Based on the event of “green credit guidelines” issued by CBRC in 2012, this paper collects the panel data of China’s 3272 listed companies from 2008 to 2019, constructs a difference-in-difference model, in which the high-polluting enterprises are regarded as the experimental group and other enterprises as the control group. We collect data from several resources. First, data about enterprise environmental information disclosure is collected manually from corporate social responsibility reports, environmental reports, sustainable development reports, and other aspects. Second, data about green innovation are from the Chinese Research Data Services Platform. Third, other data are gathered from China Stock Market & Accounting Research Database. To enable reasonable precision, we exclude firms with special treatment, firms that belong to financial industries, and firms with missing values. All the continuous variables are winsorized at 1% and 99% to exclude the outlier effect.
Empirical model
We use the following regression model to capture the effect of green credit policy on the green strategic behavior choice of high-polluting enterprises:
![](data:image/png;base64,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)
Among them, Gpat denotes the number of green invention patent applications of enterprises. Post is the policy dummy variable, Post is equal to 1 when the year is in 2012, otherwise, the value is 0; Treat represents group dummy variables, which value is 1 when the firm is in the experimental group, otherwise, the value is 0 (as the enterprises in high-polluting industries are directly affected by the green credit guidelines, they are treated as experimental group and non-high-polluting enterprises as control group). Treat×Post represents a difference-in-difference variable and α1 represents the impact of green credit on the experimental group. X denotes a set of characteristic variables of enterprises. σ and λ denote time fixed effects and firm fixed effects, respectively. In other words, this study uses the two-way fixed-effects panel model to implement the DID design, which can exclude the interference of other exogenous factors and individual firm heterogeneity issues during the study period. ε is the error term.
Meanwhile, to examine the dynamic policy effects on firms’ green behavior choices after the implementation of “Green Credit Guidelines”, the following extended difference-in-difference model is constructed :
![](data:image/png;base64,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)
In model (2), PostYear is a dummy variable for each year after the introduction of “ Green Credit Guidelines”, and PostYear×Treat is a new difference-in-difference variable to test the dynamic effects of the green credit policy.
Empirical results
Descriptive statistics
This paper is mainly based on data of 3272 listed enterprises from 2008 to 2019, and the summary of the main variables is reported in Table 1.
Table 1 Descriptive statistics
Variables
|
N
|
Mean
|
Min
|
Max
|
S.D.
|
Treat
|
20168
|
0.291
|
0
|
1
|
0.454
|
Gpat
|
20168
|
0.926
|
0
|
4.489
|
1.179
|
Gwl
|
20168
|
0.713
|
0.167
|
0.972
|
0.214
|
Size
|
20168
|
21.481
|
18.309
|
25.344
|
1.413
|
State
|
20168
|
0.391
|
0
|
1
|
0.488
|
Top
|
20168
|
0.345
|
0.032
|
0.742
|
0.149
|
Roa
|
20168
|
0.038
|
-0.235
|
0.195
|
0.057
|
Lev
|
20168
|
0.432
|
0.056
|
0.882
|
0.205
|
Tobin Q
|
20168
|
0.582
|
-0.131
|
2.134
|
0.476
|
Tangible
|
20168
|
0.218
|
0.002
|
0.709
|
0.164
|
Cash
|
20168
|
0.046
|
-0.165
|
0.241
|
0.070
|
Share
|
20168
|
0.125
|
0
|
0.676
|
0.192
|
Dual
|
20168
|
0.258
|
0
|
1
|
0.437
|
Supn
|
20168
|
1.498
|
0
|
2.565
|
0.206
|
Age
|
20168
|
1.984
|
0
|
3.219
|
0.926
|
Baseline results
This paper explores the green strategy choices of firms after being affected by green credit policy in terms of internal green technology innovation and external environmental information disclosure. High-polluting enterprises are regarded as the experimental group and other enterprises are regarded as the control group. A vital prerequisite of the DID model is that the experimental and control groups have similar trends before the policy shock (Bertrand et al., 2004). Referring to Wang et al. (2020)’s research, this study employed the event-study method to verify the parallel trend. The results of the parallel trend test in this paper are shown in columns (1) and (2) of Table 2. Before1, Before2, Before3, Before4 represent before the implementation of the green credit policy, high-polluting enterprises take the value of 1, otherwise, the value is 0. Current represents high-polluting enterprises and belongs to the year when the policy was implemented. After1, After2, After3, After4 represent after the implementation of the green credit policy, high-polluting enterprises take the value of 1, otherwise, the value is 0. When the dependent variable is Gpat, the regression results of Before4, Before3, and Before2 are not significant, the coefficient of Current is significantly negative, and the coefficients of After2 and After4 are significantly positive, indicating that the evolution of green innovation of the experimental group and the control group was almost the same before the policy implementation, but the gap between the experimental and control groups widened rapidly after the policy implementation. Similarly, when the dependent variable is Gwl, the regression results of Before1 to Before4 are all insignificant, Current, After1, and After2 are significantly positive, but After4 is significantly negative. All of the above results reflect that the experimental and control groups which meet the parallel trend assumption and the regression results in this study are reasonable to some degree.
Columns (3) and (4) of Table 2 show the DID regression results after controlling for firm and time fixed effects. The independent variable is Gpat in column (3) of Table 2, the coefficient of the Post×Treat is 0.1, which is significant at the level of 1%. This indicates that compared with other enterprises, high-polluting enterprises have increased green innovation after the implementation of the green credit policy, which confirms hypothesis 2. The independent variable is Gwl in column (4) of Table 2, the coefficient of the Post×Treat is -0.006, but not significant. This result does not confirm hypothesis 1.
Table 2 Baseline results
|
(1)
|
(2)
|
(3)
|
(4)
|
|
Gpat
|
Gwl
|
Gpat
|
Gwl
|
Post×Treat
|
|
|
0.100***
|
-0.006
|
|
|
|
(3.082)
|
(-0.784)
|
Before4
|
-0.168
|
0.015
|
|
|
|
(-1.190)
|
(0.530)
|
|
|
Before3
|
-0.065
|
0.025
|
|
|
|
(-1.010)
|
(1.628)
|
|
|
Before2
|
-0.076
|
0.013
|
|
|
|
(-1.244)
|
(0.923)
|
|
|
Before1
|
-0.142**
|
0.021
|
|
|
|
(-2.215)
|
(1.353)
|
|
|
Current
|
-0.086*
|
0.041***
|
|
|
|
(-1.938)
|
(4.209)
|
|
|
After1
|
-0.051
|
0.044***
|
|
|
|
(-1.156)
|
(4.563)
|
|
|
After2
|
0.254***
|
0.034***
|
|
|
|
(4.399)
|
(2.911)
|
|
|
After3
|
-0.017
|
0.008
|
|
|
|
(-0.387)
|
(0.960)
|
|
|
After4
|
0.065*
|
-0.015**
|
|
|
|
(1.790)
|
(-1.965)
|
|
|
Size
|
0.227***
|
-0.016***
|
0.225***
|
-0.015***
|
|
(10.544)
|
(-4.350)
|
(18.748)
|
(-5.688)
|
State
|
0.117**
|
0.015
|
0.117***
|
0.016*
|
|
(2.047)
|
(1.441)
|
(2.937)
|
(1.847)
|
Top
|
-0.138
|
-0.013
|
-0.136*
|
-0.015
|
|
(-1.221)
|
(-0.650)
|
(-1.776)
|
(-0.883)
|
Roa
|
-0.093
|
-0.027
|
-0.080
|
-0.036
|
|
(-0.661)
|
(-0.937)
|
(-0.648)
|
(-1.331)
|
Lev
|
0.080
|
0.020
|
0.080
|
0.023*
|
|
(0.982)
|
(1.270)
|
(1.416)
|
(1.857)
|
Tobin Q
|
-0.052**
|
0.013**
|
-0.056***
|
0.014***
|
|
(-2.096)
|
(2.508)
|
(-2.802)
|
(3.170)
|
Tangible
|
0.068
|
-0.027
|
0.079
|
-0.034**
|
|
(0.623)
|
(-1.323)
|
(1.124)
|
(-2.244)
|
Cash
|
-0.179*
|
-0.006
|
-0.168*
|
-0.008
|
|
(-1.911)
|
(-0.316)
|
(-1.872)
|
(-0.397)
|
Share
|
-0.167
|
-0.024
|
-0.164**
|
-0.027
|
|
(-1.407)
|
(-1.058)
|
(-2.041)
|
(-1.528)
|
Dual
|
0.006
|
0.002
|
0.006
|
0.003
|
|
(0.243)
|
(0.526)
|
(0.337)
|
(0.642)
|
Supn
|
0.183**
|
-0.013
|
0.180***
|
-0.011
|
|
(1.984)
|
(-0.781)
|
(2.875)
|
(-0.797)
|
Age
|
0.012
|
-0.008
|
0.013
|
-0.008*
|
|
(0.450)
|
(-1.496)
|
(0.648)
|
(-1.947)
|
Firm
|
Yes
|
Yes
|
Yes
|
Yes
|
Year
|
Yes
|
Yes
|
Yes
|
Yes
|
Cons
|
-4.787***
|
1.136***
|
-4.800***
|
1.112***
|
|
(-10.035)
|
(14.000)
|
(-18.149)
|
(19.335)
|
N
|
20168
|
20168
|
20168
|
20168
|
Adj. R2
|
0.218
|
0.123
|
0.216
|
0.119
|
Note: ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. T-statistics are reported in parentheses and are based on robust standard errors.
Dynamic effects
The promulgation of the “Green Credit Guidelines” in 2012 marked the formal entry of financial instruments to regulate the environmental behavior of enterprises at the policy level, but the actual effect of the policy may have a time lag. To further clarify how the policy affects the process of enterprise green strategy choice with time evolution, this paper includes dummy variables of policy years and uses model (2) to test the dynamic effect of the policy.
Table 3 shows that there is a significant time difference between green credit policy and green innovation and green-washing behavior of high-polluting enterprises. In terms of the dynamic impact of green credit policy on green innovation, the results are presented in column (1) of Table 3. The coefficients of PostYear2014×Treat, PostYear2015×Treat, PostYear2016×Treat, PostYear2018×Treat, and PostYear2019×Treat are 0.352, 0.081, 0.163, 0.1, and 0.142, respectively, and both significance at least at the level of 10%. This result indicates that the level of green innovation of high-polluting enterprises increased significantly from 2014 to 2019 after the implementation of the green credit policy. In 2012 and 2013, the coefficient of PostYear×Treat is not significant. The above results reveal that the green credit policy has some continuity and lag on enhancing the green innovation activities of high-polluting enterprises.
Column (2) shows the dynamic influence of green credit policy on enterprises’ green-washing behavior. The coefficient of the interaction term PostYear2012×Treat and PostYear2013×Treat are positive at the 1% level of significance, the coefficients interaction term PostYear2014×Treat and PostYear2015×Treat are not significant. However, the coefficient of PostYear2016×Treat, PostYear2018×Treat, and PostYear2019×Treat are significantly negative at least at the level of 5%. This reveals that although the overall effect of green credit policy on corporate green-washing behavior is not statistically significant, in terms of the time dimension, the first two years after the implementation of the policy significantly induces high-polluting enterprises to choose green-washing behavior, over time corporate green-washing behavior will be detected and the benefits of green-washing to firms are much lower than the costs of environmental regulation, which in turn inhibits the green-washing behavior of high-polluting enterprises.
The reason for the above-mentioned results is that, at the early stage of the implementation of green credit policy, high marginal of enterprise green innovation and insufficient banking supervision lead to high-polluting enterprises are more inclined to adopt green-washing with lower cost to meet the requirements of legality. However, with the development of time, the environmental information of enterprises is easier monitored and observed, and the supervision of enterprises by commercial banks is becoming more severe. When an enterprise’s green-washing is exposed, it will induce criticisms and negative evaluations and hinder the enterprise’s survival and development (Testa et al., 2018; Leonidou and Skarmeas, 2017). Symbolic green-washing behavior is ineffective and cannot essentially improve the environmental condition of enterprises. Therefore, from the long-term development perspective, it is more in line with the cost-benefit principle to carry out green innovation.
Table 3 Results of dynamic effects test
|
(1)
|
(2)
|
|
Gpat
|
Gwl
|
PostYear2012×Treat
|
0.011
|
0.022**
|
|
(0.253)
|
(2.330)
|
PostYear2013×Treat
|
0.046
|
0.025***
|
|
(1.046)
|
(2.588)
|
PostYear2014×Treat
|
0.352***
|
0.015
|
|
(6.231)
|
(1.232)
|
PostYear2015×Treat
|
0.081*
|
-0.011
|
|
(1.746)
|
(-1.078)
|
PostYear2016×Treat
|
0.163***
|
-0.034***
|
|
(3.654)
|
(-3.559)
|
PostYear2017×Treat
|
0.049
|
-0.011
|
|
(1.158)
|
(-1.212)
|
PostYear2018×Treat
|
0.100**
|
-0.020**
|
|
(2.427)
|
(-2.182)
|
PostYear2019×Treat
|
0.142***
|
-0.027***
|
|
(3.438)
|
(-2.964)
|
Size
|
0.227***
|
-0.016***
|
|
(18.884)
|
(-6.174)
|
State
|
0.119***
|
0.015*
|
|
(2.983)
|
(1.725)
|
Top
|
-0.139*
|
-0.013
|
|
(-1.807)
|
(-0.791)
|
Roa
|
-0.097
|
-0.026
|
|
(-0.790)
|
(-0.960)
|
Lev
|
0.082
|
0.019
|
|
(1.455)
|
(1.560)
|
Tobin Q
|
-0.051**
|
0.013***
|
|
(-2.544)
|
(2.979)
|
Tangible
|
0.068
|
-0.027*
|
|
(0.971)
|
(-1.769)
|
Cash
|
-0.180**
|
-0.007
|
|
(-2.002)
|
(-0.351)
|
Share
|
-0.168**
|
-0.024
|
|
(-2.092)
|
(-1.397)
|
Dual
|
0.006
|
0.002
|
|
(0.327)
|
(0.624)
|
Supn
|
0.183***
|
-0.014
|
|
(2.926)
|
(-1.000)
|
Age
|
0.012
|
-0.008*
|
|
(0.587)
|
(-1.885)
|
Firm
|
Yes
|
Yes
|
Year
|
Yes
|
Yes
|
Cons
|
-4.838***
|
1.141***
|
|
(-18.277)
|
(19.832)
|
N
|
20168
|
20168
|
Adj. R2
|
0.218
|
0.123
|
Note: ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. T-statistics are reported in parentheses and are based on robust standard errors.
Analysis of heterogeneity
Green patent type
This paper subdivides the types of green patents into green invention patents and green utility patents according to patent attributes, joint applications green patents and independent applications green patents according to patent application categories. The results are shown in Panel A of Table 4. Columns (1) and (2) show that the regression results of Post×Treat are both significantly positive, indicating that the implementation of the green credit policy significantly contributes to the increase in the number of independent and joint green patent applications by firms. The results in columns (3) and (4) show that after the implementation of the green credit policy, the number of green invention patents of enterprises did not increase significantly (t=-0.362, p>0.1), while the number of green utility patent applications increased significantly (t=3.705, p<0.01). In other words, the green credit policy only promotes the increase of the quantity of green innovation of enterprises but lacks the improvement of the quality of innovation.
Intensity of environmental regulation
Environmental policies vary widely among different regions in China. The effectiveness of the green credit policy requires the support of relevant laws and regulations, and environmental regulation, as an important institutional arrangement to solve the current environmental pollution problem, will certainly significantly affect the implementation effect of green credit. Hence, the heterogeneity of regional environmental regulation intensity on green credit and enterprise green activities should be analyzed. Based on the discharge of industrial wastewater, industrial smoke, and industrial sulfur dioxide, we calculated the environmental regulation index of different regions using the entropy method. The median value of the environmental regulation index of each province is used as the criterion to divide the whole sample into two groups: high environmental regulation and low environmental regulation. Panel B of Table 4 reported the heterogeneous impact of environmental regulations. In the high environmental regulation group, the green credit policy promotes green innovation of enterprises and reduces green-washing behavior. In contrast, enterprises in the low environmental regulation group prefer the behavior of green-washing environmental information. This suggests that the higher the intensity of regional environmental regulation, the more pronounced the green incentive effect of the green credit policy.
Spatial heterogeneity
The green credit policy through the financial market sends signals to enterprises, so the green credit policy would be affected by the development level of the regional financial. Based on the per capita GDP of provinces where the listed company is located, we further classified the sample as economically developed regions if its provinces with an index value higher than the average value and as economically underdeveloped regions otherwise. The results in columns (1) and (2) of Panel C show that green credit policy has a positive effect on green innovation of enterprises in the economically developed regions, and has a negative effect on green-washing behavior of enterprises. Columns (3) and (4) show that green credit policy has no influence on the innovation behavior of enterprises in economically underdeveloped regions, but strengthens the green-washing behavior of enterprises.
Alternative financing channel
Trade credit is a well-known alternative financing channel for enterprises facing credit financing constraints, the enterprise uses the trade credit after the commercial bank financing became unavailable (Chen et al., 2019; Wen et al., 2021). Thus, we believe that the trade credit will significantly affect the forms of green activities after the implementation of the green credit policy, which needs further explored in depth. Referring to Wen et al. (2021), trade credit is measured by the ratio of accounts payable to the total assets of a firm. We further classified firms as having a high or a low trade credit, where firms were classified as high if their index value was above the median of industry years and as low otherwise. Panel D summarized the grouped test results of trade credit for enterprises. The results show that green credit policy has a direct and effective effect on green innovation only in enterprises with low trade credit.
Table 4. Results of heterogeneous effects
Panel A Heterogeneity test of green patent type
|
|
(1)
|
(2)
|
(3)
|
(4)
|
|
joint applications green patents
|
independent applications green patents
|
green invention patents
|
green utility
patents
|
|
Gpat
|
Gpat
|
Gpat
|
Gpat
|
Post×Treat
|
0.086***
|
0.041**
|
-0.010
|
0.105***
|
|
(2.648)
|
(1.970)
|
(-0.362)
|
(3.705)
|
Controls
|
Yes
|
Yes
|
Yes
|
Yes
|
Cons
|
-4.552***
|
-1.474***
|
-3.816***
|
-3.502***
|
|
(-17.233)
|
(-8.799)
|
(-16.705)
|
(-15.250)
|
N
|
20168
|
20168
|
20168
|
20168
|
Adj. R2
|
0.170
|
0.165
|
0.198
|
0.066
|
Panel B Heterogeneity test of environmental regulation intensity
|
|
(1)
|
(2)
|
(3)
|
(4)
|
|
High environmental regulation
|
Low environmental regulation
|
|
Gpat
|
Gwl
|
Gpat
|
Gwl
|
Post×Treat
|
0.104**
|
-0.027***
|
0.067
|
0.025**
|
|
(2.194)
|
(-2.699)
|
(1.443)
|
(2.344)
|
Controls
|
Yes
|
Yes
|
Yes
|
Yes
|
Cons
|
-5.179***
|
1.188***
|
-4.366***
|
1.052***
|
|
(-13.474)
|
(14.631)
|
(-11.620)
|
(12.319)
|
N
|
10883
|
10883
|
9285
|
9285
|
Adj. R2
|
0.227
|
0.122
|
0.199
|
0.112
|
Panel C Heterogeneity test of space
|
|
(1)
|
(2)
|
(3)
|
(4)
|
|
Economically developed regions
|
Economically undeveloped regions
|
|
Gpat
|
Gwl
|
Gpat
|
Gwl
|
Post×Treat
|
0.113***
|
-0.019**
|
0.083
|
0.023**
|
|
(2.632)
|
(-2.118)
|
(1.628)
|
(1.969)
|
Controls
|
Yes
|
Yes
|
Yes
|
Yes
|
Cons
|
-4.764***
|
1.179***
|
-5.376***
|
1.227***
|
|
(-12.207)
|
(14.222)
|
(-10.811)
|
(10.606)
|
N
|
13779
|
13779
|
6352
|
6352
|
Adj. R2
|
0.209
|
0.103
|
0.231
|
0.138
|
Panel D Heterogeneity test for alternative financing channel
|
|
(1)
|
(2)
|
(3)
|
(4)
|
|
High trade credit
|
Low trade credit
|
|
Gpat
|
Gwl
|
Gpat
|
Gwl
|
Post×Treat
|
0.000
|
-0.009
|
0.149***
|
-0.006
|
|
(0.008)
|
(-0.825)
|
(3.415)
|
(-0.655)
|
Controls
|
Yes
|
Yes
|
Yes
|
Yes
|
Cons
|
-4.942***
|
1.054***
|
-4.784***
|
1.223***
|
|
(-11.442)
|
(11.498)
|
(-12.331)
|
(14.032)
|
N
|
10050
|
10050
|
10118
|
10118
|
Adj. R2
|
0.202
|
0.117
|
0.209
|
0.112
|
Note: ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. T-statistics are reported in parentheses and are based on robust standard errors.
Analysis of economic consequences: Why make such a green choice
The previous findings suggest that green credit policy significantly increases enterprises’ green innovation but has no significant effect on green-washing on the whole. From the perspective of time, enterprises will choose green-washing at the early stage of policy implementation, and then reduce green-washing behavior and increase green innovation. We argued that this may be firms’ green-washing behavior will be identified over time and bring negative impact to the firm, while the accumulation of innovative output can add competitive advantage to the firm, which eventually motivates the firm to choose innovative behavior and reduce green-washing behavior. To detect the mechanism, we try to verify how the performance of high-polluting enterprises is affected by a series of green activities under the pressure of green credit policy. Three indicators of corporate social performance, environmental performance, and financial performance are selected to examine the impact of the green credit policy on the economic consequences of different green behaviors of heavy polluters using the following model:
![](data:image/png;base64,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)
Social Performance
CSR denotes corporate social responsibility score, which is measured by the social responsibility score published by the Hexun website. The result is shown in Panel A of Table 5. The coefficients of Gpat×Treat and Gwl×Treat are not significant when the social responsibility of the firm lagged one period is used as the dependent variable. When the social responsibility of the firm lagged two periods as the dependent variable, the coefficient of Gpat×Treat is significant and positive at the level of 10%, the coefficient of Gwl×Treat is significant and negative. The above results show that the “Green Credit Guidelines” have significantly improved CSR by prompting high-polluting enterprises to engage in green innovation activities, but this effect has a lagged effect, while green-washing behavior adopted by firms does not improve CSR.
Environmental Performance
EP represents the environmental performance of the firm. Emission fee per unit of operating revenue is used as environmental performance proxy variable, and a smaller value means the better environmental performance of enterprises. The results of Panel B show that neither of the two green behaviors of firms has an impact on the environmental performance in the latter period after the implementation of the green credit policy. In terms of environmental performance with two lags, the regression coefficient of Gpat×Treat is significantly negative, reflecting that after the implementation of the policy, high-polluting enterprises increase the output of green innovation can reduce corporate emission fees. The coefficient of Gwl×Treat is significantly positive, indicating that the enterprise’s behavior of green-washing environmental information will not improve the environmental performance of the enterprise. Instead, it will lead the government to increase the punishment of enterprise pollution due to violations.
Financial Performance
ROE denotes the financial performance of a company. Empirical results are shown in Panel C of Table 5. The coefficient of Gpat×Treat is significantly positive, indicating that increasing green innovation can improve the financial performance of heavy pollution enterprises. The coefficient of Gwl×Treat is significantly negative, reflecting that the adoption of green-washing behavior by firms significantly reduces their financial performance.
Table 5. Results of economic consequences
Panel A Consequences test for corporate social responsibility performance
|
|
(1)
|
(2)
|
(3)
|
(4)
|
|
CSRt+1
|
CSRt+2
|
CSRt+1
|
CSRt+2
|
Gpat×Treat
|
-0.002
|
0.022*
|
|
|
|
(-0.209)
|
(1.826)
|
|
|
Gwl×Treat
|
|
|
0.078
|
-0.097*
|
|
|
|
(1.402)
|
(-1.696)
|
Controls
|
Yes
|
Yes
|
Yes
|
Yes
|
Cons
|
0.268
|
1.092***
|
0.441**
|
1.143***
|
|
(1.319)
|
(5.113)
|
(2.166)
|
(5.333)
|
N
|
17737
|
14716
|
17737
|
14716
|
Adj. R2
|
0.150
|
0.106
|
0.150
|
0.105
|
Panel B Consequences test for environmental performance
|
|
(1)
|
(2)
|
(3)
|
(4)
|
|
EPt+1
|
EPt+2
|
EPt+1
|
EPt+2
|
Gpat×Treat
|
-0.009
|
-0.042*
|
|
|
|
(-0.289)
|
(-1.678)
|
|
|
Gwl×Treat
|
|
|
0.254
|
0.382***
|
|
|
|
(1.567)
|
(3.108)
|
Controls
|
Yes
|
Yes
|
Yes
|
Yes
|
Cons
|
0.372
|
-0.876*
|
0.312
|
-0.867**
|
|
(0.688)
|
(-1.915)
|
(0.578)
|
(-1.978)
|
N
|
13067
|
10066
|
13067
|
10066
|
Adj. R2
|
0.007
|
0.008
|
0.007
|
0.008
|
Panel C Consequences test for financial performance
|
|
(1)
|
(2)
|
(3)
|
(4)
|
|
ROEt+1
|
ROEt+2
|
ROEt+1
|
ROEt+2
|
Gpat×Treat
|
0.018**
|
0.015**
|
|
|
|
(2.447)
|
(2.269)
|
|
|
Gwl×Treat
|
|
|
-0.093***
|
0.004
|
|
|
|
(-2.600)
|
(0.117)
|
Controls
|
Yes
|
Yes
|
Yes
|
Yes
|
Cons
|
0.087
|
0.912***
|
0.125
|
0.927***
|
|
(0.664)
|
(7.782)
|
(0.952)
|
(7.873)
|
N
|
17731
|
13293
|
17731
|
13293
|
Adj. R2
|
0.017
|
0.026
|
0.018
|
0.025
|
Note: ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. T-statistics are reported in parentheses and are based on robust standard errors.
Robustness test
To verify the robustness of the baseline regression results, the following tests are conducted.
Propensity score matching method and difference-in-differences
Since the sample may have endogeneity problems arising from selection bias, to ensure the robustness of the study findings, the propensity score matching method (PSM) and the difference-in-difference method (DID) are combined to solve this problem. First, we use the Probit model to estimate the propensity scores. Referring to the previous research literature, eight observable variables, including the number of employees (Staff), the ownership concentration of companies (Top), the return on assets measures the enterprise’s profitability (Roa), investment opportunities (Tobin Q), capital intensity (Tangible), cash flow (Cash), size of the supervisory board (Supn), and firm’s region (Area) are selected as the matching indexes of the PSM model. Second, since there have 5872 samples in the experimental and 14296 samples in the control group, we use the 1:2 nearest neighbor matching, radius matching, and kernel matching to match the experimental group (high-polluting enterprises) with the control group (non-high-polluting enterprises). We implement a balance test to ensure no significant difference between the treatment and control groups after matching. Table 6 shows the results of the balance test. Figure 1 shows the density function diagram before and after matching. As shown in figure 1 and Table 6, the experimental group and the control group are obviously different before matching, but have the same trend after matching. The results of PSM-DID are shown in columns (1) to (3) of Table 7. Regardless of which propensity score matching method is chosen, the coefficient of Treat×Post is significantly positive at least at the level of 5%, which further verifies the reliability of the conclusion in this paper.
Table 6 Balance test
Variable
|
Unmatched
matched
|
mean
|
%bias
|
t-test
|
Treated
|
Control
|
t
|
p>|t|
|
Staff
|
U
|
7.8368
|
7.6135
|
17.7
|
11.23
|
0.000
|
M
|
7.8368
|
7.8202
|
1.3
|
0.73
|
0.468
|
Top
|
U
|
0.3476
|
0.3437
|
2.6
|
1.69
|
0.092
|
M
|
0.3476
|
0.3436
|
2.7
|
1.46
|
0.146
|
Roa
|
U
|
0.0421
|
0.0365
|
9.8
|
6.31
|
0.000
|
M
|
0.0421
|
0.0431
|
-1.7
|
-0.97
|
0.333
|
Tobin Q
|
U
|
0.5300
|
0.6041
|
-15.7
|
-10.05
|
0.000
|
M
|
0.5300
|
0.5652
|
-7.4
|
-4.16
|
0.000
|
Tangible
|
U
|
0.3006
|
0.1839
|
73.6
|
48.65
|
0.000
|
M
|
0.3006
|
0.3015
|
-0.6
|
-0.29
|
0.776
|
Cash
|
U
|
0.0577
|
0.0415
|
23.7
|
15.01
|
0.000
|
M
|
0.0577
|
0.0586
|
-1.4
|
-0.78
|
0.433
|
Supn
|
U
|
1.5287
|
1.4853
|
20.5
|
13.67
|
0.000
|
|
M
|
1.5287
|
1.5239
|
2.3
|
1.16
|
0.245
|
Area
|
U
|
14.086
|
13.349
|
9.7
|
6.23
|
0.000
|
|
M
|
14.086
|
14.062
|
0.3
|
0.17
|
0.865
|
Difference-in-differences-in-differences
Since the government has enacted other environmental regulation policies after 2012, these policies will also affect enterprises’ choice of green behavior. Therefore, how to distinguish green credit policy from other environmental policies is the key problem to be solved in this paper, and we use the DDD method to overcome this problem. Because green credit requires commercial banks to grant loans to enterprises following green standards, theoretically the degree of external financing demand of enterprises will directly affect the effect of green credit policy on enterprises. Using the method of Huang et al. (2019) to reflect external financing demand by using the difference between enterprise growth and endogenous growth rate (FID). FID=(Assett−Assett−1)/Assett−Roet/(1−Roet), Asset denotes total assets, Roe denotes return on equity, the higher FID value indicates that the firm is more dependent on external funding and more influenced by green credit policy. Based on this, the FID greater than the annual industry average is set to 1; otherwise, the FID is set to 0. Thus, we add the dummy variable of external financing demand of enterprises to the original DID model to construct a DDD model to further test the robustness of the relationship between the green credit policy and enterprises’ green behaviors. The results are shown in (4) and (5) of Table 7. The regression results of Post×Treat×FID cross term on Gpat are significantly positive at the level of 10%, while regression results on Gwl do not pass the significance test. The empirical results are basically consistent with the above main regression results, indicating that the results of this paper are robust.
Table 7. Results of endogenous test
|
(1)
|
(2)
|
(3)
|
(4)
|
(5)
|
|
Nearest-neighbor matching
|
Kernel matching
|
Radius matching
|
DDD
|
DDD
|
|
Gpat
|
Gpat
|
Gpat
|
Gpat
|
Gwl
|
Post×Treat
|
0.096**
|
0.098***
|
0.098***
|
0.053
|
-0.008
|
|
(2.320)
|
(2.999)
|
(2.991)
|
(1.253)
|
(-0.896)
|
Post×Treat×FID
|
|
|
|
0.099*
|
0.006
|
|
|
|
|
(1.794)
|
(0.462)
|
Post×FID
|
|
|
|
0.030**
|
0.001
|
|
|
|
|
(2.214)
|
(0.428)
|
Treat×FID
|
|
|
|
-0.074
|
-0.003
|
|
|
|
|
(-1.477)
|
(-0.308)
|
Size
|
0.289***
|
0.224***
|
0.226***
|
0.225***
|
-0.015***
|
|
(15.998)
|
(18.633)
|
(18.575)
|
(18.759)
|
(-5.685)
|
State
|
0.171***
|
0.121***
|
0.124***
|
0.118***
|
0.016*
|
|
(2.978)
|
(3.014)
|
(3.053)
|
(2.964)
|
(1.854)
|
Top
|
-0.144
|
-0.141*
|
-0.141*
|
-0.132*
|
-0.015
|
|
(-1.341)
|
(-1.831)
|
(-1.825)
|
(-1.719)
|
(-0.872)
|
Roa
|
-0.327*
|
-0.088
|
-0.157
|
-0.056
|
-0.034
|
|
(-1.751)
|
(-0.713)
|
(-1.207)
|
(-0.453)
|
(-1.273)
|
Lev
|
0.086
|
0.078
|
0.077
|
0.053
|
0.021*
|
|
(1.060)
|
(1.368)
|
(1.341)
|
(0.931)
|
(1.727)
|
Tobin Q
|
-0.017
|
-0.057***
|
-0.054***
|
-0.052***
|
0.014***
|
|
(-0.580)
|
(-2.833)
|
(-2.694)
|
(-2.587)
|
(3.206)
|
Tangible
|
0.099
|
0.082
|
0.085
|
0.096
|
-0.033**
|
|
(1.054)
|
(1.167)
|
(1.196)
|
(1.369)
|
(-2.165)
|
Cash
|
-0.157
|
-0.169*
|
-0.159*
|
-0.139
|
-0.006
|
|
(-1.151)
|
(-1.874)
|
(-1.754)
|
(-1.539)
|
(-0.316)
|
Share
|
-0.054
|
-0.173**
|
-0.181**
|
-0.172**
|
-0.027
|
|
(-0.463)
|
(-2.157)
|
(-2.241)
|
(-2.139)
|
(-1.552)
|
Dual
|
0.020
|
0.005
|
0.002
|
0.004
|
0.002
|
|
(0.762)
|
(0.296)
|
(0.095)
|
(0.245)
|
(0.620)
|
Supn
|
0.212**
|
0.180***
|
0.181***
|
0.181***
|
-0.011
|
|
(2.465)
|
(2.887)
|
(2.884)
|
(2.890)
|
(-0.794)
|
Age
|
-0.037
|
0.011
|
0.009
|
0.012
|
-0.008**
|
|
(-1.297)
|
(0.564)
|
(0.431)
|
(0.584)
|
(-1.963)
|
Firm
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Year
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Cons
|
-6.329***
|
-4.776***
|
-4.813***
|
-4.790***
|
1.112***
|
|
(-16.079)
|
(-18.038)
|
(-18.008)
|
(-18.108)
|
(19.332)
|
N
|
11526
|
20143
|
19994
|
20168
|
20168
|
Adj. R2
|
0.231
|
0.215
|
0.216
|
0.216
|
0.120
|
Note: ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. T-statistics are reported in parentheses and are based on robust standard errors.
Other robustness tests
Replacing variables
Referring to Hong et al. (2021) and Liu et al. (2021), we use the ratio between the number of green patents and the total number of patents (GPatratio) as proxy variables for enterprises’ green innovation. In addition, considering the high risk of innovation and the relatively long time required for patent research and development, we select the number of green patent applications in t+1 and t +2 years to measure the green innovation of enterprises. The results in columns (1) to (3) of Table 8 show that the regression results of the Treat×Post cross-product term pass the significance test for both the proportion and the lagged innovation time.
Replacement model
Considering that there are some samples with the number of green innovation being 0, and the value of enterprise green-washing degree is between 0 and 1. Therefore, this paper uses the Tobit model to replace the fixed effect model to test the main fundamental regression. The results are shown in (4) and (5) of Table 8. It can be seen that the test results are consistent with the previous results, suggesting that our findings are robust.
Exclude some samples
To observe the continuity of enterprises’ behavior before and after the implementation of the green credit policy, enterprises listed after 2012 are removed from this paper. As these listed enterprises lack data on the green behaviors of enterprises before the implementation of the policy, the inclusion of these data in the sample may lead to deviations in the results. The results in columns (6) and (7) of Table 8 show that the regression result of Gpat is still significant, while the regression result of Gwl does not pass the significance test.
Table 8. Results of other robustness tests
|
(1)
|
(2)
|
(3)
|
(4)
|
(5)
|
(6)
|
(7)
|
|
GPatratio
|
GPatt+1
|
GPatt+2
|
GPat
|
Gwl
|
GPat
|
Gwl
|
Post×Treat
|
0.009*
|
0.145***
|
0.136***
|
0.292***
|
-0.004
|
0.102***
|
-0.006
|
|
(1.863)
|
(3.263)
|
(3.302)
|
(2.591)
|
(-0.362)
|
(3.101)
|
(-0.774)
|
Post
|
|
|
|
1.860***
|
-0.053***
|
|
|
|
|
|
|
(8.458)
|
(-3.174)
|
|
|
Treat
|
|
|
|
-0.270**
|
-0.080***
|
|
|
|
|
|
|
(-2.159)
|
(-7.063)
|
|
|
Size
|
0.021***
|
0.176***
|
0.134***
|
0.656***
|
-0.045***
|
0.228***
|
-0.014***
|
|
(10.608)
|
(11.169)
|
(7.441)
|
(21.271)
|
(-17.873)
|
(18.082)
|
(-5.093)
|
State
|
-0.009
|
0.192***
|
0.168***
|
0.071
|
-0.014**
|
0.155***
|
0.015*
|
|
(-1.362)
|
(3.419)
|
(2.668)
|
(0.854)
|
(-1.997)
|
(3.667)
|
(1.660)
|
Top
|
-0.020
|
-0.111
|
-0.091
|
-0.902***
|
-0.020
|
-0.167**
|
-0.012
|
|
(-1.516)
|
(-1.177)
|
(-0.867)
|
(-4.272)
|
(-1.166)
|
(-2.038)
|
(-0.667)
|
Roa
|
-0.075***
|
0.258
|
0.566***
|
-1.548***
|
-0.065*
|
-0.172
|
-0.050*
|
|
(-3.560)
|
(1.586)
|
(2.763)
|
(-3.699)
|
(-1.826)
|
(-1.277)
|
(-1.687)
|
Lev
|
-0.051***
|
0.114
|
-0.015
|
-0.278
|
0.070***
|
0.065
|
0.024*
|
|
(-5.382)
|
(1.556)
|
(-0.183)
|
(-1.559)
|
(4.792)
|
(1.082)
|
(1.811)
|
Tobin Q
|
-0.010***
|
0.006
|
-0.012
|
0.219***
|
0.006
|
-0.047**
|
0.019***
|
|
(-3.745)
|
(0.222)
|
(-0.424)
|
(3.155)
|
(1.087)
|
(-2.182)
|
(3.890)
|
Tangible
|
0.012
|
-0.013
|
0.006
|
-0.341*
|
-0.169***
|
0.063
|
-0.034**
|
|
(1.010)
|
(-0.141)
|
(0.065)
|
(-1.649)
|
(-10.078)
|
(0.863)
|
(-2.078)
|
Cash
|
0.002
|
-0.207*
|
0.085
|
-1.145***
|
-0.030
|
-0.180*
|
-0.018
|
|
(0.138)
|
(-1.889)
|
(0.703)
|
(-3.692)
|
(-1.153)
|
(-1.856)
|
(-0.862)
|
Share
|
-0.018
|
-0.092
|
-0.042
|
0.416**
|
0.030**
|
-0.117
|
-0.023
|
|
(-1.337)
|
(-0.891)
|
(-0.361)
|
(2.319)
|
(2.000)
|
(-1.293)
|
(-1.160)
|
Dual
|
0.001
|
0.035
|
-0.006
|
0.023
|
0.005
|
0.005
|
0.002
|
|
(0.354)
|
(1.539)
|
(-0.231)
|
(0.376)
|
(1.006)
|
(0.251)
|
(0.438)
|
Supn
|
0.001
|
0.141*
|
0.170*
|
0.116
|
-0.031**
|
0.175***
|
-0.005
|
|
(0.085)
|
(1.786)
|
(1.956)
|
(0.691)
|
(-2.202)
|
(2.686)
|
(-0.349)
|
Age
|
0.018***
|
0.054**
|
0.007
|
-0.257***
|
0.006*
|
0.022
|
-0.010*
|
|
(7.128)
|
(2.040)
|
(0.241)
|
(-6.709)
|
(1.849)
|
(0.902)
|
(-1.918)
|
Firm
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Year
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Cons
|
-0.350***
|
-3.716***
|
-2.657***
|
-14.505***
|
1.758***
|
-4.869***
|
1.082***
|
|
(-8.140)
|
(-10.708)
|
(-6.719)
|
(-21.756)
|
(32.755)
|
(-17.455)
|
(17.614)
|
N
|
19985
|
14693
|
12186
|
20168
|
20168
|
16470
|
16470
|
Adj. R2
|
0.031
|
0.185
|
0.159
|
-
|
-
|
0.228
|
0.124
|
Note: ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. T-statistics are reported in parentheses and are based on robust standard errors.