Carbon management level evaluation method and terminal

Abstract

The invention discloses a carbon management level assessment method and terminal, and the method comprises the steps: obtaining the input data and output data of a to-be-assessed power grid company and other power grid companies, and calculating the values of the input indexes of the to-be-assessed power grid company and other power grid companies based on the input data; based on the output data, respectively calculating output index values of the to-be-evaluated power grid company and other power grid companies, and respectively calculating total factor productivity of the to-be-evaluated power grid company and other power grid companies according to the input index values and the output index values; and determining a carbon management level assessment result of the to-be-assessed power grid company based on the total factor productivity, and improving the carbon management input structure of the to-be-assessed power grid company with a low assessment result by using other power grid companies based on the total factor productivity of other power grid companies, so that the carbon management level is assessed more accurately and comprehensively. And a power grid company is helped to identify and improve the space, optimize resource allocation and promote low-carbon development.

Description

Technical Field

[0001] The present invention relates to the field of carbon management technology, and in particular to a carbon management level assessment method and terminal. Background Art

[0002] The problems that power grid companies have in measuring carbon management levels are mainly reflected in the imperfection of the measurement system and the insufficient application of measurement results. Due to the lack of comprehensive and unified carbon emission accounting standards, it is difficult for power grid companies to accurately assess their own carbon emission levels and emission reduction effects. At the same time, the complexity of carbon asset management and carbon emission rights trading also makes performance measurement more difficult. These problems not only affect the supervision and control of carbon emissions by 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 has failed to effectively guide enterprises to formulate 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, namely, cost-benefit analysis ignores long-term environmental and social benefits; IRR is too sensitive to the time distribution of cash flow of the project; EVA ignores the impact of non-financial factors. Summary of the invention

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

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

[0006] A carbon management level assessment method comprises the following steps:

[0007] Screen the input and output indicators of carbon management, and obtain the input and output data of the power grid company to be evaluated and other power grid companies;

[0008] 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, and calculating the 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 the total factor productivity of the power grid company to be evaluated and the other power grid companies respectively according to the value of the input index and the value of the output index, 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;

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

[0011] In order to solve the above technical problems, another technical solution adopted by the present invention is:

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

[0013] Screen the input and output indicators of carbon management, and obtain the input and output data of the power grid company to be evaluated and other power grid companies;

[0014] 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, and calculating the values ​​of the output indicators of the power grid company to be evaluated and the other power grid companies based on the output data;

[0015] Calculating the total factor productivity of the power grid company to be evaluated and the other power grid companies respectively according to the value of the input index and the value of the output index, 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;

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

[0017] The beneficial effects of the present invention are as follows: screening the input indicators and output indicators of carbon management, and obtaining the input data and output data of the power grid company to be evaluated and other power grid companies, respectively calculating the values ​​of the input indicators of the power grid company to be evaluated and other power grid companies based on the input data, and respectively calculating the values ​​of the output indicators of the power grid company to be evaluated and other power grid companies based on the output data, respectively calculating the 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 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 of the power grid company to be evaluated is If the evaluation result is low, then other power grid companies can be used to improve the input structure of carbon management of the power grid company to be evaluated based on their total factor productivity. The present invention measures the efficiency of power grid companies in carbon management input and output by introducing total factor productivity, thereby realizing the quantification of carbon management levels and providing a more comprehensive and dynamic evaluation perspective, thereby more accurately and comprehensively evaluating carbon management levels. When the evaluation result of the carbon management level of the power grid company to be evaluated is low, other power grid companies can be used to improve the input structure of carbon management of the power grid company to be evaluated based on total factor productivity, thereby helping power grid companies identify room for improvement, optimize resource allocation, and promote low-carbon development. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of a carbon management level assessment method according to an embodiment of the present invention;

[0019] Figure 2 This is a structural schematic diagram of a carbon management level assessment terminal according to an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of an assessment method for assessing carbon management level in an embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram showing that as the parameter Lambda increases in the carbon management level assessment method according to an embodiment of the present invention, each variable is gradually compressed to 0;

[0022] Figure 5 This is a schematic diagram of using mean square error to tune parameters in the carbon management level assessment method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

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

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

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

[0027] Calculating the total factor productivity of the power grid company to be evaluated and the other power grid companies respectively according to the value of the input index and the value of the output index, 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 of the grid company to be evaluated is assessed to be low, the input structure of the carbon management of the grid company to be evaluated by the other grid companies is improved based on the total factor productivity of the other grid companies.

[0029] From the above description, it can be seen that the beneficial effects of the present invention are: screening the input indicators and output indicators of carbon management, and obtaining the input data and output data of the power grid company to be evaluated and other power grid companies, respectively calculating the values ​​of the input indicators of the power grid company to be evaluated and other power grid companies based on the input data, and respectively calculating the values ​​of the output indicators of the power grid company to be evaluated and other power grid companies based on the output data, respectively calculating the 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 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 of the power grid company to be evaluated is If the management level assessment result is low, then the input structure of carbon management of the grid company to be assessed can be improved by utilizing other grid companies based on their total factor productivity. The present invention measures the efficiency of grid companies in carbon management input and output by introducing total factor productivity, thereby realizing the quantification of carbon management levels and providing a more comprehensive and dynamic assessment perspective, thereby more accurately and comprehensively assessing carbon management levels. When the carbon management level assessment result of the grid company to be assessed is low, other grid companies can be used to improve the input structure of carbon management of the grid company to be assessed based on total factor productivity, thereby helping grid companies identify room for improvement, optimize resource allocation, and promote low-carbon development.

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

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

[0032] Integrate various carbon management inputs and screen to obtain carbon management input indicators, including carbon emission accounting input indicators, carbon asset management input indicators, carbon emission rights trading input indicators, carbon emission governance input indicators and carbon assessment management input indicators;

[0033] The output indicators of carbon management are screened and obtained, and the output indicators include a new energy output proportion indicator, a carbon emission indicator, a carbon intensity indicator, a main transformer load factor indicator, a new energy volatility indicator, a new energy power station investment return rate indicator and an electric energy terminal consumption proportion indicator.

[0034] From the above description, it can be seen that the input indicators include carbon emission accounting input indicators, carbon asset management input indicators, carbon emission rights trading input indicators, carbon emission governance input indicators and carbon assessment management input indicators; the output indicators include new energy output proportion indicators, carbon emission indicators, carbon intensity indicators, main transformer load rate indicators, new energy volatility indicators, new energy power station investment return rate indicators and electricity terminal consumption proportion indicators; the output indicators of carbon management take into account the low-carbon environmental protection perspective, economic perspective, technical perspective and performance perspective, and use input indicators and output indicators to consider multiple factors such as technological progress, innovation capability, and resource allocation, which can comprehensively and effectively quantify the input and output of carbon management and comprehensively reflect technological progress and efficiency changes.

[0035] Furthermore, the acquisition of input data and output data of the power grid company to be evaluated and other power grid companies includes:

[0036] Divide the operation types of the power grid company into multiple operation centers according to the operation business of the power grid company;

[0037] The power grid company to be evaluated and other power grid companies are respectively used as decision-making units, and the investment of the power grid company to be evaluated and other power grid companies in various operation centers, the investment in various carbon management operation activities, the investment in each sub-link under various carbon management operation activities, and the investment in various operation centers in the current year are collected;

[0038] Performing data standardization processing on the investment in each operating center over the years, the investment in each operating activity of carbon management over the years, the investment in each sub-link under each operating activity of carbon management over the years, and the investment in each operating center in the current year, respectively, to obtain the processed investment in each operating center over the years, the investment in each operating activity of carbon management over the years, the investment in each sub-link under each operating activity of carbon management over the years, and the investment in each operating center in the current year;

[0039] The investment data of the power grid company to be evaluated and the other power grid companies are obtained based on the processed investment in various operating centers over the years, the investment in various operating activities of carbon management over the years, the investment in each sub-link of various operating activities of carbon management over the years, and the investment in various operating centers in the current year;

[0040] Collect the new energy output, traditional energy output, carbon emissions, unit gross national product, actual load carried by the main transformer, rated capacity of the main transformer, average electricity price of the year, investment amount of power plants and primary energy conversion data of the power grid company to be evaluated and other power grid companies;

[0041] The output data of the power grid company to be evaluated and the other power grid companies are obtained based on the new energy output, the traditional energy output, the carbon emissions, the unit gross national product, the actual load borne by the main transformer, the rated capacity of the main transformer, the average electricity price of the year, the investment amount of the power plant and the primary energy conversion data.

[0042] From the above description, it can be seen that the investment data of the power grid company to be evaluated and other power grid companies are obtained according to the processed investment in various operating centers over the years, the investment in various operating activities of carbon management over the years, the investment in each sub-link under various operating activities of carbon management over the years and the investment in each operating center in the current year; the output data of the power grid company to be evaluated and other power grid companies are obtained according to the output of new energy, traditional energy, carbon emissions, unit GDP, actual load borne by the main transformer, rated capacity of the main transformer, average electricity price of the year, investment amount of power plants and primary energy conversion data, so as to accurately calculate the values ​​of input indicators and output indicators later.

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

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

[0045] From the above description, it can be seen that the input index values ​​of the power grid company to be evaluated and other power grid companies are calculated based on the input data based on the grey correlation analysis method. The grey correlation analysis not only considers the correlation between various factors, but also considers the comprehensive impact of various factors on the results. It can more comprehensively evaluate the degree of correlation between factors, thereby making the calculated input index values ​​more accurate and reliable.

[0046] Furthermore, the grey correlation analysis method is used to calculate the input index values ​​of the power grid company to be evaluated and the other power grid companies according to the input data, including:

[0047] The investment in each operating center in the past years after the processing is used as a reference sequence, the investment in each operating activity of carbon management in the past years after the processing is used as a comparison sequence, and the first correlation coefficient between the investment in each operating center in the past years after the processing and the investment in each operating activity of carbon management in the past years after the processing is calculated;

[0048] Taking the investment in various carbon management activities in the past years after the treatment as a reference sequence, taking the investment in each sub-link under the various carbon management activities in the past years after the treatment as a comparison sequence, and calculating the second correlation coefficient between the investment in various carbon management activities in the past years after the treatment and the investment in each sub-link under the various carbon management activities in the past years after the treatment;

[0049] Calculating a first grey relational degree according to the first relational coefficient, and calculating a second grey relational degree according to the second relational coefficient;

[0050] Standardizing the first grey relational degree and the second grey relational degree respectively to obtain standardized first grey relational degree and second grey relational degree, and using the standardized first grey relational degree as a first weight and using the standardized second grey relational degree as a second weight;

[0051] Multiplying the processed investment in each operation center in the current year by the first weight respectively, to obtain the investment of the grid company to be evaluated and the other grid companies in each carbon management operation activity in the current year;

[0052] The investment in each carbon management operation in the current year is multiplied by the second weight respectively to obtain the values ​​of the carbon emission accounting input index, carbon asset management input index, carbon emission rights trading input index, carbon emission governance input index and carbon assessment management input index 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 seen that after the first grey correlation degree and the second grey correlation degree are standardized respectively, the standardized first grey correlation degree is used as the first weight, and the standardized second grey correlation degree is used as the second weight. Then, according to the investment in each operation center in the year after processing, the first weight and the second weight, the carbon emission accounting input index, carbon asset management input index, carbon emission rights trading input index, carbon emission governance input index and carbon assessment management input index of the power grid company to be evaluated and other power grid companies in that year are calculated, so as to accurately evaluate the investment in carbon management.

[0054] Further, the calculating the output index values ​​of the power grid company to be evaluated and the other power grid companies based on the output data respectively includes:

[0055] Calculate the values ​​of the new energy output ratio index 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;

[0056] According to the carbon emissions, respectively obtain the values ​​of the carbon emissions index of the power grid company to be evaluated and the other power grid companies;

[0057] Calculate the values ​​of the carbon intensity index of the power grid company to be evaluated and the other power grid companies respectively according to the values ​​of the unit gross national product and the carbon emission index;

[0058] Calculating the values ​​of the main transformer load rate indexes of the power grid company to be evaluated and the other power grid companies respectively according to the load actually carried by the main transformer and the rated capacity of the main transformer;

[0059] Calculate the values ​​of the new energy volatility index of the power grid company to be evaluated and the other power grid companies respectively according to the new energy output;

[0060] Calculate the value of the investment return rate index of the new energy power station of the power grid company to be evaluated and the other power grid companies respectively according to the average electricity price of the year and the investment amount of the power plant;

[0061] The values ​​of the electric energy terminal consumption proportion indicators of the power grid company to be evaluated and the other power grid companies are calculated respectively according to the primary energy conversion data.

[0062] From the above description, it can be seen that the value of each output indicator can be obtained directly by mathematical calculation using the obtained output data, which effectively quantifies the output level of carbon management.

[0063] Further, the calculating the total factor productivity of the power grid company to be evaluated and the other power grid companies respectively according to the value of the input index and the value of the output index includes:

[0064] Taking the minimum input amount that the power grid company can compress under the condition that the output may change as the objective function, and establishing the constraint condition of the objective function based on the value of the input index and the value of the output index;

[0065] The objective function is solved under the constraint conditions to obtain the total factor productivity of the power grid company to be evaluated and the other power grid companies.

[0066] From the above description, it can be seen that the minimum input amount that the power grid company can compress while considering the change in output is taken as the objective function, and the constraints of the objective function are established based on the values ​​of the input indicators and the output indicators. The objective function is solved under the constraints to obtain the total factor productivity of the power grid company to be evaluated and other power grid companies. The introduction of total factor productivity helps power grid companies to pay attention to environmental and social responsibilities while pursuing economic benefits and promote sustainable development.

[0067] Furthermore, the objective function is to take the minimum input amount that the power grid company can compress under the condition that the output will change as follows:

[0068] minθ;

[0069] In the formula, θ represents the amount of input that the power grid company can compress while considering changes in output;

[0070] The constraint conditions for establishing the objective function based on the value of the input index and the value of the output index include:

[0071]

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

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

[0074] Furthermore, it also includes:

[0075] Select multiple characteristic variables that affect the level of carbon management;

[0076] Using Lasso regression algorithm to screen the multiple characteristic variables to obtain key characteristic variables;

[0077] Performing multiple regression using the key characteristic variables to obtain a prediction model;

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

[0079] From the above description, it can be seen that using the Lasso regression algorithm to screen multiple characteristic variables can reduce the complexity of the system and identify the factors that most significantly affect the prediction results. Combined with multiple regression, the carbon management level in future years is predicted, which improves the prediction accuracy of future carbon management input and output levels.

[0080] Please refer to Figure 2 Another embodiment of the present invention provides a carbon management level assessment terminal, including 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 assessment method when executing the computer program.

[0081] The carbon management level assessment method and terminal of the present invention can be applied to the carbon management level assessment scenario of a power grid company, and are described below through specific implementation methods:

[0082] Please refer to Figure 1 , Figures 3 to 5 , Embodiment 1 of the present invention is:

[0083] A carbon management level assessment method comprises the following steps:

[0084] S1. Screen the input and output indicators of carbon management, and obtain the input and output data of the power grid company to be evaluated and other power grid companies, such as Figure 3 As shown, specifically including S11-S18:

[0085] S11. Integrate various carbon management inputs and screen to obtain carbon management input indicators, which include carbon emission accounting input indicators, carbon asset management input indicators, carbon emission rights trading input indicators, carbon emission governance input indicators and carbon assessment management input indicators.

[0086] Among them, carbon emission accounting input indicators: Carbon emission accounting is the primary link in carbon management and the basic prerequisite for carrying out carbon emission reduction work. Carbon emission accounting refers to the process of systematically calculating and evaluating the carbon dioxide and greenhouse gas emissions generated by power grid companies over a certain period of time through quantitative and qualitative methods. Its goal is to understand and quantify the carbon footprint in order to formulate 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 indicators: Carbon assets refer to new assets generated by the carbon emission trading mechanism, that is, carbon emission quotas, emission reduction credits and related activities that can directly or indirectly affect greenhouse gas emissions under the mandatory carbon emission rights trading mechanism or resource-based carbon emission trading mechanism. Carbon asset management refers to the systematic management and optimization of assets related to carbon emissions to achieve the goals of greenhouse gas emission reduction and sustainable development. This process includes carbon credit trading, evaluation 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 indicators: Carbon emission rights trading is a global market trading mechanism adopted for the purpose of low-carbon emission reduction, that is, carbon dioxide emission rights are regarded as the subject of commodity trading, and buyers pay a certain amount of money to obtain a certain quota of carbon dioxide emission rights. The carbon market has the attributes of the capital market, and carbon emission rights trading is also uncertain like financial products. Its transaction price is affected by risks such as changes in supply and demand in the carbon market and changes in the ecological environment. However, carbon trading guides the direction of capital investment in the financial market, which will make the investment market biased towards projects and enterprises that can create carbon assets.

[0089] Carbon emission management investment indicators: Carbon emission management refers to reducing carbon emissions in the power supply process by optimizing grid operation and management to achieve the goal of sustainable development. This includes the use of advanced power dispatching and management systems, improving the access and utilization efficiency of renewable energy, and promoting the development of electric energy substitution and electric transportation. 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 facilities construction, talent training, and the improvement of monitoring and evaluation systems. These investments will support grid companies in promoting low-carbon operations, improving energy efficiency and achieving green grids. Play a key role, thereby contributing to global emission reduction goals.

[0090] Carbon assessment management investment indicators: Carbon assessment management refers to the overall management work of evaluating and assessing the company's carbon management performance based on the responsibility system. The company's carbon assessment management investment can be roughly divided into two parts, namely the construction and implementation of the company's internal carbon management assessment system, and the relevant investment in cooperating with the government, the public and the media to comprehensively use legal, economic and administrative regulatory means to supervise the company's carbon emissions.

[0091] S12. Screen and obtain output indicators of carbon management, such as Figure 3 As shown, the output indicators include new energy output proportion indicator, carbon emission indicator, carbon intensity indicator, main transformer load factor indicator, new energy volatility indicator, new energy power station investment return rate indicator and electric energy terminal consumption proportion indicator.

[0092] Among them, the new energy output ratio indicator: the new energy output ratio refers to the ratio of new energy power 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 emissions indicator: Carbon emissions refer to the emissions of greenhouse gases such as carbon dioxide produced by the combustion of fossil fuels (such as coal, natural gas, etc.) in the process of electricity production and supply, measured in tons (t).

[0094] Carbon intensity indicator: Carbon emission intensity refers to the amount of carbon dioxide emissions generated per unit of growth in gross domestic product (GDP).

[0095] Main transformer load factor index: The main transformer load factor refers to the ratio of the load borne by the main transformer to its capacity, and is one of the important indicators for measuring the efficiency of the main transformer. The main transformer load factor directly affects key factors such as the stability of power system operation, the life of equipment, and the quality of electric energy.

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

[0097] The proportion of terminal energy consumption refers to the proportion of electricity in the total terminal energy consumption. It is used to measure the importance and influence of electricity in energy use, to evaluate the efficiency of electricity use and the level of electrification, to guide energy policies and to promote the promotion of clean energy.

[0098] From the above, it can be seen that the screening of output indicators of carbon management is considered from a comprehensive perspective, such as environmental protection, economic, technical and performance. The indicators considering the environmental protection perspective include carbon emissions, the proportion of electric energy terminal consumption and the proportion of new energy output, which provide clear emission reduction targets and evaluation criteria for power grid companies. Through these indicators, companies can monitor and report their carbon footprints, evaluate the effectiveness of carbon management strategies, and respond to government policies and market demands, enhance public trust and corporate reputation, which not only helps to achieve environmental sustainability, but also is the key for companies to fulfill their social responsibilities and promote green development. The indicators considering the economic perspective include the return on investment of new energy power stations. Carbon management can reduce operating costs and reduce 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. The evaluation of the return on investment of new energy power stations helps companies understand the financial value of carbon management, make more informed investment decisions and remain competitive in the market. Indicators from a technical perspective include new energy volatility indicators and main transformer load factor indicators, which need to be included in the carbon management effectiveness assessment. These indicators are essential 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 emission reduction measures on operational efficiency and achieve a win-win situation of economic benefits and environmental responsibility. In addition, the monitoring of technical indicators is in line with national energy policies and international environmental protection standards, which helps power grid companies establish a green and efficient image in the market and promote the implementation of sustainable development strategies. Indicators from a performance perspective include carbon intensity indicators, which are essential for measuring and monitoring the effectiveness of emission reduction measures. They provide a quantitative perspective that shows the greenhouse gas emissions associated with each unit of economic output, thereby providing policymakers and companies with a clear emission reduction target and evaluation benchmark. By tracking changes in carbon intensity, it is possible to assess whether carbon management measures have effectively reduced the carbon footprint of economic activities and promoted the transition to a low-carbon economy.

[0099] Since the economic costs of some behaviors in the carbon management activities of power grid companies are difficult to quantify, the activity-based costing method is used to obtain the input data of power grid companies. The focus of the activity-based costing method is "operation", which is to verify and calculate the various operations involving the use of enterprise resources, and to include the cost of the resources used in the corresponding operations according to certain standards and principles, and then select the cost drivers, and allocate all operation costs to the cost calculation target (product or service) according to industry standards and allocation systems. The guiding ideology of the activity-based costing method is "operation consumes resources, and output consumes operation", as described in S13-S16 below.

[0100] S13. Divide the operation types of the power grid company into multiple operation centers according to the operation business of the power grid company.

[0101] In an optional implementation, the operation types of the power grid company are divided into 13 operation centers according to the power grid company's operating business, namely, planning and development center, innovation management center, production technology center, market transaction center, science and technology information center, safety supervision center, personnel work center, human resources center, financial center, policy research center, audit work center, regulations supervision center and logistics affairs center.

[0102] S14. The grid company to be evaluated and other grid companies are respectively used as decision-making units (DMUs), and the investment A1 of the grid company to be evaluated and other grid companies in various operation centers, the investment A2 of various carbon management operation activities, the investment A3 of each sub-link under various carbon management operation activities, and the investment B1 of each operation center in the current year are collected to ensure that the carbon management level of each grid company is comparable and referenceable, such as Figure 3 shown.

[0103] S15. Perform data standardization on the investment in each operating center over the years, the investment in each operating activity of carbon management over the years, the investment in each sub-link under each operating activity of carbon management over the years, and the investment in each operating center in the current year, and obtain the processed investment in each operating center over the years, the investment in each operating activity of carbon management over the years, the investment in each sub-link under each operating activity of carbon management over the years, and the investment in each operating center in the current year.

[0104] The formula for data standardization is:

[0105]

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

[0107] Let the normalized matrix be Z, and the element in Z be z ij , the expression is 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 for which data is obtained, and m represents the total number of indicators of the power grid company.

[0110] For example, for the investment of the power grid company to be evaluated in various operation centers over the years, substitute the investment of the power grid company to be evaluated in various operation centers over the years into the above formula to obtain the investment of the power grid company to be evaluated in various operation centers over the years after processing, and so on.

[0111] S16. Obtain the investment data of the power grid company to be evaluated and the other power grid companies based on the processed investment in various operating centers over the years, the investment in various operating activities of carbon management over the years, the investment in each sub-link under various operating activities of carbon management over the years, and the investment in various operating centers in the current year.

[0112] For example, the input data of the power grid company to be evaluated is obtained based on its investment in various operating centers over the years after processing, its investment in various carbon management operating activities over the years after processing, its investment in each sub-link under various carbon management operating activities over the years after processing, and its investment in each operating center in the current year after processing. The investment data of other power grid companies are obtained based on their investment in various operating centers over the years after processing, their investment in various carbon management operating activities over the years after processing, their investment in each sub-link under various carbon management operating activities over the years after processing, and their investment in each operating center in the current year after processing.

[0113] S17. Collect the new energy output, traditional energy output, carbon emissions, unit GDP, actual load borne by the main transformer, rated capacity of the main transformer, average electricity price of the year, investment amount of power plants and primary energy conversion data of the power grid company to be evaluated and other power grid companies.

[0114] S18. Obtain the output data of the power grid company to be evaluated and the other power grid companies based on the new energy output, the traditional energy output, the carbon emissions, the unit GDP, the actual load borne by the main transformer, the rated capacity of the main transformer, the average electricity price of the year, the investment amount of the power plant and the primary energy conversion data.

[0115] For example, the output data of the power grid company to be evaluated is obtained based on its new energy output, traditional energy output, carbon emissions, unit of GDP, actual load borne by the main transformer, rated capacity of the main transformer, average electricity price of the year, investment amount of power plants and primary energy conversion data; the output data of other power grid companies are obtained based on their new energy output, traditional energy output, carbon emissions, unit of GDP, actual load borne by the main transformer, rated capacity of the main transformer, average electricity price of the year, investment amount of power plants and primary energy conversion data.

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

[0117] S21. Calculate the input index values ​​of the power grid company to be evaluated and the other power grid companies respectively according to the input data based on the grey correlation analysis method. The grey correlation analysis method is based on the similarity or difference between the development trends of factors, that is, "grey correlation", as a method to measure the degree of correlation between factors. In the process of system development, if the trends of two factors are consistent, that is, the degree of synchronous change is high, then the correlation between the two factors is high, otherwise, it is low. Specifically including S211-S216:

[0118] S211. Take the investment in each operation center in the years after the processing as a reference sequence, take the investment in each operation activity of carbon management in the years after the processing as a comparison sequence, and calculate the first correlation coefficient between the investment A1 in each operation center in the years after the processing and the investment A2 in each operation activity of carbon management in the years after the processing.

[0119] S212. Take the investment in various carbon management activities in the years after the processing as a reference sequence, take the investment in each sub-link under the various carbon management activities in the years after the processing as a comparison sequence, and calculate the second correlation coefficient between the investment A2 in various carbon management activities in the years after the processing and the investment A3 in each sub-link under the various carbon management activities in the years after the processing.

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

[0121]

[0122] In the formula, x0(k) represents the reference sequence, x i(k) represents the comparison sequence, y(x0(k),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, ρ represents the resolution coefficient, and in this embodiment, the value is 0.5, k represents the year ordinal number, i represents the comparison sequence ordinal number, and e represents the total number of comparison sequences.

[0123] For example, calculate the first correlation coefficient between the investment of the power grid company to be evaluated in various operating centers in the years after processing and the investment in various operating activities of carbon management in the years after processing, then take the investment in various operating activities of carbon management in the years after processing as the reference sequence, and take the investment in each sub-link under the various operating activities of carbon management in the years after processing as the comparison sequence, substitute them into the above formula to obtain the first correlation coefficient of the power grid company to be evaluated, calculate the second correlation coefficient between the investment of the power grid company to be evaluated in various operating activities of carbon management in the years after processing and the investment in each sub-link under the various operating activities of carbon management in the years after processing, then take the investment in various operating activities of carbon management in the years after processing as the reference sequence, and take the investment in each sub-link under the various operating activities of carbon management in the years after processing as the comparison sequence, substitute them into the above formula to obtain the second correlation coefficient of the power grid company to be evaluated, and the same is true for other power grid companies.

[0124] S213, calculating a first gray correlation degree according to the first correlation coefficient, and calculating 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 grey relational degree.

[0127] For example, the first correlation coefficient of the power grid company to be evaluated is substituted into the above formula to obtain the first grey correlation degree, and the second correlation coefficient of the power grid company to be evaluated is substituted into the above formula to obtain the second grey correlation degree. The same is true for other power grid companies.

[0128] S214, respectively perform standardization processing on the first gray relational degree and the second gray relational degree to obtain standardized first gray relational degree and second gray relational degree, and use the standardized first gray relational degree as the first weight and the standardized second gray relational degree as the second weight. The standardization processing makes the first gray relational degree and the second gray relational degree between 0 and 1 for comparison.

[0129] S215. Multiply the processed investment B1 in each operating center in the current year by the first weight respectively to obtain the investment of the grid company to be evaluated and the other grid companies in various carbon management operating activities in the current year.

[0130] For example, the investment of the grid company to be evaluated in each operating center in the year after processing is multiplied by the first weight of the grid company to be evaluated, and the investment of the grid company to be evaluated in various carbon management activities in that year is obtained. The investment of other grid companies in each operating center in the year after processing is multiplied by the first weight of other grid companies, and the investment of other grid companies in various carbon management activities in that year is obtained.

[0131] S216. Multiply the investment in each carbon management operation activity in the current year by the second weight respectively to obtain the values ​​of the carbon emission accounting input index, carbon asset management input index, carbon emission rights trading input index, carbon emission governance input index and carbon assessment management input index of the power grid company to be evaluated and the other power grid companies in the current year.

[0132] For example, multiply the investment of the power grid company to be evaluated in various carbon management operations in that year by the second weight of the power grid company to be evaluated, and obtain the values ​​of the carbon emission accounting input index, carbon asset management input index, carbon emission rights trading input index, carbon emission governance input index and carbon assessment management input index of the power grid company to be evaluated in that year; multiply the investment of other power grid companies in various carbon management operations in that year by the second weights of other power grid companies, and obtain the values ​​of the carbon emission accounting input index, carbon asset management input index, carbon emission rights trading input index, carbon emission governance input index and carbon assessment management input index of other power grid companies in that year.

[0133] S22, calculating the values ​​of the new energy output ratio index of the power grid company to be evaluated and the other power grid companies according to the new energy output and the traditional energy output, specifically:

[0134]

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

[0136] For example, by substituting the new 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 new energy output ratio index of the power grid company to be evaluated, and the same is true for other power grid companies.

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

[0138]

[0139] Coal combustion emissions = coal consumption in the current year × comprehensive coal combustion emission factor;

[0140] Fuel emissions = fuel consumption of the year × comprehensive fuel emission factor;

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

[0142] The carbon dioxide emissions contained in the electricity transferred from the j-th provincial power grid = the amount of electricity transferred from the j-th provincial power grid in the region in that year × the average CO2 emission factor of the j-th provincial power grid;

[0143] The carbon dioxide emissions contained in the power transfer from this region = the amount of electricity transferred from this region × the average CO2 emission factor of the provincial power grid in this region.

[0144] The above-mentioned carbon dioxide emissions are the values ​​of the carbon emissions index.

[0145] S24. Calculate the carbon intensity index values ​​of the grid company to be evaluated and the other grid companies respectively according to the unit gross national product and the carbon emission index values, specifically:

[0146] Carbon emission intensity = value of carbon emission index / unit of gross national product.

[0147] Carbon emission intensity is the value of the carbon intensity indicator.

[0148] S25. Calculate the values ​​of the main transformer load rate index of the power grid company to be evaluated and the other power grid companies respectively according to the load actually carried by the main transformer and the rated capacity of the main transformer, specifically:

[0149] Main transformer load factor = actual load carried by the main transformer / rated capacity of the main transformer × 100%.

[0150] The load rate of the main transformer is the value of the load rate index of the main transformer. Among them, the actual load carried by the main transformer refers to the comprehensive value of the small and medium-sized electric energy of the load connected to the main transformer. The rated capacity of the main transformer refers to the maximum capacity designed by the main transformer. The load rate of the main transformer directly affects the operating status and efficiency of the main transformer.

[0151] S26. Calculate the values ​​of the new energy volatility index of the power grid company to be evaluated and the other power grid companies according to the new energy output, specifically:

[0152]

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

[0154] S27. Calculate the values ​​of the investment return rate indicators of the new energy power stations of the power grid company to be evaluated and the other power grid companies respectively 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 one-time investment amount and the subsequent total investment amount, which is specifically:

[0155] The return on investment of new energy power station = {∑[power generation × (1-yearly attenuation rate) × average electricity price of the year - annual operating cost]} / (one-time investment + subsequent total investment) × 100%;

[0156] Wherein, power generation is the total power generation of the new energy power station each year, unit is (×104kW·h), the year-on-year attenuation rate is the power generation capacity of the generator set (life of power generation components) of the power station after it is put into operation, which will decay every year. For example, the life of photovoltaic power generation components is generally 25 years, and the component life attenuation meets the requirement of less than 5% within 5 years, less than 10% within 10 years, and less than 20% within 25 years. The component decays less than 2% in the first year, and the annual attenuation rate changes linearly thereafter, with an annual average attenuation rate of less than 0.7%. The average electricity price of the year is the sum of the average daily electricity price divided by the number of calculation days, unit is yuan / day, the one-time investment amount is the total cost of the project from the start of construction to the start of operation, unit is yuan, and the subsequent total investment amount is the estimated total investment amount from the start of operation to the scrapping and withdrawal of the power station, unit is yuan.

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

[0158] The proportion of electricity in terminal energy consumption = (the proportion of primary energy to electricity conversion × the efficiency of electricity conversion) / (the proportion of primary energy to electricity conversion × the efficiency of electricity conversion + the proportion of primary energy to non-electricity conversion × the efficiency of processing conversion);

[0159] The proportion of electricity in terminal energy consumption is the value of the indicator of the proportion of terminal consumption of electricity.

[0160] S3, calculating the total factor productivity (TFP) of the power grid company to be evaluated and the other power grid companies respectively according to the value of the input index and the value of the output index, 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, specifically including S31-S33:

[0161] S31, taking the minimum input amount that the power grid company can compress under the condition that the output will change as the objective function, and establishing the constraint conditions of the objective function based on the value of the input index and the value of the output index. The objective function is specifically:

[0162] minθ;

[0163] In the formula, θ represents the amount of input that the power grid company can compress while considering changes in output, that is, total factor productivity;

[0164] The constraints are specifically:

[0165]

[0166] In the formula, x ij represents the value of the i-th input indicator of carbon management of the j-th power grid company, λ j represents the linear combination coefficient of the jth power grid company, cn represents the total number of the power grid company to be evaluated and the other power grid companies, x i0 represents the value of the current grid company's i-th input indicator in carbon management, p represents the total number of input indicators, and y rj represents the value of the rth output indicator of carbon management of the jth power grid company, q represents the total number of output indicators, and y r0 Represents the value of the current grid company’s r-th output indicator in carbon management.

[0167] For example, when calculating the total factor productivity of the power grid company to be evaluated, the minimum input amount that the power grid company to be evaluated can compress while considering changes in output is used as the objective function, and the constraints of the objective function are established based on the values ​​of the input indicators and the output indicators of the power grid company to be evaluated, as shown in the above formula. The total factor productivity of other power grid companies is similar.

[0168] S32, 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. The method for calculating the total factor productivity of the power grid company described above is a non-parametric method, i.e., the BCC model of the data envelopment method, and the calculated total factor productivity is more accurate and reliable.

[0169] S33. Determine an assessment result of the carbon management level of the power grid company to be assessed based on the total factor productivity of the power grid company to be assessed.

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

[0171] In an optional implementation, 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 model 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 model of the power grid company in carbon management is invalid, and there is a better strategy in the operation of carbon management.

[0172] S4. If the carbon management level of the power grid company to be evaluated is assessed to be low, improving the carbon management input structure of the power grid company to be evaluated by utilizing the other power grid companies based on the total factor productivity of the other power grid companies.

[0173] For example, we can select another power grid company with a total factor productivity equal to 1, refer to the input and output model of this power grid company, and improve the input structure of carbon management of the power grid company to be evaluated.

[0174] In an optional implementation, it also includes:

[0175] S5. Select multiple characteristic variables that affect the level of carbon management.

[0176] In an optional implementation, 34 characteristic variables affecting the carbon management level are selected from four aspects: administration, economy, technology and links. Among them, the positive variables are represented by H 1 Indicates that negative variables are represented by H 2 Indicates that there are three time-lagged variables, all of which have a positive impact on the carbon management investment level of power grid companies in the short term and a negative impact in the long term. 12 The 34 characteristic variables that affect the carbon management level are shown in Table 1.

[0177] Table 1 Multiple characteristic variables affecting carbon management level

[0178]

[0179]

[0180] However, there are certain differences in the degree of influence of these variables, so they need to be screened.

[0181] S6. Use the Lasso regression algorithm to screen the multiple characteristic variables to obtain key characteristic variables, specifically including S61-S62:

[0182] S61. Perform dimensionless processing on the multiple characteristic variables to obtain multiple processed characteristic variables.

[0183] In an optional implementation, the multiple characteristic variables are dimensionally processed using a z-score standardization method to obtain multiple characteristic variables after processing. The z-score standardization is also called standard deviation standardization. By converting the data into a unitless z-score value, the data standard is unified and the data comparability is improved. Specifically:

[0184]

[0185] In the formula, y i represents the i-th feature variable after processing, x i represents the i-th characteristic variable, x represents the mean of multiple characteristic variables, s represents the standard deviation, and v represents the number of samples of a certain characteristic variable.

[0186] In an optional implementation, the method further includes: in the case where the time series data of the processed multiple feature variables are missing or unavailable, using interpolation method and moving average method to fill the missing data.

[0187] S62. Use the Lasso regression algorithm to screen the processed multiple feature variables to obtain key feature variables.

[0188] Specifically, if the traditional least squares method is used for parameter estimation, the instability of the estimation results will increase due to the multicollinearity between many characteristic variables, which will affect the judgment of the indicators and ultimately lead to the inability to correctly select the key characteristic variables. The Lasso regression algorithm can solve this problem. Lasso regression can adapt to the situation where the sample size is small but the indicators are more, and it also performs well in variable selection and prediction.

[0189] Lasso is a compression estimation. Its basic idea is to add L1 norm as a constraint on the basis of ordinary least squares to compress the regression coefficients of the model, thereby eliminating unimportant variables. Its basic principles are as follows:

[0190] Multiple regression usually works better than univariate regression because it has more explanatory variables. The calculation method is:

[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 a sample size of m, X = (1, x m1 ,x m2 ,…,x mp )' represents an independent variable with a sample size of m and a dimension of p, 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 normal distribution of each error term is 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 make ε=Y-Xβ as small as possible, that is, to minimize Q(β), Q(β)=||ε|| 2 =(Y-Xβ)'(Y-Xβ), so we can further get the estimated coefficient solution:

[0194] However, the coefficient solution obtained by the least squares estimation method is often not accurate enough. When there are many variables, the system's explanatory ability is poor. The Lasso regression algorithm adds the L1 norm ||β||1 as a constraint in the objective function Q(β) to obtain the coefficient solution: Where 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 unknown coefficients can be shrunk to 0, and finally a system with fewer variables is obtained. The value of λ can be obtained by cross-validation.

[0195] Therefore, the Lasso regression algorithm is used to screen the processed multiple characteristic variables to obtain key characteristic variables. In an optional implementation, the Lasso regression algorithm is used to screen 34 characteristic variables to obtain 12 key characteristic variables.

[0196] like Figure 4 As shown, Figure 4 It shows that as the parameter Lambda increases, each variable is gradually compressed to 0. Figure 4 The horizontal axis in represents λ, which is used to control the strength of the penalty term. The stronger the penalty term, the more variable coefficients will be compressed to zero, thereby affecting the sparsity of the variable coefficients in the model, while the vertical axis represents the size of the variable coefficient. Figure 4Each point or curve in represents the path of the coefficient of a variable as λ changes. As the value of λ increases, Figure 4 The curves in the graph will gradually decrease, and some curves will eventually drop to zero, which means that the corresponding variable coefficients have been compressed to zero and thus eliminated. Figure 4 The dotted lines ① and ② in the figure represent the MinSE and MinMSE indicators respectively. MinMSE is generally selected as the optimal parameter. The corresponding point above it is the number of non-zero variables retained, that is, the number of key feature variables screened out is 12.

[0197] The optimal parameters are selected through cross-validation, and the parameters can be tuned according to the mean square error. Figure 5 .like Figure 5 As shown in the figure, the red dot represents the target parameter corresponding to each Lambda, and the dotted line represents a model with 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, which corresponds to a system with excellent performance and the least number of independent variables. At this time, Lambda = 0.14877.

[0198] In summary, after regression, the final result retains 12 key characteristic variables, including 2 positive variables, 7 negative variables, and 3 time-lagged variables. All of them have a positive impact on the carbon management investment level of power grid companies in the short term and a negative impact in the long term. The coefficients of these screened key characteristic variables are not 0, as shown in Table 2.

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

[0200]

[0201]

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

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

[0204] The present invention introduces total factor productivity to measure the carbon management level of power grid companies, which can provide a more comprehensive and dynamic perspective, consider multiple factors such as technological progress, innovation capabilities, resource allocation, etc., and more accurately evaluate the comprehensive benefits and long-term value of carbon management investment. The introduction of total factor productivity helps enterprises to pay attention to environmental and social responsibilities while pursuing economic benefits, and promote sustainable development; in addition, when predicting the future carbon management investment level of power grid companies, there are high dimensionality and multicollinearity problems of data and the risk of overfitting of the model. Lasso regression, as a regularization method, can effectively deal with these problems and improve prediction accuracy because it reduces the complexity of the prediction system through the penalty coefficient and enhances the generalization ability of the system. The above-mentioned carbon management level assessment method of the present invention can meet the carbon management needs of the region, provide strong support for achieving the carbon emission reduction goals of power grid companies, and can assist power grid companies in rationalizing carbon management investment and making strategic arrangements to promote high-quality development.

[0205] Please refer to Figure 2 , Embodiment 2 of the present invention is:

[0206] A carbon management level assessment terminal comprises 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 carbon management level assessment method in the first embodiment when executing the computer program.

[0207] In summary, the present invention provides a carbon management level assessment method and terminal, which screens the input indicators and output indicators of carbon management, and obtains the input data and output data of the power grid company to be assessed and other power grid companies, respectively calculates the values ​​of the input indicators of the power grid company to be assessed and other power grid companies based on the input data, and respectively calculates the values ​​of the output indicators of the power grid company to be assessed and other power grid companies based on the output data, respectively calculates the total factor productivity of the power grid company to be assessed and other power grid companies according to the values ​​of the input indicators and the values ​​of the output indicators, and determines the carbon management level assessment result of the power grid company to be assessed based on the total factor productivity of the power grid company to be assessed. If the power grid company to be assessed If the carbon management level of the company is evaluated as low, the input structure of the carbon management of the grid company to be evaluated can be improved by using other grid companies based on the total factor productivity of other grid companies. The present invention achieves the quantification of the carbon management level by introducing total factor productivity to measure the efficiency of grid companies in carbon management input and output, and provides a more comprehensive and dynamic evaluation perspective, so as to more accurately and comprehensively evaluate the carbon management level. When the carbon management level evaluation result of the grid company to be evaluated is low, the input structure of the carbon management of the grid company to be evaluated can be improved by using other grid companies based on total factor productivity, so as to help the grid company identify improvement space, optimize resource allocation, and promote low-carbon development. In addition, the output index of carbon management takes into account the low-carbon environmental protection perspective, economic perspective, technical perspective, and performance perspective, and uses input indicators and output indicators to consider multiple factors such as technological progress, innovation ability, and resource allocation, which can comprehensively and effectively quantify the input and output of carbon management and comprehensively reflect technological progress and efficiency changes. In addition, the use of the Lasso regression algorithm to screen multiple characteristic variables can reduce the complexity of the system and identify the factors that most significantly affect the prediction results. Combined with multiple regression, the carbon management level for future years is predicted, which improves the prediction accuracy of future carbon management input and output levels.

[0208] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A carbon management level assessment method, characterized in that: Includes steps: Screen the input and output indicators of carbon management, and obtain the input and output data of the power grid company to be evaluated and other power grid companies; 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, and calculating the 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 the total factor productivity of the power grid company to be evaluated and the other power grid companies respectively according to the value of the input index and the value of the output index, 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 of the grid company to be evaluated is assessed to be low, the input structure of the carbon management of the grid company to be evaluated by the other grid companies is improved based on the total factor productivity of the other grid companies.

2. A carbon management level assessment method according to claim 1, characterized in that: The input indicators and output indicators for screening carbon management include: Integrate various carbon management inputs and screen to obtain carbon management input indicators, including carbon emission accounting input indicators, carbon asset management input indicators, carbon emission rights trading input indicators, carbon emission governance input indicators and carbon assessment management input indicators; The output indicators of carbon management are screened and obtained, and the output indicators include a new energy output proportion indicator, a carbon emission indicator, a carbon intensity indicator, a main transformer load factor indicator, a new energy volatility indicator, a new energy power station investment return rate indicator and an electric energy terminal consumption proportion indicator.

3. A carbon management level assessment method according to claim 2, characterized in that: The acquisition of input data and output data of the power grid company to be evaluated and other power grid companies includes: Divide the operation types of the power grid company into multiple operation centers according to the operation business of the power grid company; The power grid company to be evaluated and other power grid companies are respectively used as decision-making units, and the investment of the power grid company to be evaluated and other power grid companies in various operation centers, the investment in various carbon management operation activities, the investment in each sub-link under various carbon management operation activities, and the investment in various operation centers in the current year are collected; Performing data standardization processing on the investment in each operating center over the years, the investment in each operating activity of carbon management over the years, the investment in each sub-link under each operating activity of carbon management over the years, and the investment in each operating center in the current year, respectively, to obtain the processed investment in each operating center over the years, the investment in each operating activity of carbon management over the years, the investment in each sub-link under each operating activity of carbon management over the years, and the investment in each operating center in the current year; The investment data of the power grid company to be evaluated and the other power grid companies are obtained based on the processed investment in various operating centers over the years, the investment in various operating activities of carbon management over the years, the investment in each sub-link of various operating activities of carbon management over the years, and the investment in various operating centers in the current year; Collect the new energy output, traditional energy output, carbon emissions, unit gross national product, actual load carried by the main transformer, rated capacity of the main transformer, average electricity price of the year, investment amount of power plants and primary energy conversion data of the power grid company to be evaluated and other power grid companies; The output data of the power grid company to be evaluated and the other power grid companies are obtained based on the new energy output, the traditional energy output, the carbon emissions, the unit gross national product, the actual load borne by the main transformer, the rated capacity of the main transformer, the average electricity price of the year, the investment amount of the power plant and the primary energy conversion data.

4. A carbon management level assessment method according to claim 3, characterized in that: The calculating the values ​​of the investment indicators of the power grid company to be evaluated and the other power grid companies respectively based on the investment data includes: Based on the grey correlation analysis method, the values ​​of the input indicators of the power grid company to be evaluated and the other power grid companies are calculated respectively according to the input data.

5. A carbon management level assessment method according to claim 4, characterized in that: The grey correlation analysis method is used to calculate the input index values ​​of the power grid company to be evaluated and the other power grid companies according to the input data, including: The investment in each operating center in the past years after the processing is used as a reference sequence, the investment in each operating activity of carbon management in the past years after the processing is used as a comparison sequence, and the first correlation coefficient between the investment in each operating center in the past years after the processing and the investment in each operating activity of carbon management in the past years after the processing is calculated; Taking the investment in various carbon management activities in the past years after the treatment as a reference sequence, taking the investment in each sub-link under the various carbon management activities in the past years after the treatment as a comparison sequence, and calculating the second correlation coefficient between the investment in various carbon management activities in the past years after the treatment and the investment in each sub-link under the various carbon management activities in the past years after the treatment; Calculating a first grey relational degree according to the first relational coefficient, and calculating a second grey relational degree according to the second relational coefficient; Standardizing the first grey relational degree and the second grey relational degree respectively to obtain standardized first grey relational degree and second grey relational degree, and using the standardized first grey relational degree as a first weight and using the standardized second grey relational degree as a second weight; Multiplying the processed investment in each operation center in the current year by the first weight respectively, to obtain the investment of the grid company to be evaluated and the other grid companies in each carbon management operation activity in the current year; The investment in each carbon management operation in the current year is multiplied by the second weight respectively to obtain the values ​​of the carbon emission accounting input index, carbon asset management input index, carbon emission rights trading input index, carbon emission governance input index and carbon assessment management input index of the power grid company to be evaluated and the other power grid companies in the current year.

6. A carbon management level assessment method according to claim 3, characterized in that: The calculating the output index values ​​of the to-be-evaluated power grid company and the other power grid companies based on the output data respectively includes: Calculate the values ​​of the new energy output ratio index 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; According to the carbon emissions, respectively obtain the values ​​of the carbon emissions index of the power grid company to be evaluated and the other power grid companies; Calculate the values ​​of the carbon intensity index of the power grid company to be evaluated and the other power grid companies respectively according to the values ​​of the unit gross national product and the carbon emission index; Calculating the values ​​of the main transformer load rate indexes of the power grid company to be evaluated and the other power grid companies respectively according to the load actually carried by the main transformer and the rated capacity of the main transformer; Calculate the values ​​of the new energy volatility index of the power grid company to be evaluated and the other power grid companies respectively according to the new energy output; Calculate the value of the investment return rate index of the new energy power station of the power grid company to be evaluated and the other power grid companies respectively according to the average electricity price of the year and the investment amount of the power plant; The values ​​of the electric energy terminal consumption proportion indicators of the power grid company to be evaluated and the other power grid companies are calculated respectively according to the primary energy conversion data.

7. A carbon management level assessment method according to claim 1, characterized in that: The calculating the total factor productivity of the power grid company to be evaluated and the other power grid companies respectively according to the value of the input index and the value of the output index comprises: Taking the minimum input amount that the power grid company can compress under the condition that the output may change as the objective function, and establishing the constraint condition of the objective function based on the value of the input index and the value of the output index; The objective function is solved under the constraint conditions to obtain the total factor productivity of the power grid company to be evaluated and the other power grid companies.

8. A carbon management level assessment method according to claim 7, characterized in that: The objective function is to take the minimum input amount that the power grid company can compress under the condition that the output will change as follows: minθ; In the formula, θ represents the amount of input that the power grid company can compress while considering changes in output; The constraint conditions for establishing the objective function based on the value of the input index and the value of the output index include: In the formula, x ij represents the value of the i-th input indicator of carbon management of the j-th power grid company, λ j represents the linear combination coefficient of the jth power grid company, cn represents the total number of the power grid company to be evaluated and the other power grid companies, x i0 represents the value of the current grid company's i-th input indicator in carbon management, p represents the total number of input indicators, and y rj represents the value of the rth output indicator of carbon management of the jth power grid company, q represents the total number of output indicators, and y r0 Represents the value of the current grid company’s r-th output indicator in carbon management.

9. A carbon management level assessment method according to claim 1, characterized in that: Also includes: Select multiple characteristic variables that affect the level of carbon management; Using Lasso regression algorithm to screen the multiple characteristic variables to obtain key characteristic variables; Performing multiple regression using the key characteristic variables to obtain a prediction model; The carbon management level of the power grid company to be evaluated is predicted according to the prediction model to obtain a prediction result.

10. A carbon management level assessment terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step of the carbon management level assessment method according to any one of claims 1 to 9 is implemented.

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