 1 that before the issuance of the Green Credit Guidelines, the trend of debt financing scale and debt financing cost in the experimental group and the control group was basically the same and was in line with the parallel trend assumption.

3.4.1, the impact of green credit policies on the scale of corporate debt financing
Under the premise that the experimental group and the control group meet the parallel trend assumption, this part further uses the double difference model to perform regression analysis on the panel data, and the regression results for H1 are shown in Table 3. r-square 0.2645 Note: The t-test values in parentheses, *, **, *** indicate that they are significant at the 10%, 5%, and 1% levels, respectively.

3.4.2, the impact of green credit policies on corporate debt financing costs
The impact of green credit policies on the debt financing costs of heavily polluting enterprises is discussed below, and the double difference test is carried out on H2, and the regression results are shown in Table 4. Observations control Year control r-square 0.1181 Note: The t-test values in parentheses, *, **, *** indicate that they are significant at the 10%, 5%, and 1% levels, respectively.
The coefficient of interaction terms in this table is 0.0069 and the value of t is 2.29, indicating that there is a significant positive correlation between the two. Compared with non-heavily polluting enterprises, the green credit policy significantly increases the debt financing cost of heavily polluting enterprises, validating H2.

3.4.3, The impact of green credit policies on corporate debt financing under the difference in property rights
According to property rights, the total samples were divided into state-owned enterprises and non-state-owned enterprises for double differential analysis, and the regression results are shown in Table 5 and Table 6. Table 5 Empirical analysis results of the impact of green credit on debt financing of state-owned enterprises   r-square 0.3931 0.1485 Note: The t-test values in parentheses, *, **, *** indicate that they are significant at the 10%, 5%, and 1% levels, respectively.

Conclusions
First, from the perspective of the scale of debt financing, after the introduction of the "green credit" guidelines, the proportion of debt financing for heavily polluting enterprises is significantly lower than that of non-heavy polluting enterprises.
Second, from the perspective of debt financing costs, compared with companies with lighter environmental pollution, green credit policies significantly increase the debt financing costs of heavily polluting enterprises.
Third, under different property rights attributes, whether it is the scale of debt financing or the cost of debt financing, green credit policies have a greater impact on state-owned enterprises.

Policy Recommendations
First, from the point of view of the state, it is necessary to establish a green credit system that suits China's national conditions. Improve China's green finance legal system and corresponding supporting regulations.
Second, from the company's perspective, it should take corresponding social responsibilities while maximizing its benefits.
Third, from the perspective of the banking industry, first of all, financial institutions should make banks the main body of green credit implementation in order to better fulfill their social responsibilities; Furthermore, it is necessary to continuously develop various forms of green credit loans to provide favorable conditions for corporate pollution control projects and transformation of development methods.
Finally, from the perspective of internationalization, it is necessary to actively carry out green economy management on a global scale, actively respond to the requirements of "green" development, and ensure the optimal allocation of credit to the greatest extent.