Power industry carbon emission efficiency measuring and calculating method and device based on Non-radial DDF and SBM model

Abstract

The invention provides a power industry carbon emission efficiency measuring and calculating method and device based on Non-radial DDF and SBM models, and the method comprises the steps: carrying out the decision-making unit division of a to-be-measured region, and obtaining independent decision-making units; related information of each independent decision-making unit is collected, and the related information comprises the fossil fuel consumption of each independent decision-making unit, the carbon content of fossil fuel unit energy, the employment number of the power industry and the fixed capital net value of the power industry; an SBM model and a Non-radial DDF model are constructed, and a comprehensive carbon emission efficiency evaluation system is constructed based on the constructed SBM model, the Non-radial DDF model and the related information of each independent decision unit; and determining the carbon emission efficiency based on the comprehensive carbon emission efficiency evaluation system. In this way, the carbon emission efficiency of the power industry can be measured and calculated more accurately, the overall carbon emission efficiency of the power industry is improved, and the regional carbon emission cost is reduced.

Description

Technical Field

[0001] The present disclosure relates to the electric power industry and the technical field of carbon emission rights trading, and in particular to a method and device for calculating carbon emission efficiency in the electric power industry based on Non-radialDDF and SBM models. Background Art

[0002] As the global climate change problem becomes increasingly serious, reducing carbon emissions and improving energy efficiency have become the focus of global attention. As one of the main sources of carbon emissions, the accurate measurement of the carbon emission efficiency of the power industry is the basis for formulating effective emission reduction strategies. However, traditional carbon emission efficiency measurement methods often ignore the undesirable outputs (such as carbon emissions) in the production process, so the results may be biased, and traditional carbon emission efficiency evaluation methods are mostly based on individual enterprises, ignoring the overall efficiency at the regional level. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention provides a method and device for calculating the carbon emission efficiency of the power industry based on the Non-radial DDF and SBM models, which combines the advantages of the Non-radial DDF model and the SBM model to more accurately calculate the carbon emission efficiency of the power industry, improve the overall carbon emission efficiency of the power industry, and reduce regional carbon emission costs.

[0004] According to a first aspect of the present disclosure, a method for calculating carbon emission efficiency in the power industry based on a Non-radial DDF and SBM model is provided, the method comprising: Divide the area to be tested into decision units to obtain independent decision units; Collecting relevant information of each independent decision-making unit, wherein the relevant information includes fossil fuel consumption, carbon content per unit of energy of fossil fuel, number of employees in the power industry and net value of fixed capital in the power industry of each independent decision-making unit; Constructing an SBM model and a Non-radial DDF model, and building a comprehensive carbon emission efficiency evaluation system based on the constructed SBM model and the Non-radial DDF model and the relevant information of each independent decision-making unit; Based on the comprehensive carbon emission efficiency evaluation system, the carbon emission efficiency is determined.

[0005] According to the above aspects and any possible implementation, an implementation is further provided, wherein a comprehensive carbon emission efficiency evaluation system is constructed based on the constructed SBM model and the Non-radial DDF model and the relevant information of each independent decision-making unit, including: Calculating the carbon dioxide emissions of each independent decision-making unit according to the fossil fuel consumption of each independent decision-making unit and the carbon content of the fossil fuel unit energy; A comprehensive carbon emission efficiency evaluation system is constructed according to the carbon dioxide emissions of each independent decision-making unit in combination with the constructed SBM model and the Non-radial DDF model.

[0006] According to the above aspects and any possible implementation, an implementation is further provided, wherein the constructing of the SBM model and the Non-radial DDF model includes: Constructing a possible production set including carbon emissions, and adding constraints to the possible production set including carbon emissions to generate a possible set model including carbon emissions; Based on the production possibility set model including carbon emissions, an SBM model and a Non-radial DDF model are constructed.

[0007] According to the above aspects and any possible implementation, an implementation is further provided, wherein constructing a possible production set including carbon emissions, adding constraints to the possible production set including carbon emissions, and generating a possible set model including carbon emissions includes: Determining input variables, desired outputs, and undesired outputs, and defining a production possibility set based on the input variables, desired outputs, and undesired outputs; Constraints are added to the production possibility set to make it satisfy weak disposability and zero-zero association processing, thereby generating a possibility set model including carbon emissions.

[0008] According to the above aspects and any possible implementation, a further implementation is provided, which constructs a Non-radial DDF model based on the production possibility set model including carbon emissions, including: Based on the production possibility set model including carbon emissions, the direction vector is introduced , construct a radial distance function, )}, θ represents the degree to which the measured unit approaches the frontier surface, assuming ; Set the direction vector to ; Converting the radial direction distance function into a non-radial direction distance function; According to the direction vector The Non-radial DDF model is constructed with the non-radial direction distance function.

[0009] According to the above aspects and any possible implementation, an implementation is further provided, which measures the carbon emission efficiency based on the constructed Non-radial DDF model, including: .

[0010] According to the above aspects and any possible implementation, an implementation is further provided, wherein the SBM model is constructed based on the production possibility set model including carbon emissions, including: min

[0011]

[0012]

[0013]

[0014]

[0015]

[0016] in, is the objective function; each decision unit includes Input factors, Energy input factor input and Non-energy input factors , Expected output , Undesirable output Three elements; represents the weight variable of the decision-making unit; represents the variable element of energy input; slack variable representing energy input; represents the non-energy input variable element, slack variables representing non-energy inputs; represents the expected output factor, Slack variables representing expected output; is an undesirable output factor, Slack variables representing undesirable outputs; represents the non-negative basic assumption of the slack variables; denotes the underlying assumption that weights are non-negative.

[0017] According to the above aspects and any possible implementation, an implementation is further provided, which measures the carbon emission efficiency based on the constructed SBM model, including: .

[0018] According to a second aspect of the present disclosure, a device for calculating carbon emission efficiency in the power industry based on a Non-radial DDF and SBM model is provided, the device comprising: An independent decision unit division module is used to divide the area to be tested into decision units to obtain independent decision units; A relevant information collection module, used to collect relevant information of each independent decision-making unit, wherein the relevant information includes the fossil fuel consumption of each independent decision-making unit, the carbon content of fossil fuel unit energy, the number of employees in the power industry and the net value of fixed capital in the power industry; A model building module, used to build an SBM model and a Non-radial DDF model, and to build a comprehensive carbon emission efficiency evaluation system based on the constructed SBM model and the Non-radial DDF model and the relevant information of each independent decision-making unit; The carbon emission efficiency calculation module is used to determine the carbon emission efficiency based on the comprehensive carbon emission efficiency evaluation system.

[0019] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method according to the first aspect of the present disclosure is implemented.

[0020] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0021] Compared with the prior art, the present invention achieves the following beneficial effects: The present invention provides a method for calculating the carbon emission efficiency of the power industry based on the Non-radial DDF and SBM models. By taking each province as an independent decision-making unit and combining the advantages of the Non-radial DDF model and the SBM model, the carbon emission efficiency of the power industry is calculated more accurately, the overall carbon emission efficiency of the power industry is improved, and the regional carbon emission costs are reduced.

[0022] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which: Figure 1 A flow chart of a method for calculating carbon emission efficiency in the power industry based on the Non-radial DDF and SBM models according to an embodiment of the present disclosure is shown; Figure 2 A flowchart of constructing a production possibility set model including carbon emissions according to an embodiment of the present disclosure is shown; Figure 3 A flowchart of constructing a Non-radial DDF model according to an embodiment of the present disclosure is shown; Figure 4 A block diagram of a phase regulator and grid-connected inverter coordinated control device according to an embodiment of the present disclosure is shown; Figure 5 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0025] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0026] The present disclosure provides a method for calculating the carbon emission efficiency of the power industry based on the Non-radial DDF and SBM models, the method comprising: dividing the area to be measured into decision units to obtain independent decision units; collecting relevant information of the independent decision units, wherein the relevant information includes the fossil fuel consumption of each independent decision unit, the carbon content of fossil fuel per unit energy, the number of employees in the power industry and the net value of fixed capital in the power industry; constructing an SBM model and a Non-radial DDF model, and based on the constructed SBM model and the Non-radial DDF model and the relevant information of the independent decision units, constructing a comprehensive carbon emission efficiency evaluation system; based on the comprehensive carbon emission efficiency evaluation system, determining the carbon emission efficiency. In this way, the carbon emission efficiency of the power industry can be more accurately calculated, the overall carbon emission efficiency of the power industry can be improved, and the regional carbon emission cost can be reduced.

[0027] In conjunction with the accompanying drawings, a method for calculating carbon emission efficiency in the power industry based on the Non-radial DDF and SBM models provided by the present disclosure is described below.

[0028] like Figure 1 As shown, the embodiment of the present disclosure provides a flow chart of a method for calculating carbon emission efficiency in the power industry based on the Non-radial DDF and SBM models, the method comprising: Step S110: Divide the area to be tested into decision units to obtain independent decision units.

[0029] In some embodiments, in order to effectively evaluate the carbon emission efficiency of each region, track the effect of policy implementation, and adjust strategies in a timely manner, the area to be tested can be divided into decision-making units to obtain independent decision-making units (DMU, DecisionMaking Units). For example, if the carbon emission efficiency is calculated nationwide, each province can be used as an independent decision-making unit. Through detailed analysis of each province, carbon emission issues can be managed more meticulously, resources can be allocated more reasonably, and those less efficient regions can be supported to carry out technological transformation and management improvement.

[0030] In this embodiment, relevant data on the input and output of the power industry in 29 provinces and regions in China from 2010 to 2023 can be collected. Among them, Tibet, Xinjiang, Hong Kong, Macao and Taiwan are not included due to the lack of some energy and labor data. The data of Chengdu and Chongqing are treated as the same province, and the capital-related data are deflated with 2010 as the base period.

[0031] Step S120: Collect relevant information of each independent decision-making unit, wherein the relevant information includes the fossil fuel consumption of each independent decision-making unit, the carbon content of fossil fuel unit energy, the number of employees in the power industry and the net value of fixed capital in the power industry.

[0032] In some embodiments, relevant information on the power industry in each province can be collected and processed, such as the consumption of fossil fuels (coal, oil, natural gas), the carbon content per unit energy of the above types of fossil fuels, the number of employees in the power industry in each province, and the net value of fixed capital in the power industry in each province, etc., so as to calculate the carbon emission efficiency based on the above information.

[0033] Step S130: constructing an SBM model and a Non-radial DDF model, and constructing a comprehensive carbon emission efficiency evaluation system based on the constructed SBM model and Non-radial DDF model and the relevant information of each independent decision-making unit.

[0034] In some embodiments, the carbon dioxide emissions of each independent decision-making unit are first calculated based on the fossil fuel consumption of each independent decision-making unit and the carbon content per unit energy of the fossil fuel; then, a comprehensive carbon emission efficiency evaluation system is constructed based on the carbon dioxide emissions of each independent decision-making unit combined with the constructed SBM model and Non-radial DDF model.

[0035] In some embodiments, the carbon dioxide emissions of each independent decision-making unit are calculated based on the fossil fuel consumption of each independent decision-making unit and the carbon content per unit energy of the fossil fuel, that is: , where = represents the carbon dioxide emissions generated by energy activities estimated according to the formula, in 10,000 tons; E represents energy consumption, in 10,000 tons; C represents the carbon content of each unit of energy. Multiply the consumption of each fuel by its corresponding carbon content per unit of energy, and then add up the carbon dioxide emissions of all fuels to get the carbon dioxide emissions of the power industry in each province.

[0036] In some embodiments, constructing the SBM model and the Non-radial DDF model includes: A possible production set including carbon emissions is constructed, and constraints are added to the possible production set including carbon emissions to generate a possible set model including carbon emissions; based on the possible production set model including carbon emissions, an SBM model and a Non-radial DDF model are constructed.

[0037] Specifically, constructing a production potential set that includes carbon emissions Figure 2 As shown, firstly, the input variables, expected outputs and undesired outputs are determined, and a production possibility set is defined according to the input variables, expected outputs and undesired outputs; constraints are added to the production possibility set to satisfy weak disposability and zero-zero association processing, and a possible set model including carbon emissions is generated, which specifically includes the following steps: In the first step, assume that each production unit in a country uses J energy inputs ( and N non-energy inputs ( , producing M kinds of expected outputs ( ) and Q undesirable outputs ( ), the possible production set is ; The second step is to constrain the constructed environmental production technology so that it satisfies two environmental theorems: weak processability of output, i.e. and zero-zero correlation, i.e. , θ represents the degree to which the unit under test approaches the preamble surface; Step 3: Assume that there are K production units in a country.

[0038] In the formula, ; is the weight variable, It indicates that environmental production technology has constant returns to scale.

[0039] Step 4: To reflect the zero-zero correlation, the following two formulas need to be added:

[0040]

[0041] In the formula, the two inequalities indicate that there is only one production unit in a region with an undesirable output greater than zero, or there is at least one undesirable output in a region with an output greater than zero.

[0042] In some embodiments, based on the modeling environment technology, the setting of the non-radial direction distance function (NDDF) and the DEA model building process are as follows: Figure 3 As shown, specifically: Step 1, based on the environmental production function, introduce the direction vector. , construct a directional distance function, )}, θ represents the degree of proximity of the measured unit to the front surface; Step 2, assuming , after this setting, the radial direction distance function is transformed into non-radial; Step 3: Set the direction vector to ; In the fourth step, the non-radial output-oriented directional distance function and DEA model are set as follows:

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] In the DEA model of formula, and It means that the expected output and unexpected output are expanded and compressed in different proportions, that is, the inefficiency in the two dimensions of carbon emission reduction and economic output is measured.

[0049] Based on the constructed Non-radial DDF model, the carbon emission efficiency is calculated using the following formula:

[0050] In some embodiments, based on the modeling environment technology, the SBM model building process is: min

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] In the formula, is the objective function; each decision unit includes Input factors ( Energy input factor input and n non-energy input factors )、 Expected output , Undesirable output Three elements; represents the weight variable of the decision-making unit; represents the variable element of energy input, slack variable representing energy input; represents the non-energy input variable element, slack variables representing non-energy inputs; represents the expected output factor, Slack variables representing expected output; is an undesirable output factor, Slack variables representing undesirable outputs; represents the non-negative basic assumption of the slack variables; denotes the underlying assumption that weights are non-negative.

[0057] Based on the constructed SBM model, the carbon emission efficiency is calculated using the following formula:

[0058] Step S140: Determine the carbon emission efficiency based on the comprehensive carbon emission efficiency evaluation system.

[0059] According to the embodiments of the present disclosure, the following technical effects are achieved: The present invention provides a method for calculating the carbon emission efficiency of the power industry based on the Non-radial DDF and SBM models. By taking each province as an independent decision-making unit and combining the advantages of the Non-radial DDF model and the SBM model, the carbon emission efficiency of the power industry is calculated more accurately, the overall carbon emission efficiency of the power industry is improved, and the regional carbon emission costs are reduced.

[0060] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the described order of actions, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0061] The above is an introduction to the method embodiment. The following is a further explanation of the scheme disclosed in the present invention through an apparatus embodiment.

[0062] Figure 4 FIG. 4 is a block diagram of a device 400 for calculating carbon emission efficiency in the power industry based on a Non-radial DDF and SBM model according to an embodiment of the present disclosure. Figure 4 As shown, the device 400 includes: An independent decision unit division module 410 is used to divide the area to be tested into decision units to obtain independent decision units; The relevant information collection module 420 is used to collect relevant information of each independent decision-making unit, wherein the relevant information includes the fossil fuel consumption of each independent decision-making unit, the carbon content of fossil fuel unit energy, the number of employees in the power industry and the net value of fixed capital in the power industry; A model building module 430 is used to build an SBM model and a Non-radial DDF model, and to build a comprehensive carbon emission efficiency evaluation system based on the built SBM model and the Non-radial DDF model and the relevant information of each independent decision-making unit; The carbon emission efficiency calculation module 440 is used to determine the carbon emission efficiency based on the comprehensive carbon emission efficiency evaluation system.

[0063] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0064] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0065] Figure 5 A schematic block diagram of an electronic device 300 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0066] The electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a ROM 502 or a computer program loaded from a storage unit 508 into a RAM 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An I / O interface 505 is also connected to the bus 504.

[0067] Multiple components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0068] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above, such as the method of automatically generating a new picture. For example, in some embodiments, the method of automatically generating a new picture may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the method of automatically generating a new picture described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to execute the method for automatically generating a new picture in any other appropriate manner (for example, by means of firmware).

[0069] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0070] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0071] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0072] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0073] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0074] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0075] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of this disclosure can be achieved, and this document is not limited here.

[0076] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for calculating carbon emission efficiency in the power industry based on the Non-radial DDF and SBM models, characterized in that: include: Divide the area to be tested into decision units to obtain independent decision units; Collecting relevant information of each independent decision-making unit, wherein the relevant information includes fossil fuel consumption, carbon content per unit of energy of fossil fuel, number of employees in the power industry and net value of fixed capital in the power industry of each independent decision-making unit; Constructing an SBM model and a Non-radial DDF model, and building a comprehensive carbon emission efficiency evaluation system based on the constructed SBM model and the Non-radial DDF model and the relevant information of each independent decision-making unit; Based on the comprehensive carbon emission efficiency evaluation system, the carbon emission efficiency is determined.

2. The method according to claim 1, characterized in that Based on the constructed SBM model and Non-radialDDF model and the relevant information of each independent decision-making unit, a comprehensive carbon emission efficiency evaluation system is constructed, including: Calculating the carbon dioxide emissions of each independent decision-making unit according to the fossil fuel consumption of each independent decision-making unit and the carbon content of the fossil fuel unit energy; A comprehensive carbon emission efficiency evaluation system is constructed according to the carbon dioxide emissions of each independent decision-making unit in combination with the constructed SBM model and Non-radialDDF model.

3. The method according to claim 1, characterized in that The construction of the SBM model and the Non-radial DDF model includes: Constructing a possible production set including carbon emissions, and adding constraints to the possible production set including carbon emissions to generate a possible set model including carbon emissions; Based on the production possibility set model including carbon emissions, an SBM model and a Non-radial DDF model are constructed.

4. The method according to claim 3, characterized in that The constructing of a possible production set including carbon emissions, and adding constraints to the possible production set including carbon emissions to generate a possible set model including carbon emissions, includes: Determining input variables, desired outputs, and undesired outputs, and defining a production possibility set based on the input variables, desired outputs, and undesired outputs; Constraints are added to the production possibility set to make it satisfy weak disposability and zero-zero association processing, thereby generating a possibility set model including carbon emissions.

5. The method according to claim 3, characterized in that: Based on the production possibility set model including carbon emissions, a Non-radial DDF model is constructed, including: Based on the production possibility set model including carbon emissions, the direction vector is introduced , construct a radial distance function, )}, θ represents the degree to which the measured unit approaches the frontier surface, assuming ; Set the direction vector to ; Converting the radial direction distance function into a non-radial direction distance function; According to the direction vector The Non-radialDDF model is constructed with the non-radial direction distance function.

6. The method according to claim 5, characterized in that Based on the constructed Non-radial DDF model, the carbon emission efficiency is calculated, including: 。 7. The method according to claim 3, characterized in that The SBM model is constructed based on the production possibility set model including carbon emissions, including: min in, is the objective function; each decision unit includes Input factors, Energy input factor input and Non-energy input factors , Expected output , Undesirable output Three elements; represents the weight variable of the decision-making unit; represents the variable element of energy input; slack variable representing energy input; represents the non-energy input variable element, slack variables representing non-energy inputs; represents the expected output factor, Slack variables representing expected output; is an undesirable output factor, Slack variables representing undesirable outputs; represents the non-negative basic assumption of the slack variables; denotes the underlying assumption that weights are non-negative.

8. The method according to claim 7, characterized in that Based on the constructed SBM model, the carbon emission efficiency is calculated, including: 。 9. A device for calculating carbon emission efficiency in the power industry based on the Non-radial DDF and SBM model, characterized in that: include: An independent decision unit division module is used to divide the area to be tested into decision units to obtain independent decision units; A relevant information collection module, used to collect relevant information of each independent decision-making unit, wherein the relevant information includes the fossil fuel consumption of each independent decision-making unit, the carbon content of fossil fuel unit energy, the number of employees in the power industry and the net value of fixed capital in the power industry; A model building module, used to build an SBM model and a Non-radial DDF model, and to build a comprehensive carbon emission efficiency evaluation system based on the constructed SBM model and the Non-radial DDF model and the relevant information of each independent decision-making unit; The carbon emission efficiency calculation module is used to determine the carbon emission efficiency based on the comprehensive carbon emission efficiency evaluation system.

10. An electronic device, characterized in that: It includes a processor, and a memory and a network interface connected to the processor; the network interface is connected to a non-volatile memory in a server; when the processor is running, it calls a computer program from the non-volatile memory through the network interface, and runs the computer program through the memory to execute the method described in any one of claims 1 to 8.

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