Method and system for evaluating influence of policy factors on enterprises

Abstract

The invention belongs to the technical field of policy evaluation, and particularly relates to a method and system for evaluating the influence of policy factors on enterprises, and the method comprises the steps: obtaining an experimental group and a control group before and after a green credit policy is executed; inputting the experimental group and the control group into a first double difference model and outputting a first difference result; if the first difference result meets a first trigger condition, constructing a first regression model to represent an influence mechanism of the green credit policy on enterprise technology innovation, and if the first regression result meets a second trigger condition, constructing a second regression model to represent the influence of the green credit policy on enterprise performance, and judging the influence level of the green credit policy on the enterprise technology innovation based on the first regression result and the second regression result. According to the method, the double difference model and the reference regression model are constructed, so that the influence of policies on enterprise technology innovation, financing constraint, financing cost and enterprise performance can be evaluated respectively, and more comprehensive policy effect analysis is provided.

Description

Technical Field

[0001] The present invention belongs to the technical field of policy evaluation, and specifically relates to an evaluation method and system for the impact of policy factors on enterprises. Background Art

[0002] In the field of policy impact assessment on enterprises, existing technologies usually focus on the analysis of single-aspect policy effects, such as the impact of policies on the economic performance or environmental performance of enterprises. However, these technologies often ignore the comprehensive impact of policies on enterprises in multiple aspects and at multiple levels, especially policies such as green credit policies that aim to promote sustainable development and environmental protection. Therefore, existing technologies have obvious defects in comprehensively and systematically assessing the impact of policies on enterprises.

[0003] Specifically, the limitations of existing technologies are mainly reflected in the following aspects: first, there is a lack of an evaluation method that can simultaneously consider the impact of policies on corporate technological innovation, financing status, and overall performance; second, when evaluating policy effects, existing methods often fail to fully consider the differences in policy responses of different corporate attributes (such as heavily polluting enterprises and non-heavily polluting enterprises); finally, existing technologies may be overly simplified in data processing and model construction, resulting in evaluation results that are not robust and reliable enough. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for evaluating the impact of policy factors on enterprises, so as to solve the problems raised in the background technology.

[0005] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0006] In a first aspect, the present invention proposes a method for evaluating the impact of policy factors on enterprises, the method comprising:

[0007] Obtain a sample data set of enterprises before and after the implementation of green credit policies, and divide them into experimental and control groups;

[0008] Constructing a first double difference model to characterize the impact of green credit policy on enterprise technological innovation, inputting the experimental group and the control group into the first double difference model and outputting a first difference result, and performing a robustness analysis on the first difference result to determine the reliability of the first difference result;

[0009] If the first difference result meets the first trigger condition, a first regression model is constructed to characterize the impact mechanism of green credit policy on enterprise technological innovation, the experimental group and the control group are input into the first regression model and a first regression result is output;

[0010] If the first regression result meets the second trigger condition, a second regression model is constructed to characterize the impact of the green credit policy on corporate performance, the experimental group and the control group are input into the second regression model and a second regression result is output;

[0011] Based on the first regression results and the second regression results, the impact level of green credit policy on enterprise technological innovation is judged.

[0012] Furthermore, the sample data set of enterprises before and after the implementation of the green credit policy is obtained and divided into an experimental group and a control group, including:

[0013] The heavily polluting enterprises are taken as the experimental group, and the non-heavily polluting enterprises are taken as the control group. The sample data set includes enterprise patent data and enterprise financial data before and after the implementation of the green financial credit policy.

[0014] Furthermore, the first double difference model is constructed to characterize the impact of green credit policy on enterprise technological innovation, including:

[0015] The total number of patent applications, invention patent applications, utility model and design patent applications of enterprises are used to characterize the enterprise technological innovation variables and serve as explained variables in the double difference model.

[0016] The interaction term between green credit policy and enterprise attributes is used as the core explanatory variable of the double difference model;

[0017] Tobin's Q value, return on equity, book-to-market ratio, fixed asset ratio, enterprise maturity, enterprise size and independent director ratio are used as control variables in the double difference model.

[0018] Further, performing a robustness analysis on the first differential result to determine the reliability of the first differential result includes:

[0019] The baseline regression is re-performed by changing the weight distribution of the explained variable by changing the explained variable, lagged the core explanatory variable by one period, using a placebo test, and using a double difference estimation based on the propensity matching score. If the obtained interaction term coefficient is still significantly positive, it indicates that the first difference result is reliable.

[0020] Furthermore, if the first difference result meets the first trigger condition, a first regression model is constructed to characterize the impact mechanism of green credit policy on enterprise technological innovation, the experimental group and the control group are input into the first regression model and a first regression result is output, including:

[0021] If the interaction term coefficient of the interaction term is significantly positive, it means that the green credit policy significantly promotes enterprise technological innovation. Financing constraints and financing costs are taken as the explained variables of the first regression model, and the interaction term of the financing constraint variable SA index and the financing cost FC index is taken as the core explanatory variable of the first regression model. The control variables are the same as the control variables of the first double difference model.

[0022] Furthermore, if the second regression result satisfies the second trigger condition, a second regression model is constructed to characterize the impact of the green credit policy on corporate performance, the experimental group and the control group are input into the second regression model and a second regression result is output, including:

[0023] If the coefficient of the interaction term between the financing constraint variable SA index and the financing cost FC index is significantly positive, it means that the green credit policy has reduced the financing constraints and financing costs of enterprises. Enterprise performance is taken as the explained variable of the second regression model, and the interaction term between environmental performance and financial performance is taken as the core explanatory variable of the second regression model. The control variables are the same as those of the first double difference model.

[0024] Furthermore, judging the impact of green credit policy on enterprise technological innovation based on the first regression result and the second regression result includes:

[0025] If the interaction coefficients of the financing constraint variable SA index and the financing cost FC index, and the interaction coefficients of environmental performance and financial performance are both significantly positive, it is determined that the green credit policy reduces the financing constraints and financing costs of enterprises, promotes enterprise technological innovation, and improves enterprise performance.

[0026] Furthermore, the evaluation method further comprises:

[0027] The second double difference model is constructed to characterize the difference in the trend of green technology innovation between the experimental group and the control group before and after the implementation of the green credit policy;

[0028] The changing trend is taken as the explained variable of the second double difference model, and the interaction term of the green technological innovation between the experimental group and the control group is taken as the core explanatory variable; if the second difference result of the second double difference model shows that the interaction term coefficient between the experimental group and the control group was not significant before the implementation of the policy, and the interaction term coefficient changes significantly after the implementation of the policy, it indicates that the green credit policy has a significant impact on the technological innovation of enterprises.

[0029] In a second aspect, the present invention proposes an evaluation system for the impact of policy factors on enterprises, which is applied to implement any of the above-mentioned evaluation methods, and the system comprises:

[0030] The data input module is used to obtain sample data sets of enterprises before and after the implementation of the green credit policy, and divide them into experimental and control groups;

[0031] A first difference module, used for constructing a first double difference model to characterize the impact of green credit policy on enterprise technological innovation, inputting the experimental group and the control group into the first double difference model and outputting a first difference result, and performing a robustness analysis on the first difference result to determine the reliability of the first difference result;

[0032] A first regression module, for constructing a first regression model to characterize the impact mechanism of green credit policy on enterprise technological innovation if the first difference result meets the first trigger condition, inputting the experimental group and the control group into the first regression model and outputting a first regression result;

[0033] A second regression module, for constructing a second regression model to characterize the impact of the green credit policy on corporate performance if the second regression result meets the second trigger condition, inputting the experimental group and the control group into the second regression model and outputting a second regression result;

[0034] An evaluation output module is used to determine the impact level of the green credit policy on enterprise technological innovation based on the first regression result and the second regression result.

[0035] The beneficial effects of the present invention are:

[0036] 1. The present invention improves the comprehensiveness and accuracy of the evaluation. Specifically, by constructing a double difference model and a benchmark regression model, the present solution can evaluate the impact of policies on enterprise technological innovation, financing constraints and financing costs, and enterprise performance, thereby providing a more comprehensive policy effect analysis.

[0037] 2. The present invention takes into account the differences in enterprise attributes. By fully considering the differences in enterprise attributes in the setting of the experimental group and the control group, the heavily polluting enterprises are used as the experimental group and the non-heavily polluting enterprises are used as the control group, thereby more accurately reflecting the differences in the responses of different enterprises to policies.

[0038] 3. The present invention enhances the robustness of the evaluation results. By conducting robustness analysis on the differential results, including changing the explained variables, lagged core explanatory variables, placebo tests, and double difference estimation based on propensity matching scores, this scheme ensures the robustness and reliability of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flow chart of a method for evaluating the impact of policy factors on enterprises provided in an embodiment of the present application.

[0040] Figure 2This is a schematic diagram of the control variables and their definitions of the double difference model in the specific implementation manner of this application.

[0041] Figure 3 The fourth double difference model regression result in the specific implementation mode of this application is under the 95% confidence interval δ t Schematic diagram of the estimated coefficients. DETAILED DESCRIPTION

[0042] The present application is further described in detail below in conjunction with the accompanying drawings. It is necessary to point out here that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technical personnel in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0043] Example 1

[0044] like Figure 1 As shown, this embodiment proposes a method for evaluating the impact of policy factors on enterprises, and the method may include the following steps:

[0045] S1. Obtain a sample data set of enterprises before and after the implementation of the green credit policy, and divide it into an experimental group and a control group; take the heavily polluting enterprises as the experimental group and the non-heavily polluting enterprises as the control group. The sample data set includes the enterprise patent data and enterprise financial data before and after the implementation of the green financial credit policy.

[0046] S2. Construct the first double difference model to characterize the impact of green credit policy on enterprise technological innovation, input the experimental group and the control group into the first double difference model and output the first difference result, and conduct a robustness analysis on the first difference result to determine the reliability of the first difference result.

[0047] S3. If the first differential result meets the first trigger condition, a first regression model is constructed to characterize the impact mechanism of green credit policy on enterprise technological innovation, the experimental group and the control group are input into the first regression model and the first regression result is output.

[0048] S4. If the first regression result meets the second trigger condition, a second regression model is constructed to characterize the impact of green credit policy on corporate performance, the experimental group and the control group are input into the second regression model and the second regression result is output.

[0049] S5. Determine the impact of green credit policy on enterprise technological innovation based on the first and second regression results.

[0050] It is understandable that the above evaluation method aims to achieve a comprehensive evaluation scheme for the impact of policies on enterprises, especially to evaluate the impact of green credit policies on enterprise technological innovation and corporate performance. Through the double difference model (DID) and its extension, combined with data analysis and model verification, a scientific and systematic evaluation scheme is provided.

[0051] Further preferably, a first double difference model is constructed to characterize the impact of green credit policy on enterprise technological innovation, including: using the total number of enterprise patent applications, the number of invention patent applications, and the number of utility model and design patent applications to characterize enterprise technological innovation variables, and as the explained variables of the double difference model; using the interaction term between green credit policy and enterprise attributes as the core explanatory variable of the double difference model; using Tobin's Q value, return on equity, book-to-market ratio, fixed asset ratio, enterprise maturity, enterprise scale and independent director ratio as control variables of the double difference model.

[0052] In this embodiment, the patent data of an enterprise includes the number of applications for invention patents, utility model patents and design patents. The financial data related to an enterprise includes information such as the enterprise's financial indicators and governance structure. Step S1 also includes sample processing: eliminating ST (listed companies that have been "specially treated" by the stock exchange), ST* (listed companies facing more serious financial problems) and PT (listed companies that have been "specially transferred"). Eliminate companies in the financial and insurance industries. Eliminate companies with debt-to-asset ratios less than 0 and greater than 1. Eliminate companies with serious data missing.

[0053] More specifically, this embodiment constructs a first double difference model to test the role of green credit policy on enterprise technological innovation and evaluate the impact of green credit policy on enterprise technological innovation. Using enterprise patent application data as the explained variable, the interaction term between green credit policy and enterprise attributes as the core explanatory variable, and controlling other related variables, the formula is as follows:

[0054] Patent it =α0+α1treat i +α2policy t +α3treat i ×policy t +γX it +δ i +λ t +σ it

[0055] Among them, the explained variable patentit is the performance of enterprise technological innovation (this embodiment uses the number of patent applications to measure the level of enterprise technological innovation, mainly because the number of patent applications is more stable and timely than the number of patent authorizations, and can better reflect the innovation ability of enterprises. Specifically, my country's patents are mainly divided into three categories, namely invention patents, utility model patents and design patents. Invention patents are used to represent new technical solutions proposed by enterprises for products, methods, etc.; utility model and design patents reflect the market attributes of technological innovation. This paper sums up the number of applications for the three types of patents to obtain the total amount of technological innovation pat1, uses the number of invention patent applications pat2 to measure the quality of technological innovation, and uses the number of utility model and design patent applications pat3 to measure the quantity of technological innovation. At the same time, in order to solve the problem of right-skewed distribution of patent application data, the number of patent applications is added by 1 and the logarithm is taken, and finally the explained variables lnpat1, lnpat2 and lnpat3 of this paper are obtained); the core explanatory variable is the cross product of green credit policy and enterprise attributes treati X Policyt, where treat is a grouping dummy variable. If the sample enterprise belongs to Class A and Class B, the sample enterprise is a heavily polluting enterprise and the grouping variable is 1; otherwise, it is a non-heavy polluting enterprise and the variable is 0; policy is a time dummy variable; the interaction coefficient a3 should be paid attention to. If it is significantly greater than 0, it means that the implementation of the green credit policy has significantly promoted the technological innovation of heavily polluting enterprises. Otherwise, it means that there is no significant promoting effect.

[0056] The enterprise-level control variables xit include: Tobin Q value qa, return on net assets roe, book-to-market ratio mtb, fixed asset ratio ppe, enterprise maturity lnage, enterprise size size, and independent director ratio ind. In addition, in order to obtain more robust regression results, a double fixed effect model is used. The definition of variables is shown in Figure 2 .

[0057] Further preferably, a robustness analysis is performed on the first difference result to determine the reliability of the first difference result, including: changing the weight distribution of the explained variable by changing the explained variable, lagged the core explanatory variable by one period, using a placebo test, and re-performing the baseline regression using a double difference estimation based on the propensity matching score. If the obtained interaction term coefficient is still significantly positive, it indicates that the first difference result is reliable.

[0058] Further preferably, if the first difference result meets the first trigger condition, a first regression model is constructed to characterize the impact mechanism of green credit policy on enterprise technological innovation, the experimental group and the control group are input into the first regression model and the first regression result is output, including: if the interaction term coefficient of the interaction term is significantly positive, it represents that the green credit policy significantly promotes enterprise technological innovation, financing constraints and financing costs are used as the explained variables of the first regression model, the interaction term of the financing constraint variable SA index and the financing cost FC index is used as the core explanatory variable of the first regression model, and the control variables are the same as the control variables of the first double difference model.

[0059] It can be understood that the first trigger condition in this embodiment is specifically: if the first difference result of the first double difference model (significantly positive) shows that the policy has a significant impact on green technology innovation, the impact mechanism is further analyzed; it is mainly explained by changes in financing constraints and financing costs.

[0060] In this embodiment, the results of the baseline regression of the first double difference model are shown in Table 1. Columns 1 to 3 are the regression results with the total number of patent applications, invention patent applications, and utility model and design patent applications as explained variables and without control variables. It can be seen that the coefficients of the interaction terms are all positive and significant at the 1% level. Columns 4 to 6 are the results of adding control variables on the basis of the previous regression. The coefficients of the interaction terms have changed slightly, but they are still significantly positive overall, which shows that the regression results have a certain significance.

[0061] Table 1: First double difference model benchmark regression results

[0062]

[0063] More specifically, the first regression model in this embodiment is:

[0064] med it =β0+β1treat i +β2policy t +β3treat i ×policy t +β4X it +δ i +λ t +σ it

[0065] Patent it =θ0+θ1treat i +θ2policy t +θ3treat i ×policy t +medit +θ5X it +δ i +λ t +σ it

[0066] Among them, medit represents the mediating variable. Compared with other financing constraint measurement indicators, the biggest advantage of the SA index is that it effectively eliminates the interference of endogenous financing variables. The larger its absolute value, the stronger the financing balance constraint faced by the enterprise. The SA index is constructed using the total assets (Size) and the age (Age) of the enterprise. The calculation formula is:

[0067] SA=…0.737×size+0.043×size 2 -0.04×Age.

[0068] Combined with the regression results shown in Table 2, it can be concluded that at the 1% significance level, the implementation of the green credit policy reduces the financing constraints SA (negative indicator) and financing costs FC of heavily polluting enterprises, thereby promoting enterprise technological innovation.

[0069] Table 2: Regression results of the first regression model

[0070]

[0071] Further preferably, if the first regression result meets the second trigger condition, a second regression model is constructed to characterize the impact of the green credit policy on corporate performance, the experimental group and the control group are input into the second regression model and the second regression result is output, including:

[0072] If the coefficient of the interaction term between the financing constraint variable SA index and the financing cost FC index is significantly positive, it means that the green credit policy has reduced the financing constraints and financing costs of enterprises. Enterprise performance is taken as the explained variable of the second regression model, and the interaction term between environmental performance and financial performance is taken as the core explanatory variable of the second regression model. The control variables are the same as those of the first double difference model.

[0073] It can be understood that the second trigger condition in this embodiment is specifically: if the first regression model shows that financing constraints and financing costs are significantly reduced, then evaluate how these changes affect corporate performance.

[0074] More specifically, the second regression model in this embodiment is:

[0075] Y it =ξ t +ξ i +τ1(treat×policy)+γX it +σ it

[0076] Among them, yit represents the explained variable enterprise performance, and here enterprise performance is divided into environmental performance Eidq and financial performance ROA. Regarding environmental performance, this paper uses environmental information disclosure quality to characterize it, and divides the environmental information disclosure quality of enterprises into two types of information: monetary information and non-monetary information. There are five aspects in total, covering 25 scoring items. The scores of these items are summed up and logarithmically processed to obtain Eidq. This data mainly comes from the environmental research database in the CSMAR database. Regarding financial performance, ROA return on total assets is used. Considering that the pilot policy stimulates enterprise technological innovation and is converted into an increase in enterprise value in actual business activities, this may be a process with lag, so this paper uses the ROA of the current period, t+1 period and t+2 period as the explained variable. The remaining control variables are consistent with the double difference model.

[0077] Table 3 reports the regression results of the second regression model. In column (1) of Table (8), the coefficient of the interaction term is significantly positive at the 1% level, indicating that the green credit policy has an improving effect on the quality of corporate environmental information disclosure. The coefficient of the interaction term corresponding to the current ROA is not significant, but the coefficient of the interaction term corresponding to the ROA of the next period and t+2 period is significantly positive at the 10% level, indicating that the green credit policy does not have an immediate effect on the financial performance of the enterprise, but from the perspective of the long-term development of the enterprise, the development and improvement of the green financial system based on green credit will have a positive impact on the enterprise.

[0078] Table 3: Second regression model regression results

[0079]

[0080] Further preferably, the evaluation method also includes: constructing a second double difference model to characterize the difference in the changing trend of green technology innovation between the experimental group and the control group before and after the implementation of the green credit policy; taking the changing trend as the explained variable of the second double difference model, and taking the interaction term of green technology innovation between the experimental group and the control group as the core explanatory variable; if the second difference result of the second double difference model shows that the interaction term coefficient between the experimental group and the control group was not significant before the policy implementation, and the interaction term coefficient changed significantly after the policy implementation, it indicates that the green credit policy has a significant impact on the technological innovation of enterprises.

[0081] More specifically, the second double difference model is:

[0082]

[0083] Among them, 2011, the year before the implementation of the green credit policy, is used as the base year, and the focus is on the δ tThe coefficient estimate changes to determine whether the parallel trend test is passed. Figure 3 , plotting a 95% confidence interval for δ t Estimated coefficients for 2008-2010 t This indicates that there is no significant difference in the trend of technological innovation between the experimental group and the control group before the implementation of the green credit policy, which meets the parallel trend hypothesis. t The coefficient has been significant since 2013, which shows that the impact of green credit policy on enterprise technological innovation has begun to become prominent. Especially after 2015, the significance has increased and the coefficient has increased. From the subsequent long-term effects, the effect of green credit policy is lasting.

[0084] It can be understood that this embodiment constructs a second double difference model to evaluate the difference in the trend of green technology innovation between the experimental group and the control group before and after the policy implementation, so as to further confirm the policy effect.

[0085] Based on the same inventive concept, this embodiment also proposes an evaluation system for the impact of policy factors on enterprises, which is applied to implement the above evaluation method. The system includes:

[0086] The data input module is used to obtain sample data sets of enterprises before and after the implementation of the green credit policy, and divide them into experimental and control groups;

[0087] The first difference module is used to construct a first double difference model to characterize the impact of green credit policies on enterprise technological innovation, input the experimental group and the control group into the first double difference model and output the first difference result, and conduct a robustness analysis on the first difference result to determine the reliability of the first difference result;

[0088] A first regression module is used to construct a first regression model to characterize the impact mechanism of green credit policy on enterprise technological innovation if the first difference result meets the first trigger condition, input the experimental group and the control group into the first regression model and output the first regression result;

[0089] A second regression module is used to construct a second regression model to characterize the impact of the green credit policy on corporate performance if the first regression result meets the second trigger condition, input the experimental group and the control group into the second regression model and output the second regression result;

[0090] The evaluation output module is used to judge the impact level of green credit policy on enterprise technological innovation based on the first regression results and the second regression results.

[0091] It should be noted here that each module in the above-mentioned evaluation system corresponds to the implementation of steps S1 to S5 in the above-mentioned evaluation method. The instances and application scenarios implemented by multiple modules are the same as those of the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiment 1. For their specific functions and the technical effects brought about, please refer to the method embodiment part, which will not be repeated here.

[0092] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for evaluating the impact of policy factors on enterprises, characterized in that: The method comprises: Obtain a sample data set of enterprises before and after the implementation of green credit policies, and divide them into experimental and control groups; Constructing a first double difference model to characterize the impact of green credit policy on enterprise technological innovation, inputting the experimental group and the control group into the first double difference model and outputting a first difference result, and performing a robustness analysis on the first difference result to determine the reliability of the first difference result; If the first difference result meets the first trigger condition, a first regression model is constructed to characterize the impact mechanism of green credit policy on enterprise technological innovation, the experimental group and the control group are input into the first regression model and a first regression result is output; If the first regression result meets the second trigger condition, a second regression model is constructed to characterize the impact of the green credit policy on corporate performance, the experimental group and the control group are input into the second regression model and a second regression result is output; Based on the first regression results and the second regression results, the impact level of green credit policy on enterprise technological innovation is judged.

2. The method for evaluating the impact of policy factors on enterprises according to claim 1, characterized in that: The sample data set of enterprises before and after the implementation of the green credit policy is obtained and divided into an experimental group and a control group, including: The heavily polluting enterprises are taken as the experimental group, and the non-heavily polluting enterprises are taken as the control group. The sample data set includes enterprise patent data and enterprise financial data before and after the implementation of the green financial credit policy.

3. The method for evaluating the impact of policy factors on enterprises according to claim 1, characterized in that: The first double difference model is constructed to characterize the impact of green credit policy on enterprise technological innovation, including: The total number of patent applications, invention patent applications, utility model and design patent applications of enterprises are used to characterize the enterprise technological innovation variables and serve as explained variables in the double difference model. The interaction term between green credit policy and enterprise attributes is used as the core explanatory variable of the double difference model; Tobin's Q value, return on equity, book-to-market ratio, fixed asset ratio, enterprise maturity, enterprise size and independent director ratio are used as control variables in the double difference model.

4. The method for evaluating the impact of policy factors on enterprises according to claim 3, characterized in that: The performing a robustness analysis on the first differential result to determine the reliability of the first differential result includes: The baseline regression is re-performed by changing the weight distribution of the explained variable by changing the explained variable, lagged the core explanatory variable by one period, using a placebo test, and using a double difference estimation based on the propensity matching score. If the obtained interaction term coefficient is still significantly positive, it indicates that the first difference result is reliable.

5. The method for evaluating the impact of policy factors on enterprises according to claim 3, characterized in that: If the first difference result meets the first trigger condition, a first regression model is constructed to characterize the impact mechanism of green credit policy on enterprise technological innovation, the experimental group and the control group are input into the first regression model and a first regression result is output, including: If the interaction term coefficient of the interaction term is significantly positive, it means that the green credit policy significantly promotes enterprise technological innovation. Financing constraints and financing costs are taken as the explained variables of the first regression model, and the interaction term of the financing constraint variable SA index and the financing cost FC index is taken as the core explanatory variable of the first regression model. The control variables are the same as the control variables of the first double difference model.

6. The method for evaluating the impact of policy factors on enterprises according to claim 4, characterized in that: If the first regression result meets the second trigger condition, a second regression model is constructed to characterize the impact of the green credit policy on corporate performance, the experimental group and the control group are input into the second regression model and a second regression result is output, including: If the coefficient of the interaction term between the financing constraint variable SA index and the financing cost FC index is significantly positive, it means that the green credit policy has reduced the financing constraints and financing costs of enterprises. Enterprise performance is taken as the explained variable of the second regression model, and the interaction term between environmental performance and financial performance is taken as the core explanatory variable of the second regression model. The control variables are the same as those of the first double difference model.

7. The method for evaluating the impact of policy factors on enterprises according to claim 5, characterized in that: The determining of the impact of the green credit policy on enterprise technological innovation based on the first regression result and the second regression result includes: If the interaction coefficients of the financing constraint variable SA index and the financing cost FC index, and the interaction coefficients of environmental performance and financial performance are both significantly positive, it is determined that the green credit policy reduces the financing constraints and financing costs of enterprises, promotes enterprise technological innovation, and improves enterprise performance.

8. The method for evaluating the impact of policy factors on enterprises according to claim 1, characterized in that: The evaluation method further comprises: The second double difference model is constructed to characterize the difference in the trend of green technology innovation between the experimental group and the control group before and after the implementation of the green credit policy; The changing trend is taken as the explained variable of the second double difference model, and the interaction term of the green technological innovation between the experimental group and the control group is taken as the core explanatory variable; if the regression results of the second double difference model show that the interaction term coefficient between the experimental group and the control group is not significant before the implementation of the policy, and the interaction term coefficient changes significantly after the implementation of the policy, it indicates that the green credit policy has a significant impact on the technological innovation of enterprises.

9. An evaluation system for the impact of policy factors on enterprises, characterized in that: Applied to executing the evaluation method according to any one of claims 1 to 8, the system comprises: The data input module is used to obtain sample data sets of enterprises before and after the implementation of the green credit policy, and divide them into experimental and control groups; A first difference module, used for constructing a first double difference model to characterize the impact of green credit policy on enterprise technological innovation, inputting the experimental group and the control group into the first double difference model and outputting a first difference result, and performing a robustness analysis on the first difference result to determine the reliability of the first difference result; A first regression module, for constructing a first regression model to characterize the impact mechanism of green credit policy on enterprise technological innovation if the first difference result meets the first trigger condition, inputting the experimental group and the control group into the first regression model and outputting a first regression result; A second regression module, for constructing a second regression model to characterize the impact of the green credit policy on corporate performance if the first regression result meets the second trigger condition, inputting the experimental group and the control group into the second regression model and outputting a second regression result; An evaluation output module is used to determine the impact level of the green credit policy on enterprise technological innovation based on the first regression result and the second regression result.

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