Industrial electric power carbon emission influence factor decomposition analysis method and device based on green economy

Abstract

The invention relates to an industrial electric power carbon emission influence factor decomposition analysis method and device based on green economy, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring power carbon emission data of at least one target industry in a target region within a preset time interval; performing quantitative decomposition on the electric power carbon emission data through an LMDI decomposition model to obtain dynamic electric power carbon emission influence factors corresponding to each target industry; the dynamic electric power carbon emission influence factors comprise carbon emission influence factors taking a preset time interval as a unit; the carbon emission influence factors at least comprise an electric power influence factor and a green economic influence factor; and for any target industry, determining the influence degree of each dynamic electric power carbon emission influence factor of any target industry on the electric power carbon emission according to the Dihell index corresponding to each dynamic electric power carbon emission influence factor of any target industry. By adopting the method, the influence factors of the power carbon emission of the industry can be analyzed more accurately.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for decomposing and analyzing factors affecting industry electric power carbon emissions based on a green economy. Background Art

[0002] The traditional power industry uses fossil fuels as its main energy source, and the use of these energy sources leads to a large amount of greenhouse gas emissions such as carbon dioxide, exacerbating the problems of global warming and climate change. Therefore, it is particularly important to develop an industry electricity carbon factor monitoring method, which can more accurately analyze and monitor the carbon emissions generated by specific industries and specific enterprises in the process of electricity production.

[0003] At present, the global call for environmental protection and sustainable development is growing. The technology of analyzing and evaluating the factors affecting the carbon emissions of electricity from emission-controlled enterprises in a specific industry has broad application prospects in the context of the energy industry. It not only helps companies understand their own carbon emissions and formulate emission reduction strategies, but also assists governments and international organizations in monitoring carbon emission trends and promoting the goal of global carbon neutrality. This method is of great significance and application prospects for the power industry to achieve low-carbon transformation, respond to climate change, and improve energy efficiency.

[0004] Therefore, in order to achieve emission reduction targets, how to accurately analyze the factors affecting the industry’s electricity carbon emissions has become an urgent issue to be addressed. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for decomposing and analyzing the influencing factors of industry electricity carbon emissions based on the green economy, which can more accurately analyze the influencing factors of industry electricity carbon emissions in response to the above-mentioned technical problems.

[0006] In the first aspect, the present application provides a method for decomposing and analyzing factors affecting carbon emissions of industry electricity based on green economy, including:

[0007] Obtain electricity carbon emission data for at least one target industry in a target region within a preset time interval;

[0008] The electricity carbon emission data is quantitatively decomposed through the LMDI decomposition model to obtain the dynamic electricity carbon emission impact factor corresponding to each of the target industries; the dynamic electricity carbon emission impact factor includes the carbon emission impact factor with the preset time interval as the unit; the carbon emission impact factor includes at least the electricity impact factor and the green economy impact factor;

[0009] For any target industry, the degree of influence of each of the dynamic electricity carbon emission influencing factors of any target industry on electricity carbon emissions is determined based on the Digg index corresponding to each of the dynamic electricity carbon emission influencing factors of any target industry.

[0010] In one of the embodiments, the electricity impact factor represents the impact of electricity events in the target industry on the electricity carbon emissions; the green economy impact factor represents the impact of economic events in the target industry on the electricity carbon emissions.

[0011] In one embodiment, the electricity influencing factors include at least one of the following: carbon emission factor of power generation, renewable energy power generation coefficient, renewable energy power self-sufficiency rate, renewable energy power demand coefficient, renewable energy power supply-demand ratio, renewable energy power supply proportion, power supply efficiency, renewable energy power consumption coefficient, and renewable energy power consumption intensity; the green economy influencing factors include at least one of the following: social responsibility investment coefficient, green investment coefficient, average green investment, and number of enterprises.

[0012] In one embodiment, the electricity carbon emission data is quantitatively decomposed by the LMDI decomposition model to obtain the dynamic electricity carbon emission impact factors corresponding to each target industry, including:

[0013] Extracting carbon emission impact data of each target industry based on the electricity carbon emission data; the carbon emission impact data includes electricity impact data and green economy impact data;

[0014] The power impact data includes at least one of total power generation, total renewable energy power generation, total power demand, total renewable energy power demand, total renewable energy power supply, total power supply, total power usage, and total renewable energy power usage; the green economic impact data includes at least one of gross domestic product, total social responsibility-related investment cost, total green investment cost, and the number of enterprises;

[0015] The carbon emission impact data of each target industry is quantitatively decomposed through the LMDI decomposition model to obtain the dynamic electricity carbon emission impact factor corresponding to each target industry.

[0016] In one embodiment, the carbon emission impact data of each target industry is quantitatively decomposed by the LMDI decomposition model to obtain the dynamic electricity carbon emission impact factor corresponding to each target industry, including:

[0017] For any of the target industries, the carbon emission impact data of any of the target industries is substituted into the factor decomposition formula to obtain the dynamic electricity carbon emission impact factor corresponding to any of the target industries;

[0018] The factor decomposition formula is expressed as:

[0019]

[0020] in, for area The carbon emissions of electricity from the industry, in MtCO2; for area Total electricity generation by industry; for area Total renewable energy generation by industry; for area Total electricity demand of the industry; for area Total renewable electricity demand by sector; for area Total renewable electricity supply by sector; for area Total electricity demand of the industry; for area Total electricity usage by industry; for area Total renewable electricity use by industry; for area The gross output of the industry; for area The total social responsibility-related investment costs of the industry; for area Total green investment costs for the industry; for area The number of companies in the industry.

[0021] In one embodiment, substituting the carbon emission impact data of any target industry into a factor decomposition formula to obtain a dynamic electricity carbon emission impact factor corresponding to any target industry includes:

[0022] Simplifying the factor decomposition formula, and using each item in the simplified factor decomposition formula as the dynamic electricity carbon emission influencing factor;

[0023] The simplified factor decomposition formula is:

[0024]

[0025] The dynamic electricity carbon emission influencing factors include:

[0026] in, for area The carbon emission factors for electricity generation by industry; for area Renewable energy generation coefficients for the industry; for area The industry’s renewable electricity self-sufficiency rate; for area Renewable energy electricity demand factor for the industry; for area The industry’s renewable energy electricity supply-demand ratio; for area The proportion of electricity supplied by renewable energy in the industry; for area efficiency of electricity supply to the industry; for area Renewable energy electricity consumption coefficient of the industry; for area Renewable energy electricity consumption intensity of the industry; for area The industry's socially responsible investment coefficient; for area The green investment coefficient of the industry; for area Average green investment by industry.

[0027] In a second aspect, the present application also provides a device for decomposing and analyzing factors affecting carbon emissions of industry electricity based on green economy, including:

[0028] An acquisition module, used to acquire electricity carbon emission data of at least one target industry in a target area within a preset time interval;

[0029] A decomposition module, used to quantitatively decompose the electricity carbon emission data through the LMDI decomposition model to obtain the dynamic electricity carbon emission impact factor corresponding to each of the target industries; the dynamic electricity carbon emission impact factor includes the carbon emission impact factor in units of the preset time interval; the carbon emission impact factor includes at least an electricity impact factor and a green economy impact factor;

[0030] A determination module is used to determine, for any target industry, the degree of influence of each of the dynamic electricity carbon emission influencing factors of the target industry on electricity carbon emissions based on the Digg index corresponding to each of the dynamic electricity carbon emission influencing factors of the target industry.

[0031] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the above method are implemented.

[0032] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0033] In a fifth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0034] The above-mentioned method, device, computer equipment, computer-readable storage medium and computer program product for decomposing and analyzing the influencing factors of industry electricity carbon emissions based on the green economy obtain electricity carbon emission data of at least one target industry in the target area within a preset time interval; through the LMDI decomposition model, the electricity carbon emission data is quantitatively decomposed to obtain the dynamic electricity carbon emission influencing factors corresponding to each target industry; the dynamic electricity carbon emission influencing factors include carbon emission influencing factors with preset time intervals as units; the carbon emission influencing factors include at least electricity influencing factors and green economy influencing factors; for any target industry, according to the Dirichlet index corresponding to each dynamic electricity carbon emission influencing factor of any target industry, the degree of influence of each dynamic electricity carbon emission influencing factor of any target industry on electricity carbon emissions is determined.

[0035] In this way, by obtaining the electricity carbon emission data of at least one target industry in the target area within the preset time interval; through the LMDI decomposition model, the electricity carbon emission data is quantitatively decomposed to obtain the dynamic electricity carbon emission impact factor corresponding to each target industry; the dynamic electricity carbon emission impact factor includes the carbon emission impact factor in units of the preset time interval; the carbon emission impact factor includes at least the electricity impact factor and the green economy impact factor; thus, through the decomposition of the impact factor of the preset time interval, dynamic measurement can be achieved, based on the carbon emission impact factor in units of the preset time interval, the authenticity required for actual operation can be considered when achieving low carbon, and then according to the Di index corresponding to each dynamic electricity carbon emission impact factor, the impact of each dynamic electricity carbon emission impact factor of the target industry on electricity carbon emissions can be more accurately determined, and a more comprehensive analysis of the electricity carbon emissions of the target industry in the target area can be achieved, and the impact of different impact factors on the electricity carbon emissions of the target industry can be analyzed. This analysis method has high accuracy and real-time performance, can dynamically manage electricity carbon emissions, provide customized solutions for different industries, can conduct detailed analysis of carbon emissions in various industries, provide support for the formulation and adjustment of environmental policies, and help to formulate more effective carbon emission reduction measures and promote the realization of environmental protection and sustainable development goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A schematic diagram of a flow chart of a method for decomposing and analyzing factors affecting carbon emissions of power industry based on green economy in one embodiment;

[0038] Figure 2 A schematic diagram of a flow chart of steps for quantifying and decomposing electricity carbon emission data in one embodiment;

[0039] Figure 3 A schematic flow chart of a method for decomposing and analyzing factors affecting carbon emissions of power industry based on green economy in another embodiment;

[0040] Figure 4 It is a structural block diagram of a device for decomposing and analyzing influencing factors of industry power carbon emissions based on green economy in one embodiment;

[0041] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0043] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0044] The method for decomposing and analyzing factors affecting carbon emissions from industry electricity based on a green economy provided in an embodiment of the present application can be used in a computer device, which can be a server or a terminal. The server can be a single server or a server cluster composed of multiple servers. The terminal can be but is not limited to various personal computers, laptops, smart phones and tablet devices. The method for decomposing and analyzing factors affecting carbon emissions from industry electricity based on a green economy provided in an embodiment of the present application can also be applied to a system including a terminal and a server, and implemented through the interaction between the terminal and the server.

[0045] In one embodiment, Figure 1 As shown, a method for decomposing and analyzing influencing factors of industry electricity carbon emissions based on green economy is provided. The method is described by taking the method for computer equipment as an example. The method includes the following steps:

[0046] Step S110, obtaining electricity carbon emission data of at least one target industry in a target area within a preset time interval.

[0047] Among them, the target area refers to the area where analysis of factors affecting electricity carbon emissions is required.

[0048] Among them, the target industry refers to the industry that needs to analyze the influencing factors of electricity carbon emissions.

[0049] Among them, electricity carbon emission data refers to the recorded data of carbon emissions caused by the use of electricity in the target area over the past period of time. Electricity carbon emission data usually includes annual or quarterly carbon emissions, as well as information data related to carbon emissions, such as the energy structure of power supply and energy efficiency level.

[0050] The preset time interval refers to the interval for extracting electricity carbon emission data. In practical applications, the preset time interval may be every hour, and the length of the preset time interval is not specifically limited here.

[0051] Among them, by utilizing the electricity and energy information base, computer equipment can obtain electricity usage and carbon emission information data related to the target area, and specifically can obtain electricity carbon emission data corresponding to the industry and preset time interval scale.

[0052] In an embodiment of the present application, the computer device may obtain electricity carbon emission data corresponding to the regional identifier from multiple data sources based on the regional identifier of the target area.

[0053] Step S120, using the LMDI decomposition model, quantify and decompose the electricity carbon emission data to obtain the dynamic electricity carbon emission impact factor corresponding to each target industry.

[0054] Among them, the LMDI (Log-Mean Divisia Index) decomposition model can be used for factor decomposition analysis.

[0055] Among them, the dynamic electricity carbon emission impact factor includes a carbon emission impact factor in units of preset time intervals.

[0056] Among them, carbon emission influencing factors include at least electricity influencing factors and green economy influencing factors.

[0057] Among them, the power impact factor represents the impact of power events in the target industry on power carbon emissions.

[0058] Among them, the green economy impact factor represents the impact of economic events in the target industry on electricity carbon emissions.

[0059] Among them, the electricity influencing factors include at least one of the carbon emission factor of power generation, renewable energy power generation coefficient, renewable energy power self-sufficiency rate, renewable energy power demand coefficient, renewable energy power supply-demand ratio, renewable energy power supply proportion, power supply efficiency, renewable energy power consumption coefficient, and renewable energy power consumption intensity.

[0060] Among them, the green economy influencing factors include at least one of the social responsibility investment coefficient, green investment coefficient, average green investment, and number of enterprises.

[0061] In some embodiments, before the electricity carbon emission data is quantitatively decomposed through the LMDI decomposition model, the computer device may preprocess the electricity carbon emission data. Specifically, the computer device may split the acquired electricity carbon emission data, filter the useless data information in the split electricity carbon emission data, merge and process the useful data information, and convert it into a commonly used data format. After confirming that the converted data format meets the data format required by the calculation formula, the preprocessed electricity carbon emission data is obtained, and the preprocessed electricity carbon emission data is stored, thereby performing quantitative decomposition based on the preprocessed electricity carbon emission data.

[0062] In the specific implementation, the computer equipment can use the LMDI decomposition model to quantitatively decompose the pre-processed electricity carbon emission data to obtain the dynamic electricity carbon emission impact factors corresponding to each target industry; the dynamic electricity carbon emission impact factors include carbon emission impact factors in units of preset time intervals; the carbon emission impact factors include at least electricity impact factors and green economy impact factors.

[0063] In some embodiments, Figure 2 As shown, step S120, through the LMDI decomposition model, the power carbon emission data is quantitatively decomposed to obtain the dynamic power carbon emission impact factor corresponding to each target industry, including the following steps:

[0064] Step S210, extracting the carbon emission impact data of each target industry based on the electricity carbon emission data.

[0065] Among them, carbon emission impact data refers to the data required to determine the values ​​of all influencing factors.

[0066] Among them, carbon emission impact data includes electricity impact data and green economy impact data.

[0067] Among them, the power impact data includes at least one of total power generation, total renewable energy power generation, total power demand, total renewable energy power demand, total renewable energy power supply, total power demand, total power usage, and total renewable energy power usage.

[0068] Among them, the green economic impact data includes at least one of gross domestic product, total social responsibility-related investment costs, total green investment costs, and the number of enterprises.

[0069] Step S220, using the LMDI decomposition model, quantitatively decompose the carbon emission impact data of each target industry to obtain the dynamic electricity carbon emission impact factor corresponding to each target industry.

[0070] In the specific implementation, computer equipment can use the LMDI decomposition model to quantitatively decompose the carbon emission impact data of each target industry and obtain the dynamic electricity carbon emission impact factor corresponding to each target industry.

[0071] In some embodiments, Figure 3 As shown, in the process of quantitatively decomposing the carbon emission impact data of each target industry through the LMDI decomposition model to obtain the dynamic electricity carbon emission impact factor corresponding to each target industry, for any target industry, the computer device substitutes the carbon emission impact data of any target industry into the factor decomposition formula to obtain the dynamic electricity carbon emission impact factor corresponding to any target industry.

[0072] The factor decomposition formula is:

[0073]

[0074] in, for area The carbon emissions of electricity from the industry, in MtCO2; for area Total electricity generation by industry; for area Total renewable energy generation by industry; for area Total electricity demand of the industry; for area Total renewable electricity demand by sector; for area Total renewable electricity supply by sector; for area Total electricity supply to the industry; for area Total electricity usage by industry; for area Total renewable electricity use by industry; for area The gross output of the industry; for area The industry’s total social responsibility-related investment costs; for area Total green investment costs for the industry; for area The number of companies in the industry.

[0075] In this embodiment, Region can refer to the target area; Industry can refer to any of the target industries.

[0076] In some embodiments, when the computer device substitutes the carbon emission impact data of any target industry into the factor decomposition formula to obtain the dynamic electricity carbon emission impact factor corresponding to any target industry, the computer device may simplify the factor decomposition formula and use each item in the simplified factor decomposition formula as the dynamic electricity carbon emission impact factor; the simplified factor decomposition formula is:

[0077]

[0078] Among them, dynamic electricity carbon emission influencing factors include:

[0079] in, for area The carbon emission factor of power generation in the industry represents the carbon dioxide emitted per unit of electricity generated; for area The industry's renewable energy generation coefficient indicates the proportion of electricity generated by renewable energy. The larger the coefficient, the smaller the replacement rate of fossil fuels by renewable energy electricity. for area The industry's renewable energy electricity self-sufficiency rate, which represents the proportion of renewable energy electricity in total electricity demand; for area The industry's renewable energy power demand coefficient indicates the proportion of renewable energy power demand. The larger the coefficient, the smaller the demand for renewable energy power. for area The industry's renewable energy power supply-demand ratio, which represents the ratio of renewable energy power demand to supply; for area The proportion of renewable energy electricity supply in the industry, which represents the proportion of renewable energy electricity supply in the total electricity supply; for area The power supply efficiency of the industry, which indicates the power loss in the power supply process; for area The renewable energy power consumption coefficient of the industry indicates the proportion of renewable energy power consumption. The larger the coefficient, the smaller the proportion of renewable energy power consumption. for area The renewable energy power consumption intensity of the industry represents the ratio of renewable energy power to gross output value. From an economic perspective, the greater the intensity, the lower the power consumption efficiency; for area The industry's socially responsible investment coefficient indicates the proportion of socially responsible investment costs. The larger the coefficient, the smaller the related investment. for area The industry's green investment coefficient represents the proportion of green investment costs in social responsibility investment costs. The larger the coefficient, the smaller the related investment. for area The average green investment of an industry represents the average green investment cost of the industry, expressed in currency / number of enterprises. The larger the value, the greater the related investment.

[0080] Among them, the proportion of renewable energy power supply is the ratio of renewable energy power supply to total power supply, which shows the degree of renewable energy power supply in the industry. The power supply-demand ratio of the industry is the ratio of the power supply of the industry to the power demand, which reflects the accuracy of the prediction of the power demand of the industry. The power consumption level of the industry, that is, the number of enterprises corresponding to the unit power consumption, the larger the value, the smaller the average power consumption of the enterprise, suggesting a more low-carbon trend.

[0081] Step S130, for any target industry, according to the Dirichlet index corresponding to each dynamic electricity carbon emission influencing factor of any target industry, determine the influence degree of each dynamic electricity carbon emission influencing factor of any target industry on electricity carbon emission.

[0082] Specifically, the computer device can determine the degree of influence of each dynamic electricity carbon emission influencing factor of any target industry on electricity carbon emissions based on the Dirichlet index corresponding to each dynamic electricity carbon emission influencing factor of any target industry.

[0083] Furthermore, in order to obtain the Diggs index, the LMDI decomposition model also includes: an impact effect quantification formula and an index calculation formula. The computer equipment can determine the power carbon emission change value of any target industry between the first moment and the second moment based on the dynamic power carbon emission impact factors of any target industry, and the first moment is greater than the second moment; for each dynamic power carbon emission impact factor, the power carbon emission change value corresponding to the dynamic power carbon emission impact factor is substituted into the impact effect quantification formula to calculate the impact effect value corresponding to the dynamic power carbon emission impact factor; each impact effect value is substituted into the index calculation formula to calculate the Diggs index corresponding to each dynamic power carbon emission impact factor.

[0084] Among them, The calculation formula for the change in electricity carbon emissions of any target industry between the first moment and the second moment is as follows:

[0085]

[0086] Among them, the first moment is a specific end moment t, the second moment is a specific initial moment 0, and the first moment is greater than the second moment.

[0087]

[0088] in, , , , , , , , , , , , , is the impact effect value corresponding to each dynamic electricity carbon emission influencing factor.

[0089] The index calculation formula for calculating the Divison index (DI (Division Index)) corresponding to each dynamic electricity carbon emission impact factor is as follows:

[0090] ;

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] ;

[0102] ;

[0103] ;

[0104] in, for The corresponding Dirichlet index; C is The corresponding Dirichlet index; for The corresponding Dirichlet index; for The corresponding Dirichlet index; for The corresponding Dirichlet index; for The corresponding Dirichlet index; for The corresponding Dirichlet index; for The corresponding Dirichlet index; for The corresponding Dirichlet index; for The corresponding Dirichlet index; for The corresponding Dirichlet index; The Dixie index corresponding to the average green investment; for The corresponding Dirichlet index.

[0105] After determining the Dirichlet index corresponding to each dynamic electricity carbon emission influencing factor as described above, the impact degree corresponding to each dynamic electricity carbon emission influencing factor is determined according to the identity of electricity carbon emission and each Dirichlet index. The identity is as follows:

[0106]

[0107] In the formula, the sum of all Dietrich indexes is equal to the change in electricity carbon emissions between the time period t and 0, and the influence of each dynamic electricity carbon emission influencing factor can be calculated.

[0108] In the above-mentioned decomposition and analysis method of influencing factors of industry electricity carbon emissions based on green economy, electricity carbon emission data of at least one target industry in the target area within a preset time interval is obtained; the electricity carbon emission data is quantitatively decomposed through the LMDI decomposition model to obtain the dynamic electricity carbon emission influencing factors corresponding to each target industry; the dynamic electricity carbon emission influencing factors include carbon emission influencing factors with preset time intervals as units; the carbon emission influencing factors include at least electricity influencing factors and green economy influencing factors; for any target industry, according to the Dirichlet index corresponding to each dynamic electricity carbon emission influencing factor of any target industry, the degree of influence of each dynamic electricity carbon emission influencing factor of any target industry on electricity carbon emissions is determined.

[0109] In this way, by obtaining the electricity carbon emission data of at least one target industry in the target area within the preset time interval; through the LMDI decomposition model, the electricity carbon emission data is quantitatively decomposed to obtain the dynamic electricity carbon emission impact factor corresponding to each target industry; the dynamic electricity carbon emission impact factor includes the carbon emission impact factor in units of the preset time interval; the carbon emission impact factor includes at least the electricity impact factor and the green economy impact factor; thus, through the decomposition of the impact factor of the preset time interval, dynamic measurement can be achieved, based on the carbon emission impact factor in units of the preset time interval, the authenticity required for actual operation can be considered when achieving low carbon, and then according to the Di index corresponding to each dynamic electricity carbon emission impact factor, the impact of each dynamic electricity carbon emission impact factor of the target industry on electricity carbon emissions can be more accurately determined, and a more comprehensive analysis of the electricity carbon emissions of the target industry in the target area can be achieved, and the impact of different impact factors on the electricity carbon emissions of the target industry can be analyzed. This analysis method has high accuracy and real-time performance, can dynamically manage electricity carbon emissions, provide customized solutions for different industries, can conduct detailed analysis of carbon emissions in various industries, provide support for the formulation and adjustment of environmental policies, and help to formulate more effective carbon emission reduction measures and promote the realization of environmental protection and sustainable development goals.

[0110] In another embodiment, Figure 3 As shown, a method for decomposing and analyzing factors affecting carbon emissions of industry electricity based on green economy is provided. The method is applied to computer equipment as an example, and includes the following steps:

[0111] Step S302, obtaining electricity carbon emission data of at least one target industry in a target area within a preset time interval.

[0112] Step S304: extracting the carbon emission impact data of each target industry based on the electricity carbon emission data.

[0113] Step S306: for any target industry, substitute the carbon emission impact data of any target industry into the factor decomposition formula.

[0114] Step S308, simplifying the factor decomposition formula, and using each item in the simplified factor decomposition formula as a dynamic electricity carbon emission influencing factor.

[0115] Step S310, for any target industry, according to the Dirichlet index corresponding to each dynamic electricity carbon emission influencing factor of any target industry, determine the influence degree of each dynamic electricity carbon emission influencing factor of any target industry on electricity carbon emission.

[0116] It should be noted that the specific limitations of the above steps can refer to the specific limitations of the above method for decomposing and analyzing factors affecting industry electricity carbon emissions based on a green economy.

[0117] This application is based on the industry's dynamic electricity carbon emission impact factors, determines the degree of influence of each dynamic electricity carbon emission impact factor on electricity carbon emissions, and combines the collection, processing and analysis of data to provide strong support for environmental management and sustainable development.

[0118] This application can focus on enterprises with high energy and resource demands and special attention to emission control, and aim to reduce carbon emissions more effectively by decomposing the factors affecting electricity carbon emissions, rather than covering all industry data. At the micro level, the following methods can be adopted to achieve emergency measures for low carbonization in various industries: First, dynamic measurement is achieved through the decomposition of hourly electricity carbon emission factors. Considering the supply of renewable energy in various industries is crucial to ensuring the reliability of electricity carbon emission factors dominated by fossil fuels. This is because it is necessary not only to reduce the use of fossil fuels, but also to consider converting them to renewable energy, and then measure the impact of carbon emission factors. At the same time, analyze the hourly renewable energy electricity supply to assess its volatility, compared with the relatively stable fossil fuel consumption, to analyze the impact factors brought about by energy conversion. At the same time, taking into account different industries, especially the challenges that may be brought about by the relatively irregular renewable energy electricity supply, is crucial to considering electricity carbon emission factors.

[0119] Low-carbon scheduling aims to reduce the negative impact of a company, product or activity on the climate by quantifying its carbon emissions; low-carbon certification certifies companies, products or services that meet certain low-carbon standards to ensure that their impact on the environment is minimized. Therefore, low-carbon scheduling provides important support for companies to obtain low-carbon certification. It helps companies quantify their carbon footprint and develop targeted low-carbon strategies by collecting, analyzing and tracking carbon emission data. This scheduling supports companies to meet low-carbon standards and ensure that they meet certification requirements. Transparent emission data and effective carbon management strategies enhance the company's environmental image and increase its credibility, while also having a positive impact on the company's low-carbon reputation and market competitiveness.

[0120] In summary, the decomposition analysis of the factors affecting carbon emissions of electricity based on regions and industries in this application plays an important role in environmental management and sustainable development. This monitoring method has high accuracy and real-time performance, can dynamically manage carbon emissions of electricity, and provide customized solutions for different industries.

[0121] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0122] Based on the same inventive concept, the embodiment of the present application also provides a device for decomposing and analyzing factors affecting carbon emissions of industry electricity based on green economy, which is used to implement the above-mentioned method for decomposing and analyzing factors affecting carbon emissions of industry electricity based on green economy. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more devices for decomposing and analyzing factors affecting carbon emissions of industry electricity based on green economy provided below can be referred to the limitations of the method for decomposing and analyzing factors affecting carbon emissions of industry electricity based on green economy above, and will not be repeated here.

[0123] In an exemplary embodiment, Figure 4 As shown, a device for decomposing and analyzing influencing factors of industry power carbon emissions based on green economy is provided, including: an acquisition module 410, a decomposition module 420 and a determination module 430, wherein:

[0124] The acquisition module 410 is used to acquire electricity carbon emission data of at least one target industry in a target area within a preset time interval.

[0125] Decomposition module 420 is used to quantitatively decompose the electricity carbon emission data through the LMDI decomposition model to obtain the dynamic electricity carbon emission impact factor corresponding to each target industry; the dynamic electricity carbon emission impact factor includes the carbon emission impact factor in units of the preset time interval; the carbon emission impact factor includes at least an electricity impact factor and a green economy impact factor.

[0126] The determination module 430 is used to determine, for any target industry, the degree of influence of each of the dynamic electricity carbon emission influencing factors of the target industry on electricity carbon emissions based on the Dirichlet index corresponding to each of the dynamic electricity carbon emission influencing factors of the target industry.

[0127] In one of the embodiments, the electricity impact factor represents the impact of electricity events in the target industry on the electricity carbon emissions; the green economy impact factor represents the impact of economic events in the target industry on the electricity carbon emissions.

[0128] In one embodiment, the electricity influencing factors include at least one of the following: carbon emission factor of power generation, renewable energy power generation coefficient, renewable energy power self-sufficiency rate, renewable energy power demand coefficient, renewable energy power supply-demand ratio, renewable energy power supply proportion, power supply efficiency, renewable energy power consumption coefficient, and renewable energy power consumption intensity; the green economy influencing factors include at least one of the following: social responsibility investment coefficient, green investment coefficient, average green investment, and number of enterprises.

[0129] In one embodiment, the decomposition module 420 is specifically used to extract the carbon emission impact data of each of the target industries based on the electricity carbon emission data; the carbon emission impact data includes electricity impact data and green economy impact data; the electricity impact data includes at least one of total power generation, total renewable energy power generation, total electricity demand, total renewable energy power demand, total renewable energy power supply, total electricity supply, total electricity usage, and total renewable energy power usage; the green economy impact data includes at least one of gross domestic product, total social responsibility-related investment cost, total green investment cost, and the number of enterprises; through the LMDI decomposition model, the carbon emission impact data of each of the target industries is quantitatively decomposed to obtain the dynamic electricity carbon emission impact factor corresponding to each of the target industries.

[0130] In one embodiment, the decomposition module 420 is specifically used to substitute the carbon emission impact data of any target industry into a factor decomposition formula for any target industry to obtain a dynamic electricity carbon emission impact factor corresponding to any target industry; the factor decomposition formula is expressed as:

[0131]

[0132] in, for area The carbon emissions of electricity from the industry, in MtCO2; for area Total electricity generation by industry; for area Total renewable energy generation by industry; for area Total electricity demand of the industry; for area Total renewable electricity demand by sector; for area Total renewable electricity supply by sector; for area Total electricity demand of the industry; for area Total electricity usage by industry; for area Total renewable electricity use by industry; for area The gross output of the industry; for area The industry’s total social responsibility-related investment costs; for area Total green investment costs for the industry; for area The number of companies in the industry.

[0133] In one embodiment, the decomposition module 420 is specifically used to simplify the factor decomposition formula, and use each item in the simplified factor decomposition formula as the dynamic electricity carbon emission impact factor;

[0134] The simplified factor decomposition formula is:

[0135]

[0136] The dynamic electricity carbon emission influencing factors include: in, for area The carbon emission factors for electricity generation by industry; for area Renewable energy generation coefficients for the industry; for area The industry’s renewable electricity self-sufficiency rate; for area Renewable energy electricity demand factor for the industry; for area The industry’s renewable energy electricity supply-demand ratio; for area The proportion of electricity supplied by renewable energy in the industry; for area efficiency of electricity supply to the industry; for area Renewable energy electricity consumption coefficient of the industry; for area Renewable energy electricity consumption intensity of the industry; for area The industry's socially responsible investment coefficient; for area The green investment coefficient of the industry; for area Average green investment by industry.

[0137] Each module in the above-mentioned green economy-based industry power carbon emission influencing factor decomposition and analysis device can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0138] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store electricity carbon emission data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for decomposing and analyzing the influencing factors of industry electricity carbon emissions based on a green economy is implemented.

[0139] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0140] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0141] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0142] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0143] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0144] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0145] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0146] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for decomposing and analyzing factors affecting carbon emissions of industry electricity based on green economy, characterized in that: The method comprises: Obtain electricity carbon emission data for at least one target industry in a target region within a preset time interval; The electricity carbon emission data is quantitatively decomposed through the LMDI decomposition model to obtain the dynamic electricity carbon emission impact factor corresponding to each of the target industries; the dynamic electricity carbon emission impact factor includes the carbon emission impact factor with the preset time interval as the unit; the carbon emission impact factor includes at least the electricity impact factor and the green economy impact factor; For any target industry, the degree of influence of each of the dynamic electricity carbon emission influencing factors of any target industry on electricity carbon emissions is determined based on the Digg index corresponding to each of the dynamic electricity carbon emission influencing factors of any target industry.

2. The method according to claim 1, characterized in that The electricity impact factor represents the impact of electricity events in the target industry on the electricity carbon emissions; the green economy impact factor represents the impact of economic events in the target industry on the electricity carbon emissions.

3. The method according to claim 1, characterized in that The electricity influencing factors include at least one of the following: carbon emission factor of power generation, renewable energy power generation coefficient, renewable energy power self-sufficiency rate, renewable energy power demand coefficient, renewable energy power supply-demand ratio, renewable energy power supply proportion, power supply efficiency, renewable energy power consumption coefficient, and renewable energy power consumption intensity; the green economy influencing factors include at least one of the following: social responsibility investment coefficient, green investment coefficient, average green investment, and number of enterprises.

4. The method according to claim 1, characterized in that The LMDI decomposition model is used to quantitatively decompose the electricity carbon emission data to obtain the dynamic electricity carbon emission impact factors corresponding to each target industry, including: Extracting carbon emission impact data of each target industry based on the electricity carbon emission data; the carbon emission impact data includes electricity impact data and green economy impact data; The power impact data includes at least one of total power generation, total renewable energy power generation, total power demand, total renewable energy power demand, total renewable energy power supply, total power supply, total power usage, and total renewable energy power usage; the green economic impact data includes at least one of gross domestic product, total social responsibility-related investment cost, total green investment cost, and the number of enterprises; The carbon emission impact data of each target industry is quantitatively decomposed through the LMDI decomposition model to obtain the dynamic electricity carbon emission impact factor corresponding to each target industry.

5. The method according to claim 4, characterized in that The LMDI decomposition model is used to quantitatively decompose the carbon emission impact data of each target industry to obtain the dynamic electricity carbon emission impact factor corresponding to each target industry, including: For any of the target industries, the carbon emission impact data of any of the target industries is substituted into the factor decomposition formula to obtain the dynamic electricity carbon emission impact factor corresponding to any of the target industries; The factor decomposition formula is expressed as: in, for area The carbon emissions of electricity from the industry, in MtCO2; for area Total electricity generation by industry; for area Total renewable energy generation by industry; for area Total electricity demand of the industry; for area Total renewable electricity demand by sector; for area Total renewable electricity supply by sector; for area Total electricity demand of the industry; for area Total electricity usage by industry; for area Total renewable electricity use by industry; for area The gross output of the industry; for area The industry’s total social responsibility-related investment costs; for area Total green investment costs for the industry; for area The number of companies in the industry.

6. The method according to claim 5, characterized in that Substituting the carbon emission impact data of any target industry into the factor decomposition formula to obtain the dynamic electricity carbon emission impact factor corresponding to any target industry includes: Simplifying the factor decomposition formula, and using each item in the simplified factor decomposition formula as the dynamic electricity carbon emission influencing factor; The simplified factor decomposition formula is: The dynamic electricity carbon emission influencing factors include: in, for area The carbon emission factors for electricity generation by industry; for area Renewable energy generation coefficients for the industry; for area The industry’s renewable electricity self-sufficiency rate; for area Renewable energy electricity demand factor for the industry; for area The industry’s renewable energy electricity supply-demand ratio; for area The proportion of electricity supplied by renewable energy in the industry; for area efficiency of electricity supply to the industry; for area Renewable energy electricity consumption coefficient of the industry; for area Renewable energy electricity consumption intensity of the industry; for area The industry's socially responsible investment coefficient; for area The green investment coefficient of the industry; for area Average green investment by industry.

7. A device for decomposing and analyzing factors affecting carbon emissions from industrial electricity based on green economy, characterized in that: The device comprises: An acquisition module, used to acquire electricity carbon emission data of at least one target industry in a target area within a preset time interval; A decomposition module, used to quantitatively decompose the electricity carbon emission data through the LMDI decomposition model to obtain the dynamic electricity carbon emission impact factor corresponding to each of the target industries; the dynamic electricity carbon emission impact factor includes the carbon emission impact factor in units of the preset time interval; the carbon emission impact factor includes at least an electricity impact factor and a green economy impact factor; A determination module is used to determine, for any target industry, the degree of influence of each of the dynamic electricity carbon emission influencing factors of the target industry on electricity carbon emissions based on the Digg index corresponding to each of the dynamic electricity carbon emission influencing factors of the target industry.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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