Carbon management level evaluation method and terminal

Abstract

The application discloses a kind of carbon management level evaluation method and terminal, obtain the input data and output data of the power grid company to be evaluated and other power grid company, based on input data respectively calculate the value of input index of the power grid company to be evaluated and other power grid company, and based on output data respectively calculate the value of output index of the power grid company to be evaluated and other power grid company, according to the value of input index and the value of output index respectively calculate the total factor productivity of the power grid company to be evaluated and other power grid company, and based on total factor productivity determine the carbon management level evaluation result of the power grid company to be evaluated, based on the total factor productivity of other power grid company, the input structure of the carbon management of the power grid company to be evaluated for low evaluation result is improved using other power grid company, to more accurately, comprehensively evaluate carbon management level, and help power grid company to identify improvement space, optimize resource allocation, promote low carbon development.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of carbon management, and particularly relates to a carbon management level evaluation method and a terminal. BACKGROUND

[0002] The problems of power grid companies in carbon management level measurement mainly manifest in the imperfect measurement system and insufficient application of measurement results. Due to the lack of comprehensive and unified carbon emission accounting standards, power grid companies are difficult to accurately evaluate their own carbon emission level and emission reduction effect. At the same time, the complexity of carbon asset management and carbon emission right trading also makes performance measurement more difficult. These problems not only affect the regulation and control of carbon emission of power grid companies, but also limit their competitiveness and participation in the carbon market. The application of carbon management level measurement results is not widespread enough, and it cannot effectively guide enterprises to develop scientific emission reduction strategies and optimize resource allocation.

[0003] At present, common carbon management level measurement methods mainly include cost-benefit analysis, internal rate of return (IRR) and economic value added (EVA). Although these methods play an important role in evaluating input-output efficiency, they have some shortcomings, that is, cost-benefit analysis ignores long-term environmental benefits and social benefits; IRR is too sensitive to the time distribution of project cash flow; EVA ignores the influence of non-financial factors. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a carbon management level evaluation method and a terminal, which can more accurately and comprehensively evaluate the carbon management level.

[0005] In order to solve the above technical problems, a technical solution adopted by the present application is as follows:

[0006] A carbon management level evaluation method, comprising the steps of:

[0007] screening input indicators and output indicators of carbon management, and obtaining input data and output data of a power grid company to be evaluated and other power grid companies;

[0008] calculating values of the input indicators of the power grid company to be evaluated and the other power grid companies based on the input data, and calculating values of the output indicators of the power grid company to be evaluated and the other power grid companies based on the output data;

[0009] calculating total factor productivity of the power grid company to be evaluated and the other power grid companies according to the values of the input indicators and the values of the output indicators, and determining a carbon management level evaluation result of the power grid company to be evaluated based on the total factor productivity of the power grid company to be evaluated;

[0010] If the carbon management level evaluation result of the to-be-evaluated power grid company is low, the input structure of the other power grid companies to the carbon management of the to-be-evaluated power grid company is improved based on the total factor productivity of the other power grid companies.

[0011] To solve the above technical problems, another technical solution adopted by the present application is:

[0012] A carbon management level evaluation terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0013] The input indicators and output indicators of carbon management are screened, and input data and output data of a to-be-evaluated power grid company and other power grid companies are obtained;

[0014] The values of the input indicators of the to-be-evaluated power grid company and the other power grid companies are calculated based on the input data, and the values of the output indicators of the to-be-evaluated power grid company and the other power grid companies are calculated based on the output data;

[0015] The total factor productivity of the to-be-evaluated power grid company and the other power grid companies is calculated according to the values of the input indicators and the values of the output indicators, and the carbon management level evaluation result of the to-be-evaluated power grid company is determined based on the total factor productivity of the to-be-evaluated power grid company;

[0016] If the carbon management level evaluation result of the to-be-evaluated power grid company is low, the input structure of the other power grid companies to the carbon management of the to-be-evaluated power grid company is improved based on the total factor productivity of the other power grid companies.

[0017] The beneficial effects of the present application are that: the input indicators and output indicators of carbon management are screened, the input data and output data of the to-be-evaluated power grid company and other power grid companies are obtained, the values of the input indicators of the to-be-evaluated power grid company and other power grid companies are calculated based on the input data respectively, the values of the output indicators of the to-be-evaluated power grid company and other power grid companies are calculated based on the output data respectively, the total factor productivity of the to-be-evaluated power grid company and other power grid companies is calculated according to the values of the input indicators and the values of the output indicators respectively, and the carbon management level evaluation result of the to-be-evaluated power grid company is determined based on the total factor productivity of the to-be-evaluated power grid company. If the carbon management level evaluation result of the to-be-evaluated power grid company is low, the input structure of the to-be-evaluated power grid company in carbon management is improved based on the total factor productivity of other power grid companies. The present application measures the efficiency of the power grid company in carbon management input and output by introducing the total factor productivity, realizes the quantification of the carbon management level, provides a more comprehensive and dynamic evaluation perspective, so as to more accurately and comprehensively evaluate the carbon management level, and when the carbon management level evaluation result of the to-be-evaluated power grid company is low, the input structure of the to-be-evaluated power grid company in carbon management can be improved based on the total factor productivity of other power grid companies, so as to help the power grid company to identify the improvement space, optimize the resource allocation, and promote the low-carbon development. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A step flow chart of a carbon management level evaluation method according to an embodiment of the present application;

[0019] Figure 2 A structural schematic diagram of a carbon management level evaluation terminal according to an embodiment of the present application;

[0020] Figure 3 An evaluation schematic diagram in a carbon management level evaluation method according to an embodiment of the present application;

[0021] Figure 4 A schematic diagram in a carbon management level evaluation method according to an embodiment of the present application, in which as the parameter Lambda increases, each variable is gradually compressed to 0;

[0022] Figure 5 A schematic diagram in a carbon management level evaluation method according to an embodiment of the present application, in which the mean square error is used to optimize the parameter. DETAILED DESCRIPTION

[0023] To make the technical content, the achieved purposes and effects of the present application clear, the following will be described in detail in combination with the embodiments and the accompanying drawings.

[0024] Please refer to Figure 1 A carbon management level evaluation method, comprising the steps of:

[0025] screening input indicators and output indicators of carbon management, and obtaining input data and output data of the power grid company to be evaluated and other power grid companies;

[0026] calculating values of the input indicators of the power grid company to be evaluated and the other power grid companies respectively based on the input data, and calculating values of the output indicators of the power grid company to be evaluated and the other power grid companies respectively based on the output data;

[0027] calculating total factor productivity of the power grid company to be evaluated and the other power grid companies respectively according to the values of the input indicators and the values of the output indicators, and determining the carbon management level evaluation result of the power grid company to be evaluated based on the total factor productivity of the power grid company to be evaluated;

[0028] if the carbon management level evaluation result of the power grid company to be evaluated is low, improving the input structure of carbon management of the power grid company to be evaluated by the other power grid companies based on the total factor productivity of the other power grid companies.

[0029] From the above description, the beneficial effects of the present application are that: screening input indicators and output indicators of carbon management, and obtaining input data and output data of the power grid company to be evaluated and other power grid companies, calculating values of the input indicators of the power grid company to be evaluated and the other power grid companies respectively based on the input data, and calculating values of the output indicators of the power grid company to be evaluated and the other power grid companies respectively based on the output data, calculating total factor productivity of the power grid company to be evaluated and the other power grid companies respectively according to the values of the input indicators and the values of the output indicators, and determining the carbon management level evaluation result of the power grid company to be evaluated based on the total factor productivity of the power grid company to be evaluated, if the carbon management level evaluation result of the power grid company to be evaluated is low, improving the input structure of carbon management of the power grid company to be evaluated by the other power grid companies based on the total factor productivity of the other power grid companies, the present application introduces total factor productivity to measure the efficiency of power grid companies in carbon management input and output, realizes the quantification of carbon management level, provides a more comprehensive and dynamic evaluation perspective, so as to more accurately and comprehensively evaluate the carbon management level, and when the carbon management level evaluation result of the power grid company to be evaluated is low, the input structure of carbon management of the power grid company to be evaluated can be improved by the other power grid companies based on the total factor productivity, so as to help the power grid company to identify the improvement space, optimize the resource allocation, and promote the low-carbon development.

[0030] Further, the screening input indicators and output indicators of carbon management include:

[0031] The screening input indicators and output indicators of carbon management include:

[0032] The input indexes of carbon management are screened, and the input indexes include carbon emission accounting input indexes, carbon asset management input indexes, carbon emission right transaction input indexes, carbon emission treatment input indexes, and carbon assessment management input indexes.

[0033] The output indexes of carbon management are screened, and the output indexes include new energy output proportion indexes, carbon emission amount indexes, carbon intensity indexes, main transformer load rate indexes, new energy fluctuation indexes, new energy power station investment return rate indexes, and electric energy terminal consumption proportion indexes.

[0034] As can be known from the above description, the input indexes include carbon emission accounting input indexes, carbon asset management input indexes, carbon emission right transaction input indexes, carbon emission treatment input indexes, and carbon assessment management input indexes, and the output indexes include new energy output proportion indexes, carbon emission amount indexes, carbon intensity indexes, main transformer load rate indexes, new energy fluctuation indexes, new energy power station investment return rate indexes, and electric energy terminal consumption proportion indexes. The output indexes of carbon management consider low-carbon environmental protection, economic, technical, and performance angles. The input indexes and the output indexes consider technical progress, innovation capability, resource allocation, and other factors, can comprehensively and effectively quantify the input and output conditions of carbon management, and comprehensively reflect technical progress and efficiency changes.

[0035] Further, the input data and the output data of the power grid company to be evaluated and other power grid companies are obtained, including:

[0036] According to the operation business of the power grid company, the operation types of the power grid company are divided into multiple operation centers;

[0037] The power grid company to be evaluated and other power grid companies are respectively taken as decision units, and the input of the power grid company to be evaluated and the other power grid companies in each operation center in previous years, the input in each operation activity of carbon management in previous years, the input of each sub-link under each operation activity of carbon management in previous years, and the input in each operation center in the current year are collected;

[0038] The input in each operation center in previous years, the input in each operation activity of carbon management in previous years, the input of each sub-link under each operation activity of carbon management in previous years, and the input in each operation center in the current year are respectively subjected to data standardization processing to obtain the processed input in each operation center in previous years, the input in each operation activity of carbon management in previous years, the input of each sub-link under each operation activity of carbon management in previous years, and the input in each operation center in the current year;

[0039] According to the processed input of each operation center in previous years, the input of each operation activity in carbon management in previous years, the input of each sub-link under each operation activity in carbon management in previous years, and the input of each operation center in the current year, input data of the power grid company to be evaluated and the other power grid companies are obtained;

[0040] New energy output, traditional energy output, carbon emissions, unit GDP, actual load of main transformer, rated capacity of main transformer, average electricity price in the current year, investment amount of power plant, and primary energy conversion data of the power grid company to be evaluated and the other power grid companies are collected;

[0041] According to the new energy output, the traditional energy output, the carbon emissions, the unit GDP, the actual load of main transformer, the rated capacity of main transformer, the average electricity price in the current year, the investment amount of power plant, and the primary energy conversion data, output data of the power grid company to be evaluated and the other power grid companies are obtained.

[0042] As can be seen from the above description, according to the processed input of each operation center in previous years, the input of each operation activity in carbon management in previous years, the input of each sub-link under each operation activity in carbon management in previous years, and the input of each operation center in the current year, input data of the power grid company to be evaluated and the other power grid companies are obtained, and according to the new energy output, the traditional energy output, the carbon emissions, the unit GDP, the actual load of main transformer, the rated capacity of main transformer, the average electricity price in the current year, the investment amount of power plant, and the primary energy conversion data, output data of the power grid company to be evaluated and the other power grid companies are obtained, so as to accurately calculate the values of input indicators and output indicators subsequently.

[0043] Further, the calculation of the values of input indicators of the power grid company to be evaluated and the other power grid companies based on the input data respectively includes:

[0044] The values of input indicators of the power grid company to be evaluated and the other power grid companies are calculated based on the input data respectively according to the grey correlation degree analysis method.

[0045] As can be seen from the above description, the values of input indicators of the power grid company to be evaluated and the other power grid companies are calculated based on the input data respectively according to the grey correlation degree analysis method, the grey correlation degree analysis not only considers the correlation between various factors, but also considers the comprehensive influence of various factors on the result, which can more comprehensively evaluate the correlation degree between factors, and further make the calculated values of input indicators more accurate and reliable.

[0046] Further, the input indicators of the power grid company to be evaluated and the other power grid companies are calculated based on the grey correlation degree analysis method according to the input data, and the calculation includes:

[0047] The processed input of each operation center in each year is taken as a reference sequence, the processed input of each operation activity in carbon management in each year is taken as a comparison sequence, and a first correlation coefficient between the processed input of each operation center in each year and the processed input of each operation activity in carbon management in each year is calculated.

[0048] The processed input of each operation activity in carbon management in each year is taken as a reference sequence, the processed input of each sub-link under each operation activity in carbon management in each year is taken as a comparison sequence, and a second correlation coefficient between the processed input of each operation activity in carbon management in each year and the processed input of each sub-link under each operation activity in carbon management in each year is calculated.

[0049] A first grey correlation degree is calculated according to the first correlation coefficient, and a second grey correlation degree is calculated according to the second correlation coefficient.

[0050] The first grey correlation degree and the second grey correlation degree are respectively standardized to obtain a standardized first grey correlation degree and a standardized second grey correlation degree, and the standardized first grey correlation degree is taken as a first weight and the standardized second grey correlation degree is taken as a second weight.

[0051] The processed input of each operation center in the current year is multiplied by the first weight to obtain the input of each operation activity in carbon management in the current year of the power grid company to be evaluated and the other power grid companies.

[0052] The input of each operation activity in carbon management in the current year is multiplied by the second weight to obtain the values of the carbon emission accounting input indicator, the carbon asset management input indicator, the carbon emission right transaction input indicator, the carbon emission treatment input indicator and the carbon assessment management input indicator of the power grid company to be evaluated and the other power grid companies in the current year.

[0053] From the above description, it can be known that after the first grey correlation degree and the second grey correlation degree are respectively normalized, the normalized first grey correlation degree is taken as the first weight, the normalized second grey correlation degree is taken as the second weight, and then the values of the carbon emission accounting input indicators, the carbon asset management input indicators, the carbon emission right transaction input indicators, the carbon emission treatment input indicators and the carbon assessment management input indicators of the grid company to be evaluated and other grid companies in the current year are calculated according to the processed input, the first weight and the second weight in each operation center in the current year, so that the input situation of carbon management is accurately evaluated.

[0054] Further, the calculation of the output indicator values of the grid company to be evaluated and the other grid companies based on the output data respectively includes:

[0055] According to the new energy output and the traditional energy output, the new energy output proportion indicators of the grid company to be evaluated and the other grid companies are calculated respectively;

[0056] According to the carbon emissions, the carbon emission indicators of the grid company to be evaluated and the other grid companies are obtained respectively;

[0057] According to the unit gross national product and the carbon emission indicators, the carbon intensity indicators of the grid company to be evaluated and the other grid companies are calculated respectively;

[0058] According to the load actually borne by the main transformer and the rated capacity of the main transformer, the main transformer load rate indicators of the grid company to be evaluated and the other grid companies are calculated respectively;

[0059] According to the new energy output, the new energy fluctuation indicators of the grid company to be evaluated and the other grid companies are calculated respectively;

[0060] According to the average electricity price in the current year and the investment amount of the power plant, the new energy power station investment return rate indicators of the grid company to be evaluated and the other grid companies are calculated respectively;

[0061] According to the primary energy conversion data, the terminal consumption proportion of electric energy indicators of the grid company to be evaluated and the other grid companies are calculated respectively.

[0062] From the above description, it can be known that the values of each output indicator can be directly obtained by mathematical calculation using the obtained output data, and the output level of carbon management is effectively quantified.

[0063] Further, the calculation of the total factor productivity of the grid company to be evaluated and the other grid companies based on the input indicator values and the output indicator values respectively includes:

[0064] a minimum input amount that can be compressed by the grid company under the consideration that the output will change as a target function, and establishing constraint conditions of the target function based on values of the input indicators and values of the output indicators;

[0065] solving the target function under the constraint conditions to obtain total factor productivity of the grid company to be evaluated and the other grid companies.

[0066] From the above description, it can be seen that a minimum input amount that can be compressed by the grid company under the consideration that the output will change is taken as a target function, and constraint conditions of the target function are established based on values of the input indicators and values of the output indicators, the target function is solved under the constraint conditions, and total factor productivity of the grid company to be evaluated and the other grid companies is obtained. The introduction of total factor productivity helps the grid company to pursue economic benefits while also paying attention to environmental and social responsibilities and promoting sustainable development.

[0067] Further, the minimum input amount that can be compressed by the grid company under the consideration that the output will change is taken as the target function specifically as:

[0068] min θ;

[0069] In the formula, θ represents an input amount that can be compressed by the grid company under the consideration that the output will change;

[0070] The constraint conditions of the target function established based on the values of the input indicators and the values of the output indicators include:

[0071]

[0072] In the formula, x ij represents a value of an i-th input indicator of carbon management of a j-th grid company, λ j represents a linear combination coefficient of the j-th grid company, cn represents a total number of the grid company to be evaluated and the other grid companies, x i0 represents a value of an i-th input indicator of carbon management of a current grid company, p represents a total number of input indicators, y rj represents a value of an r-th output indicator of carbon management of the j-th grid company, q represents a total number of output indicators, y r0 represents a value of an r-th output indicator of carbon management of the current grid company.

[0073] From the above description, it can be seen that the BCC model of the data envelopment method is used to solve the total factor productivity. The BCC model assumes that the decision unit is in the variable return to scale (VRS) condition, is used to measure pure technical efficiency and scale efficiency, can more accurately reflect the actual situation, and realizes more reasonable carbon management level evaluation.

[0074] Further comprising:

[0075] Selecting a plurality of characteristic variables affecting the carbon management level;

[0076] Screening the plurality of characteristic variables using a Lasso regression algorithm to obtain key characteristic variables;

[0077] Using the key characteristic variables for multiple regression to obtain a prediction model;

[0078] According to the prediction model, the carbon management level of the to-be-evaluated power grid company is predicted to obtain a prediction result.

[0079] As can be seen from the above description, using the Lasso regression algorithm to screen the plurality of characteristic variables can reduce the system complexity, identify the most significant factors affecting the prediction result, and then combine the multiple regression to predict the carbon management level in future years, thereby improving the prediction accuracy of the future carbon management input and output level.

[0080] Referring to Figure 2 Another embodiment of the present application provides a carbon management level evaluation terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements each step of the above-mentioned carbon management level evaluation method when executing the computer program.

[0081] The above-mentioned carbon management level evaluation method and terminal of the present application can be applied to the carbon management level evaluation scene of a power grid company, and the following specific embodiments are described:

[0082] Referring to Figure 1 , Figures 3 to 5 An embodiment of the present application is:

[0083] A carbon management level evaluation method, comprising the steps of:

[0084] S1, screening input indicators and output indicators of carbon management, and obtaining input data and output data of a to-be-evaluated power grid company and other power grid companies, as shown in Figure 3 Specifically, S1-S18 are included:

[0085] S11, integrating various inputs of carbon management to obtain input indicators of carbon management, wherein the input indicators include carbon emission accounting input indicators, carbon asset management input indicators, carbon emission right trading input indicators, carbon emission treatment input indicators, and carbon assessment management input indicators.

[0086] Among them, the carbon emission accounting input index: carbon emission accounting is the primary step of carbon management, and is the basic prerequisite for carrying out carbon emission reduction work. Carbon emission accounting refers to the process of systematically calculating and evaluating the amount of carbon dioxide and greenhouse gas emissions generated by the power grid company within a certain period of time through quantitative and qualitative methods. Its goal is to understand and quantify carbon footprint in order to develop emission reduction strategies, improve energy efficiency and promote sustainable development. This process usually involves data collection, application of emission factors and compliance with reporting standards.

[0087] Carbon asset management input index: carbon assets refer to new assets generated by carbon emission trading mechanisms, i.e. under the mandatory carbon emission trading mechanism or the resource-based carbon emission trading mechanism, carbon emission quotas, emission reduction credit and related activities that can directly or indirectly affect greenhouse gas emissions. Carbon asset management refers to the systematic management and optimization of carbon-related assets to achieve the goal of greenhouse gas emission reduction and sustainable development. This process includes carbon credit trading, assessment and development of carbon offset projects, and risk management related to climate change policies. Carbon assets can be carbon credits obtained through emission reduction activities, carbon storage projects or related technology investments.

[0088] Carbon emission rights trading input index: carbon emission rights trading is a global market trading mechanism aimed at low-carbon emission reduction, i.e. treating carbon dioxide emission rights as a commodity for trading. The buyer pays a certain amount of money to obtain a certain amount of carbon dioxide emission rights. Carbon market has the attribute characteristics of capital market, and carbon emission rights trading, like financial commodities, has uncertainty, and its transaction price is affected by the risks of changes in carbon market supply and demand, ecological environment, etc. However, carbon trading guides the direction of capital investment in the financial market, and will make the investment market biased towards projects and enterprises that can create carbon assets.

[0089] Carbon emission governance input index: carbon emission governance refers to the optimization of power grid operation and management to reduce carbon emissions in the process of power supply to achieve the goal of sustainable development. This includes the use of advanced power dispatching and management systems, the improvement of renewable energy access and utilization efficiency, and the promotion of electric energy substitution and electric transportation development. Power grid companies also need to invest in smart grid technology and energy storage systems to enhance the flexibility and reliability of the grid. In terms of investment, it mainly includes technology research and development and introduction, supporting facility construction, personnel training and improvement of monitoring and evaluation system. These inputs will support the power grid company in promoting low-carbon operation, improving energy efficiency and realizing green power grid, thus contributing to the global emission reduction target.

[0090] Carbon management investment indicators: Carbon management refers to the overall management of evaluating and assessing the performance of carbon management in the company based on the responsibility system. The company's carbon management investment can be divided into two parts: the construction and implementation of the company's internal carbon management evaluation system, and the related investment in the comprehensive use of legal, economic and administrative regulations by the government, the public and the media to regulate the company's carbon emissions.

[0091] S12, screening the output indicators of carbon management, as shown in the figure, the output indicators include new energy output ratio index, carbon emission index, carbon intensity index, main transformer load rate index, new energy volatility index, new energy power station investment return rate index and terminal energy consumption ratio index. Figure 3

[0092] Among them, the new energy output ratio index: the new energy output ratio refers to the proportion of new energy generation to total power generation within a certain period of time, which is used to measure the contribution of new energy in the power grid.

[0093] Carbon emission index: carbon emission refers to the emission of greenhouse gases such as carbon dioxide in the process of power production and supply due to the burning of fossil fuels (such as coal, natural gas, etc.), measured in tons (t).

[0094] Carbon intensity index: carbon intensity refers to the amount of carbon dioxide emissions per unit of GDP growth.

[0095] Main transformer load rate index: main transformer load rate refers to the ratio of the load of main transformer to its capacity, which is one of the important indicators to measure the efficiency of main transformer. The level of main transformer load rate directly affects the stability of power system operation, the life of equipment and the quality of electric energy and other key factors.

[0096] New energy volatility index: new energy volatility refers to the instability of power generation in the process of renewable energy power generation such as wind and solar energy due to natural factors (such as weather changes, light intensity and wind speed fluctuations). Compared with traditional energy, the power generation capacity of new energy is greatly affected by environmental and climate conditions, so its output power will show great uncertainty and change range.

[0097] Terminal energy consumption ratio index: terminal energy consumption ratio refers to the proportion of electric energy in total terminal energy consumption, which is used to measure the importance and influence of electric energy in energy use, to evaluate the use efficiency and electrification level of electric energy, to guide energy policy and to promote the promotion of clean energy.

[0098] ​From the above, the selection of output indicators for carbon management is considered from a comprehensive perspective, such as environmental, economic, technical and performance perspectives. The indicators considering environmental perspective include carbon emission indicators, terminal consumption of electric energy proportion indicators and new energy output proportion indicators, which provide clear emission reduction targets and evaluation criteria for power grid companies. Through these indicators, companies can monitor and report their carbon footprint, evaluate the effectiveness of carbon management strategies, respond to government policies and market demand, and enhance public trust and corporate reputation, which not only helps to achieve environmental sustainability, but also is the key to fulfilling social responsibility and promoting green development. The indicators considering economic perspective include new energy power station investment return rate indicators. Carbon management can reduce operating costs and energy waste by optimizing energy use, improving energy efficiency and adopting clean technologies, thereby bringing direct economic benefits to power grid companies. In addition, through carbon trading, tax incentives and green financial incentives, carbon management can also open up new sources of income, and the evaluation of new energy power station investment return rate indicators helps enterprises understand the financial value of carbon management, make more informed investment decisions, and maintain competitiveness in the market. The indicators considering technical perspective include new energy volatility indicators and main transformer load rate indicators, etc. The effectiveness of carbon management needs to be evaluated by including these indicators, which are crucial for ensuring grid stability, optimizing energy distribution, reducing losses and improving energy efficiency. Through these indicators, power grid companies can accurately measure the impact of carbon reduction measures on operational efficiency, achieving a win-win situation of economic benefits and environmental responsibility. In addition, monitoring of technical indicators conforms to national energy policies and international environmental standards, helping power grid companies establish a green and efficient image in the market and promote the implementation of sustainable development strategies. The indicators considering performance perspective include carbon intensity indicators, which are crucial for measuring and monitoring the effectiveness of emission reduction measures. It provides a quantitative perspective, showing the amount of greenhouse gas emissions per unit of economic output, thereby providing policymakers and businesses with a clear emission reduction target and evaluation benchmark. By tracking changes in carbon intensity, it can be assessed whether carbon management measures effectively reduce the carbon footprint of economic activities and promote the transition to a low-carbon economy.

[0099] Due to the difficulty of quantifying the economic cost of some behaviors in the carbon management activities of power grid companies, the activity-based costing method is used to obtain the input data of power grid companies. The focus of activity-based costing is "activity", which is to determine and calculate various activities involving the use of enterprise resources. The cost of resources used is allocated to the corresponding activities according to certain standards and principles, and then the cost driver is selected to allocate all activity costs to the cost calculation target (product or service) according to industry standards and distribution system. The guiding ideology of activity-based costing is "activity consumes resources, output consumes activity", which is described in detail in S13-S16.

[0100] S13, dividing the operation types of the power grid company into a plurality of operation centers according to the operation business of the power grid company.

[0101] In an optional embodiment, the operation types of the power grid company are divided into 13 operation centers, i.e., a plan development center, an innovation management center, a production technology center, a market transaction center, a science and technology information center, a safety supervision center, a personnel work center, a human resource center, a financial center, a policy research center, an audit work center, a regulation supervision center, and a logistics affairs center, according to the operation business of the power grid company.

[0102] S14, taking the power grid company to be evaluated and other power grid companies as decision-making units (DMUs) respectively, and collecting the input A1 of each operation center of the power grid company to be evaluated and the other power grid companies in previous years, the input A2 of each operation activity of carbon management in previous years, the input A3 of each sub-link under each operation activity of carbon management in previous years, and the input B1 of each operation center in the current year, to ensure that the carbon management levels of each power grid company are comparable and referable, as shown in Figure 3 .

[0103] S15, performing data standardization processing on the input of each operation center in previous years, the input of each operation activity of carbon management in previous years, the input of each sub-link under each operation activity of carbon management in previous years, and the input of each operation center in the current year, to obtain the processed input of each operation center in previous years, the input of each operation activity of carbon management in previous years, the input of each sub-link under each operation activity of carbon management in previous years, and the input of each operation center in the current year.

[0104] The formula of the data standardization processing is as follows:

[0105]

[0106] In the formula, x ij represents the value of the jth index of the i th year of the power grid company, x j represents the mean value of the jth index in all years, and z ij represents the value of the jth index of the i th year of the power grid company after standardization.

[0107] Let the standardization matrix be Z, and the elements in Z be z ij , and the expression be as follows:

[0108] Z=(z ij ) n×m , (i=1, 2, 3,..., n; j=1, 2, 3,..., m);

[0109] In the formula, n represents the total number of years of the obtained data, and m represents the total number of indexes of the power grid company.

[0110] For example, for the input of the power grid company in each operation center in each year to be evaluated, the input of the power grid company in each operation center in each year to be evaluated is substituted into the above formula, and the processed input of the power grid company in each operation center in each year to be evaluated is obtained, and the like.

[0111] S16, obtaining the input data of the power grid company to be evaluated and the other power grid companies according to the processed input of each operation center in each year, the input of each operation activity of carbon management in each year, the input of each sub-link under each operation activity of carbon management in each year, and the input of each operation center in the current year.

[0112] For example, the input data of the power grid company to be evaluated is obtained according to the processed input of each operation center in each year, the processed input of each operation activity of carbon management in each year, the processed input of each sub-link under each operation activity of carbon management in each year, and the processed input of each operation center in the current year, and the input data of the other power grid companies is obtained according to the processed input of each operation center in each year, the processed input of each operation activity of carbon management in each year, the processed input of each sub-link under each operation activity of carbon management in each year, and the processed input of each operation center in the current year.

[0113] S17, collecting new energy output, traditional energy output, carbon emission, unit gross national product, actual load of main transformer, rated capacity of main transformer, average electricity price in the current year, investment amount of power plant, and primary energy conversion data of the power grid company to be evaluated and the other power grid companies.

[0114] S18, obtaining output data of the power grid company to be evaluated and the other power grid companies according to the new energy output, the traditional energy output, the carbon emission, the unit gross national product, the actual load of main transformer, the rated capacity of main transformer, the average electricity price in the current year, the investment amount of power plant, and the primary energy conversion data.

[0115] For example, the output data of the power grid company to be evaluated is obtained according to the new energy output, traditional energy output, carbon emission, unit GDP, actual load of main transformer, rated capacity of main transformer, average price of the current year, investment amount of power plant and primary energy conversion data of the power grid company to be evaluated, and the output data of other power grid companies is obtained according to the new energy output, traditional energy output, carbon emission, unit GDP, actual load of main transformer, rated capacity of main transformer, average price of the current year, investment amount of power plant and primary energy conversion data of other power grid companies.

[0116] S2, calculating the values of input indicators of the power grid company to be evaluated and the other power grid companies respectively based on the input data, and calculating the values of output indicators of the power grid company to be evaluated and the other power grid companies respectively based on the output data, specifically including S21-S28:

[0117] S21, calculating the values of input indicators of the power grid company to be evaluated and the other power grid companies respectively based on the input data according to the grey correlation degree analysis method, the grey correlation degree analysis method is a method for measuring the correlation degree between factors according to the similarity or difference of the development trend of the factors, that is, the "grey correlation degree". In the system development process, if the change trend of two factors has consistency, that is, the degree of synchronization change is high, it can be said that the correlation degree of the two is high, otherwise, it is low. Specifically including S211-S216:

[0118] S211, taking the processed input of each operation center over the years as a reference sequence, taking the processed input of each operation activity of carbon management over the years as a comparison sequence, and calculating the first correlation coefficient between the processed input A1 of each operation center over the years and the processed input A2 of each operation activity of carbon management over the years.

[0119] S212, taking the processed input of each operation activity of carbon management over the years as a reference sequence, taking the processed input of each sub-link under each operation activity of carbon management over the years as a comparison sequence, and calculating the second correlation coefficient between the processed input A2 of each operation activity of carbon management over the years and the processed input A3 of each sub-link under each operation activity of carbon management over the years.

[0120] The formula for calculating the correlation coefficient between the reference sequence and the comparison sequence (including the first correlation coefficient and the second correlation coefficient) is:

[0121]

[0122] In the formula, x0(k) represents the reference sequence, x i(k) represents the correlation coefficient between the reference sequence and the comparison sequence, a represents the minimum difference between the two poles, b represents the maximum difference between the two poles, p represents the resolution coefficient, which is 0.5 in the present embodiment, k represents the year sequence number, i represents the comparison sequence number, and e represents the total number of comparison sequences. i (k)) represents the correlation coefficient between the reference sequence and the comparison sequence, a represents the minimum difference between the two poles, b represents the maximum difference between the two poles, p represents the resolution coefficient, which is 0.5 in the present embodiment, k represents the year sequence number, i represents the comparison sequence number, and e represents the total number of comparison sequences.

[0123] For example, the first correlation coefficient between the processed historical investment of each operation center of the power grid company to be evaluated and the processed historical investment in each operation activity of carbon management is calculated, the processed historical investment in each operation activity of carbon management is taken as the reference sequence, the processed historical investment in each sub-link of each operation activity of carbon management is taken as the comparison sequence, and the above formula is substituted to obtain the first correlation coefficient of the power grid company to be evaluated. The second correlation coefficient between the processed historical investment in each operation activity of carbon management and the processed historical investment in each sub-link of each operation activity of carbon management is calculated, the processed historical investment in each operation activity of carbon management is taken as the reference sequence, the processed historical investment in each sub-link of each operation activity of carbon management is taken as the comparison sequence, and the above formula is substituted to obtain the second correlation coefficient of the power grid company to be evaluated. The same is true for other power grid companies.

[0124] S213, calculating a first gray correlation degree according to the first correlation coefficient and a second gray correlation degree according to the second correlation coefficient. The formula for calculating the gray correlation degree (including the first gray correlation degree and the second gray correlation degree) is:

[0125]

[0126] In the formula, y(x0, x i ) represents the gray correlation degree.

[0127] For example, the first gray correlation degree is obtained by substituting the first correlation coefficient of the power grid company to be evaluated into the formula, the second gray correlation degree is obtained by substituting the second correlation coefficient of the power grid company to be evaluated into the formula, and the same is true for other power grid companies.

[0128] S214, standardizing the first gray correlation degree and the second gray correlation degree respectively to obtain a standardized first gray correlation degree and a standardized second gray correlation degree, and taking the standardized first gray correlation degree as a first weight and the standardized second gray correlation degree as a second weight. The standardization processing makes the first gray correlation degree and the second gray correlation degree between 0 and 1 for comparison.

[0129] S215. Multiply the processed input B1 at each operation center in the current year by the first weight to obtain the input of the power grid company to be evaluated and the other power grid companies in various carbon management operations in the current year.

[0130] For example, the input of the power grid company to be evaluated in each operation center in the current year after processing is multiplied by the first weight of the power grid company to be evaluated to obtain the input of the power grid company to be evaluated in various carbon management operations in the current year. The input of other power grid companies in each operation center in the current year after processing is multiplied by the first weight of other power grid companies to obtain the input of other power grid companies in various carbon management operations in the current year.

[0131] S216. Multiply the inputs in various carbon management activities in the current year by the second weight to obtain the values ​​of the input indicators for carbon emission accounting, carbon asset management, carbon emission trading, carbon emission control, and carbon assessment management for the current year of the power grid company to be evaluated and the other power grid companies.

[0132] For example, multiplying the input of the power grid company to be evaluated in various carbon management activities in that year by the second weight of the power grid company to be evaluated yields the values ​​of the input indicators for carbon emission accounting, carbon asset management, carbon emission trading, carbon emission control, and carbon assessment management for that power grid company in that year. Multiplying the input of other power grid companies in various carbon management activities in that year by the second weight of other power grid companies yields the values ​​of the input indicators for carbon emission accounting, carbon asset management, carbon emission trading, carbon emission control, and carbon assessment management for other power grid companies in that year.

[0133] S22. Calculate the value of the renewable energy output ratio for the power grid company to be evaluated and the other power grid companies based on the renewable energy output and the traditional energy output, respectively. Specifically:

[0134]

[0135] In the formula, F represents the value of the proportion of new energy power output, W1 represents the power output of new energy, and W2 represents the power output of traditional energy, including thermal power, nuclear power, hydropower, natural gas and biomass energy.

[0136] For example, by substituting the renewable energy output and traditional energy output of the power grid company to be evaluated into the above formula, we can obtain the value of the renewable energy output ratio of the power grid company to be evaluated. The same applies to other power grid companies.

[0137] S23, obtaining the value of the carbon emission index of the power grid company to be evaluated and the other power grid companies according to the carbon emission, specifically:

[0138]

[0139] Coal-fired emission = coal consumption in the current year x comprehensive coal-fired emission factor;

[0140] Fuel oil emission = oil consumption in the current year x comprehensive fuel oil emission factor;

[0141] Gas emission = natural gas consumption in the current year x comprehensive gas emission factor;

[0142] Carbon dioxide emission contained in the power imported from the jth provincial power grid = power imported from the jth provincial power grid in the current year x average CO2 emission factor of the jth provincial power grid;

[0143] Carbon dioxide emission contained in the power exported from the local area = power exported from the local area x average CO2 emission factor of the provincial power grid in the local area.

[0144] The above carbon dioxide emission is the value of the carbon emission index.

[0145] S24, calculating the value of the carbon intensity index of the power grid company to be evaluated and the other power grid companies according to the unit GNP and the value of the carbon emission index, specifically:

[0146] Carbon emission intensity = value of carbon emission index / unit GNP.

[0147] The carbon emission intensity is the value of the carbon intensity index.

[0148] S25, calculating the value of the main transformer load rate index of the power grid company to be evaluated and the other power grid companies according to the actual load of the main transformer and the rated capacity of the main transformer, specifically:

[0149] Main transformer load rate = actual load of main transformer / rated capacity of main transformer x 100%.

[0150] The main transformer load rate is the value of the main transformer load rate index. The actual load of the main transformer refers to the comprehensive value of the small electric energy connected to the main transformer, the rated capacity of the main transformer refers to the maximum capacity designed for the main transformer, and the main transformer load rate directly affects the operation state and use efficiency of the main transformer.

[0151] S26, calculating the value of the new energy fluctuation index of the power grid company to be evaluated and the other power grid companies according to the new energy output, specifically:

[0152]

[0153] In the formula, K represents the value of the new energy volatility index, P WT,t represents the output value of wind power at time t, P WT,t-1 represents the output value of wind power at time t-1, P WT,max represents the maximum output of wind power, P PV,t represents the output value of photovoltaic at time t, P PV,t-1 represents the output value of photovoltaic at time t-1, P PV,max represents the maximum output of photovoltaic.

[0154] S27, respectively calculate the value of the new energy power station investment return rate index of the to-be-evaluated power grid company and the other power grid companies according to the average electricity price of the year and the investment amount of the power plant, the investment amount of the power plant includes the initial investment amount and the total subsequent investment amount, specifically:

[0155] New energy power station investment return rate = {∑[power generation × (1-yearly decay rate) × average electricity price of the year - yearly operation cost]} / (initial investment amount + total subsequent investment amount) × 100%;

[0156] In the formula, the power generation is the total power generation of the new energy power station per year, the unit is (×104kW·h), the yearly decay rate is that the power generation capacity of the generator set (service life of the power generation component) of the power station will decay every year after being put into operation, for example, the service life of the photovoltaic power generation component is generally 25 years, the component life decay satisfies that it is less than 5% within 5 years, less than 10% within 10 years, and less than 20% within 25 years, the component decay is less than 2% in the first year, and the yearly decay rate is linearly changed thereafter, the annual average electricity price is the sum of the daily average electricity price divided by the calculation days, the unit is yuan / day, the initial investment amount is the total cost of the project from the beginning of construction to the beginning of operation, the unit is yuan, and the total subsequent investment amount is the total investment amount expected from the beginning of operation to the retirement of the power station, the unit is yuan.

[0157] S28, respectively calculate the value of the terminal energy consumption proportion of electric energy index of the to-be-evaluated power grid company and the other power grid companies according to the primary energy conversion data, specifically:

[0158] Proportion of electric energy in terminal energy consumption = (primary energy electric energy conversion proportion × electric energy conversion efficiency) / (primary energy electric energy conversion proportion × electric energy conversion efficiency + primary energy non-electric energy conversion proportion × processing conversion efficiency);

[0159] The proportion of electric energy in terminal energy consumption is the value of the terminal energy consumption proportion of electric energy index.

[0160] S3, calculate total factor productivity (TFP) of the to-be-evaluated power grid company and the other power grid companies respectively according to the value of the input index and the value of the output index, and determine the carbon management level evaluation result of the to-be-evaluated power grid company based on the total factor productivity of the to-be-evaluated power grid company, and specifically comprising S31-S33:

[0161] S31, take the minimum input amount that the power grid company can compress under the condition that the output will change as a target function, and establish constraint conditions of the target function based on the value of the input index and the value of the output index. Wherein, the target function is specifically:

[0162] min theta;

[0163] In the formula, theta represents the input amount that the power grid company can compress under the condition that the output will change, that is, the total factor productivity;

[0164] The constraint conditions are specifically:

[0165]

[0166] In the formula, x ij represents the value of the i-th input index of the j-th power grid company in carbon management, lambda j represents the linear combination coefficient of the j-th power grid company, cn represents the sum of the number of the to-be-evaluated power grid company and the other power grid companies, x i0 represents the value of the i-th input index of the current power grid company in carbon management, p represents the total number of input indexes, y rj represents the value of the r-th output index of the j-th power grid company in carbon management, q represents the total number of output indexes, y r0 represents the value of the r-th output index of the current power grid company in carbon management.

[0167] For example, to calculate the total factor productivity of the to-be-evaluated power grid company, take the minimum input amount that the to-be-evaluated power grid company can compress under the condition that the output will change as a target function, and establish constraint conditions of the target function based on the value of the input index and the value of the output index of the to-be-evaluated power grid company, as shown in the above formula, and the total factor productivity of the other power grid companies is also similar.

[0168] S32, solve the target function under the constraint conditions to obtain the total factor productivity of the to-be-evaluated power grid company and the other power grid companies. The method for calculating the total factor productivity of the power grid company in the present application is a non-parametric method, that is, the BCC model of the data envelopment method, and the calculated total factor productivity is more accurate and reliable.

[0169] S33, determining the carbon management level evaluation result of the power grid company to be evaluated based on the total factor productivity of the power grid company to be evaluated.

[0170] Specifically, if the total factor productivity of the power grid company to be evaluated is equal to the preset value, it is determined that the carbon management level evaluation result of the power grid company to be evaluated is high, and if the total factor productivity of the power grid company to be evaluated is less than the preset value, it is determined that the carbon management level evaluation result of the power grid company to be evaluated is low.

[0171] In an optional embodiment, the preset value is 1. For example, if the total factor productivity of the power grid company to be evaluated is equal to 1, the carbon management level evaluation result of the power grid company to be evaluated is high, indicating that the input and output mode of the power grid company in carbon management is effective, and if the total factor productivity of the power grid company to be evaluated is less than 1, the carbon management level evaluation result of the power grid company to be evaluated is low, indicating that the input and output mode of the power grid company in carbon management is ineffective, and there is a more optimal strategy in carbon management.

[0172] S4, if the carbon management level evaluation result of the power grid company to be evaluated is low, improving the input structure of the power grid company to be evaluated in carbon management based on the total factor productivity of the other power grid companies and the input structure of the other power grid companies to the power grid company to be evaluated in carbon management.

[0173] For example, an other power grid company with a total factor productivity equal to 1 is selected, and the input structure of the power grid company to be evaluated in carbon management is improved by referring to the input and output mode of the power grid company.

[0174] In an optional embodiment, the method further comprises:

[0175] S5, selecting a plurality of characteristic variables affecting the carbon management level.

[0176] In an optional embodiment, 34 characteristic variables affecting the carbon management level are selected from four aspects of administration, economy, technology and link, wherein a positive variable is represented by H 1 , a negative variable is represented by H 2 , there are three time lag variables, and all of them are variables that have a positive impact on the carbon management input level of the power grid company in the short term and a negative impact in the long term, represented by H 12 . The 34 characteristic variables affecting the carbon management level are shown in Table 1.

[0177] Table 1: A plurality of characteristic variables affecting the carbon management level

[0178]

[0179]

[0180] But the influence degree of these variables is different, so it is necessary to screen them.

[0181] S6, screening the plurality of characteristic variables using a Lasso regression algorithm to obtain key characteristic variables, specifically comprising S61-S62:

[0182] S61, non-dimensionalizing the plurality of characteristic variables to obtain a plurality of processed characteristic variables.

[0183] In an optional implementation, the plurality of characteristic variables are non-dimensionalized using a z-score standardization method to obtain a plurality of processed characteristic variables, and the z-score standardization is also called standard deviation standardization. By converting the data into z-score values without units, the data standardization is unified, and the data comparability is improved. Specifically, the plurality of characteristic variables are non-dimensionalized using the z-score standardization method to obtain a plurality of processed characteristic variables, and the plurality of processed characteristic variables are obtained.

[0184]

[0185] In the formula, y i represents the i-th processed characteristic variable, x i represents the i-th characteristic variable, x represents the mean of the plurality of characteristic variables, s represents the standard deviation, and v represents the sample size of a certain characteristic variable.

[0186] In an optional implementation, the method further comprises: in the case that the time series data of the plurality of processed characteristic variables is missing or unavailable, filling the missing data using an interpolation method and a moving average method.

[0187] S62, screening the plurality of processed characteristic variables using a Lasso regression algorithm to obtain key characteristic variables.

[0188] Specifically, if a traditional least squares method is used for parameter estimation, due to the existence of multicollinearity among many characteristic variables, the instability of the estimation result is increased, which further affects the judgment of the index, and finally leads to the failure to correctly select the key characteristic variables. The Lasso regression algorithm can solve such problems. The Lasso regression can adapt to the case that the sample size is small but the index is large, and has good performance in variable selection and prediction.

[0189] Lasso is a compression estimation, and its basic idea is to add L1 norm as a constraint to compress the regression coefficients of the model on the basis of the ordinary least squares method, so as to eliminate unimportant variables. The basic principle is as follows:

[0190] Multivariate regression is usually better than univariate regression due to more explanatory variables, and the calculation method is as follows:

[0191] Y=X (m)β+ε,ε~N(0,σ 2 I m );

[0192] In the formula, Y=(y1,y2,…,y m )' represents the dependent variable with sample size m, X = (1, x m1 ,x m2 ,…,x mp )' represents an independent variable with sample size m and dimension p, where β = (β1, β2, ..., β) m )' represents the regression coefficient solution, ε=(ε1,ε2,…,ε m )' represents a random disturbance term with a mean of 0 and a variance of σ. 2 The error terms follow a normal distribution and are independent of each other. m Represents the identity matrix.

[0193] The coefficient solution β can be estimated using least squares estimation. The main idea is to minimize ε = Y - Xβ, which means minimizing Q(β), where Q(β) = ||ε|| 2 = (Y-Xβ)'(Y-Xβ), and thus we can further obtain the estimated coefficient solution:

[0194] However, the accuracy of the coefficient solutions obtained using the least squares estimation method is often insufficient, and the explanatory power of the system is poor when there are many variables. The Lasso regression algorithm adds the L1 norm ||β||1 as a constraint to the objective function Q(β) to obtain the coefficient solutions: In the formula, λ represents the penalty factor. The complexity of Lasso regression is controlled by λ. When λ is 0, t tends to infinity, which is equivalent to no constraint. When the value of λ is appropriately large, some undetermined coefficients can be reduced to 0, and finally a system with fewer variables can be obtained. The value of λ can be obtained by cross-validation.

[0195] Therefore, by using the Lasso regression algorithm to filter the processed feature variables, the key feature variables can be obtained. In one optional implementation, the Lasso regression algorithm is used to filter 34 feature variables, resulting in 12 key feature variables.

[0196] like Figure 4 As shown, Figure 4 This demonstrates how, as the parameter Lambda increases, the variables are gradually compressed to 0. Figure 4 The horizontal axis represents λ, which controls the strength of the penalty term. The stronger the penalty term, the more variable coefficients will be compressed to zero, thus affecting the sparsity of variable coefficients in the model. The vertical axis represents the magnitude of the variable coefficients. Figure 4Each point or curve in the figure represents the path of the coefficient of a variable as λ changes. As the value of λ increases, Figure 4 The curve in the figure gradually decreases, and some curves eventually decrease to zero, which means that the corresponding variable coefficient has been compressed to zero and thus eliminated. Figure 4 The dashed line ① and the dashed line ② in the figure represent the MinSE and MinMSE indicators, respectively. In general, MinMSE is selected as the best parameter, and the corresponding point above it is the number of non-zero variables retained, that is, the number of key feature variables screened is 12.

[0197] The optimal parameters are selected by cross-validation, and the parameters can be optimized according to the mean square error to obtain Figure 5 As shown in Figure 5 , the red dots in the figure represent the target parameters corresponding to each Lambda, and the dashed line represents a model that has both good performance and the least non-zero variables within a variance range of the MSE value, that is, the Lambda value corresponding to the simplest system. The given is a system with excellent performance and the least number of independent variables, and at this time Lambda = 0.14877.

[0198] In summary, after regression, the final result retains 12 key feature variables, of which 2 are positive variables, 7 are negative variables, and 3 are time lag variables. All of them are variables that have a positive impact on the short-term and a negative impact on the long-term of the carbon management input level of the power grid company. The coefficients of these key feature variables selected are not 0, and the specific values are shown in Table 2.

[0199] Table 2 Key feature variables affecting carbon management level

[0200]

[0201]

[0202] S7, performing multiple regression using the key feature variables to obtain a prediction model.

[0203] S8, predicting the carbon management level of the power grid company to be evaluated according to the prediction model to obtain a prediction result. The prediction result is the predicted input of carbon management.

[0204] The present application introduces the total factor productivity to measure the carbon management level of the power grid company, which can provide a more comprehensive and dynamic perspective, consider technical progress, innovation ability, resource allocation and other factors, and more accurately evaluate the comprehensive benefits and long-term value of carbon management investment. The introduction of total factor productivity helps enterprises to pursue economic benefits while also paying attention to environmental and social responsibility and promoting sustainable development. In addition, when predicting the future carbon management investment level of the power grid company, there are problems of high dimensionality, multicollinearity of data and risk of overfitting of the model. Lasso regression, as a regularization method, can effectively handle these problems and improve prediction accuracy, because it reduces the complexity of the prediction system by penalizing the coefficients and enhances the generalization ability of the system. The above-mentioned carbon management level evaluation method of the present application can meet the carbon management needs of the region, provide strong support for achieving the carbon emission reduction target of the power grid company, and can assist the power grid company in rationalizing carbon management investment and making strategic arrangements to promote high-quality development.

[0205] For reference Figure 2 Embodiment two of the present application is:

[0206] A carbon management level evaluation terminal, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to realize each step in the carbon management level evaluation method of embodiment one.

[0207] In summary, the present application provides a carbon management level evaluation method and terminal, screens carbon management input indicators and output indicators, and obtains input data and output data of the power grid company to be evaluated and other power grid companies, respectively calculates values of the input indicators of the power grid company to be evaluated and other power grid companies based on the input data, respectively calculates values of the output indicators of the power grid company to be evaluated and other power grid companies based on the output data, respectively calculates total factor productivity of the power grid company to be evaluated and other power grid companies according to the values of the input indicators and the values of the output indicators, and determines a carbon management level evaluation result of the power grid company to be evaluated based on the total factor productivity of the power grid company to be evaluated. If the carbon management level evaluation result of the power grid company to be evaluated is low, the input structure of the carbon management of the power grid company to be evaluated is improved based on the total factor productivity of other power grid companies. The present application measures the efficiency of the power grid company in carbon management input and output by introducing total factor productivity, realizes the quantification of the carbon management level, provides a more comprehensive and dynamic evaluation perspective, and thus more accurately and comprehensively evaluates the carbon management level. When the carbon management level evaluation result of the power grid company to be evaluated is low, the input structure of the carbon management of the power grid company to be evaluated can be improved based on the total factor productivity of other power grid companies, so as to help the power grid company identify the improvement space, optimize the resource allocation, and promote low-carbon development. In addition, the output indicators of the carbon management consider the low-carbon environmental protection, economic, technical and performance angles, and the input indicators and output indicators consider technical progress, innovation ability, resource allocation and other factors, which can comprehensively and effectively quantify the input and output of the carbon management, and comprehensively reflect the technical progress and efficiency change. Moreover, the Lasso regression algorithm is used to screen multiple characteristic variables, which can reduce the system complexity, identify the most significant factors affecting the prediction result, and then combine multiple regression to predict the carbon management level in future years, thereby improving the prediction accuracy of the future carbon management input and output level.

[0208] The above only describes the embodiments of the present application, and does not limit the patent scope of the present application. Any equivalent transformation or direct or indirect application in related technical fields based on the content of the present application specification and drawings is also included in the patent protection scope of the present application.

Claims

1. A carbon management level assessment method, characterized by, The method comprises the steps of: screening input indicators and output indicators of carbon management, and obtaining input data and output data of the power grid company to be evaluated and other power grid companies; calculating values of the input indicators of the power grid company to be evaluated and the other power grid companies based on the input data, and calculating values of the output indicators of the power grid company to be evaluated and the other power grid companies based on the output data; calculating total factor productivity of the power grid company to be evaluated and the other power grid companies based on the values of the input indicators and the values of the output indicators, and determining a carbon management level evaluation result of the power grid company to be evaluated based on the total factor productivity of the power grid company to be evaluated; if the carbon management level evaluation result of the power grid company to be evaluated is low, improving an input structure of carbon management of the power grid company to be evaluated based on the total factor productivity of the other power grid companies; the step of calculating the values of the input indicators of the power grid company to be evaluated and the other power grid companies based on the input data comprises: calculating the values of the input indicators of the power grid company to be evaluated and the other power grid companies based on a grey correlation degree analysis method according to the input data; the step of calculating the values of the input indicators of the power grid company to be evaluated and the other power grid companies based on the grey correlation degree analysis method according to the input data comprises: taking the processed input of each operation center in each year as a reference sequence, taking the processed input of each operation activity in carbon management in each year as a comparison sequence, and calculating a first correlation coefficient between the processed input of each operation center in each year and the processed input of each operation activity in carbon management in each year; taking the processed input of each operation activity in carbon management in each year as a reference sequence, taking the processed input of each sub-link under each operation activity in carbon management in each year as a comparison sequence, and calculating a second correlation coefficient between the processed input of each operation activity in carbon management in each year and the processed input of each sub-link under each operation activity in carbon management in each year; calculating a first grey correlation degree according to the first correlation coefficient, and calculating a second grey correlation degree according to the second correlation coefficient; standardizing the first grey correlation degree and the second grey correlation degree to obtain a standardized first grey correlation degree and a standardized second grey correlation degree, and taking the standardized first grey correlation degree as a first weight and taking the standardized second grey correlation degree as a second weight; multiplying the processed input of each operation center in the current year by the first weight to obtain the input of each operation activity in carbon management of the power grid company to be evaluated and the other power grid companies in the current year. The input of each year in the carbon management operation activities is multiplied by the second weight respectively, to obtain the value of the carbon emission accounting input index, the carbon asset management input index, the carbon emission right transaction input index, the carbon emission treatment input index and the carbon assessment management input index of the power grid company to be evaluated and the other power grid companies.

2. The method of claim 1, wherein, The input index and the output index of the carbon management screening include: The input index of the carbon management is obtained by integrating the carbon management inputs, and the input index includes the carbon emission accounting input index, the carbon asset management input index, the carbon emission right transaction input index, the carbon emission treatment input index and the carbon assessment management input index; The output index of the carbon management is obtained by screening, and the output index includes the new energy output proportion index, the carbon emission amount index, the carbon intensity index, the main transformer load rate index, the new energy fluctuation index, the new energy power station investment return rate index and the electric energy terminal consumption proportion index.

3. The method of claim 2, wherein, The input data and the output data of the power grid company to be evaluated and the other power grid companies include: The operation types of the power grid company are divided into multiple operation centers according to the operation business of the power grid company; The power grid company to be evaluated and the other power grid companies are taken as decision units respectively, and the input of each year in each operation center, the input of each year in the carbon management operation activities, the input of each year in each sub-link under the carbon management operation activities and the input of this year in each operation center of the power grid company to be evaluated and the other power grid companies are collected; The input of each year in each operation center, the input of each year in the carbon management operation activities, the input of each year in each sub-link under the carbon management operation activities and the input of this year in each operation center are respectively subjected to data standardization processing to obtain the processed input of each year in each operation center, the input of each year in the carbon management operation activities, the input of each year in each sub-link under the carbon management operation activities and the input of this year in each operation center; The input data of the power grid company to be evaluated and the other power grid companies are obtained according to the processed input of each year in each operation center, the input of each year in the carbon management operation activities, the input of each year in each sub-link under the carbon management operation activities and the input of this year in each operation center; The new energy output, the traditional energy output, the carbon emission amount, the unit gross national product, the actual load of the main transformer, the rated capacity of the main transformer, the average electricity price of this year, the investment amount of the power plant and the primary energy conversion data of the power grid company to be evaluated and the other power grid companies are collected; The output data of the power grid company to be evaluated and the other power grid companies are obtained according to the new energy output, the traditional energy output, the carbon emission amount, the unit gross national product, the actual load of the main transformer, the rated capacity of the main transformer, the average electricity price of this year, the investment amount of the power plant and the primary energy conversion data.

4. The method of claim 3, wherein, The calculating the values of the output indicators of the power grid company to be evaluated and the other power grid companies respectively based on the output data comprises: calculating the values of the new energy output proportion indicators of the power grid company to be evaluated and the other power grid companies respectively according to the new energy output and the traditional energy output; obtaining the values of the carbon emission indicators of the power grid company to be evaluated and the other power grid companies respectively according to the carbon emissions; calculating the values of the carbon intensity indicators of the power grid company to be evaluated and the other power grid companies respectively according to the values of the carbon emission indicators and the unit GNP; calculating the values of the main transformer load rate indicators of the power grid company to be evaluated and the other power grid companies respectively according to the actual load of the main transformer and the rated capacity of the main transformer; calculating the values of the new energy fluctuation indicators of the power grid company to be evaluated and the other power grid companies respectively according to the new energy output; calculating the values of the new energy power station investment return rate indicators of the power grid company to be evaluated and the other power grid companies respectively according to the average electricity price of the current year and the investment amount of the power plant; calculating the values of the terminal consumption proportion of electric energy indicators of the power grid company to be evaluated and the other power grid companies respectively according to the primary energy conversion data.

5. The method of claim 1, wherein, The calculating the total factor productivity of the power grid company to be evaluated and the other power grid companies respectively based on the values of the input indicators and the values of the output indicators comprises: taking the minimum input amount that the power grid company can compress under the condition that the output will change as an objective function, and establishing constraint conditions of the objective function based on the values of the input indicators and the values of the output indicators; solving the objective function under the constraint conditions to obtain the total factor productivity of the power grid company to be evaluated and the other power grid companies.

6. The method of claim 5, wherein, The taking the minimum input amount that the power grid company can compress under the condition that the output will change as an objective function specifically comprises: ； wherein represents the amount of input that the grid company can compress to, taking into account that the output will change; The establishing constraint conditions of the objective function based on the values of the input indicators and the values of the output indicators comprises: ； ； ； In the formula, x ij Indicates the first j The first power grid company in carbon management i The value of each input indicator, Indicates the first j The linear combination coefficients of individual power grid companies cn This represents the total number of the power grid company to be evaluated and the other power grid companies. x i0 This indicates the current level of carbon management by power grid companies. i The value of each input indicator, p This indicates the total number of input indicators. y rj Indicates the first j The first power grid company in carbon management r The value of each output indicator, q This indicates the total number of output indicators. y r0 This indicates the current level of carbon management by power grid companies. r The value of each output indicator.

7. The method of claim 1, wherein, Further comprising: selecting a plurality of characteristic variables affecting the carbon management level; using a Lasso regression algorithm to screen the plurality of characteristic variables to obtain key characteristic variables; using the key characteristic variables for multiple regression to obtain a prediction model; performing carbon management level prediction on the power grid company to be evaluated according to the prediction model to obtain a prediction result.

8. A carbon management level assessment terminal comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements each step in the carbon management level evaluation method of any one of claims 1 to 7 when executing the computer program.

Images