Inter-regional power industry green total factor productivity comprehensive evaluation method and system

Abstract

The invention discloses an inter-regional power industry green total factor productivity comprehensive evaluation method and system, and relates to the field of production resources, and the method comprises the steps: obtaining power industry production data of a target region, and carrying out the arrangement of the data to obtain a limited production possibility set; according to the limited production possibility set, constructing a super-efficiency SBM model considering unexpected output and solving the super-efficiency SBM model to obtain a directional distance function; obtaining a green total element productivity based on the directional distance function; the green total factor productivity is decomposed into a technical efficiency change and a technical progress change, the technical efficiency change and the technical progress change are compared, and key factors causing the green total factor productivity change are determined. According to the method, the traditional input and output indexes are considered, and the unexpected output is also included, so that the green total factor productivity of the power industry can be comprehensively and accurately evaluated.

Description

Technical Field

[0001] The present invention relates to the field of production resource technology, and more specifically to a method and system for comprehensively evaluating green total factor productivity of an inter-regional electric power industry. Background Art

[0002] Under the general background of low-carbon development and sustainable development, as an important source of energy consumption and pollution emissions, it is crucial for the power industry to improve its green development efficiency.

[0003] Traditional methods for evaluating the productivity of the power industry still have limitations. For example, the Solow residual method leads to evaluation bias because its assumptions are difficult to meet in reality. Although the random frontier function method takes random errors into account, it relies on model assumptions and has subjectivity and uncertainty. The semi-parametric method is not widely used at the regional level. Although non-parametric methods such as data envelopment analysis have been improved, they have not been able to fully solve the problem of unexpected output. In addition, the existing evaluation of the production efficiency of the power industry is mostly focused on traditional productivity measurement, which often ignores the wide-ranging impact of unexpected output (such as pollutant emissions) on the environment and cannot fully and accurately reflect the green transformation process of the power industry. For example, the traditional Malmquist index does not take into account the problem of unexpected output and cannot be used for green development efficiency analysis.

[0004] Therefore, how to achieve accurate evaluation of the green total factor productivity of the power industry is an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0005] In view of this, the present invention provides a comprehensive evaluation method and system for the green total factor productivity of the inter-regional power industry, which overcomes the shortcomings of existing evaluation methods in dealing with unexpected outputs and provides a scientific and effective analysis method for the green transformation of the power industry.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention discloses a comprehensive evaluation method for green total factor productivity of the inter-regional power industry, comprising:

[0008] Obtain the power industry production data of the target area and organize it to obtain a finite production possibility set;

[0009] According to the finite production possibility set, a super-efficiency SBM model considering non-desirable output is constructed and solved to obtain a directional distance function; based on the directional distance function, green total factor productivity is obtained;

[0010] The green total factor productivity is decomposed into changes in technical efficiency and changes in technical progress, and the changes in technical efficiency and the changes in technical progress are compared to determine the key factors leading to the changes in the green total factor productivity.

[0011] Furthermore, the power industry production data includes input indicators and output indicators for each period and region; the input indicators represent the resource input of the power industry, including labor, installed capacity, and energy consumption; the output indicators include expected output and unexpected output, the expected output is power generation, and the unexpected output is CO2 emissions and SO2 emissions.

[0012] Furthermore, each region is considered as a production decision unit (DMU). j (j=1,2,...,n), n is the total number of regions; each of the production decision units includes an input vector x, an expected output y 1 , undesirable output y 2 , and efficiency weight θ; the finite production possibility set is expressed as:

[0013]

[0014] Among them, P|(x0,y0) represents the finite production possibility set, are x and y respectively 1 ,y 2 The corresponding slack variable.

[0015] Furthermore, the super efficiency SBM model is expressed as:

[0016]

[0017] Among them, β is the efficiency value of the decision-making unit, k represents the index of the production decision-making unit currently being evaluated, r is the total number of input types, p is the total number of expected output types, and q is the total number of undesired output types.

[0018] Furthermore, the directional distance function It is expressed as:

[0019]

[0020] Among them, G represents the global concept and t is the time period.

[0021] Furthermore, the green total factor productivity is expressed as:

[0022]

[0023] in, Represents the green total factor productivity from period t to period t+1.

[0024] Furthermore, the green total factor productivity is decomposed into technical efficiency change and technical progress change, and the formula is:

[0025]

[0026] in, They represent the green total factor productivity, technical efficiency change, and technological progress change from period t to period t+1, respectively. The logarithmic difference function of the green total factor productivity in period t is used to measure the change in production efficiency during period t.

[0027] Furthermore, the key factors causing the change in green total factor productivity are determined as follows: when the technical efficiency change is greater than 1, technical efficiency improvement is determined as the key factor; when the technological progress change is greater than 1, technological progress improvement is determined as the key factor.

[0028] The present invention also discloses a comprehensive evaluation system for green total factor productivity of the inter-regional power industry, including:

[0029] Data acquisition module: obtain the power industry production data of the target area and organize it to obtain a limited production possibility set;

[0030] SBM model module: based on the finite production possibility set, construct and solve the super-efficiency SBM model taking into account non-desirable output to obtain a directional distance function; based on the directional distance function, obtain the green total factor productivity;

[0031] Decomposition analysis module: decompose the green total factor productivity into technical efficiency changes and technical progress changes, compare the technical efficiency changes and the technical progress changes, and determine the key factors leading to the changes in the green total factor productivity.

[0032] Through the above technical solutions, it can be known that compared with the prior art, the present invention discloses a method and system for comprehensive evaluation of green total factor productivity in the power industry between regions, which not only considers traditional input and output indicators, but also incorporates non-expected output (pollution indicators), so as to comprehensively and accurately evaluate the green total factor productivity of the power industry. The shortcomings of the traditional Malmquist index method in dealing with non-expected output problems have been improved. By adopting the GML index, the dynamic changes in the green total factor productivity of the power industry can be effectively measured; further decomposed into technical efficiency changes and technological progress changes, the key factors that promote the improvement of green productivity in different periods can be identified, so that the performance of the power industry in resource utilization and environmental impact can be comprehensively evaluated, helping the power industry to better understand the efficiency improvement and technological progress in the green transformation process. The present invention realizes the accurate evaluation of the green total factor productivity of the power industry and provides data support for the formulation of more effective green development policies. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0034] Figure 1 It is a schematic diagram of the overall process of an embodiment of the present invention.

[0035] Figure 2 This is a schematic diagram of the fluctuation trend of the green total factor productivity index and decomposition results of the power industry according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] The embodiment of the present invention discloses a comprehensive evaluation method for green total factor productivity of the power industry in different regions. Figure 1 As shown, including:

[0038] Obtain the power industry production data of the target area and organize it to obtain a finite production possibility set;

[0039] According to the limited production possibility set, a super-efficiency SBM model considering non-desirable output is constructed and solved to obtain the directional distance function; based on the directional distance function, the green total factor productivity is obtained;

[0040] Decompose green total factor productivity into changes in technical efficiency and changes in technological progress, compare the changes in technical efficiency and technological progress, and identify the key factors that lead to changes in green total factor productivity.

[0041] In a specific embodiment, the production data of the power industry includes input indicators and output indicators for each period and each region; the input indicators represent the resource input of the power industry, including labor, installed capacity, and energy consumption; the output indicators include expected output and unexpected output, the expected output is power generation, and the unexpected output is CO2 emissions and SO2 emissions.

[0042] Specifically, the green total factor productivity of the power industry is the ratio of the input of the production unit factors to their output in the power industry. Therefore, the input index and output index of the power industry are mainly obtained, where the input index table is the resource input of the production unit, the expected output represents the effective output of the production unit, and the non-expected output index is the pollution index, which represents the negative impact of the production unit on the environment. By combining the data availability and the number of model input and output indicators (the number of indicators is less than or equal to one-third of the number of decision-making units), 6 key indicators are selected to construct the green total factor productivity index. The specific indicator variables are shown in Table 1.

[0043] Table 1 Definition and data sources of input-output variables in the power industry

[0044]

[0045] In a specific embodiment, each region is regarded as a production decision unit DMU j (j=1,2,...,n), n is the total number of regions; each production decision unit includes input vector x, expected output y 1 , undesirable output y 2 , and efficiency weight θ; the finite production possibility set is expressed as:

[0046]

[0047] Among them, P|(x0,y0) represents the finite production possibility set, x0 and y0 represent the input vector and output vector of the DMU being evaluated; are x and y respectively 1 ,y 2 The corresponding slack variables. The parameters i and j are used to index different DMUs.

[0048] In a specific embodiment, the super-efficiency SBM model is expressed as:

[0049]

[0050] Among them, β is the efficiency value of the decision-making unit, which represents the green development efficiency value of the power industry to be measured. When β<1, it indicates that the decision-making unit is in an invalid state. When β>1, it indicates that reducing output while keeping input unchanged or increasing input while keeping output unchanged can achieve an effective state. k represents the index of the production decision-making unit currently being evaluated, r is the total number of input types, p is the total number of expected output types, and q is the total number of unexpected output types.

[0051] In a specific embodiment, solving the super-efficiency SBM model under the current production possibility set can obtain the directional distance function It is expressed as:

[0052]

[0053] The above formula is used to measure the decision-making unit under a given input vector x t , expected output vector y 1t and the undesired output vector y 2t Here, G represents a global concept, i.e., a measurement process based on a global reference set, and t represents the time period.

[0054] In a specific embodiment, green total factor productivity is expressed as:

[0055]

[0056] in, represents the green total factor productivity from period t to period t+1; if An index value greater than 1 indicates that the green transformation efficiency has improved compared with the previous period.

[0057] Specifically, the super-efficiency SBM model considering non-expected output can only be used to measure the static green development efficiency of the power industry, but cannot measure its dynamic changes. The Malmquist index rule can effectively measure the change of total factor productivity from t to t+1, but the Malmquist index method does not consider the problem of non-expected output. The directional distance function is combined with the traditional Malmquist index to construct a productivity index considering non-expected output, so as to realize the dynamic analysis of green total factor productivity in the power industry. However, since this index does not have cyclic transitivity and has problems such as linear programming without solution, the global idea is integrated into it to construct a new GML index.

[0058] In a specific embodiment, green total factor productivity is decomposed into technical efficiency change and technical progress change, and the formula is:

[0059]

[0060] in, They represent the green total factor productivity, technical efficiency change, and technological progress change from period t to period t+1, respectively. The logarithmic difference function of green total factor productivity in period t is used to measure the change in production efficiency in period t. Technical efficiency change (GEC) refers to the maximum output change achieved by a production process under given input conditions; while technological change (GTC) measures the efficiency improvement or output improvement brought about by improvements in production technology or methods. The decomposition principle is used to subdivide the change in green total factor productivity into technical efficiency change and technological change, so as to identify and quantify the independent contribution of these two to productivity changes.

[0061] In a specific embodiment, the key factors leading to changes in green total factor productivity are determined as follows: when the change in technical efficiency is greater than 1, technical efficiency improvement is determined as the key factor; when the change in technological progress is greater than 1, technological progress improvement is determined as the key factor.

[0062] Specifically, after calculating and analyzing the dynamic changes of green total factor productivity in the power industry, the GML index is further used to decompose the green technology efficiency change index and green technology progress change index in different periods. By comparing the GEC and GTC of each region or decision-making unit in different periods, the key factors leading to changes in green total factor productivity in the power industry are identified. Specifically, by comparing the changes in GEC and GTC values ​​in each period, it can be determined whether the improvement of technical efficiency (GEC>1) or the promotion of technological progress (GTC>1) contributes more to the green transformation efficiency of the power industry. The results of this analysis will provide data support for optimizing the green development strategy of the power industry, and then provide a theoretical basis and practical guidance for improving the overall green production efficiency of the industry.

[0063] In a specific embodiment, the relevant data of the power industry from 2005 to 2021 are used as the research object, and the total factor productivity of the power industry in 30 provinces is calculated and analyzed. Due to the lack of special statistical data on human resource input in the power industry, this patent uses the number of employees at the end of the year in the power and heat production and supply industry as an alternative indicator for the total number of employees in the power industry. The descriptive statistical results are shown in Table 2.

[0064] Table 2 Descriptive statistics of input-output indicators from 2006 to 2021

[0065]

[0066] Based on the panel data of 30 provinces and cities, the green total factor productivity of the power industry in each province and city was calculated using MaxDea Pro software with an input-oriented approach. The research results show that from 2005 to 2021, the green total factor productivity index of the power industry showed the characteristics of cyclical fluctuations, and did not show an obvious upward or downward trend. The specific trends are as follows: Figure 2 shown.

[0067] From the perspective of overall decomposition, the change in technological progress is greater than that in technical efficiency, and the trend of technological progress and the green total factor productivity index is the same, so technological progress is the main driving force for changes in total factor productivity. The reason why the green total factor productivity index fluctuated greatly in the early stage is mainly due to the extensive production model at the expense of resources and the environment, which hindered the improvement of the green total factor productivity index. However, in recent years, the power industry is reshaping the power ecosystem towards a more low-carbon, electrified, intelligent, and market-oriented approach, and the changes in the green total factor productivity index have gradually stabilized and improved. The specific index mean values ​​and rankings are shown in Table 3. According to the calculation results of Table 3, there are significant differences in the mean values ​​of green total factor productivity in the power industry in various provinces and cities from 2005 to 2021.

[0068] Table 3 Mean and ranking of GTFP index of provinces and cities

[0069]

[0070] The embodiment of the present invention also discloses a comprehensive evaluation system for green total factor productivity of the inter-regional power industry, including:

[0071] Data acquisition module: obtain the power industry production data of the target area and organize it to obtain a limited production possibility set;

[0072] SBM model module: Based on the limited production possibility set, a super-efficiency SBM model considering non-expected output is constructed and solved to obtain a directional distance function; based on the directional distance function, green total factor productivity is obtained;

[0073] Decomposition analysis module: decompose green total factor productivity into changes in technical efficiency and changes in technological progress, compare the changes in technical efficiency and technological progress, and determine the key factors that lead to changes in green total factor productivity.

[0074] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0075] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A comprehensive evaluation method for green total factor productivity of the inter-regional power industry, characterized by: include: Obtain the power industry production data of the target area and organize it to obtain a finite production possibility set; According to the finite production possibility set, a super-efficiency SBM model considering non-desirable output is constructed and solved to obtain a directional distance function; based on the directional distance function, green total factor productivity is obtained; The green total factor productivity is decomposed into changes in technical efficiency and changes in technical progress, and the changes in technical efficiency and the changes in technical progress are compared to determine the key factors leading to the changes in the green total factor productivity.

2. According to claim 1, a comprehensive evaluation method for green total factor productivity of the inter-regional power industry is characterized by: The production data of the power industry includes input indicators and output indicators for each period and region; the input indicators represent the resource input of the power industry, including labor, installed capacity, and energy consumption; the output indicators include expected output and unexpected output, the expected output is power generation, and the unexpected output is CO2 emissions and SO2 emissions.

3. According to claim 1, a comprehensive evaluation method for green total factor productivity of the inter-regional power industry is characterized by: Treat each region as a production decision unit (DMU) j (j=1,2,...,n), n is the total number of regions; each of the production decision units includes an input vector x, an expected output y 1 , undesirable output y 2 , and efficiency weight θ; the finite production possibility set is expressed as: Among them, P|(x0,y0) represents the finite production possibility set, x, y respectively 1 ,y 2 The corresponding slack variable.

4. According to claim 3, a comprehensive evaluation method for green total factor productivity of the inter-regional power industry is characterized in that: The super-efficiency SBM model is expressed as: Among them, β is the efficiency value of the decision-making unit, k represents the index of the production decision-making unit currently being evaluated, r is the total number of input types, p is the total number of expected output types, and q is the total number of undesired output types.

5. According to claim 3, a comprehensive evaluation method for green total factor productivity of the inter-regional power industry is characterized in that: The directional distance function It is expressed as: Among them, G represents the global concept and t is the time period.

6. A comprehensive evaluation method for green total factor productivity of the inter-regional power industry according to claim 5, characterized in that: The green total factor productivity is expressed as: in, Represents the green total factor productivity from period t to period t+1.

7. The method for comprehensive evaluation of green total factor productivity of the inter-regional power industry according to claim 1 is characterized in that: The green total factor productivity is decomposed into technical efficiency change and technical progress change, and the formula is: in, They represent the green total factor productivity, technical efficiency change, and technological progress change from period t to period t+1, respectively. The logarithmic difference function of the green total factor productivity in period t is used to measure the change in production efficiency during period t.

8. The method for comprehensive evaluation of green total factor productivity of the inter-regional power industry according to claim 1 is characterized in that: The key factors causing the change in green total factor productivity are determined as follows: when the change in technical efficiency is greater than 1, technical efficiency improvement is determined as the key factor; when the change in technological progress is greater than 1, technological progress improvement is determined as the key factor.

9. A system for comprehensive evaluation of green total factor productivity in the inter-regional power industry, using a method for comprehensive evaluation of green total factor productivity in the inter-regional power industry as claimed in any one of claims 1 to 8, characterized in that: include: Data acquisition module: obtain the power industry production data of the target area and organize it to obtain a limited production possibility set; SBM model module: based on the finite production possibility set, construct and solve the super-efficiency SBM model taking into account non-desirable output to obtain a directional distance function; based on the directional distance function, obtain the green total factor productivity; Decomposition analysis module: decompose the green total factor productivity into technical efficiency changes and technical progress changes, compare the technical efficiency changes and the technical progress changes, and determine the key factors leading to the changes in the green total factor productivity.

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