Evaluation method and system for enterprise green total factor productivity influence mechanism

Abstract

The invention belongs to the technical field of enterprise management, and particularly relates to an evaluation method and system for an enterprise green total factor productivity influence mechanism, and the method comprises the steps: obtaining related data before and after the optimization of an enterprise business environment, constructing a parallel trend detection model, carrying out the empirical analysis, and extracting target variable data. On the basis, a business environment influence regression model and a regulation effect regression model are constructed, and an influence mechanism of the enterprise green total factor productivity is determined through reference regression. And the specific adjustment effect of the enterprise digital transformation between the business environment optimization and the enterprise green total factor productivity can be clarified. Therefore, powerful support is provided for an enterprise to formulate a targeted optimization strategy, and the enterprise is helped to better utilize digital transformation and business environment optimization to improve the green total factor productivity. And finally, through defining the adjustment effect of enterprise digital transformation, the method helps the enterprise to break through the dilemma that the current influence mechanism is unknown, and promotes the sustainable development of the enterprise.

Description

Technical Field

[0001] The present invention belongs to the technical field of enterprise management, and in particular relates to an evaluation method and system for an enterprise's green total factor productivity impact mechanism. Background Art

[0002] Under the current trend of global economic integration, enterprises are facing increasingly fierce competition and a constantly changing market environment. As an important factor affecting the development of enterprises, the degree of optimization of the business environment is directly related to the operational efficiency and competitiveness of enterprises. However, there is still a lack of clear conclusions on how the business environment affects the green total factor productivity of enterprises.

[0003] The green total factor productivity of an enterprise is an important indicator for measuring the sustainable development capability of an enterprise. It reflects the efficiency of the enterprise's use of environmental resources in the production process. Clarifying the impact of the business environment on the green total factor productivity of an enterprise will help enterprises formulate targeted optimization strategies, improve resource utilization efficiency, and achieve sustainable development. However, most existing studies focus on the impact of the business environment on the macroeconomy or a single economic indicator, and pay insufficient attention to the comprehensive indicator of the green total factor productivity of an enterprise.

[0004] In addition, as an inevitable trend in the development of enterprises today, enterprise digital transformation plays an important role in the relationship between business environment optimization and enterprise green total factor productivity. Digital transformation can improve enterprise operational efficiency, reduce production costs, and promote innovation, thus having a positive impact on enterprise green total factor productivity. However, there is currently a lack of in-depth research and clear conclusions on the regulatory role of enterprise digital transformation in the optimization of the business environment and enterprise green total factor productivity.

[0005] Therefore, there is a situation where the influencing mechanism of the current green total factor productivity of enterprises is unclear. This unclear situation restricts the formulation and implementation of business environment optimization and digital transformation strategies by enterprises, and affects the sustainable development capabilities of enterprises. In order to solve this problem, the present invention proposes an evaluation method and system for the influencing mechanism of the green total factor productivity of enterprises. Summary of the invention

[0006] The purpose of the present invention is to provide an evaluation method and system for the green total factor productivity influencing mechanism of an enterprise, so as to solve the problems raised in the background technology.

[0007] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0008] In a first aspect, the present invention proposes an evaluation method for the impact mechanism of green total factor productivity of an enterprise, characterized in that the method comprises:

[0009] Obtain the green total factor productivity and digital transformation level of enterprises before and after the optimization of the business environment, and use them as variable data;

[0010] Using the variable data to construct a parallel trend detection model; performing empirical analysis on the variable data to extract target variable data that meets the parallel trend test in the parallel trend detection model;

[0011] The green total factor productivity in the target variable data is used as the explained variable, the enterprise's multi-dimensional business environment data is used as the explanatory variable, the enterprise's digital transformation level is used as the moderating variable, and the company control variables and the province control variables are used as the control variables;

[0012] A business environment impact regression model is constructed based on the impact of business environment optimization on the green total factor productivity of enterprises, and a moderating effect regression model is constructed based on the moderating effect of enterprise digital transformation on the relationship between business environment optimization and enterprise green total factor productivity;

[0013] The business environment impact regression model and the regulatory effect regression model are subjected to a baseline regression based on the target variable data, and the influencing mechanism of the enterprise's green total factor productivity is determined based on the significance level of the interaction term coefficient in the regression results.

[0014] Further, the parallel trend check includes:

[0015] (a) Setting the parallel trend hypothesis: before the business environment is optimized, there is no significant change in the correlation between the green total factor productivity of enterprises, the multi-dimensional business environment data of enterprises, and the level of digital transformation of enterprises;

[0016] (b) constructing parallel trend detection statistics: constructing statistics using the variable data to quantify the changes in the correlation between the variables before and after the optimization of the business environment;

[0017] (c) Performing a parallel trend test: comparing the statistic with a preset significance level. If the significance level of the statistic exceeds the preset value, the parallel trend hypothesis is rejected, and it is considered that there is a non-parallel trend correlation change between the variables; conversely, if the significance level of the statistic does not exceed the preset value, the parallel trend hypothesis is accepted, and it is considered that the variables maintain a parallel trend.

[0018] (d) Screening target variable data: Based on the results of the parallel trend test, screen out the variable data that meets the parallel trend test as the target variable data.

[0019] Furthermore, the company control variables include enterprise scale, enterprise growth, operating cash flow, enterprise value and enterprise age; the province control variables include transportation infrastructure level, industrial structure upgrading, tax burden level, environmental regulation, fiscal support and urbanization level.

[0020] Furthermore, the business environment impact regression model is constructed according to the impact of business environment optimization on the green total factor productivity of enterprises in the target variable data, including:

[0021] The following business environment impact regression model is constructed to characterize the impact of business environment optimization on the green total factor productivity of enterprises:

[0022] GTFP it =α0+α1Envi it +α2∑Controls it +∑Year+∑Industry+ε it

[0023] Among them, GTFP it represents the green total factor productivity of enterprise i in year t, Envi it represents the business environment of enterprise i in year t, Controls it represents the control variable, Year and Industry represent the time fixed effect and industry fixed effect respectively, ε it represents the error disturbance term, and α0, α1, and α2 are coefficients.

[0024] Furthermore, the regulatory effect regression model is constructed based on the regulatory effect of enterprise digital transformation on the relationship between business environment optimization and enterprise green total factor productivity in the target variable data, including:

[0025] The following moderating effect regression model is constructed to characterize the moderating effect of enterprise digital transformation on the business environment and enterprise total factor productivity:

[0026] GTFP it =β0+β1Envi it +β2DCG it +β3Envi it ×DCG it +β4∑Controls it +∑Year+

[0027] ∑Industry+ε it

[0028] Among them, DCG it represents the digital transformation level of enterprise i in year t, Envi it×DCG it It is the interaction term between business environment and enterprise digital transformation, and β0, β1, β2, β3 and β4 are coefficients.

[0029] Furthermore, the influencing mechanism of the green total factor productivity of the enterprise is determined according to the significance level of the interaction term coefficient in the regression result, including:

[0030] If the coefficient of the business environment on the green total factor productivity of the enterprise is significantly positive, it is determined that the optimization of the business environment can improve the green total factor productivity of the enterprise;

[0031] If the coefficient of the interaction term between the business environment and enterprise digital transformation is significantly positive, it means that enterprise digital transformation has a significant positive regulatory effect on the improvement of enterprise green total factor productivity through business environment optimization.

[0032] In a second aspect, the present invention proposes an evaluation system for the impact mechanism of green total factor productivity of an enterprise, which is applied to execute any of the above-mentioned evaluation methods, and the system comprises:

[0033] The data collection module is used to obtain the green total factor productivity and digital transformation level of enterprises before and after the optimization of the business environment, and use them as variable data;

[0034] The first construction module is used to construct a parallel trend detection model using the variable data; perform empirical analysis on the variable data to extract target variable data that meets the parallel trend test in the parallel trend detection model;

[0035] A variable setting module, used to use the green total factor productivity in the target variable data as the explained variable, the enterprise's multi-dimensional business environment data as the explanatory variable, the enterprise's digital transformation level as the moderating variable, and the company control variable and the province control variable as the control variable;

[0036] The second building block is used to construct a business environment impact regression model based on the impact of business environment optimization on the green total factor productivity of enterprises, and to construct a regulatory effect regression model based on the regulatory effect of enterprise digital transformation on the relationship between business environment optimization and enterprise green total factor productivity;

[0037] A regression analysis module is used to perform a baseline regression on the business environment impact regression model and the regulatory effect regression model according to the target variable data, and determine the influencing mechanism of the enterprise's green total factor productivity according to the significance level of the interaction term coefficient in the regression result.

[0038] The beneficial effects of the present invention are:

[0039] 1. This invention scientifically and accurately evaluates the effect of business environment optimization on the green total factor productivity of enterprises by constructing a parallel trend detection model. By clarifying the impact of the business environment on the green total factor productivity of enterprises, enterprises can formulate optimization strategies more targetedly, improve resource utilization efficiency, and thus achieve sustainable development.

[0040] 2. The present invention also clarifies the regulatory role of enterprise digital transformation in business environment optimization and enterprise green total factor productivity. This evaluation method not only reveals the important role of digital transformation in enterprise development, but also provides guidance for enterprises on how to better utilize business environment optimization to improve green total factor productivity during digital transformation. Through the evaluation method of the present invention, enterprises can more clearly understand the benefits of digital transformation, thereby more firmly carrying out digital transformation and enhancing the competitiveness and sustainable development capabilities of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a schematic diagram of the process of the evaluation method of the present invention;

[0042] Figure 2 It is a schematic diagram of the process of parallel trend test in the evaluation method of the present invention;

[0043] Figure 3 Figure 2 is a system architecture diagram of the evaluation system in this application. DETAILED DESCRIPTION

[0044] 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.

[0045] Example 1

[0046] like Figure 1-2 As shown, this implementation method proposes an evaluation method for the impact mechanism of green total factor productivity of an enterprise, and the method includes the following steps:

[0047] S1. Obtain the green total factor productivity and digital transformation level of enterprises before and after the optimization of the business environment, and use them as variable data;

[0048] S2. constructing a parallel trend detection model using the variable data; performing empirical analysis on the variable data to extract target variable data that meets the parallel trend test in the parallel trend detection model;

[0049] S3. Take the green total factor productivity in the target variable data as the explained variable, the enterprise's multi-dimensional business environment data as the explanatory variable, the enterprise's digital transformation level as the moderating variable (currently mainly measured by the proportion of total intangible assets and the construction of digital transformation indicators), and the company control variables and the province control variables as control variables;

[0050] S4. Construct a business environment impact regression model based on the impact of business environment optimization on the green total factor productivity of enterprises, and construct a regulatory effect regression model based on the regulatory effect of enterprise digital transformation on the relationship between business environment optimization and enterprise green total factor productivity;

[0051] S5. Perform a baseline regression on the business environment impact regression model and the regulatory effect regression model based on the target variable data, and determine the influencing mechanism of the enterprise's green total factor productivity based on the significance level of the interaction term coefficient in the regression results.

[0052] By constructing a parallel trend detection model, the present invention ensures that the experimental group and the control group have a common trend before the business environment is optimized, thereby improving the accuracy of the evaluation. At the same time, by introducing the level of enterprise digital transformation as a moderating variable, as well as company control variables and provincial control variables as control variables, the technical solution can more comprehensively evaluate the impact mechanism of enterprise green total factor productivity.

[0053] The present invention proposes a parallel trend detection model. The model is used to test whether the green total factor productivity of enterprises in the experimental group and the control group (based on the above variable data division) have a common trend before the business environment is optimized, that is, whether the parallel trend hypothesis is met.

[0054] Further preferably, the parallel trend verification comprises the following steps:

[0055] (a) Setting the parallel trend hypothesis: before the business environment is optimized, there is no significant change in the correlation between the green total factor productivity of enterprises, the multi-dimensional business environment data of enterprises, and the level of digital transformation of enterprises;

[0056] (b) constructing parallel trend detection statistics: constructing statistics using the variable data to quantify the changes in the correlation between the variables before and after the optimization of the business environment;

[0057] (c) Performing a parallel trend test: comparing the statistic with a preset significance level. If the significance level of the statistic exceeds the preset value, the parallel trend hypothesis is rejected, and it is considered that there is a non-parallel trend correlation change between the variables; conversely, if the significance level of the statistic does not exceed the preset value, the parallel trend hypothesis is accepted, and it is considered that the variables maintain a parallel trend.

[0058] (d) Screening target variable data: Based on the results of the parallel trend test, screen out the variable data that meets the parallel trend test as the target variable data.

[0059] Further preferably, the company control variables include enterprise scale, enterprise growth, operating cash flow, enterprise value and enterprise age, and the province control variables include transportation infrastructure level, industrial structure upgrading, tax burden level, environmental regulation, fiscal support intensity and urbanization level.

[0060] As an example, the target variable data obtained in step S2 is shown in Table 1.

[0061] Table 1 Variable description

[0062]

[0063] Further preferably, a business environment impact regression model is constructed according to the impact of business environment optimization on the green total factor productivity of enterprises in the target variable data, including:

[0064] The following business environment impact regression model is constructed to characterize the impact of business environment optimization on the green total factor productivity of enterprises:

[0065] GTFP it =α0+α1Envi it +α2∑Controls it +∑Year+∑Industry+ε it

[0066] Among them, GTFP it represents the green total factor productivity of enterprise i in year t, Envi it represents the business environment of enterprise i in year t, Controls it represents the control variable, Year and Industry represent the time fixed effect and industry fixed effect respectively, ε it represents the error disturbance term, and α0, α1, and α2 are coefficients.

[0067] Further preferably, a regulatory effect regression model is constructed based on the regulatory effect of enterprise digital transformation on the relationship between business environment optimization and enterprise green total factor productivity in the target variable data, including:

[0068] The following moderating effect regression model is constructed to characterize the moderating effect of enterprise digital transformation on the business environment and enterprise total factor productivity:

[0069] GTFP it =β0+β1Envi it +β2DCGit +β3Envi it ×DCG it +β4∑Controls it +∑Year+

[0070] ∑Industry+ε it

[0071] Among them, DCG it represents the digital transformation level of enterprise i in year t, Envi it ×DCG it It is the interaction term between business environment and enterprise digital transformation, and β0, β1, β2, β3 and β4 are coefficients.

[0072] Further preferably, the influencing mechanism of the green total factor productivity of the enterprise is determined according to the significance level of the interaction term coefficient in the regression result, including:

[0073] If the coefficient of the business environment on the green total factor productivity of the enterprise is significantly positive, it is determined that the optimization of the business environment can improve the green total factor productivity of the enterprise;

[0074] If the coefficient of the interaction term between the business environment and enterprise digital transformation is significantly positive, it means that enterprise digital transformation has a significant positive regulatory effect on the improvement of enterprise green total factor productivity through business environment optimization.

[0075] According to the above embodiment of the present invention, firstly, by obtaining data such as green total factor productivity and enterprise digital transformation level before and after the optimization of the business environment of enterprises, and introducing company control variables and provincial control variables, the comprehensiveness and accuracy of the evaluation are ensured. Then, a parallel trend detection model is constructed using these data to test whether the experimental group and the control group have a common trend before the optimization of the business environment, thereby satisfying the parallel trend hypothesis and improving the reliability of the evaluation.

[0076] Then, based on the target variable data, the business environment impact regression model and the moderating effect regression model are constructed. These two models are used to characterize the impact of business environment optimization on the green total factor productivity of enterprises, and the moderating effect of enterprise digital transformation on the relationship between business environment optimization and enterprise green total factor productivity. Through the construction of these two models, we can have a deeper understanding of the influencing mechanism of enterprise green total factor productivity.

[0077] Finally, the business environment impact regression model and the moderating effect regression model are benchmarked and the influencing mechanism of the green total factor productivity of enterprises is determined based on the significance level of the interaction coefficient in the regression results. This step aims to draw accurate conclusions and suggestions to provide strong support for the decision-making and development of enterprises.

[0078] like Figure 3 As shown, based on the same inventive concept, this embodiment also proposes an evaluation system for the impact mechanism of green total factor productivity of an enterprise, which is applied to execute the above evaluation method, and the system includes:

[0079] The data collection module is used to obtain the green total factor productivity and digital transformation level of enterprises before and after the optimization of the business environment, and use them as variable data;

[0080] The first construction module is used to construct a parallel trend detection model using the variable data; perform empirical analysis on the variable data to extract target variable data that meets the parallel trend test in the parallel trend detection model;

[0081] A variable setting module, used to use the green total factor productivity in the target variable data as the explained variable, the enterprise's multi-dimensional business environment data as the explanatory variable, the enterprise's digital transformation level as the moderating variable, and the company control variable and the province control variable as the control variable;

[0082] The second building block is used to construct a business environment impact regression model based on the impact of business environment optimization on the green total factor productivity of enterprises, and to construct a regulatory effect regression model based on the regulatory effect of enterprise digital transformation on the relationship between business environment optimization and enterprise green total factor productivity;

[0083] A regression analysis module is used to perform a baseline regression on the business environment impact regression model and the regulatory effect regression model according to the target variable data, and determine the influencing mechanism of the enterprise's green total factor productivity according to the significance level of the interaction term coefficient in the regression result.

[0084] It should be noted here that each module in the above-mentioned evaluation system corresponds to steps S1 to S5 in implementing the above-mentioned evaluation method, and the instances and application scenarios implemented by multiple modules and corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiment 1.

[0085] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0086] In addition, each functional module in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0087] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0088] 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 mechanism of enterprise green total factor productivity, characterized in that: The method comprises: Obtain the green total factor productivity and digital transformation level of enterprises before and after the optimization of the business environment, and use them as variable data; Using the variable data to construct a parallel trend detection model; performing empirical analysis on the variable data to extract target variable data that meets the parallel trend test in the parallel trend detection model; The green total factor productivity in the target variable data is used as the explained variable, the enterprise's multi-dimensional business environment data is used as the explanatory variable, the enterprise's digital transformation level is used as the moderating variable, and the company control variables and the province control variables are used as the control variables; A business environment impact regression model is constructed based on the impact of business environment optimization on the green total factor productivity of enterprises, and a moderating effect regression model is constructed based on the moderating effect of enterprise digital transformation on the relationship between business environment optimization and enterprise green total factor productivity; The business environment impact regression model and the regulatory effect regression model are subjected to a baseline regression based on the target variable data, and the influencing mechanism of the enterprise's green total factor productivity is determined based on the significance level of the interaction term coefficient in the regression results.

2. The method for evaluating the impact mechanism of green total factor productivity of an enterprise according to claim 1, characterized in that: The parallel trend check includes: (a) Setting the parallel trend hypothesis: before the business environment is optimized, there is no significant change in the correlation between the green total factor productivity of enterprises, the multi-dimensional business environment data of enterprises, and the level of digital transformation of enterprises; (b) constructing parallel trend detection statistics: constructing statistics using the variable data to quantify the changes in the correlation between the variables before and after the optimization of the business environment; (c) Performing a parallel trend test: comparing the statistic with a preset significance level. If the significance level of the statistic exceeds the preset value, the parallel trend hypothesis is rejected, and it is considered that there is a non-parallel trend correlation change between the variables; conversely, if the significance level of the statistic does not exceed the preset value, the parallel trend hypothesis is accepted, and it is considered that the variables maintain a parallel trend. (d) Screening target variable data: Based on the results of the parallel trend test, screen out the variable data that meets the parallel trend test as the target variable data.

3. The method for evaluating the impact mechanism of green total factor productivity of an enterprise according to claim 1, characterized in that: The company control variables include enterprise scale, enterprise growth, operating cash flow, enterprise value and enterprise age; the province control variables include transportation infrastructure level, industrial structure upgrading, tax burden level, environmental regulation, fiscal support and urbanization level.

4. The method for evaluating the impact mechanism of green total factor productivity of an enterprise according to claim 1 is characterized by: The step of constructing a business environment impact regression model based on the impact of business environment optimization on the green total factor productivity of enterprises in the target variable data includes: The following business environment impact regression model is constructed to characterize the impact of business environment optimization on the green total factor productivity of enterprises: GTFP it =α0+α1Envi it +α2∑Controls it +∑Year+∑Industry+e it Among them, GTFP it represents the green total factor productivity of enterprise i in year t, Envi it represents the business environment of enterprise i in year t, Controls it represents the control variable, Year and Industry represent the time fixed effect and industry fixed effect respectively, ε it represents the error disturbance term, and α0, α1, and α2 are coefficients.

5. The method for evaluating the impact mechanism of green total factor productivity of an enterprise according to claim 1 is characterized by: The regulatory effect regression model is constructed based on the regulatory effect of enterprise digital transformation on the relationship between business environment optimization and enterprise green total factor productivity in the target variable data, including: The following moderating effect regression model is constructed to characterize the moderating effect of enterprise digital transformation on the business environment and enterprise total factor productivity: GTFP it =β0+β1Envi it +β2DCG it +β3Envi it ×DCG it +β4∑Controls it +∑Year+ ∑Industry+e it Among them, DCG it represents the digital transformation level of enterprise i in year t, Envi it ×DCG it It is the interaction term between business environment and enterprise digital transformation, and β0, β1, β2, β3 and β4 are coefficients.

6. The method for evaluating the impact mechanism of green total factor productivity of an enterprise according to claim 5 is characterized by: The influencing mechanism of the green total factor productivity of an enterprise is determined according to the significance level of the interaction term coefficient in the regression result, including: If the coefficient of the business environment on the green total factor productivity of the enterprise is significantly positive, it is determined that the optimization of the business environment can improve the green total factor productivity of the enterprise; If the coefficient of the interaction term between the business environment and enterprise digital transformation is significantly positive, it means that enterprise digital transformation has a significant positive regulatory effect on the improvement of enterprise green total factor productivity through business environment optimization.

7. An evaluation system for the impact mechanism of enterprise green total factor productivity, characterized in that: Applied to executing the evaluation method according to any one of claims 1 to 6, the system comprises: The data collection module is used to obtain the green total factor productivity and digital transformation level of enterprises before and after the optimization of the business environment, and use them as variable data; The first construction module is used to construct a parallel trend detection model using the variable data; perform empirical analysis on the variable data to extract target variable data that meets the parallel trend test in the parallel trend detection model; A variable setting module, used to use the green total factor productivity in the target variable data as the explained variable, the enterprise's multi-dimensional business environment data as the explanatory variable, the enterprise's digital transformation level as the moderating variable, and the company control variable and the province control variable as the control variable; The second building block is used to construct a business environment impact regression model based on the impact of business environment optimization on the green total factor productivity of enterprises, and to construct a regulatory effect regression model based on the regulatory effect of enterprise digital transformation on the relationship between business environment optimization and enterprise green total factor productivity; A regression analysis module is used to perform a baseline regression on the business environment impact regression model and the regulatory effect regression model according to the target variable data, and determine the influencing mechanism of the enterprise's green total factor productivity according to the significance level of the interaction term coefficient in the regression result.

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