Regional power industry carbon emission prediction method and system based on PCEE model

Abstract

The invention discloses a regional industry carbon emission prediction method based on a PCEE model, and the method comprises the steps: obtaining a total carbon emission accounting formula based on a PCEE method, introducing a comprehensive emission coefficient into the total carbon emission accounting formula, and obtaining a total carbon emission precise calculation formula; according to the input-output model, reflecting the production and consumption relationship of the regional industry by using an algebraic matrix; constructing a regional industry environment input-output model; the regional industry carbon emission variation is further obtained; and predicting a future regional industry carbon emission prediction value in combination with the regional industry-side conversion coefficient. The method can effectively predict the future carbon emission in the power industry, reduces the manual calculation investment, and saves the time and cost.

Description

Technical Field

[0001] The patent of this invention relates to the technical field of carbon emission prediction in the power industry, and relates to a method and system for predicting carbon emissions in the regional power industry based on the PCEE model. Background Art

[0002] With the rapid development of my country's power industry construction and the continuous rise of diversified industries, the society's demand for electricity is increasing day by day, and the country's attention to the carbon emissions generated by the power industry is also increasing. Since the traditional forecasting method considers the analysis and forecast of future carbon emissions of various industries in China, it does not focus on the power industry in a certain region, and does not fully consider the characteristic factors such as energy types and energy consumption in each region, resulting in insufficient calculation of future carbon emissions in the power industry, which may lead to inaccurate prediction of regional carbon emissions. However, the power industry has many comprehensive factors, such as regional energy types and stronger professional focus, which requires a more sophisticated forecasting model for calculation.

[0003] As the energy consumption in the power industry grows rapidly, the corresponding carbon emissions continue to increase. At the same time, the requirements for power carbon emission management are becoming more and more stringent. Therefore, it is necessary to strengthen management methods and build prediction models in the field of power energy consumption. It is urgent to form a set of calculation models suitable for predicting carbon emissions in the power industry, obtain more professional and focused carbon emission prediction values, and provide auxiliary basis for energy authorities to formulate work requirements. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for predicting regional industry carbon emissions based on the PCEE model (the PCEE model is a carbon emissions prediction algorithm model for electric power), so as to accurately predict the carbon emissions of regional industries.

[0005] To achieve this purpose, the present invention designs a regional industry carbon emission prediction method based on the PCEE model, which includes the following steps:

[0006] Step 1: Based on the PCEE method, according to the regional energy consumption heat, carbon production regression equation factors, and the final economic demand of the industry region, the total carbon emission calculation formula of the region industry is obtained, and the regional energy consumption and regional carbon-containing matter production are introduced to decompose the total carbon emission calculation formula into the total carbon emission accounting formula;

[0007] Step 2: Based on the input-output model, use algebraic matrix to reflect the production and consumption relationship of regional industries;

[0008] Step 3, introduce the comprehensive emission coefficient into the total carbon emission accounting formula to obtain an accurate calculation formula for the total carbon emission;

[0009] Step 4: Based on the production and consumption relationship of the regional industry, the total carbon emissions accurate calculation formula is used to construct the regional industry environmental input-output model;

[0010] Step 5: Conduct a structural decomposition analysis on the regional industry environmental input-output model, decompose the regional industry carbon emissions into the product of the industry carbon emission intensity driving factors, input-output structure driving factors, final carbon emission demand structure driving factors, final carbon emission demand scale driving factors, and final demand return rate driving factors, and further decompose the impact of the change in each driving factor on the change in regional industry carbon emissions to obtain the change in regional industry carbon emissions;

[0011] Step 6: Calculate the regional industry-side conversion coefficient based on the regional industry's historical carbon emissions, and then predict the future regional industry carbon emissions based on the regional industry-side conversion coefficient and the change in regional industry carbon emissions.

[0012] Beneficial effects of the present invention:

[0013] The present invention provides a regional industry carbon emission prediction method based on the PCEE model. On the basis of the traditional regional industry carbon emission prediction method, the low calorific value of energy consumption, the regional industry consumption return rate, the investment return rate and the net export return rate are introduced, which can effectively and accurately predict the future carbon emissions of the power industry, reduce the input of manual measurement, and thus save time and cost. At the same time, the settlement has the ability of continuous learning, and can continuously improve and optimize the model according to new data and feedback, improve the accuracy of its prediction and decision-making, and thus obtain a more accurate prediction value of carbon emissions in the power industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0015] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0016] Example 1

[0017] A regional industry carbon emission prediction method based on the PCEE model includes the following steps;

[0018] Step 1: Based on the PCEE method, according to the regional energy consumption heat, carbon production regression equation factors, and the final economic demand of the industry region, the total carbon emission calculation formula of the region industry is obtained, and the regional energy consumption and regional carbon-containing matter production are introduced to decompose the total carbon emission calculation formula into the total carbon emission accounting formula;

[0019] Step 2: Based on the input-output model, use algebraic matrix to reflect the production and consumption relationship of regional industries;

[0020] Step 3, introduce the comprehensive emission coefficient into the total carbon emission accounting formula to obtain an accurate calculation formula for the total carbon emission;

[0021] Step 4: Based on the production and consumption relationship of the regional industry, the total carbon emissions accurate calculation formula is used to construct the regional industry environmental input-output model;

[0022] Step 5: Conduct a structural decomposition analysis on the regional industry environmental input-output model, decompose the regional industry carbon emissions into the product of the industry carbon emission intensity driving factors, input-output structure driving factors, final carbon emission demand structure driving factors, final carbon emission demand scale driving factors, and final demand return rate driving factors, and further decompose the impact of the change in each driving factor on the change in regional industry carbon emissions to obtain the change in regional industry carbon emissions;

[0023] Step 6: Calculate the regional industry-side conversion coefficient based on the regional industry's historical carbon emissions, and then predict the future regional industry carbon emissions based on the regional industry-side conversion coefficient and the change in regional industry carbon emissions.

[0024] The present invention creates a prediction model based on the carbon emission data of electricity energy consumption in a certain province over the years, obtains reliable parameters, and can effectively predict carbon emission data for future years such as 2025 and 2026.

[0025] A regional industry carbon emission prediction method based on the PCEE model is based on the PCEE method. According to the regional energy consumption heat, carbon production regression equation factors, and the final economic demand of the industry region, the total carbon emission calculation formula of the regional industry is obtained, and the regional energy consumption and regional carbon-containing matter production are introduced. The specific method of decomposing the total carbon emission calculation formula into the total carbon emission accounting formula is as follows:

[0026] As shown in formula (1), Q represents the total carbon emissions of the regional industry, E represents the energy consumption heat of the regional industry, G represents the regression equation factor of carbon generation, and V represents the economic scale of the regional industry;

[0027] Q=E / G*V (1)

[0028] In combination with social development and energy consumption, it is necessary to introduce parameters such as regional energy consumption and carbon-containing matter production to increase the prediction accuracy. T represents regional industry energy consumption, and B represents regional industry carbon-containing matter production. Formula (1) can be expressed as;

[0029] Q=E / T*T / B*B / G*V (2)

[0030] Decompose equation (2) and let E / T = ξ, T / B = C, B / G = D to obtain the total carbon emissions accounting formula:

[0031] Q=ξCDV (3)

[0032] Among them, ξ is a constant representing the low calorific value of energy consumption, Q represents the total carbon emissions; C is the carbon emission intensity of the regional industry; and D is the carbon emission benchmark regression equation.

[0033] A regional industry carbon emission prediction method based on the PCEE model is a specific method that uses algebraic matrices to reflect the production and consumption relationship of regional industries according to the input-output model:

[0034] Assuming that the regional industry energy consumption system consists of n categories, the final regional industry product column vector V(n*1) is linked to the regional industry total output value column vector X(n*1) through formula (4).

[0035] AX(n*1)+V(n*1)=X(n*1) (4)

[0036] Transform the formula into:

[0037] X(n*1)=(1-A) -1 *V(n*1) (5)

[0038] Among them, A is the direct consumption coefficient matrix, (1-A) -1 It is the inverse matrix of regional industry emission equivalents. The elements in the matrix are called inverse coefficients, which represent the complete consumption of intermediate products of all sectors when obtaining the final product of regional industry units.

[0039] A regional industry carbon emission prediction method based on the PCEE model introduces the comprehensive emission coefficient into the total carbon emission accounting formula, and the specific method to obtain the accurate calculation formula of the total carbon emission is as follows:

[0040] Let e ​​be the comprehensive emission coefficient. The comprehensive emission coefficient e is determined by querying the National Statistical Yearbook, which can improve the prediction accuracy;

[0041] make:

[0042] C=e*Q / ξ*X(n*1)*D (6)

[0043] The conversion yields:

[0044] Q = ξ*C*D*X(n*1) / e (7).

[0045] A regional industry carbon emission prediction method based on the PCEE model. The specific method of constructing a regional industry environmental input-output model based on the production and consumption relationship of the regional industry and the precise calculation formula of the total carbon emissions is as follows:

[0046] Combining equations (5) and (7), we construct an environmental input-output model, as shown in equation (8):

[0047] Q=ξC(1-A) -1 *D*V(n*1) / e (8).

[0048] A regional industry carbon emission prediction method based on the PCEE model performs structural decomposition analysis on the regional industry environmental input-output model, decomposes the regional industry carbon emissions into the product of industry carbon emission intensity driving factors, input-output structure driving factors, final carbon emission demand structure driving factors, final carbon emission demand scale driving factors, and final demand return rate driving factors, and further decomposes the impact of changes in industry carbon emission intensity driving factors, input-output structure driving factors, final carbon emission demand structure driving factors, final carbon emission demand scale driving factors, and final demand return rate driving factors on changes in regional industry carbon emissions. The specific method for obtaining changes in regional industry carbon emissions is as follows:

[0049] This method is a fixed formula splitting method, which combines the carbon emissions and resource conditions of a province to decompose the demand vector into multiple driving factors;

[0050] In formula (7), let the input-output structure (1-A) -1 Reflect the correlation between regional industries; the final demand vector V(n*1)*(1-A) -1 / e is decomposed into the product of the final carbon emission demand structure driving factor Z (n*r), the final carbon emission demand scale driving factor S (r*1) and the final demand rate of return driving factor W (r*1). The elements in Z represent the proportion of various energy consumption, investment and net export proportions of the regional industry. The elements in S are the total consumption, investment and net export of the regional industry. The elements in W are the consumption rate of return, investment rate of return and net export rate of return of the regional industry. The carbon emissions can be decomposed into the product of the industry carbon emission intensity driving factor, input-output structure driving factor, final carbon emission demand structure driving factor, final carbon emission demand scale driving factor and final demand rate of return driving factor:

[0051] Q=ξCDZSW (9)

[0052] If we start the decomposition from the calculation period (i.e. period t), we have:

[0053] △Q1=ξ△CD t Z t S t W t +ξC0△DZ t S t W t+ξC0D0△ZS t W t +ξC0D0Z0△SW t +ξC0D0Z0S0△W(10)

[0054] If we start the decomposition from the base period (i.e. period 0), we have:

[0055] △Q2=ξ△CD0Z0S0W0+ξC t △DZ0S0W0+ξC t D t △ZS0W0+ξC t D t Z t △SW0+ξC t D t Z t S t △W(11)

[0056] Among them, △Q1 is the impact of the factor change obtained by decomposition from the calculation period on the change of regional industry carbon emissions; △Q2 is the impact of the factor change obtained by decomposition from the base period on the change of regional industry carbon emissions;

[0057] According to the two-pole decomposition method (existing algorithm), taking the arithmetic mean of equation (10) and equation (11), we can get:

[0058] △Q=0.5*(ξ△CD t Z t S t W t +ξ△CD0Z0S0W0)+0.5*(ξC0△

[0059] DZ t S t W t +ξC t △DZ0S0W0)+0.5*(ξC0D0△ZS t W t +ξC t D t △ZS0W0)+0.5*(ξC0D0Z0△SW t +ξC t D t Z t △SW0)+0.5*(ξC0D0Z0S0△W+ξC t D t Z t S t △W)(12)

[0060] The arithmetic mean is calculated based on various driving factors using the same set of data. The arithmetic mean is fixed and will not change due to the rearrangement or selection of data, which has a high degree of certainty.

[0061] Let △Q C =0.5*(ξ△CD t Z t S t W t +ξ△CD0Z0S0W0) represents the carbon emission intensity effect, that is, the impact of changes in the industry's carbon emission intensity driving factor C on the change in carbon emissions △Q;

[0062] Let △Q D =0.5*(ξC0△DZ t S t W t +ξC t △DZ0S0W0) represents the input-output structure effect, that is, the impact of the change of the input-output structure driving factor D on the change of carbon emissions △Q;

[0063] Let △Q Z =0.5*(ξC0D0△ZS t W t +ξC t D t △ZS0W0) represents the final demand structure effect, that is, the impact of the change of the final demand structure driving factor Z on the carbon emission change △Q;

[0064] Let △Q S =0.5*(ξC0D0Z0△SW t +ξC t D t Z t △SW0) represents the final demand scale effect, that is, the impact of the change of the final demand scale driving factor S on the change of carbon emissions △Q;

[0065] Let △Q W =0.5*(ξC0D0Z0S0△W+ξC t D t Z t S t △W) represents the final demand rate of return effect, that is, the impact of the change in the final demand rate of return driving factor W on the change in carbon emissions △Q;

[0066] Then formula (12) can be changed to:

[0067] △Q=△Q C +△Q D +△Q Z +△Q S +△QW (13)

[0068] △Q is the impact of changes in industry carbon emission intensity driving factors, input-output structure driving factors, final carbon emission demand structure driving factors, final carbon emission demand scale driving factors, and final demand return rate driving factors on changes in regional industry carbon emissions.

[0069] Among them, C0, D0, Z0 and S0 are the driving factors of industry carbon emission intensity, input-output structure, final demand structure, final demand scale and final demand return rate respectively corresponding to the base period;

[0070] C t , D t , Z t and S t They are respectively the driving factors of industry carbon emission intensity, input-output structure driving factors, final demand structure driving factors, final demand scale driving factors, and final demand return rate driving factors corresponding to the tth calculation period.

[0071] A regional industry carbon emission prediction method based on the PCEE model calculates the regional industry side conversion coefficient according to the regional industry carbon emissions over the years, and then predicts the future regional industry carbon emission forecast value according to the regional industry side conversion coefficient and the regional industry carbon emission change. The specific method is as follows:

[0072] The calculated value of the present invention is the prediction of the future carbon emissions of the regional industry. It is necessary to multiply the obtained △Q result by the regional industry side conversion coefficient f. By increasing the conversion coefficient, it is possible to combine the carbon emission data of previous years to obtain a stable prediction value and reduce excessive data fluctuations. The calculation formula for f is:

[0073] f=[(Q n-5 -Q n-6 ) / Q n-6 +(Q n-4 -Q n-5 ) / Q n-5 +(Q n-3 -Q n-4 ) / Q n-4 +(Q n-2 -Q n-3 ) /

[0074] Q n-3 +(Q n-1 -Q n-2 ) / Q n-2 ] / 5(14)

[0075] The regional industry conversion coefficient f takes the carbon emissions of the five years before the measurement year as the calculation base, and n is the forecast year;

[0076] Among them, Q n-6 , Q n-5 , Q n-4 , Q n-3 , Q n-2 , Q n-1 It is the carbon emissions of the regional industry from year n-6 to n-1.

[0077] Forecast value of future carbon emissions of regional industries Q n for:

[0078] Q n =Q+△Q*(1+f) (15)

[0079] A regional industry carbon emission prediction method based on the PCEE model is a specific method for calculating the carbon emission benchmark regression equation based on the regional industry population, regional industry economy, and regional industry manufacturing volume:

[0080] D=￡1Ina+￡2Inb+￡3Inc (16)

[0081] This method combines the characteristics of carbon emission factors in a certain province, takes population, economy, and manufacturing as analysis factors, and obtains a carbon emission regression equation, thereby obtaining a carbon emission forecast value that is more in line with the province. Among them, a represents the regional industry population, b represents the regional industry economic benefits, c represents the amount of electrical materials manufactured by the regional industry, ￡1 represents the regional industry population regression system parameter, ￡2 represents the regional industry economic regression system parameter, and ￡3 represents the regional industry material manufacturing regression system parameter. The statistical data is shown in the following table. The model can be trained using the existing current regression method in combination with the historical data of a certain province from 2019 to 2023 to obtain the regional industry population regression system parameter ￡ for the forecast year 2024. n1 , regional industry economic regression system parameters in 2024 n2 , Regional Industry Materials Manufacturing Regression System Parameters in 2024 n3 , and then the regression equation D is obtained.

[0082] Table 1 Statistics of economic population manufacturing in the power industry of a province in the past five years:

[0083]

[0084] A regional industry carbon emission prediction method based on the PCEE model, and a verification method according to the total carbon emission accounting formula is as follows:

[0085] Q=ΣEC i *EP i *EY i *D*44 / 12,i=1,2,3,4,5 (17)

[0086] In the formula, ECi is the consumption of various types of fossil energy in regional industries, EP i is the carbon emission coefficient of various fossil energy sources in the regional industry, EY i is the efficiency of various fossil energy consumption in the regional industry, and 44 / 12 is the coefficient for converting carbon into carbon dioxide.

[0087] Formula (17) is also a regional industry carbon emissions formula, which is used to support formula (1) and is used for verification. This formula can be compared with the PCEE prediction model in this paper. If the difference between the two is small, a more accurate PCEE model can be obtained.

[0088] Example 2

[0089] A regional industry carbon emission prediction system based on the PCEE model, which includes the following carbon emission total amount accounting module, algebraic matrix conversion module, comprehensive emission coefficient introduction module, input-output model construction module, carbon emission change calculation module and carbon emission prediction module;

[0090] The total carbon emission accounting module is used to obtain the total carbon emission calculation formula of the regional industry based on the PCEE method according to the regional energy consumption heat, the carbon production regression equation factor, and the final economic demand of the industry region, and introduce the regional energy consumption and the regional carbon-containing matter production to decompose the total carbon emission calculation formula into the total carbon emission accounting formula;

[0091] The algebraic matrix conversion module is used to reflect the production and consumption relationship of regional industries using algebraic matrices according to the input-output model;

[0092] The comprehensive emission coefficient introduction module is used to introduce the comprehensive emission coefficient into the total carbon emission accounting formula to obtain an accurate calculation formula for the total carbon emission;

[0093] The input-output model building module is used to build a regional industry environmental input-output model based on the production and consumption relationship of the regional industry and the precise calculation formula of the total carbon emissions;

[0094] The carbon emission change calculation module is used to perform structural decomposition analysis on the regional industry environmental input-output model, decomposing the regional industry carbon emissions into the product of the industry carbon emission intensity driving factors, input-output structure driving factors, final carbon emission demand structure driving factors, final carbon emission demand scale driving factors, and final demand return rate driving factors, and further decompose the impact of the change in each driving factor on the change in regional industry carbon emissions to obtain the regional industry carbon emission change;

[0095] The carbon emission prediction module is used to calculate the regional industry side conversion coefficient based on the regional industry's carbon emissions over the years, and then predict the future regional industry carbon emission forecast value based on the regional industry side conversion coefficient and the regional industry carbon emission change.

[0096] Example 3

[0097] A computer program product includes a computer program / instruction, and when the computer program / instruction is executed by a processor, the method described in embodiment 1 is implemented.

[0098] The contents not described in detail in this specification belong to the prior art known to those skilled in the art. It should be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0099] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims of the invention.

[0103] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

Claims

1. A regional industry carbon emission prediction method based on the PCEE model, characterized by: It includes the following steps; Based on the PCEE method, according to the regional energy consumption heat, carbon production regression equation factors, and the final economic demand of the industry region, the total carbon emission calculation formula of the region industry is obtained, and the regional energy consumption and regional carbon-containing matter production are introduced to decompose the total carbon emission calculation formula into the total carbon emission accounting formula; According to the input-output model, the algebraic matrix is ​​used to reflect the production and consumption relationship of regional industries; The comprehensive emission coefficient is introduced into the total carbon emission accounting formula to obtain an accurate calculation formula for the total carbon emission; Construct a regional industry environmental input-output model based on the production and consumption relationship of the regional industry and the precise calculation formula of the total carbon emissions; Conduct a structural decomposition analysis on the regional industry environmental input-output model, decompose the regional industry carbon emissions into the product of the industry carbon emission intensity driving factors, input-output structure driving factors, final carbon emission demand structure driving factors, final carbon emission demand scale driving factors, and final demand return rate driving factors, and further decompose the impact of the changes in various driving factors on the changes in regional industry carbon emissions to obtain the changes in regional industry carbon emissions; The regional industry conversion coefficient is calculated based on the regional industry's carbon emissions over the years, and then the future regional industry carbon emissions are predicted based on the regional industry conversion coefficient and the change in regional industry carbon emissions.

2. According to the method for predicting regional industry carbon emissions based on the PCEE model of claim 1, it is characterized in that: Based on the PCEE method, according to the regional energy consumption heat, carbon production regression equation factors, and the final economic demand of the industry region, the total carbon emission calculation formula of the regional industry is obtained, and the regional energy consumption and regional carbon-containing matter production are introduced to decompose the total carbon emission calculation formula into the total carbon emission accounting formula. The specific method is as follows: As shown in formula (1), Q represents the total carbon emissions of the regional industry, E represents the energy consumption heat of the regional industry, G represents the regression equation factor of carbon generation, and V represents the economic scale of the regional industry; Q=E / G*V(1) In order to improve the accuracy of the model, regional industry-specific parameters are introduced, T represents the regional industry energy consumption, B represents the regional industry carbon content generation, and formula (1) can be expressed as; Q=E / T*T / B*B / G*V(2) Decompose equation (2) and let E / T = ξ, T / B = C, B / G = D to obtain the total carbon emissions accounting formula: Q=ξCDV(3) Among them, ξ is a constant representing the low calorific value of energy consumption, Q represents the total carbon emissions; C is the carbon emission intensity of the regional industry; and D is the carbon emission benchmark regression equation.

3. According to claim 2, a method for predicting regional industry carbon emissions based on the PCEE model is characterized in that: The specific method of using algebraic matrices to reflect the production and consumption relationship of regional industries based on the input-output model is: Assuming that the regional industry energy consumption system consists of n categories, the final regional industry product column vector V(n*1) is linked to the regional industry total output value column vector X(n*1) through formula (4); AX(n*1)+V(n*1)=X(n*1)(4) Transform the formula into: X(n*1)=(1-A) -1 *V(n*1)(5) Among them, A is the direct consumption coefficient matrix, (1-A) -1 It is the inverse matrix of regional industry emission equivalents. The elements in the matrix are called inverse coefficients, which represent the complete consumption of intermediate products of all sectors when obtaining the final product of regional industry units.

4. According to claim 3, a regional industry carbon emission prediction method based on the PCEE model is characterized in that: The specific method of introducing the comprehensive emission coefficient into the total carbon emission accounting formula to obtain the accurate calculation formula of the total carbon emission is as follows: Assume e is the comprehensive emission coefficient, and let: C=e*Q / ξ*X(n*1)*D(6) The conversion yields: Q = ξ*C*D*X(n*1) / e(7).

5. According to the method for predicting regional industry carbon emissions based on the PCEE model as claimed in claim 4, it is characterized in that: According to the production and consumption relationship of regional industries, the specific method of accurately calculating the total carbon emissions formula to construct the regional industry environmental input-output model is as follows: Combining equations (5) and (7), we construct an environmental input-output model, as shown in equation (8): Q=ξC(1-A) -1 *D*V(n*1) / e(8)。 6. According to claim 5, a method for predicting regional industry carbon emissions based on a PCEE model is characterized in that: A structural decomposition analysis is conducted on the regional industry environmental input-output model, and the regional industry carbon emissions are decomposed into the product of the industry carbon emission intensity driving factors, input-output structure driving factors, final carbon emission demand structure driving factors, final carbon emission demand scale driving factors, and final demand return rate driving factors. The impact of changes in industry carbon emission intensity driving factors, input-output structure driving factors, final carbon emission demand structure driving factors, final carbon emission demand scale driving factors, and final demand return rate driving factors on the changes in regional industry carbon emissions are further decomposed. The specific method for obtaining the changes in regional industry carbon emissions is as follows: In formula (7), let the input-output structure (1-A) -1 Reflect the correlation between regional industries; the final demand vector V(n*1)*(1-A) -1 / e is decomposed into the product of the final carbon emission demand structure driving factor Z (n*r), the final carbon emission demand scale driving factor S (r*1) and the final demand rate of return driving factor W (r*1). The elements in Z represent the proportion of various energy consumption, investment and net export proportions of the regional industry, the elements in S are the total consumption, investment and net export of the regional industry, and the elements in W are the consumption rate of return, investment rate of return and net export rate of return of the regional industry. The carbon emissions can be decomposed into the product of the driving factors of industry carbon emission intensity, input-output structure, final carbon emission demand structure, final carbon emission demand scale and final demand rate of return: Q=ξCDZSW(9) If we start the decomposition from the calculation period, we have: △Q1=ξ△CD t Z t S t W t +ξC0△DZ t S t W t +ξC0D0△ZS t W t +ξC0D0Z0△SW t +ξC0D0Z0S0△W(10) If we start the decomposition from the base period, we have: △Q2=ξ△CD0Z0S0W0+ξC t △DZ0S0W0+ξC t D t △ZS0W0+ξC t D t Z t △SW0+ξC t D t Z t S t △W(11) Among them, △Q1 is the impact of the factor change obtained by decomposition from the calculation period on the change of regional industry carbon emissions; △Q2 is the impact of the factor change obtained by decomposition from the base period on the change of regional industry carbon emissions; According to the two-pole decomposition method, taking the arithmetic mean of equation (9) and equation (10), we can get: △Q=0.5*(ξ△CD t Z t S t W t +ξ△CD0Z0S0W0)+0.5*(ξC0△ DZ t S t W t +ξC t △DZ0S0W0)+0.5*(ξC0D0△ZS t W t +ξC t D t △ZS0W0)+0.5*(ξC0D0Z0△SW t +ξC t D t Z t △SW0)+0.5*(ξC0D0Z0S0△W+ξC t D t Z t S t △W)(12) Let △Q C =0.5*(ξ△CD t Z t S t W t +ξ△CD0Z0S0W0) represents the carbon emission intensity effect, that is, the impact of changes in the industry's carbon emission intensity driving factor C on the change in carbon emissions △Q; Let △Q D =0.5*(ξC0△DZ t S t W t +ξC t △DZ0S0W0) represents the input-output structure effect, that is, the impact of the change of the input-output structure driving factor D on the change of carbon emissions △Q; Let △Q Z =0.5*(ξC0D0△ZS t W t +ξC t D t △ZS0W0) represents the final demand structure effect, that is, the impact of the change of the final demand structure driving factor Z on the carbon emission change △Q; Let △Q S =0.5*(ξC0D0Z0△SW t +ξC t D t Z t △SW0) represents the final demand scale effect, that is, the impact of the change of the final demand scale driving factor S on the change of carbon emissions △Q; Let △Q W =0.5*(ξC0D0Z0S0△W+ξC t D t Z t S t △W) represents the final demand rate of return effect, that is, the impact of the change in the final demand rate of return driving factor W on the change in carbon emissions △Q; Then formula (12) can be changed to: △Q=△Q C +△Q D +△Q Z +△Q S +△Q W (13) △Q is the impact of changes in the driving factors of industry carbon emission intensity, input-output structure, final carbon emission demand structure, final carbon emission demand scale, and final demand return rate on changes in regional industry carbon emissions. Among them, C0, D0, Z0 and S0 are the driving factors of industry carbon emission intensity, input-output structure, final demand structure, final demand scale and final demand return rate respectively corresponding to the base period; C t , D t , Z t and S t They are respectively the driving factors of industry carbon emission intensity, input-output structure driving factors, final demand structure driving factors, final demand scale driving factors, and final demand return rate driving factors corresponding to the tth calculation period.

7. A regional industry carbon emission prediction method based on PCEE model according to claim 6, characterized in that: The specific method for calculating the regional industry-side conversion coefficient based on the regional industry's carbon emissions over the years and then predicting the future regional industry carbon emissions forecast value based on the regional industry-side conversion coefficient and the change in regional industry carbon emissions is as follows: The calculated value is the forecast of future carbon emissions of regional industries. It is necessary to multiply the obtained △Q result by the conversion coefficient f on the regional industry side. The calculation formula for f is: f=[(Q n-5 -Q n-6 ) / Q n-6 +(Q n-4 -Q n-5 ) / Q n-5 +(Q n-3 -Q n-4 ) / Q n-4 +(Q n-2 -Q n-3 ) / Q n -3 +(Q n-1 -Q n-2 ) / Q n-2 ] / 5(14) The regional industry conversion coefficient f takes the carbon emissions of the five years before the measurement year as the calculation base, and n is the forecast year; Where Q n-6 , Q n-5 , Q n-4 , Q n-3 , Q n-2 , Q n-1 is the carbon emissions of the regional industry from year n-6 to n-1; Forecast value of future carbon emissions of regional industries Q n for: Q n =Q+△Q*(1+f)(15)。 8. According to the method for predicting regional industry carbon emissions based on the PCEE model as claimed in claim 1, it is characterized in that: The carbon emission benchmark regression equation is obtained based on the regional industry population, regional industry economy, and regional industry manufacturing volume: D=￡1Ina+￡2Inb+￡3Inc(16) Among them, a represents the regional industry population, b represents the regional industry economic benefits, c represents the amount of electrical materials manufactured by the regional industry, £1 represents the regional industry population regression system parameters, £2 represents the regional industry economic regression system parameters, and £3 represents the regional industry material manufacturing regression system parameters.

9. A regional industry carbon emission prediction system based on the PCEE model, characterized by: It includes the following carbon emission total amount accounting module, algebraic matrix conversion module, comprehensive emission coefficient introduction module, input-output model construction module, carbon emission change calculation module and carbon emission prediction module; The total carbon emission accounting module is used to obtain the total carbon emission calculation formula of the regional industry based on the PCEE method according to the regional energy consumption heat, the carbon production regression equation factor, and the final economic demand of the industry region, and introduce the regional energy consumption and the regional carbon-containing matter production to decompose the total carbon emission calculation formula into the total carbon emission accounting formula; The algebraic matrix conversion module is used to reflect the production and consumption relationship of regional industries using algebraic matrices according to the input-output model; The comprehensive emission coefficient introduction module is used to introduce the comprehensive emission coefficient into the total carbon emission accounting formula to obtain an accurate calculation formula for the total carbon emission; The input-output model building module is used to build a regional industry environmental input-output model based on the production and consumption relationship of the regional industry and the precise calculation formula of the total carbon emissions; The carbon emission change calculation module is used to perform structural decomposition analysis on the regional industry environmental input-output model, decomposing the regional industry carbon emissions into the product of the industry carbon emission intensity driving factors, input-output structure driving factors, final carbon emission demand structure driving factors, final carbon emission demand scale driving factors, and final demand return rate driving factors, and further decompose the impact of the change in each driving factor on the change in regional industry carbon emissions to obtain the regional industry carbon emission change; The carbon emission prediction module is used to calculate the regional industry side conversion coefficient based on the regional industry's carbon emissions over the years, and then predict the future regional industry carbon emission forecast value based on the regional industry side conversion coefficient and the regional industry carbon emission change.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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