CN116993041A - Quantitative analysis method, system, equipment and terminal for electric power carbon emission influence factors - Google Patents

Quantitative analysis method, system, equipment and terminal for electric power carbon emission influence factors Download PDF

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CN116993041A
CN116993041A CN202310479635.3A CN202310479635A CN116993041A CN 116993041 A CN116993041 A CN 116993041A CN 202310479635 A CN202310479635 A CN 202310479635A CN 116993041 A CN116993041 A CN 116993041A
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吴杏平
周春雷
宣东海
袁启恒
宋金伟
陈翔
何东
陈建
林建华
陈岸青
张羽舒
刘文思
张澄心
李金湖
康毅滨
于浩
王斌
余仰淇
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State Grid Information and Telecommunication Co Ltd
Information and Telecommunication Branch of State Grid Zhejiang Electric Power Co Ltd
Great Power Science and Technology Co of State Grid Information and Telecommunication Co Ltd
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Abstract

The invention belongs to the technical field of analysis of carbon emission influence factors, and discloses a quantitative analysis method, a system, equipment and a terminal of electric power carbon emission influence factors, wherein factor decomposition is carried out on the processes of power generation, power transmission, power transformation, power distribution and power utilization based on LMDI, and the influence factors of carbon emission coefficients, energy structure coefficients, fire intensity coefficients, thermal power duty ratio coefficients, residual electric power coefficients, power utilization coefficients and economic intensity coefficients of all energy sources are selected, so that the contribution of all the influence factors is analyzed at the national level; the important influence factors of the national level are selected, and the comprehensive analysis is carried out by combining the country and the region based on the PDA decomposition method and analysis of the carbon emission change rule of all provinces. The invention compares and selects typical years through the influence degree of factors, recognizes the driving effect on the basis of the same year, and provides theoretical support and decision reference for national policies by utilizing the exploration of the change rule of the national electric power carbon emission and the formulation of the electric power policy.

Description

Quantitative analysis method, system, equipment and terminal for electric power carbon emission influence factors
Technical Field
The invention belongs to the technical field of analysis of carbon emission influencing factors, and particularly relates to a method, a system, equipment and a terminal for quantitatively analyzing electric power carbon emission influencing factors.
Background
At present, china is the first large country of global carbon emission and power generation, and the proportion of carbon emission in the power industry exceeds 40% of the total amount of all industries. The power industry is the most carbon emission industry, and is worthy of important attention of researchers. The contribution and the driving effect of the influence factors of the carbon emission of the electric power in China are researched, a decision basis can be provided for effectively controlling the carbon emission of the electric power in China, and important theoretical and practical significance is achieved.
At present, in the research of carbon emission influencing factors, an exponential decomposition method is convenient for numerical operation by the characteristic of quantitatively analyzing the influence degree of each factor, and is widely adopted by students. To search for the influencing factors of carbon emissions of fossil energy, myers used LMDI decomposition (logarithmic average di index) for the first time in 1978, after which LMDI was increasingly more focused, and LMDI has become the first quantitative index decomposition analysis method of no date by 2000. In order to research the change rule of carbon emission in the production process of the China power industry, hou Jianchao and other precursors apply the LMDI method to the analysis power industry. After that, zhang et al studied the influence factor of the carbon emission of the electricity in china on the national level based on the LMDI method. The LMDI method is further used by Zhou et al to quantitatively analyze carbon grid driving factors at various grid levels. In order to analyze the change reasons of the electric power carbon intensity in China, peng and the like quantitatively analyze the electric power low-carbon technology and the power generation structure based on an LMDI decomposition model. Chen Guijing and the like quantitatively analyze the law of the change of the Jibei electric power carbon emission on the provincial level based on the LMDI method. Liu et al decomposed the electric carbon emissions of all provinces on a provincial level based on the LMDI method.
Because LMDI cannot quantify the nature of qualitative factors, researchers have further proposed PDA (production theory decomposition analysis) methods based on the research needs of efficiency and technical coefficients. The Pasurka is based on a PDA model to perform qualitative and quantitative research on the change condition of greenhouse gas at the national level for the first time. Zhou et al put forward a carbon emission influencing factor decomposition framework on a national level based on PDA theory. Zhang et al theories innovation on the PDA model, and introduces a plurality of factor inputs and a plurality of distance functions into the decomposition model, thereby expanding the applicability and reliability of the PDA model.
At present, the research on carbon emission influencing factors of certain industries in China at home and abroad is quite plentiful, and the research on carbon emission influencing factors of analysis areas is quite plentiful, but comprehensive research for combining countries and regions by using various decomposition models is lacking. Meanwhile, the existing electric power carbon emission influence factor research basically does not consider an electric energy transmission link, but the electric energy transmission link gradually plays an increasingly larger role along with the continuous enhancement of the dispatching effect of the national power grid. Therefore, there is a need to design a new quantitative analysis method and system for the influence factors of the carbon emission of electric power.
Through the above analysis, the problems and defects existing in the prior art are as follows: firstly, the prior art does not fully consider that the carbon emission coefficient, the energy structure coefficient, the fire intensity coefficient, the thermal power duty ratio coefficient, the residual power coefficient, the used power coefficient and the economic intensity coefficient of different areas of China have obvious differences, and the change trend of the electric power carbon emission influence factor can be comprehensively and thoroughly analyzed only by combining the national and regional layers. The index decomposition method can carry out numerical operation on quantitative influence factors, but LMDI cannot quantify the characteristics of qualitative factors, cannot fully consider the influence of efficiency and technical coefficients on carbon emission, and needs to further study a carbon emission influence factor decomposition frame to carry out qualitative and quantitative study on the change condition of greenhouse exhaust gas. Thirdly, in the existing technology for researching carbon emission influencing factors, comprehensive research for combining countries and regions by using various decomposition models is lacking, and meanwhile, the existing electric power carbon emission influencing factor research basically does not consider an electric energy transmission link.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a method, a system, equipment and a terminal for quantitatively analyzing electric power carbon emission influence factors, and particularly relates to a method, a system, a medium, equipment and a terminal for quantitatively analyzing electric power carbon emission influence factors based on a decomposition method.
The invention is realized in such a way that the method for quantitatively analyzing the influence factors of the electric carbon emission comprises the following steps:
step one, adopting a standard carbon emission accounting method to carry out carbon emission accounting; inputting data required by calculating the carbon number; outputting carbon emission; the carbon emission is used for quantitatively decomposing the carbon emission and analyzing regional carbon emission influencing factors in an LMDI decomposition model and a production theoretical decomposition method of the subsequent steps;
constructing an LMDI (least mean square) decomposition model of an accumulated quantity index decomposition method, quantitatively decomposing the carbon emission obtained in the step one, and evaluating the weight and contribution of each collected influence factor; inputting the collected influence factor data; outputting the values of the coefficients after decomposition; the coefficient values are used for analyzing the electric power carbon emission influencing factors subsequently;
Analyzing regional carbon emission influence factors based on a production theory decomposition method; inputting ideal energy input generating capacity, electric power carbon emission amount, non-energy input generating capacity and electric power carbon emission amount of each region obtained according to the step one; outputting the ratio of ideal energy input generating capacity to electric carbon emission, the ratio of actual energy input generating capacity to electric carbon emission, the energy input generating distance function and the electric carbon emission distance function.
Further, the step two of evaluating the weight and contribution of the collected influence factors specifically includes: based on LMDI, carrying out factor decomposition on the processes of power generation, power transmission, power transformation, power distribution and power utilization, selecting the influencing factors of fossil energy carbon emission coefficients such as coal, petroleum, natural gas and the like, energy structure coefficients, fire intensity coefficients, thermal power duty ratio coefficients, residual power coefficients, power utilization coefficients and economic intensity coefficients, and analyzing the weight of each influencing factor on the change of the power carbon emission; according to the change of the contribution degree of the influence factors, whether each influence factor plays a pulling role or a suppressing role on the electric power emission or not in different years and whether the effect is enhanced or weakened can be obtained, so that the electric power industry can be guided to manufacture a more targeted emission reduction strategy; important influencing factors are selected, and the national and regional comprehensive analysis of the electric power carbon emission rule is combined based on the production theory decomposition method and the carbon emission data of the investigated provinces.
Further, the weight of each influencing factor on the electric power carbon emission variable is analyzed, and the contribution level of each factor on the electric power carbon emission variable is determined;
the contribution degree is calculated based on the LMDI model and the total electric power emission; the method is used for analyzing the contribution degree of each influence factor to the change amount of the electric power carbon emission in the electric power carbon emission influence factor quantitative analysis method;namely, the EMI contribution value corresponding to the EF factor; f contribution degree is EMI effect (t-1,t)/ΔCO 2 e (t-1, t), i.e. the ratio of the contribution of factor EF year t to the carbon emission;
the importance of the factors is determined by combining the positive and negative of the contribution value of the influence factors and the annual change quantity of the contribution value of the influence factors.
Further, the carbon emission accounting method in the first step includes:
the electric carbon emission is particularly referred to as carbon dioxide emission generated in the thermal power generation process; based on the related requirements of the national carbon list guide, an international general carbon emission accounting method is adopted, and the following formula is adopted:
C＝∑E i U i ；
wherein C represents the amount of carbon dioxide discharged in the national power production process; e (E) i 、U i The consumption and the carbon emission coefficient of the thermal power generation of the ith energy source in the China electric power industry are respectively.
The national carbon list guide classifies energy into two types, namely non-thermal energy, and the calculation process of the carbon emission coefficient of the non-thermal energy is shown as follows:
U i ＝NCV i ×CC i ×O i ×44/12；
In the formula, NCV i 、CC i And O i Respectively the low heat value, the carbon content of the unit heat value and the oxidation rate of carbon dioxide of the ith power generation energy source in the national power industry; 44/12 is the carbon dioxide conversion factor.
And secondly, the thermal energy is obtained, and the calculation process of the thermal energy carbon emission coefficient is shown as the following formula:
wherein C is τ And Q τ Carbon emissions and heat generated in the national power production process are respectively.
Further, the constructing the LMDI decomposition model in the second step includes:
the method comprises the steps of carrying out quantitative decomposition on carbon emission by adopting a logarithmic average weight function of LMDI to obtain products of a series of indexes representing each influence factor, wherein the products are shown in the following formula:
wherein C represents a carbon emission amount; c (C) i And E is i Respectively representing the generated carbon emission amount and the standard consumption amount of the ith energy source; E. p, Q, F and G respectively represent the standard consumption total amount of energy, the generated energy, the production total amount, the terminal consumption amount and the economic strength; f (f) i 、e i H, p and q respectively represent carbon emission coefficient, energy structure coefficient, energy consumption intensity coefficient, thermal power duty ratio coefficient and residual power system of each energy sourceNumber and use power factor.
According to the links of power generation, power transmission and power consumption of power generation, the influence factors are divided into f based on an LMDI method i 、e i And the 7 influencing factors including h, p, q, G and G respectively represent the carbon emission coefficient, the energy structure coefficient, the fire intensity coefficient, the thermal power duty ratio coefficient, the surplus power coefficient, the electricity utilization coefficient and the economic intensity coefficient of each energy source. The LMDI model is used to decompose the delta change in the electric carbon emission from the t-th to the t+1st year of the country into the sum of the 7 factor change delta deltacf (i), deltace (i), deltach, deltacp, deltacq, deltacg and deltacg as shown in the following formula:
ΔC＝C t -C 0 ＝ΔCf(i)+ΔCe(i)+ΔCh+ΔCp+ΔCq+ΔCg+ΔCG；
wherein Δcf (i), Δce (i), Δch, Δcp, Δcq, Δcg, and Δcg are contributions of each energy carbon emission coefficient, energy structure coefficient, fire intensity coefficient, thermal power duty coefficient, surplus power coefficient, usage power coefficient, and economic intensity coefficient, respectively.
And taking the year as the minimum measurement unit, defining the contribution rate of the electric power carbon emission influencing factors in the t year as the ratio of the contribution quantity in the t year to the carbon emission quantity, and for example, the contribution rate of the carbon emission coefficient of each energy source is delta Cf (i)/delta C.
Further, the carbon emission coefficient of each energy source is the ratio of the carbon emission amount of the energy source to the standard consumption amount, the energy source structure coefficient is the ratio of the standard consumption amount of the energy source to the total consumption amount, the energy consumption intensity coefficient is the ratio of the standard consumption amount of the energy source to the total generation amount, the thermal power duty ratio coefficient is the ratio of the standard generation amount of the energy source to the total generation amount, the residual power coefficient is the ratio of the standard production amount of the energy source to the total consumption amount of the terminal, and the use power coefficient is the total production amount of the standard energy source to the total consumption amount of the terminal.
Further, the method for analyzing the regional carbon emission influencing factors based on the production theory decomposition method in the third step comprises the following steps:
the PDA decomposition model is adopted to model the distance function of the energy efficiency coefficient, the energy saving coefficient and the emission reduction coefficient decision unit DMU as follows, and carbon emission influence factor analysis of technology and efficiency is carried out:
wherein E is j,ideal 、CO 2 e j,ideal 、E j And CO 2 e j Respectively representing ideal energy input generating capacity, electric power carbon emission, non-energy input generating capacity and electric power carbon emission in each region; EE j 、CO 2 e j 、D E,j And D C,j The ratio of the ideal energy input power generation amount to the electric carbon emission amount, the ratio of the actual energy input power generation amount to the electric carbon emission amount, the energy input power generation distance function and the electric carbon emission distance function are respectively expressed.
The ratio of the ideal energy input power generation amount to the electric carbon emission amount is equal to the reciprocal of the energy input power generation distance function, and the ratio of the actual energy input power generation amount to the electric carbon emission amount is equal to the electric carbon emission distance function. And (3) calculating an energy input power generation distance function and an electric carbon emission distance function, wherein the calculation is shown in the following formula:
in the method, in the process of the invention,and Y is equal to t Respectively representing the distance function of the fire intensity coefficient, the energy source structure coefficient, the power source structure coefficient, the electricity consumption intensity coefficient and the economic intensity coefficient from the j th year to the t th year, < > >And (3) withRepresenting the efficiency and the technical function of the j-th to t-th years, respectively.
Another object of the present invention is to provide an electric power carbon emission influence factor quantitative analysis system to which the electric power carbon emission influence factor quantitative analysis method is applied, the electric power carbon emission influence factor quantitative analysis system comprising:
the carbon emission accounting module is used for carrying out carbon emission accounting by adopting a standard carbon emission accounting method; inputting data required by calculating the carbon number; outputting carbon emission; the carbon emission is used for quantifying the carbon emission in the LMDI decomposition model and the production theoretical decomposition method of the subsequent steps and analyzing regional carbon emission influencing factors
The quantitative decomposition module is used for constructing an LMDI decomposition model and quantitatively decomposing the carbon emission; carrying out weight and contribution evaluation on all the collected influence factors; inputting the collected influence factor data; outputting the values of the coefficients after decomposition; the coefficient values are used for analyzing the electric power carbon emission influencing factors subsequently;
the influence analysis module is used for analyzing regional carbon emission influence factors based on the production theoretical decomposition method, and inputting ideal energy input power generation amount, electric power carbon emission amount, non-energy input power generation amount and electric power carbon emission amount of each region; outputting the ratio of ideal energy input generating capacity to electric carbon emission, the ratio of actual energy input generating capacity to electric carbon emission, the energy input generating distance function and the electric carbon emission distance function.
It is a further object of the present invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
based on LMDI, carrying out factor decomposition on the processes of power generation, power transmission, power transformation, power distribution and power utilization, selecting the carbon emission coefficient, the energy structure coefficient, the fire intensity coefficient, the thermal power duty ratio coefficient, the residual power coefficient, the power utilization coefficient and the economic intensity coefficient of each energy source, and analyzing the contribution of each influence factor at the national level; the important influence factors of the national level are selected, and the comprehensive analysis is carried out by combining the country and the region based on the PDA decomposition method and analysis of the carbon emission change rule of all provinces.
Another object of the present invention is to provide a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
step one, adopting a standard carbon emission accounting method to carry out carbon emission accounting; inputting data required by calculating the carbon number; outputting carbon emission; the carbon emission is used for quantitatively decomposing the carbon emission and analyzing regional carbon emission influencing factors in an LMDI decomposition model and a production theoretical decomposition method of the subsequent steps;
Constructing an LMDI (least mean square) decomposition model of an accumulated quantity index decomposition method, quantitatively decomposing the carbon emission obtained in the step one, and evaluating the weight and contribution of each collected influence factor; inputting the collected influence factor data; outputting the values of the coefficients after decomposition; the coefficient values are used for analyzing the electric power carbon emission influencing factors subsequently;
analyzing regional carbon emission influence factors based on a production theory decomposition method; inputting ideal energy input generating capacity, electric power carbon emission amount, non-energy input generating capacity and electric power carbon emission amount of each region obtained according to the step one; outputting the ratio of ideal energy input generating capacity to electric carbon emission, the ratio of actual energy input generating capacity to electric carbon emission, the energy input generating distance function and the electric carbon emission distance function.
Another object of the present invention is to provide an information data processing terminal for implementing the electric power carbon emission influence factor quantitative analysis system.
In combination with the technical scheme and the technical problems to be solved, the technical scheme to be protected has the following advantages and positive effects:
Firstly, the invention carries out factor decomposition on the power generation, transmission, transformation, distribution and utilization processes based on LMDI, comprehensively selects 7 large influencing factors of carbon emission coefficient, energy structure coefficient, fire intensity coefficient, thermal power duty ratio coefficient, residual power coefficient, power utilization coefficient and economic intensity coefficient of each energy source, solves the problem that the carbon emission of the power transmission link is not fully considered in the prior art, and further considers the influence of the distribution and utilization processes on the carbon emission.
Based on LMDI factor decomposition, the invention analyzes the carbon emission influence factors of different areas by adopting PDA theory to realize the combination of national and area-level carbon emission analysis on the 7 large influence factors of the carbon emission coefficient, the energy structure coefficient, the fire intensity coefficient, the thermal power duty ratio coefficient, the residual power coefficient, the use power coefficient and the economic intensity coefficient of 30 provincial area energy sources in the whole country.
According to the invention, the factors of the fire intensity coefficient, the energy structure coefficient, the energy efficiency coefficient, the emission efficiency coefficient, the energy saving coefficient, the emission reduction coefficient, the power structure coefficient, the electricity consumption intensity coefficient and the economic intensity coefficient of 30 provincial areas are decomposed by the PDMA model, so that the combination of quantitative factor analysis and qualitative factor analysis is realized, and the problem that the traditional LMDI decomposition only can analyze quantitative factors is solved. The analysis can obtain that the economic strength coefficient is the most main factor (forward driving) for promoting the increase of the regional electric power carbon emission, and the fire consumption strength coefficient, the energy source structure coefficient, the energy efficiency coefficient, the energy saving coefficient, the emission reduction coefficient, the power source structure coefficient and the electric power consumption strength coefficient play an unoriented driving role similar to the result obtained based on the LMDI decomposition model.
In the invention, the positive driving trend of the regional thermal power generation energy efficiency coefficient on the increase of carbon emission is greater than that of the negative driving, which is not beneficial to energy conservation and emission reduction; the regional thermal power generation efficiency coefficient has a negative driving trend to carbon emission increase greater than a positive trend.
Secondly, the invention compares the influence degree of factors, needs to select typical years, identifies the driving effect on the basis of the same year, and utilizes the exploration national electricity carbon emission change rule and the electricity policy formulation.
The invention selects important influence factors on the national level and simultaneously gives consideration to efficiency and technology, analyzes the carbon emission change rule of all provinces based on a PDA decomposition method, combines the country and the region for comprehensive research, and provides theoretical support and decision reference for national policy.
Thirdly, as inventive supplementary evidence of the claims of the present invention, the following important aspects are also presented:
(1) The expected benefits and commercial values after the technical scheme of the invention is converted are as follows:
the invention provides a set of analysis method and system for carbon emission in the power industry aiming at the carbon emission reduction requirements in the power industry, which can be applied to the carbon emission analysis in the power industry after technical transformation, and can be used for guiding the carbon emission reduction in the power industry more effectively, thereby saving the carbon emission reduction investment in the power industry and promoting the sustainable development of the power industry in China.
(2) The technical scheme of the invention fills the technical blank in the domestic and foreign industries:
the invention compares the influence degree of factors, needs to select typical years, identifies the driving effect on the basis of the same year, and utilizes the exploration of the national change rule of electric carbon emission and the formulation of electric policy.
The invention selects important influence factors on the national level and simultaneously gives consideration to efficiency and technology, analyzes the carbon emission change rule of all provinces based on a PDA decomposition method, combines the country and the region for comprehensive research, and provides theoretical support and decision reference for national policy.
(3) Whether the technical scheme of the invention solves the technical problems that people want to solve all the time but fail to obtain success all the time is solved:
first, in the research of exploring carbon emission influencing factors in the power industry of China by applying an exponential decomposition analysis method, not only the internal factors related to the power generation department are involved, but also the influence of the internal factors related to the transmission and distribution department is proposed. The effect difference of different influence factors is compared in the national view, and a comprehensive analysis framework of the carbon emission influence factors in the electric power industry is constructed.
Secondly, the production theory decomposition analysis method is applied to the research of greenhouse gas emission influencing factors in the power industry in China, and the fire intensity coefficient, the energy structure coefficient, the energy efficiency coefficient, the emission efficiency coefficient, the energy saving coefficient, the emission reduction coefficient, the power structure coefficient, the electricity consumption intensity coefficient and the economic intensity coefficient are innovatively brought into the decomposition model, so that the effect of the efficiency and the technical factors on the greenhouse gas emission change in the power industry under different visual angles is quantized, and the index system of the greenhouse gas emission influencing factors in the power industry is further enriched and perfected.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for quantitatively analyzing influence factors of electric power carbon emission provided by an embodiment of the invention;
FIG. 2 is a block diagram of a system for quantitatively analyzing the influence factors of electric power carbon emission according to an embodiment of the present invention;
FIG. 3 is a graph showing the amount of carbon emission and the intensity of carbon emission of the electric power according to the embodiment of the present invention;
FIG. 4 is a schematic diagram showing the driving effect of the electric carbon emission influencing factors according to the embodiment of the present invention;
FIG. 5 is a schematic diagram of the influence degree of different provincial thermal power generation efficiency coefficients and discharge efficiency coefficients provided by the embodiment of the invention;
FIG. 5 (a) is a schematic diagram showing the influence degree of different provincial thermal power generation efficiency factor factors provided by the embodiment of the invention;
FIG. 5 (b) is a schematic diagram of the influence degree of different provincial thermal power generation efficiency coefficient provided by the embodiment of the invention;
FIG. 6 is a schematic diagram showing the influence of factors of energy saving coefficient and emission reduction coefficient of different provincial thermal power generation provided by the embodiment of the invention;
FIG. 6 (a) is a schematic diagram showing the influence of different provincial thermal power generation energy saving coefficients provided by the embodiment of the invention;
FIG. 6 (b) is a schematic diagram showing the influence of different provincial thermal power generation emission reduction coefficients provided by the embodiment of the invention;
in the figure: 1. a carbon emission accounting module; 2. a quantization and decomposition module; 3. and an influence analysis module.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Analyzing the weight of each influence factor on the electric power carbon emission variable quantity, wherein the weight is actually the contribution degree of each factor on the electric power carbon emission variable quantity, and the influence of the factors with high contribution degree on the electric power carbon emission variable quantity is larger, so the weight is larger than that of other factors.
The contribution degree is the contribution rate and is calculated based on the LMDI model and the total electric power emission. Such asNamely the EMI contribution value corresponding to the EF factor. EF contribution degree is EMI effect (t-1,t)/ΔCO 2 e (t-1, t), i.e. the ratio of the contribution of factor EF in the t-th year to the carbon emission.
In the embodiment of the invention, how to select the important influencing factors:
the specific method comprises the following steps: and determining the importance of the factors by combining the positive and negative of the contribution values of the influence factors and the annual change quantity. If the contribution value of the economic strength factor is always positive in the actual data and the value is maximum compared with other factors, the economic strength factor is selected as an important influencing factor.
In the embodiment of the invention, how to select the important influencing factors:
the specific method comprises the following steps: and determining the importance of the factors by combining the positive and negative of the contribution values of the influence factors and the annual change quantity. If the contribution value of the economic strength factor is always positive in the actual data and the value is maximum compared with other factors, the economic strength factor is selected as an important influencing factor.
Aiming at the problems existing in the prior art, the invention provides a method, a system, equipment and a terminal for quantitatively analyzing electric power carbon emission influence factors, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the method for quantitatively analyzing the influence factors of the carbon emission of the electric power provided by the embodiment of the invention comprises the following steps:
S101, performing carbon emission accounting by adopting an international carbon emission accounting method;
in the embodiment of the invention, the carbon emission accounting module performs carbon emission accounting by adopting a carbon emission accounting method recommended by the inter-national government climate change specialized committee (IPCC).
S102, constructing an LMDI decomposition model, and quantitatively decomposing the carbon emission;
s103, analyzing regional carbon emission influence factors based on analysis of production theory decomposition methods.
As shown in fig. 2, the system for quantitatively analyzing the influence factors of the carbon emission of the electric power provided by the embodiment of the invention comprises:
a carbon emission accounting module 1 for performing carbon emission accounting by using an internationally used carbon emission accounting method;
the quantitative decomposition module 2 is used for constructing an LMDI decomposition model and quantitatively decomposing the carbon emission;
and an influence analysis module 3 for analyzing regional carbon emission influence factors based on the analysis of the production theory decomposition method.
As a preferred embodiment of the present invention:
the quantitative analysis method for the electric power carbon emission influence factors comprises the following steps:
step one, adopting a standard carbon emission accounting method to carry out carbon emission accounting; inputting data required by calculating the carbon number; outputting carbon emission; the carbon emission is used for quantitatively decomposing the carbon emission and analyzing regional carbon emission influencing factors in an LMDI (logarithmic average Diels index method) decomposition model and a production theory decomposition method of the subsequent steps;
Constructing an accumulated quantity index decomposition method LMDI (logarithmic average Diels index method) decomposition model, quantitatively decomposing the carbon emission obtained in the step one, and evaluating the weight and contribution degree of each collected influence factor; inputting the collected influence factor data; outputting the values of the coefficients after decomposition; the coefficient values are used for analyzing the electric power carbon emission influencing factors subsequently;
analyzing regional carbon emission influence factors based on a production theory decomposition method; inputting ideal energy input generating capacity, electric power carbon emission amount, non-energy input generating capacity and electric power carbon emission amount of each region obtained according to the step one; outputting the ratio of ideal energy input generating capacity to electric carbon emission, the ratio of actual energy input generating capacity to electric carbon emission, the energy input generating distance function and the electric carbon emission distance function.
The technical scheme of the invention is further described below with reference to specific embodiments.
Based on LMDI, factor decomposition is carried out on the power generation, transmission, transformation, distribution and use processes, the carbon emission coefficient, the energy structure coefficient, the fire intensity coefficient, the thermal power duty ratio coefficient, the residual power coefficient, the used power coefficient and the economic intensity coefficient of each energy source are comprehensively selected, and the contribution of each influence factor is analyzed on the national level. The method selects important influence factors on the national level and simultaneously gives consideration to efficiency and technology, analyzes the carbon emission change rule of all provinces based on a PDA decomposition method, combines the country and the region to carry out comprehensive research, and provides theoretical support and decision reference for national policy.
1. Carbon emission accounting method
Because carbon emission is not generated basically in the non-fossil energy power generation process and can be ignored, the invention refers to the electric carbon emission as carbon dioxide emission generated in the thermal power generation process, so as to simplify the analysis process. Based on the related requirements of the national carbon inventory guidelines, to ensure the reproducibility of the study, an internationally universal carbon emission accounting method is employed, as shown in the following formula:
C＝∑E i U i (1)
in the formula (1), C represents the carbon dioxide emission amount in the national power production process; e (E) i 、U i The consumption and the carbon emission coefficient of the thermal power generation of the ith energy source in the China electric power industry are respectively.
The national carbon list guide divides energy into two types, namely non-thermal energy, and the calculation process of the carbon emission coefficient is complex, as shown in the following formula:
U i ＝NCV i ×CC i ×O i ×44/12 (2)
in formula (2), NCV i 、CC i And O i Respectively the low heat value, the carbon content of the unit heat value and the oxidation rate of carbon dioxide of the ith power generation energy source in the national power industry; 44/12 is the carbon dioxide conversion factor.
And secondly, the calculation process of the carbon emission coefficient of the thermal energy is direct, and the calculation process is shown as the following formula:
in the formula (3), C τ And Q τ Carbon emissions and heat generated in the national power production process are respectively.
LMDI decomposition model
In order to construct a Chinese electric power carbon emission influence factor analysis framework, the logarithmic average weight function of the LMDI is adopted to quantitatively decompose the carbon emission quantity, so as to obtain a series of products representing indexes of each influence factor, wherein the products are shown in the following formula:
In the formula (4), C represents a carbon emission amount; c (C) i And E is i Respectively representing the generated carbon emission amount and the standard consumption amount of the ith energy source; E. p, Q, F and G respectively represent the standard consumption total amount of energy, the generated energy, the production total amount, the terminal consumption amount and the economic strength; f (f) i 、e i H, p, q each represent a carbon emission coefficient (ratio of carbon emission amount to standard consumption amount), an energy structure coefficient (ratio of standard consumption amount to total consumption amount), and an energy consumption intensity coefficient (standard total consumption amount)Ratio to power generation amount), thermal power duty ratio coefficient (ratio of power generation amount to total production amount of energy standard), surplus power coefficient (ratio of total production amount of energy standard to terminal consumption amount), and usage power coefficient (total production amount of energy standard to terminal consumption amount).
According to the change condition of carbon emission in the national power industry and the links of power generation, power transmission and power consumption in power production, the influence factors are divided into f based on an LMDI method i 、e i The 7 influencing factors of h, p, q, G and G respectively represent the carbon emission coefficient, the energy structure coefficient, the fire consumption intensity coefficient, the thermal power duty ratio coefficient, the surplus power coefficient, the used power coefficient and the economic intensity coefficient of each energy source. Further using the LMDI model, the national 2005-2020 electric carbon emission delta was decomposed into the sum of these 7 factor delta changes (Δcf (i), Δce (i), Δch, Δcp, Δcq, Δcg and Δcg) as shown in the following formula:
ΔC＝C t -C 0 ＝ΔCf(i)+ΔCe(i)+ΔCh+ΔCp+ΔCq+ΔCg+ΔCG (5)
In the formulas (5-13), Δcf (i), Δce (i), Δch, Δcp, Δcq, Δcg, and Δcg are the contribution amounts of the carbon emission coefficient, the energy structure coefficient, the fire intensity coefficient, the thermal power duty coefficient, the surplus power coefficient, the used power coefficient, and the economic intensity coefficient, respectively.
And taking the year as the minimum measurement unit, defining the contribution rate of the electric power carbon emission influencing factors in the t year as the ratio of the contribution quantity in the t year to the carbon emission quantity, and for example, the contribution rate of the carbon emission coefficient of each energy source is delta Cf (i)/delta C.
3. Regional carbon emission influence factor analysis based on production theory decomposition method analysis
Because the LMDI can not quantify the characteristic of qualitative factors, when researching the influence factors of electric carbon emission on the aspect of the area with great difference between the power generation technology and the power generation efficiency, the influence degree analysis of the technology and the efficiency factors can not be considered in a targeted manner. In order to study carbon emission influencing factors for accounting for technology and efficiency, a PDA decomposition model is adopted to model a distance function of an energy efficiency coefficient, an emission efficiency coefficient, an energy saving coefficient and emission reduction coefficient decision unit (Decision Making Unit, DMU for short), and the following formula is shown:
in the formula (14-15), E j,ideal 、CO 2 e j,ideal 、E j And CO 2 e j Respectively representing ideal energy input generating capacity, electric power carbon emission, non-energy input generating capacity and electric power carbon emission in each region; EE j 、CO 2 e j 、D E,j And D C,j The ratio of the ideal energy input power generation amount to the electric carbon emission amount, the ratio of the actual energy input power generation amount to the electric carbon emission amount, the energy input power generation distance function and the electric carbon emission distance function are respectively expressed.
Analytical formulae (14-15), wherein the ratio (efficiency value) of the ideal energy input power generation amount to the electric power carbon emission amount is equal to the inverse of the energy input power generation distance function, and the ratio (efficiency value) of the actual energy input power generation amount to the electric power carbon emission amount is equal to the electric power carbon emission distance function. And (3) calculating an energy input power generation distance function and an electric carbon emission distance function, wherein the calculation is shown in the following formula:
in the formula (16-20),and Y is equal to t Respectively represent the distance functions of the fire consumption intensity coefficient, the energy source structure coefficient, the power source structure coefficient, the electricity consumption intensity coefficient and the economic intensity coefficient from the j th year to the t th year,and->Respectively represent the j thEfficiency and technical function from year to t.
Therefore, the increase in the amount of electric carbon emissions in the region of the t-th year in 2005-2020 is decomposed into products taking into account various factors in terms of structure, strength, efficiency, technology, and the like, as shown in the following formula:
in the formula (21), D pef 、D pei 、D ce 、D ctech 、D ee 、D etech 、D gs 、D pi And D py The method respectively represents the influence degree of the regional fire intensity coefficient, the energy structure coefficient, the energy efficiency coefficient, the energy saving coefficient, the emission reduction coefficient, the power structure coefficient, the electricity consumption intensity coefficient and the economic intensity coefficient on the electric power carbon emission.
The experimental scheme is developed around researching the influence factors of greenhouse gas emission in the power industry of China, and the effects of different types of factors on carbon emission change in the power industry are explored by respectively applying an exponential decomposition analysis method and a production theory decomposition analysis method. On the basis, the power supply structure of the common important emission reduction factors obtained by the two models is subjected to deep analysis and research.
The following describes the implementation of the present invention by taking the quantitative analysis of carbon emissions in the power industry in China 2005-2020 as an example.
On the national level, the basic data of carbon emission coefficient, energy structure coefficient, fire intensity coefficient, thermal power duty ratio coefficient, residual power coefficient, used power coefficient and economic intensity coefficient of each energy source are selected. On the regional level, the rest 30 provinces are selected for research in order to ensure the research consistency requirement due to the partial missing reasons of the data in the four areas of Tibet, hong Kong, australian and Taiwan. As 2005 is a key year for realizing the goal of power reform and the task of link reform in China, the invention takes 2005 and 2020 as the beginning and ending years of research respectively. The electric power carbon emission and the carbon emission intensity of China are shown in figure 3.
Based on 7 influencing factors, namely, an energy carbon emission coefficient, an energy structure coefficient, a fire intensity coefficient, a thermal power duty ratio coefficient, a residual power coefficient, a used power coefficient and an economic intensity coefficient, which are obtained by decomposing by an LDMI method, the annual contribution of each influencing factor from 2005-2020 is calculated for identifying the driving effect of each influencing factor, as shown in table 1.
TABLE 1 contribution increment of electric carbon emission influencing factors in China
For the research of the differential decomposition of the carbon emission factors in the areas of each province in China, the electric power carbon emission ratio (greenhouse gas emission ratio) of each province in China in 2005-2020 is calculated and obtained, as shown in FIG. 4. Setting the electric power carbon emission ratio equal to 1 as the reference line, it can be seen that in 2005-2020, except for the two provinces of Yunnan and Sichuan, the electric power carbon emission ratio of all provinces is greater than 1, i.e., each factor contributes to the electric power carbon emission increase as a whole. And setting the reference line of the 2 nd electric carbon emission ratio as 1.05 according to the distribution condition of the carbon emission ratios of the various provinces. The results of the provinces above the datum line are Anhui (1.05), shanxi (1.06), xinjiang (1.07), guangdong (1.08), jiangsu (1.08), shandong (1.11) and inner Mongolia (1.14) in a small-to-large arrangement. The 6 provinces with smaller electric carbon emission except Yunnan and Sichuan are classified into the following 3 categories: firstly, the small overall economic scale limits the increase of the electric power carbon emission ratio, such as Qinghai and Hainan; secondly, the local energy conservation and emission reduction and the green GDP policy limit the increase of the electric power carbon emission ratio, such as Shanghai and Beijing; thirdly, the local non-fossil energy power generation environment is excellent, such as Ningxia and Hebei.
The factors of the fire intensity coefficient, the energy structure coefficient, the energy efficiency coefficient, the energy saving coefficient, the emission reduction coefficient, the power structure coefficient, the electricity consumption intensity coefficient and the economic intensity coefficient of the 30 provincial areas are decomposed by a PDMA model, as shown in the table 2.
TABLE 2 decomposition results of various influencing factors of regional electric carbon emission ratio
The effect of the invention in analyzing the carbon emissions of the electric power industry is described below based on the quantized results of the carbon emissions of the electric power industry in the middle China 2005-2020.
As can be seen from the results in table 1, the driving effect of the economic strength factor is always positive, and the driving effect is strongest, which is the most important factor for promoting the carbon emission growth in the power industry. The driving effect of other influencing factors has obvious fluctuation in 2005-2020, sometimes positive effect and sometimes negative effect, so that typical years need to be selected for comparing the influence degree of the factors.
Since the financial crisis is generated in 2008, the contribution amount of the economic strength coefficient as the first forward driving factor is obviously reduced, and the increment of the electric carbon emission is also reduced along with the contribution amount, the economic strength coefficient is selected as a typical year for researching the driving effect of the electric carbon emission influencing factor in China in 2008. The financial crisis in 2008 is not only represented by the obvious reduction of the economic strength coefficient, but also the energy structure coefficient, the fire consumption strength coefficient, the thermal power duty ratio coefficient, the residual power coefficient and the used power coefficient of each energy source are reduced to a certain extent. The method has a direct relation with primary and secondary hazards caused by financial crisis, for example, the method has an irreversible adverse effect on an energy structure in a short time after the productivity of each industry is reduced before the needs of each industry are difficult to recover to the crisis in a short time after foam is broken.
The analysis of the data in the table shows that the increase of the electric carbon emission occurs negatively for the first time in 2014, and the negative increase has important research significance (inflection point), so 2014 is selected as a typical year for researching the driving effect of the influence factors of the carbon emission in China. The 2014 forward driving factors include energy structure coefficients of various energy sources besides economic strength coefficients; the negative driving factors are a fire intensity coefficient, a thermal power duty ratio coefficient, a residual power coefficient and a used power coefficient. This has a direct relationship with the development of new energy power generation and green GDP. Wherein, the fire intensity coefficient of each energy source is increased by 246.88Mt in a negative way, and the fire intensity coefficient is obviously reduced, which is a direct result regulated and controlled by the national energy policy; the economic strength factor contribution increment is reduced by 21.63Mt compared with 2013, which has a certain connection with the green GDP concept.
Similarly, the increment of electric carbon emission in 2017 is highest, and the electric carbon emission has key research significance (peak point), so 2017 is selected as a typical year for researching the driving effect of the influence factors of Chinese carbon emission. In 2017, the positive driving factors comprise energy source structure coefficients, fire consumption intensity coefficients, thermal power duty ratio coefficients, surplus power coefficients, used power coefficients and economic intensity coefficients of various energy sources, and no negative driving factors exist. The economic strength coefficient contribution rate is increased 455.19, is the maximum value in the research period of 2005-2020, and has a direct relation with the total GDP in 2017, which reaches 12.31 trillion yuan, and the speed is increased rapidly to 6.9%. Meanwhile, the thermal power energy consumption intensity, the thermal power duty ratio and the power utilization coefficient which play a role in inhibiting in the past year are converted into forward driving factors at 2017, and the forward driving factors are connected with the increase of the power generation scale, the power consumption scale and the proportion of thermal power equipment in 2017 to a certain extent.
Through the study of the driving effect of the carbon emission influencing factors in the 3 typical years of 2008, 2014 and 2017, the contribution value of the positive driving effect factors is reduced as much as possible, and the factors playing the non-directional driving effect should be made to play the negative driving (inhibiting) effect as much as possible.
As can be obtained from the analysis results of the carbon emissions of the different provinces in table 2, similar to the results obtained based on the LMDI decomposition model, the economic strength coefficient is the most important factor (positive driving) for promoting the increase of the carbon emissions of the regional power, and the fire strength coefficient, the energy structure coefficient, the energy efficiency coefficient, the energy saving coefficient, the emission reduction coefficient, the power structure coefficient and the electricity consumption strength coefficient play a role in non-directional driving (some provinces are positive and some provinces are negative).
From the aspect of efficiency factors, the energy efficiency and the efficiency coefficient of regional thermal power generation have important significance, as shown in fig. 5. In fig. 5 (a), the median of thermal power generation efficiency coefficients of different provinces is 1.01, and the sum is in the [0.97,1.04] interval; in fig. 5 (b), the median of thermal power generation efficiency coefficient of different provinces is 0.99, and the total is in the [0.97,1.005] interval. Therefore, the positive driving trend of the thermal power generation energy efficiency coefficient of the region on the increase of carbon emission is larger than that of the region on the negative driving, so that energy conservation and emission reduction are not facilitated; the regional thermal power generation efficiency coefficient has a negative driving trend to carbon emission increase greater than a positive trend.
From the technical factor, the energy conservation and emission reduction coefficient of regional thermal power generation have the same key significance as shown in fig. 6. In fig. 6 (a), the thermal power generation energy saving coefficient of different provinces has a median of 0.997 and is generally in the [0.98,1.005] interval; in FIG. 6 (b), the median of the emission reduction coefficients of different provincial thermal power generation is 0.994 and is in the [0.982,1.018] interval. Therefore, the overall regional thermal power generation efficiency coefficient has a tendency of increasing negative driving of carbon emission larger than that of positive driving, and is beneficial to energy conservation and emission reduction; the negative driving trend of the regional thermal power generation emission reduction coefficient to the carbon emission increase is greater than that of the positive driving, and the regional thermal power generation emission reduction coefficient is also beneficial to the realization of emission reduction. Comparing the efficiency with the technical factor, the driving effect of the efficiency factor (the energy efficiency and the emission efficiency coefficient of regional thermal power generation) is slightly larger than that of the technical factor (the energy saving and emission reduction coefficient of regional thermal power generation).
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.

Claims (10)

1. The quantitative analysis method for the electric power carbon emission influence factors is characterized by comprising the following steps of:
step one, adopting a standard carbon emission accounting method to carry out carbon emission accounting; inputting data required by calculating the carbon number; outputting carbon emission; the carbon emission is used for quantitatively decomposing the carbon emission and analyzing regional carbon emission influencing factors in an LMDI decomposition model and a production theoretical decomposition method of the subsequent steps;
constructing an LMDI (least mean square) decomposition model of an accumulated quantity index decomposition method, quantitatively decomposing the carbon emission obtained in the step one, and evaluating the weight and contribution of each collected influence factor; inputting the collected influence factor data; outputting the values of the coefficients after decomposition; the coefficient values are used for analyzing the electric power carbon emission influencing factors subsequently;
Analyzing regional carbon emission influence factors based on a production theory decomposition method; inputting ideal energy input generating capacity, electric power carbon emission amount, non-energy input generating capacity and electric power carbon emission amount of each region obtained according to the step one; outputting the ratio of ideal energy input generating capacity to electric carbon emission, the ratio of actual energy input generating capacity to electric carbon emission, the energy input generating distance function and the electric carbon emission distance function.
2. The method for quantitatively analyzing the influence factors of the carbon emission of the electric power according to claim 1, wherein the step two of evaluating the weight and the contribution degree of each collected influence factor specifically comprises:
based on LMDI, carrying out factor decomposition on the processes of power generation, power transmission, power transformation, power distribution and power utilization, selecting carbon emission coefficients, energy structure coefficients, fire intensity coefficients, thermal power duty ratio coefficients, residual power coefficients, power utilization coefficients and economic intensity coefficients of fossil energy sources of coal, petroleum and natural gas, and analyzing the weight of each influence factor on the change of the power carbon emission;
according to the change of the contribution degree of the influence factors, whether each influence factor plays a pulling effect or a suppressing effect on the electric power emission in different years and whether the effect is enhanced or weakened is obtained;
And selecting important influencing factors, and comprehensively analyzing the electric power carbon emission rule based on the production theory decomposition method and the carbon emission data of the inspected provinces.
3. The method for quantitatively analyzing the influence factors of the electric power carbon emission according to claim 2, wherein the analyzing the weights of the influence factors on the change amount of the electric power carbon emission is used for determining the contribution level of each factor to the change of the electric power carbon emission;
the contribution degree is calculated based on the LMDI model and the total electric power emission; the method is used for analyzing the contribution degree of each influence factor to the change amount of the electric power carbon emission in the electric power carbon emission influence factor quantitative analysis method;namely, the EMI contribution value corresponding to the EF factor; f contribution degree is EMI effect (t-1,t)/ΔCO 2 e (t-1, t), i.e. the ratio of the contribution of factor EF year t to the carbon emission;
the importance of the factors is determined by combining the positive and negative of the contribution value of the influence factors and the annual change quantity of the contribution value of the influence factors.
4. The method for quantitatively analyzing the influence factor of carbon emission of electric power according to claim 1, wherein the method for accounting for carbon emission in the first step includes:
the adopted carbon emission accounting method is shown as the following formula:
C＝∑E i U i ；
wherein C represents the amount of carbon dioxide discharged in the national power production process; e (E) i 、U i The consumption and the carbon emission coefficient of thermal power generation of the ith energy source in the China electric power industry are respectively;
the calculation process of the non-thermal energy carbon emission coefficient is shown as follows:
U i ＝NCV i ×CC i ×O i ×44/12；
in the formula, NCV i 、CC i And O i Respectively the low heat value, the carbon content of the unit heat value and the oxidation rate of carbon dioxide of the ith power generation energy source in the national power industry; 44/12 is the carbon dioxide conversion coefficient;
the calculation process of the carbon emission coefficient of the thermal energy is shown as follows:
wherein C is τ And Q τ Carbon emissions and heat generated in the national power production process are respectively.
5. The method for quantitatively analyzing the influence factors of the carbon emission of the electric power according to claim 1, wherein the constructing of the LMDI decomposition model in the second step includes:
the method comprises the steps of carrying out quantitative decomposition on carbon emission by adopting a logarithmic average weight function of LMDI to obtain products of a series of indexes representing each influence factor, wherein the products are shown in the following formula:
wherein C represents a carbon emission amount; c (C) i And E is i Respectively representing the generated carbon emission amount and the standard consumption amount of the ith energy source; E. p, Q, F and G respectively represent the standard consumption total amount of energy, the generated energy, the production total amount, the terminal consumption amount and the economic strength; f (f) i 、e i H, p and q respectively represent the carbon emission coefficient, the energy structure coefficient, the energy consumption intensity coefficient, the thermal power duty ratio coefficient, the residual power coefficient and the used power coefficient of each energy source;
According to the links of power generation, power transmission and power consumption of power generation, the influence factors are divided into f based on an LMDI method i 、e i 7 influencing factors including h, p, q, G and G respectively represent the carbon emission coefficient, the energy structure coefficient, the fire intensity coefficient, the thermal power duty ratio coefficient, the residual power coefficient, the power utilization coefficient and the economic intensity coefficient of each energy source; the LMDI model is used to decompose the delta change in the electric carbon emission from the t-th to the t+1st year of the country into the sum of the 7 factor change delta deltacf (i), deltace (i), deltach, deltacp, deltacq, deltacg and deltacg as shown in the following formula:
ΔC＝C t -C 0 ＝ΔCf(i)+ΔCe(i)+ΔCh+ΔCp+ΔCq+ΔCg+ΔCG；
wherein, deltaCf (i), deltaCe (i), deltaCh, deltaCp, deltaCq, deltaCg and DeltaCG are the contribution of each energy carbon emission coefficient, energy structure coefficient, fire intensity coefficient, thermal power duty ratio coefficient, surplus power coefficient, used power coefficient and economic intensity coefficient respectively;
and defining the contribution rate of the electric power carbon emission influencing factors in the t year as the ratio of the contribution quantity in the t year to the carbon emission quantity by taking the year as the minimum measurement unit.
6. The method according to claim 5, wherein the carbon emission coefficient of each energy source is a ratio of an energy source carbon emission amount to a standard consumption amount, the energy source structural coefficient is a ratio of an energy source standard consumption amount to a total consumption amount, the energy consumption strength coefficient is a ratio of an energy source standard consumption amount to a power generation amount, the thermal power duty coefficient is a ratio of an energy source standard power generation amount to a total production amount, the remaining power coefficient is a ratio of an energy source standard total production amount to a terminal consumption amount, and the used power coefficient is a ratio of an energy source standard total production amount to a terminal consumption amount.
7. The method for quantitatively analyzing the influence factors of the carbon emission of the electric power according to claim 1, wherein the method for analyzing the influence factors of the carbon emission of the region based on the production theory decomposition method in the third step comprises the steps of:
the PDA decomposition model is adopted to model the distance function of the energy efficiency coefficient, the energy saving coefficient and the emission reduction coefficient decision unit DMU as follows, and carbon emission influence factor analysis of technology and efficiency is carried out:
wherein E is j,ideal 、CO 2 e j,ideal 、E j And CO 2 e j Respectively representing ideal energy input generating capacity, electric power carbon emission, non-energy input generating capacity and electric power carbon emission in each region; EE j 、CO 2 e j 、D E,j And D C,j The method is characterized in that the method comprises the steps of respectively representing the ratio of ideal energy input generating capacity to electric carbon emission, the ratio of actual energy input generating capacity to electric carbon emission, an energy input generating distance function and an electric carbon emission distance function;
the ratio of the ideal energy input generating capacity to the electric carbon emission is equal to the reciprocal of the energy input generating distance function, and the ratio of the actual energy input generating capacity to the electric carbon emission is equal to the electric carbon emission distance function; and (3) calculating an energy input power generation distance function and an electric carbon emission distance function, wherein the calculation is shown in the following formula:
in the method, in the process of the invention,and Y is equal to t Respectively representing the distance function of the fire intensity coefficient, the energy source structure coefficient, the power source structure coefficient, the electricity consumption intensity coefficient and the economic intensity coefficient from the j th year to the t th year, < >>And (3) withRepresenting the efficiency and the technical function of the j-th to t-th years, respectively.
8. An electric power carbon emission influence factor quantitative analysis system applying the electric power carbon emission influence factor quantitative analysis method according to any one of claims 1 to 7, characterized in that the electric power carbon emission influence factor quantitative analysis system includes:
the carbon emission accounting module is used for carrying out carbon emission accounting by adopting a standard carbon emission accounting method; inputting data required by calculating the carbon number; outputting carbon emission; the carbon emission is used for quantitatively decomposing the carbon emission and analyzing regional carbon emission influencing factors in an LMDI decomposition model and a production theoretical decomposition method of the subsequent steps;
the quantitative decomposition module is used for constructing an LMDI decomposition model and quantitatively decomposing the carbon emission; carrying out weight and contribution evaluation on all the collected influence factors; inputting the collected influence factor data; outputting the values of the coefficients after decomposition; the coefficient values are used for analyzing the electric power carbon emission influencing factors subsequently;
The influence analysis module is used for analyzing regional carbon emission influence factors based on the production theoretical decomposition method, and inputting ideal energy input power generation amount, electric power carbon emission amount, non-energy input power generation amount and electric power carbon emission amount of each region; outputting the ratio of ideal energy input generating capacity to electric carbon emission, the ratio of actual energy input generating capacity to electric carbon emission, the energy input generating distance function and the electric carbon emission distance function.
9. A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
step one, adopting a standard carbon emission accounting method to carry out carbon emission accounting; inputting data required by calculating the carbon number; outputting carbon emission; the carbon emission is used for quantitatively decomposing the carbon emission and analyzing regional carbon emission influencing factors in an LMDI decomposition model and a production theoretical decomposition method of the subsequent steps;
constructing an LMDI (least mean square) decomposition model of an accumulated quantity index decomposition method, quantitatively decomposing the carbon emission obtained in the step one, and evaluating the weight and contribution of each collected influence factor; inputting the collected influence factor data; outputting the values of the coefficients after decomposition; the coefficient values are used for analyzing the electric power carbon emission influencing factors subsequently;
Analyzing regional carbon emission influence factors based on a production theory decomposition method; inputting ideal energy input generating capacity, electric power carbon emission amount, non-energy input generating capacity and electric power carbon emission amount of each region obtained according to the step one; outputting the ratio of ideal energy input generating capacity to electric carbon emission, the ratio of actual energy input generating capacity to electric carbon emission, the energy input generating distance function and the electric carbon emission distance function.
10. An information data processing terminal for realizing the electric power carbon emission influence factor quantitative analysis system according to claim 8.
CN202310479635.3A 2023-04-28 2023-04-28 Quantitative analysis method, system, equipment and terminal for electric power carbon emission influence factors Pending CN116993041A (en)

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CN118279114A (en) * 2024-06-04 2024-07-02 华南理工大学 Multi-factor decomposition and quantification method for pollution source volatile organic compound emission influence

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