A city carbon emission calculation method based on multi-dimensional information fusion

Abstract

The application provides a kind of city carbon emission calculation method based on multi-dimensional information fusion, it is related to carbon emission management technical field, the method is to city carbon emission energy dimension data, city carbon emission economic dimension data, city carbon emission environmental dimension data, city carbon emission policy dimension data are calculated respectively, corresponding energy dimension carbon emission estimation result, economic dimension carbon emission estimation result, environmental dimension correction result and policy dimension correction result are obtained;Using entropy weight method, the weight of city carbon emission energy dimension data and city carbon emission economic dimension data is analyzed, and the dynamic weight result is obtained;Using multivariate linear regression error correction model, city carbon emission correction dimension data is analyzed, and the prediction deviation result is obtained;Through comprehensive calculation, the city carbon emission calculation result is obtained.The application solves the problem that the existing city carbon emission accounting dimension is single, data is fragmented and linear weighting is insufficient.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of carbon emission management, and particularly relates to a city carbon emission calculation method based on multi-dimensional information fusion. BACKGROUND

[0002] At present, the existing city carbon emission accounting method generally has the technical pain points of single dimension, data fragmentation and insufficient adaptability, and is mostly dependent on fixed coefficient linear weighted summation, ignores the nonlinear influence within the dimension, the dynamic correlation between the dimensions and the weight adaptability, and is difficult to meet the demand for accurate quantification of carbon emission under complex city system. The carbon emission amount based on single-dimensional data accounting is easy to lead to unbalanced regional carbon quota allocation, unrealistic emission reduction target setting, and lack of depth correction of environmental and policy dimensions, so that the carbon emission accounting result is difficult to serve as a scientific basis for policy effectiveness evaluation, and restricts the fine level of city carbon management. SUMMARY

[0003] In view of the above-mentioned deficiencies in the prior art, the city carbon emission calculation method based on multi-dimensional information fusion provided by the present application solves the problems of single dimension, data fragmentation and insufficient linear weighting of the existing city carbon emission accounting.

[0004] In order to achieve the above-mentioned application purposes, the technical scheme adopted by the present application is as follows: a city carbon emission calculation method based on multi-dimensional information fusion, comprising:

[0005] S1: obtaining city carbon emission energy dimension data, city carbon emission economic dimension data, city carbon emission environmental dimension data, city carbon emission policy dimension data and city carbon emission correction dimension data;

[0006] S2: calculating the city carbon emission energy dimension data, the city carbon emission economic dimension data, the city carbon emission environmental dimension data, the city carbon emission policy dimension data respectively, to obtain the corresponding energy dimension carbon emission estimation result, the economic dimension carbon emission estimation result, the environmental dimension correction result and the policy dimension correction result;

[0007] S3: using entropy weight method to analyze the weight of the city carbon emission energy dimension data and the city carbon emission economic dimension data, to obtain the dynamic weight result;

[0008] S4: using a multivariate linear regression error correction model to analyze the city carbon emission correction dimension data, to obtain the prediction deviation result;

[0009] S5: using the energy dimension carbon emission estimation result, the economic dimension carbon emission estimation result, the environmental dimension correction result, the policy dimension correction result, the dynamic weight result and the prediction deviation result to calculate the city carbon emission calculation result, to complete the city carbon emission calculation based on multi-dimensional information fusion.

[0010] Further, S2 includes:

[0011] Based on urban carbon emission energy dimension data, the energy dimension carbon emission estimation results are obtained by calculating the basic carbon emission and correcting for energy utilization efficiency.

[0012] Based on urban carbon emission economic dimension data, the economic dimension carbon emission estimation results are obtained by calculating the basic carbon emission volume of each industry and correcting it with economic growth rate.

[0013] Based on urban carbon emission environmental dimension data, the environmental dimension correction results are obtained by calculating the carbon source constraint coefficient and carbon sink offset amount;

[0014] Based on data from urban carbon emission policy dimensions, the policy dimension correction results were obtained through calculation.

[0015] Furthermore, the expression for the carbon emission estimation result from the energy dimension is as follows:

[0016] ;

[0017] ;

[0018] ;

[0019] in, This indicates the carbon emission estimation results from the energy dimension. This represents the basic carbon emissions from the consumer end. This represents the energy efficiency correction factor. Indicates the first i Carbon emission coefficient of energy type Indicates the first i Energy consumption This indicates the carbon emission coefficient of new energy power generation. This represents the total amount of new energy power generation. This indicates the rate of decrease in energy consumption per unit of GDP in a city each year. This indicates the percentage of renewable energy consumption in a city's annual total.

[0020] Furthermore, the expression for the carbon emission estimation result in the economic dimension is as follows:

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] in, This indicates the results of carbon emission estimates from an economic perspective. This indicates the basic carbon emissions by industry sector. This represents the elasticity coefficient of economic growth. This indicates the city's annual GDP growth rate. This represents the city's average annual GDP growth rate. Indicates the carbon emission intensity of the primary industry. Indicates the output value of the primary industry. Indicates the carbon emission intensity of high-energy-consuming industries. This represents the output value of energy-intensive industries. This indicates the carbon emission intensity of general industry. This represents general industrial output. Indicates the carbon intensity of transportation and buildings. Indicates the output value of transportation and construction. Indicates the carbon emission intensity of the service sector. Indicates the output value of the service industry. Represents the city's gross domestic product. This indicates the proportion of output value in the primary industry. This indicates the proportion of output value of the secondary industry. This indicates the proportion of output value of the tertiary industry.

[0027] Furthermore, the environmental dimension correction result includes an environmental dimension carbon source constraint coefficient and an environmental dimension carbon sink offset, wherein the expressions for the environmental dimension carbon source constraint coefficient and the environmental dimension carbon sink offset are as follows:

[0028] ;

[0029] ;

[0030] in, This represents the carbon source constraint coefficient from an environmental perspective. This represents the first regression calibration coefficient. Indicates smoke and dust emission density, This represents the second regression calibration coefficient. Indicates the rate of untreated wastewater. This represents the third regression calibration coefficient. Indicates forest coverage. This indicates the amount of carbon offsetting from an environmental perspective. This indicates the carbon emissions from urban forests. Indicates the area of ​​urban forests. This indicates the carbon emissions from urban wetlands. This indicates the area of ​​urban wetlands.

[0031] Furthermore, the expression for the policy dimension correction result is as follows:

[0032] ;

[0033] in, This indicates the results of policy dimension corrections. This represents empirical calibration data on the effectiveness of the first policy. This indicates the proportion of ecological civilization construction work in the performance evaluation. This indicates empirical calibration data for the effectiveness of the second policy. This indicates the compliance rate of the carbon emissions trading market. This represents the empirical calibration data for the effectiveness of the third policy. Indicates the coverage rate of carbon footprint labeling certification. This indicates the empirical calibration data for the effectiveness of the fourth policy. This indicates the policy timeliness coefficient.

[0034] Furthermore, the expression for the dynamic weight result is:

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] in, Represents the dynamic weights of the energy dimension. This represents the information entropy at the industry's consumer end. This represents the entropy of basic industry information. This represents the dynamic weighting of the economic dimension. Represents the information entropy of the j-th dimension. Represent a constant. express The weighting over the past 5 years, This represents the standardized data. Represents the original data. This represents the function that takes the minimum value. This represents the standardized data sequence. This represents the function that takes the maximum value. This represents the basic carbon emissions from the consumer end. This represents data on basic carbon emissions from the consumer sector over the past five years. This represents the data for basic carbon emissions from the consumer side in the first year. This represents the data for basic carbon emissions from the consumption side in the second year. This represents the data for basic carbon emissions from the consumer end within the third year. This represents the data for basic carbon emissions from the consumer side in the fourth year. This represents the data for basic carbon emissions from the consumer sector over the fifth year. This indicates the basic carbon emissions by industry sector. This represents the baseline carbon emissions data for each industry over the past five years. This represents the baseline carbon emissions data for the first year by industry sector. This represents the baseline carbon emissions data for the second year by industry. This represents the baseline carbon emissions data for the third year by industry. This represents the baseline carbon emissions data for each industry within the fourth year. This represents the baseline carbon emissions data for each industry within the fifth year.

[0043] Further, S4 includes:

[0044] We construct feature sets and label sets using urban carbon emission correction dimension data;

[0045] The feature set and label set are input into the multiple linear regression error correction model, and the optimal parameters are solved based on the objective function to obtain the prediction bias result.

[0046] Furthermore, the expression for the feature set is:

[0047] ;

[0048] The expression for the tag set is:

[0049] ;

[0050] ;

[0051] The expression for the multiple linear regression model is:

[0052] ;

[0053] The expression for the objective function is:

[0054] ;

[0055] ;

[0056] The expression for the prediction bias result is:

[0057] ;

[0058] in, Represents the feature set, This represents the estimated carbon emissions of the city in the energy dimension for year 1. This represents the estimated carbon emissions of the city in the energy dimension for the second year. This represents the estimated carbon emissions of the city in the energy dimension for year T. This represents the estimated urban carbon emissions for the first year based on the economic dimension. This represents the estimated urban carbon emissions in the second year based on the economic dimension. This represents the estimated urban carbon emissions in year T, based on the economic dimension. This represents the carbon source constraint coefficient for the environmental dimension in year 1. This represents the carbon source constraint coefficient in the environmental dimension for the second year. This represents the carbon source constraint coefficient in the environmental dimension for year T. This represents the policy dimension coordination correction coefficient for year 1. This represents the policy dimension coordination correction coefficient for the second year. This represents the policy dimension coordination correction coefficient for year T. This indicates the amount of carbon offset in the first year from an environmental perspective. This indicates the amount of carbon offset in the second year from an environmental perspective. This represents the amount of carbon offset in year T from an environmental perspective. This indicates the dynamic weight of the energy dimension in year 1. This indicates the dynamic weight of the energy dimension in the second year. This represents the dynamic weight of the energy dimension in year T. This indicates the dynamic weight of the economic dimension in year 1. This indicates the dynamic weight of the economic dimension in the second year. This represents the dynamic weight of the economic dimension in year T. Represents a tag set. This indicates the deviation of the main accounting framework in year 1. This indicates the deviation of the main accounting framework in the second year. This indicates the deviation of the main accounting framework in year T. Represents the transpose of a matrix. This indicates the deviation of the main accounting framework in year t. This represents the city's actual annual carbon emissions verification value in year t. This represents the output value of the master accounting framework in year t. Represented as the intercept term, This represents the regression weight of the estimated urban carbon emissions in the energy dimension for year t. This represents the estimated carbon emissions of the city in the energy dimension for year t. This represents the regression weight of the estimated urban carbon emissions in the economic dimension for year t. This represents the estimated urban carbon emissions in year t, based on the economic dimension. This represents the regression weight of the carbon source constraint coefficient in the environmental dimension for year 1. This represents the carbon source constraint coefficient for the environmental dimension in year 1. This represents the regression weight of the policy dimension collaborative correction coefficient in year t. This represents the policy dimension coordination correction coefficient for year T. This represents the regression weight of the carbon sequestration offset in year t, representing the environmental dimension. This represents the amount of carbon offset in year T from an environmental perspective. This represents the regression weights of the dynamic weights of the energy dimension in year t. This represents the dynamic weight of the energy dimension in year t. This represents the regression weights of the dynamic weights of the economic dimension in year t. This represents the dynamic weight of the economic dimension in year t. Represents the random error term. This means minimizing the squared residuals. This represents the expression that minimizes the value of the expression. The value, where T represents the Tth year. Represents the parameter vector , This indicates the prediction bias result. This represents the intercept term in a multiple linear regression model. Represents the estimated carbon emissions at the source level. The regression coefficients, This represents the estimated carbon emissions from the energy dimension. This represents the estimated carbon emissions from an economic perspective. Estimates of regression coefficients, This represents the estimated carbon emissions from an economic perspective. Represents the carbon source constraint coefficient in the environmental dimension. Estimates of regression coefficients, This represents the carbon source constraint coefficient from an environmental perspective. Indicates the policy dimension collaborative correction coefficient Estimates of regression coefficients, Indicates carbon offset amount from an environmental perspective Estimates of regression coefficients, This represents an estimate of the regression weights for the environmental dimension of carbon sequestration offset. This indicates the amount of carbon offsetting from an environmental perspective. Represents the dynamic weights of the energy dimension. Estimates of regression coefficients, Represents the dynamic weights of the energy dimension. Indicates the dynamic weight of the economic dimension Estimates of regression coefficients, This represents the dynamic weight of the economic dimension.

[0059] Furthermore, the expression for the calculated urban carbon emissions is as follows:

[0060] ;

[0061] in, This indicates the results of urban carbon emission calculations. Represents the dynamic weights of the energy dimension. This indicates the carbon emission estimation results from the energy dimension. This represents the carbon source constraint coefficient from an environmental perspective. This represents the dynamic weighting of the economic dimension. This indicates the results of carbon emission estimates from an economic perspective. This indicates the results of policy dimension corrections. This indicates the amount of carbon offsetting from an environmental perspective. This indicates the prediction bias result.

[0062] The beneficial effects of this invention are as follows: This invention provides a method for calculating urban carbon emissions based on multi-dimensional information fusion. It comprehensively calculates and corrects errors in urban carbon emission energy dimension data, urban carbon emission economic dimension data, urban carbon emission environmental dimension data, urban carbon emission policy dimension data, and urban carbon emission correction dimension data to obtain the calculated urban carbon emissions result. Through intra-dimensional scenario modeling, non-linear coupling and fusion between dimensions, dynamic weight assignment, and machine learning error correction, quantitative models for each dimension are established, scientific coefficients and correction mechanisms are set, and finally, a multi-dimensional information fusion-based total carbon emission estimate is formed, improving the accuracy of urban carbon emission calculation. Attached Figure Description

[0063] The present invention will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, the same reference numerals denote the same structures, wherein:

[0064] Figure 1 This is an exemplary flowchart of a method for calculating urban carbon emissions based on multidimensional information fusion, as shown in some embodiments of the present invention. Detailed Implementation

[0065] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0066] Example

[0067] Figure 1 This is an exemplary flowchart illustrating a method for calculating urban carbon emissions based on multi-dimensional information fusion, according to some embodiments of the present invention. Figure 1 As shown, the process includes the following steps. In some embodiments, the process may be executed by a processor.

[0068] S1: Obtain urban carbon emission energy dimension data, urban carbon emission economic dimension data, urban carbon emission environmental dimension data, urban carbon emission policy dimension data, and urban carbon emission correction dimension data.

[0069] The energy dimension data for urban carbon emissions includes data by energy type, consumption volume by consumer end, carbon emission coefficient of end energy, energy consumption reduction rate per unit of GDP, and the proportion of renewable energy.

[0070] The economic dimension data of urban carbon emissions includes urban GDP and output value of each industry, carbon emission intensity of each industry, and urban and national GDP growth rates.

[0071] Urban carbon emission environmental dimension data includes forest and wetland area and carbon sink coefficient.

[0072] Data on urban carbon emission policies includes assessment percentage, compliance rate, and certification coverage rate.

[0073] The data for the city's carbon emission correction dimension includes the annual actual carbon emission verification value.

[0074] In some embodiments, the processor can obtain city carbon emission correction dimension data by querying historical carbon verification values.

[0075] S2: Calculate the energy dimension data, economic dimension data, environmental dimension data, and policy dimension data of urban carbon emissions respectively to obtain the corresponding energy dimension carbon emission estimation results, economic dimension carbon emission estimation results, environmental dimension correction results, and policy dimension correction results.

[0076] In some embodiments, the processor can obtain energy-dimensional carbon emission estimation results based on urban carbon emission energy dimension data by calculating the basic carbon emission amount and energy utilization efficiency correction; obtain economic-dimensional carbon emission estimation results based on urban carbon emission economic dimension data by calculating the basic carbon emission amount by industry and economic growth rate correction; obtain environmental-dimensional correction results based on urban carbon emission environmental dimension data by calculating the carbon source constraint coefficient and carbon sink offset amount; and obtain policy-dimensional correction results based on urban carbon emission policy dimension data.

[0077] In some embodiments, the expression for the carbon emission estimation result from the energy dimension can be:

[0078] ;

[0079] ;

[0080] ;

[0081] in, This indicates the carbon emission estimation results from the energy dimension. This represents the basic carbon emissions from the consumer end. This represents the energy efficiency correction factor. Indicates the first i Carbon emission coefficient of energy type Indicates the first i Energy consumption This indicates the carbon emission coefficient of new energy power generation. This represents the total amount of new energy power generation. This indicates the rate of decrease in energy consumption per unit of GDP in a city each year. This indicates the percentage of renewable energy consumption in a city's annual total.

[0082] In some embodiments, i =1, 2, 3 correspond to coal, crude oil, and natural gas, respectively. i =4 corresponds to new energy power generation (wind power / photovoltaic / hydropower / nuclear power), and industrial coal. a 1=2.52, crude oil transportation a 2=2.18, Building Natural Gas a 3 = 1.55 a 4 = 0.098 is the carbon emission coefficient of new energy power generation.

[0083] In some embodiments, the expression for the carbon emission estimation result from the economic dimension can be:

[0084] ;

[0085] ;

[0086] ;

[0087] ;

[0088] ;

[0089] in, This indicates the results of carbon emission estimates from an economic perspective. This indicates the basic carbon emissions by industry sector. This represents the elasticity coefficient of economic growth. This indicates the city's annual GDP growth rate. This represents the city's average annual GDP growth rate. Indicates the carbon emission intensity of the primary industry. Indicates the output value of the primary industry. Indicates the carbon emission intensity of high-energy-consuming industries. This represents the output value of energy-intensive industries. This indicates the carbon emission intensity of general industry. This represents general industrial output. Indicates the carbon intensity of transportation and buildings. Indicates the output value of transportation and construction. Indicates the carbon emission intensity of the service sector. Indicates the output value of the service industry. Represents the city's gross domestic product. This indicates the proportion of output value in the primary industry. This indicates the proportion of output value of the secondary industry. This indicates the proportion of output value of the tertiary industry.

[0090] In some embodiments, γ 1 = 0.02 γ 2 = 0.8 γ 3 = 0.15 γ 4 = 0.3 γ 5 = 0.05.

[0091] In some embodiments, the environmental dimension correction result includes an environmental dimension carbon source constraint coefficient and an environmental dimension carbon sink offset, wherein the expressions for the environmental dimension carbon source constraint coefficient and the environmental dimension carbon sink offset are as follows:

[0092] ;

[0093] ;

[0094] in, This represents the carbon source constraint coefficient from an environmental perspective. This represents the first regression calibration coefficient. Indicates smoke and dust emission density, This represents the second regression calibration coefficient. Indicates the rate of untreated wastewater. This represents the third regression calibration coefficient. Indicates forest coverage. This indicates the amount of carbon offsetting from an environmental perspective. This indicates the carbon emissions from urban forests. Indicates the area of ​​urban forests. This indicates the carbon emissions from urban wetlands. This indicates the area of ​​urban wetlands.

[0095] The environmental dimension carbon source constraint coefficient is a coefficient used to constrain the carbon emission estimation results from the energy dimension.

[0096] The environmental dimension of carbon offset is the amount of carbon dioxide absorbed by afforestation that offsets the carbon emissions generated by daily activities.

[0097] In some embodiments, c 1 = 0.05 c 2 = 0.03 c 3 = 0.08 δ 1=10、 δ 2 = 8.

[0098] In some embodiments, the expression for the policy dimension correction result can be:

[0099] ;

[0100] in, This indicates the results of policy dimension corrections. This represents empirical calibration data on the effectiveness of the first policy. This indicates the proportion of ecological civilization construction work in the performance evaluation. This indicates empirical calibration data for the effectiveness of the second policy. This indicates the compliance rate of the carbon emissions trading market. This represents the empirical calibration data for the effectiveness of the third policy. Indicates the coverage rate of carbon footprint labeling certification. This indicates the empirical calibration data for the effectiveness of the fourth policy. This indicates the policy timeliness coefficient.

[0101] In some embodiments, d 1 = 0.3 d 2 = 0.4 d 3 = 0.2 d 4 = 0.002, calibrated using empirical data on policy effectiveness; ,in,, t Indicates the duration of the policy implementation.

[0102] S3: Using the entropy weight method, analyze the weights of urban carbon emission energy dimension data and urban carbon emission economic dimension data to obtain dynamic weight results.

[0103] The dynamic weighting result is the weighting ratio of urban carbon emission energy dimension data and urban carbon emission economic dimension data. For example, the dynamic weighting result can include dynamic weights for the energy dimension and dynamic weights for the economic dimension.

[0104] In some embodiments, the expression for the dynamic weight result is:

[0105] ;

[0106] ;

[0107] ;

[0108] ;

[0109] ;

[0110] ;

[0111] ;

[0112] in, Represents the dynamic weights of the energy dimension. This represents the information entropy at the industry's consumer end. This represents the entropy of basic industry information. This represents the dynamic weighting of the economic dimension. Represents the information entropy of the j-th dimension. Represent a constant. express The weighting over the past 5 years, This represents the standardized data. Represents the original data. This represents the function that takes the minimum value. This represents the standardized data sequence. This represents the function that takes the maximum value. This represents the basic carbon emissions from the consumer end. This represents data on basic carbon emissions from the consumer sector over the past five years. This represents the data for basic carbon emissions from the consumer side in the first year. This represents the data for basic carbon emissions from the consumption side in the second year. This represents the data for basic carbon emissions from the consumer end within the third year. This represents the data for basic carbon emissions from the consumer side in the fourth year. This represents the data for basic carbon emissions from the consumer sector over the fifth year. This indicates the basic carbon emissions by industry sector. This represents the baseline carbon emissions data for each industry over the past five years. This represents the baseline carbon emissions data for the first year by industry sector. This represents the baseline carbon emissions data for the second year by industry. This represents the baseline carbon emissions data for the third year by industry. This represents the baseline carbon emissions data for each industry within the fourth year. This represents the baseline carbon emissions data for each industry within the fifth year.

[0113] S4: Using a multiple linear regression error correction model, analyze the data on the correction dimension of urban carbon emissions to obtain the prediction bias results.

[0114] The prediction bias result reflects the deviation of the predicted values ​​for urban carbon emissions.

[0115] In some embodiments, the processor can use urban carbon emission correction dimension data to construct a feature set and a label set; input the feature set and label set into a multiple linear regression error correction model, solve for the optimal parameters based on the objective function, and obtain the prediction bias result.

[0116] The feature set is a set of features constructed from the core parameters of the master accounting framework of the city carbon emission correction dimension data.

[0117] The label set is a set of deviation vectors constructed based on the actual values ​​of the local ecological and environmental bureaus.

[0118] In some embodiments, the processor can construct a sample matrix: X (8 rows and 8 columns) and a label vector Y (8 rows and 1 column); calculate X T X (8×8 matrix): (the t-th row vector); find the inverse matrix. ;calculate , ;Will and Multiply to obtain the parameter vector The main accounting parameters of the year to be estimated are substituted into the regression equation to calculate the prediction deviation and obtain the prediction deviation result.

[0119] In some embodiments, the feature set is expressed as:

[0120] .

[0121] In some embodiments, the expression for the tag set is:

[0122] ;

[0123] .

[0124] In some embodiments, the expression for the multiple linear regression model is:

[0125] .

[0126] In some embodiments, the expression for the objective function is:

[0127] ;

[0128] .

[0129] In some embodiments, the expression for the prediction bias result is:

[0130] ;

[0131] in, Represents the feature set, This represents the estimated carbon emissions of the city in the energy dimension for year 1. This represents the estimated carbon emissions of the city in the energy dimension for the second year. This represents the estimated carbon emissions of the city in the energy dimension for year T. This represents the estimated urban carbon emissions for the first year based on the economic dimension. This represents the estimated urban carbon emissions in the second year based on the economic dimension. This represents the estimated urban carbon emissions in year T, based on the economic dimension. This represents the carbon source constraint coefficient for the environmental dimension in year 1. This represents the carbon source constraint coefficient in the environmental dimension for the second year. This represents the carbon source constraint coefficient in the environmental dimension for year T. This represents the policy dimension coordination correction coefficient for year 1. This represents the policy dimension coordination correction coefficient for the second year. This represents the policy dimension coordination correction coefficient for year T. This indicates the amount of carbon offset in the first year from an environmental perspective. This indicates the amount of carbon offset in the second year from an environmental perspective. This represents the amount of carbon offset in year T from an environmental perspective. This indicates the dynamic weight of the energy dimension in year 1. This indicates the dynamic weight of the energy dimension in the second year. This represents the dynamic weight of the energy dimension in year T. This indicates the dynamic weight of the economic dimension in year 1. This indicates the dynamic weight of the economic dimension in the second year. This represents the dynamic weight of the economic dimension in year T. Represents a tag set. This indicates the deviation of the main accounting framework in year 1. This indicates the deviation of the main accounting framework in the second year. This indicates the deviation of the main accounting framework in year T. Represents the transpose of a matrix. This indicates the deviation of the main accounting framework in year t. This represents the city's actual annual carbon emissions verification value in year t. This represents the output value of the master accounting framework in year t. Represented as the intercept term, This represents the regression weight of the estimated urban carbon emissions in the energy dimension for year t. This represents the estimated carbon emissions of the city in the energy dimension for year t. This represents the regression weight of the estimated urban carbon emissions in the economic dimension for year t. This represents the estimated urban carbon emissions in year t, based on the economic dimension. This represents the regression weight of the carbon source constraint coefficient in the environmental dimension for year 1. This represents the carbon source constraint coefficient for the environmental dimension in year 1. This represents the regression weight of the policy dimension collaborative correction coefficient in year t. This represents the policy dimension coordination correction coefficient for year T. This represents the regression weight of the carbon sequestration offset in year t, representing the environmental dimension. This represents the amount of carbon offset in year T from an environmental perspective. This represents the regression weights of the dynamic weights of the energy dimension in year t. This represents the dynamic weight of the energy dimension in year t. This represents the regression weights of the dynamic weights of the economic dimension in year t. This represents the dynamic weight of the economic dimension in year t. Represents the random error term. This means minimizing the squared residuals. This represents the expression that minimizes the value of the expression. The value, where T represents the Tth year. Represents the parameter vector , This indicates the prediction bias result. This represents the intercept term in a multiple linear regression model. Represents the estimated carbon emissions at the source level. The regression coefficients, This represents the estimated carbon emissions from the energy dimension. This represents the estimated carbon emissions from an economic perspective. Estimates of regression coefficients, This represents the estimated carbon emissions from an economic perspective. Represents the carbon source constraint coefficient in the environmental dimension. Estimates of regression coefficients, This represents the carbon source constraint coefficient from an environmental perspective. Indicates the policy dimension collaborative correction coefficient Estimates of regression coefficients, Indicates carbon offset amount from an environmental perspective Estimates of regression coefficients, This represents an estimate of the regression weights for the environmental dimension of carbon sequestration offset. This indicates the amount of carbon offsetting from an environmental perspective. Represents the dynamic weights of the energy dimension. Estimates of regression coefficients, Represents the dynamic weights of the energy dimension. Indicates the dynamic weight of the economic dimension Estimates of regression coefficients, This represents the dynamic weight of the economic dimension.

[0132] In some embodiments, the matrix form is simplified to: ,in For parameter vectors, This is the error vector.

[0133] In some embodiments, by differentiating the objective function and setting the derivative to zero, the parametric closed-form solution (normal equation) is derived: .

[0134] The multiple linear regression error correction model establishes a linear mapping relationship between the core parameters of the main accounting framework and the accounting deviation, and corrects the estimation error with a simple and interpretable statistical method. It does not require complex machine learning algorithms and has the advantages of easy understanding of the principle, low computational load, and easy implementation.

[0135] S5: Calculate the city's carbon emissions by using the carbon emission estimation results from the energy dimension, the carbon emission estimation results from the economic dimension, the correction results from the environmental dimension, the correction results from the policy dimension, the dynamic weighting results, and the prediction bias results, thus completing the city's carbon emission calculation through multi-dimensional information fusion.

[0136] The results of the urban carbon emission calculation are the calculated urban carbon emissions after eliminating errors.

[0137] In some embodiments, the expression for calculating urban carbon emissions is:

[0138] ;

[0139] in, This indicates the results of urban carbon emission calculations. Represents the dynamic weights of the energy dimension. This indicates the carbon emission estimation results from the energy dimension. This represents the carbon source constraint coefficient from an environmental perspective. This represents the dynamic weighting of the economic dimension. This indicates the results of carbon emission estimates from an economic perspective. This indicates the results of policy dimension corrections. This indicates the amount of carbon offsetting from an environmental perspective. This indicates the prediction bias result.

[0140] In some embodiments of the present invention, a method for calculating urban carbon emissions based on multi-dimensional information fusion is provided. This method comprehensively calculates and corrects errors in urban carbon emission energy dimension data, urban carbon emission economic dimension data, urban carbon emission environmental dimension data, urban carbon emission policy dimension data, and urban carbon emission correction dimension data to obtain the calculated urban carbon emissions result. Through intra-dimensional scenario modeling, non-linear coupling and fusion between dimensions, dynamic weight assignment, and machine learning error correction, quantitative models for each dimension are established, scientific coefficients and correction mechanisms are set, ultimately forming a multi-dimensional information fusion-based total carbon emission estimate, thus improving the accuracy of urban carbon emission calculation.

Claims

1.A method for calculating urban carbon emission based on multi-dimensional information fusion, characterized in that, The method comprises the following steps: S1: obtaining urban carbon emission energy dimension data, urban carbon emission economic dimension data, urban carbon emission environmental dimension data, urban carbon emission policy dimension data and urban carbon emission correction dimension data; S2: calculating the urban carbon emission energy dimension data, urban carbon emission economic dimension data, urban carbon emission environmental dimension data and urban carbon emission policy dimension data respectively to obtain corresponding energy dimension carbon emission estimation results, economic dimension carbon emission estimation results, environmental dimension correction results and policy dimension correction results; S3: using the entropy weight method to analyze the weights of the urban carbon emission energy dimension data and the urban carbon emission economic dimension data to obtain dynamic weight results; The expression of the dynamic weight results is: ； ； ； ； ； ； ； wherein, represents the dynamic weight of the energy dimension, represents the industry consumption end information entropy, represents the industry foundation end information entropy, represents the dynamic weight of the economic dimension, represents the information entropy of the jth dimension, represents a constant, represents the weight in 5 years, represents the standardized data, represents the original data, represents the minimum value function, represents the standardized data sequence, represents the maximum value function, represents the consumption end basic carbon emission, represents the consumption end basic carbon emission data in 5 years, represents the consumption end basic carbon emission data in the first year, represents the consumption end basic carbon emission data in the second year, represents the consumption end basic carbon emission data in the third year, represents the consumption end basic carbon emission data in the fourth year, represents the consumption end basic carbon emission data in the fifth year, represents the industry basic carbon emission, represents the industry basic carbon emission data in 5 years, represents the industry basic carbon emission data in the first year, represents the industry basic carbon emission data in the second year, represents the industry basic carbon emission data in the third year, represents the industry basic carbon emission data in the fourth year, represents the industry basic carbon emission data in the fifth year. S4: using a multivariate linear regression error correction model to analyze the urban carbon emission correction dimension data to obtain prediction deviation results; comprising: using the urban carbon emission correction dimension data to construct a feature set and a label set; inputting the feature set and the label set into the multivariate linear regression error correction model, solving the optimal parameters based on an objective function, and obtaining the prediction deviation results; The expression of the feature set is: ； The expression of the label set is: ； ； The expression of the multivariate linear regression error correction model is: ； The expression of the objective function is: ； ； The expression of the prediction deviation results is: ； wherein, represents a characteristic set, represents the estimated value of the city carbon emission in the first year in the energy dimension, represents the estimated value of the city carbon emission in the second year in the energy dimension, represents the estimated value of the city carbon emission in the Tth year in the energy dimension, represents the estimated value of the city carbon emission in the first year in the economic dimension, represents the estimated value of the city carbon emission in the second year in the economic dimension, represents the estimated value of the city carbon emission in the Tth year in the economic dimension, represents the carbon source constraint coefficient in the first year in the environmental dimension, represents the carbon source constraint coefficient in the second year in the environmental dimension, represents the carbon source constraint coefficient in the Tth year in the environmental dimension, represents the policy dimension synergy correction coefficient in the first year, represents the policy dimension synergy correction coefficient in the second year, represents the policy dimension synergy correction coefficient in the Tth year, represents the carbon sink offset amount in the first year in the environmental dimension, represents the carbon sink offset amount in the second year in the environmental dimension, represents the carbon sink offset amount in the Tth year in the environmental dimension, represents the dynamic weight in the first year in the energy dimension, represents the dynamic weight in the second year in the energy dimension, represents the dynamic weight in the Tth year in the energy dimension, represents the dynamic weight in the first year in the economic dimension, represents the dynamic weight in the second year in the economic dimension, represents the dynamic weight in the Tth year in the economic dimension, represents a label set, represents the main accounting framework deviation in the first year, represents the main accounting framework deviation in the second year, represents the main accounting framework deviation in the Tth year, represents the transpose of a matrix, represents the main accounting framework deviation in the tth year, represents the actual city annual carbon emission verification value in the tth year, represents the main accounting framework output value in the tth year, represents an intercept term, represents the regression weight of the estimated value of the city carbon emission in the tth year in the energy dimension, represents the estimated value of the city carbon emission in the tth year in the energy dimension, represents the regression weight of the estimated value of the city carbon emission in the tth year in the economic dimension, represents the estimated value of the city carbon emission in the tth year in the economic dimension, regression weight representing the environmental dimension carbon source constraint coefficient in year 1, regression weight representing the environmental dimension carbon source constraint coefficient in year 1, regression weight representing the policy dimension synergy modification coefficient in year t, regression weight representing the policy dimension synergy modification coefficient in year T, regression weight representing the environmental dimension carbon sink offset in year t, regression weight representing the environmental dimension carbon sink offset in year T, regression weight representing the energy dimension dynamic weight in year t, regression weight representing the energy dimension dynamic weight in year t, regression weight representing the economic dimension dynamic weight in year t, regression weight representing the economic dimension dynamic weight in year t, representing a random error term, representing a residual square, representing a minimum value of the equation, value, T represents year T, representing a parameter vector , representing an objective function, representing a transpose of a feature set, representing a prediction bias result, representing a multiple linear regression model intercept term, representing a regression coefficient of the energy dimension carbon emission estimate , representing an energy dimension carbon emission estimate, representing a regression coefficient of the economic dimension carbon emission estimate , representing an economic dimension carbon emission estimate, representing a regression coefficient of the environmental dimension carbon source constraint coefficient , representing an environmental dimension carbon source constraint coefficient, representing a regression coefficient of the policy dimension synergy modification coefficient , representing a regression coefficient of the environmental dimension carbon sink offset , representing an estimate of the regression weight of the environmental dimension carbon sink offset, representing an environmental dimension carbon sink offset, representing a regression coefficient of the energy dimension dynamic weight , representing a dynamic weight of the energy dimension, representing a regression coefficient of the economic dimension dynamic weight , Dynamic weights representing economic dimensions; S5: using the energy dimension carbon emission estimation results, the economic dimension carbon emission estimation results, the environmental dimension correction results, the policy dimension correction results, the dynamic weight results and the prediction deviation results to calculate the urban carbon emission calculation results, and completing the multi-dimensional information fusion of the urban carbon emission calculation. 2.The method of claim 1, wherein, The S2 comprises: based on the urban carbon emission energy dimension data, the energy dimension carbon emission estimation results are obtained by calculating the basic carbon emission and the energy utilization efficiency correction; based on the urban carbon emission economic dimension data, the economic dimension carbon emission estimation results are obtained by calculating the basic carbon emission and the economic growth correction; based on the urban carbon emission environmental dimension data, the environmental dimension correction results are obtained by calculating the carbon source constraint coefficient and the carbon sink offset; based on the urban carbon emission policy dimension data, the policy dimension correction results are obtained by calculation. 3.The method of claim 2, wherein, The expression of the energy dimension carbon emission estimation results is: ； ； ； wherein, represents the estimated result of energy dimension carbon emission, represents the carbon emission of the consumption end, represents the energy efficiency correction coefficient, represents the carbon emission coefficient of the energy of the i class, represents the energy consumption of the energy of the i class, represents the carbon emission coefficient of new energy power generation, represents the total amount of new energy power generation, represents the annual unit GDP energy consumption reduction rate of the city, represents the annual renewable energy consumption proportion of the city. 4.The method of claim 2, wherein, The expression of the economic dimension carbon emission estimation results is: ； ； ； ； ； wherein, represents the economic dimension carbon emission estimation result, represents the industry-based carbon emission, represents the economic growth elasticity coefficient, represents the city annual GDP growth rate, represents the city annual average GDP growth rate, represents the first industry carbon emission intensity, represents the first industry output value, represents the high energy consumption industry carbon emission intensity, represents the high energy consumption industry output value, represents the general industry carbon emission intensity, represents the general industry output value, represents the transportation and construction carbon emission intensity, represents the transportation and construction output value, represents the service industry carbon emission intensity, represents the service industry output value, represents the city gross product, represents the first industry output value proportion, represents the second industry output value proportion, represents the third industry output value proportion. 5.The method for calculating urban carbon emission based on multi-dimensional information fusion according to claim 2, characterized in that, The environmental dimension correction results comprise an environmental dimension carbon source constraint coefficient and an environmental dimension carbon sink offset, wherein the expressions of the environmental dimension carbon source constraint coefficient and the environmental dimension carbon sink offset are respectively: ； ； wherein, represents an environmental dimension carbon source constraint coefficient, represents a first regression calibration coefficient, represents a soot emission density, represents a second regression calibration coefficient, represents a wastewater untreated rate, represents a third regression calibration coefficient, represents a forest coverage rate, represents an environmental dimension carbon sink offset amount, represents a carbon emission amount of urban forest, represents an urban forest area, represents a carbon emission amount of urban wetland, represents an urban wetland area. 6.The method for calculating urban carbon emission based on multi-dimensional information fusion according to claim 2, characterized in that, The expression of the policy dimension correction results is: ； wherein, represents the policy dimension correction result, represents the first policy effectiveness empirical calibration data, represents the proportion of ecological civilization construction work in performance evaluation, represents the second policy effectiveness empirical calibration data, represents the carbon emission rights trading market compliance completion rate, represents the third policy effectiveness empirical calibration data, represents the carbon footprint identification certification coverage rate, represents the fourth policy effectiveness empirical calibration data, represents the policy time effectiveness coefficient. 7.The method of claim 1, wherein, The expression of the urban carbon emission calculation results is: ； wherein, represents the calculation result of urban carbon emissions, represents the dynamic weight of the energy dimension, represents the estimation result of carbon emissions of the energy dimension, represents the carbon source constraint coefficient of the environmental dimension, represents the dynamic weight of the economic dimension, represents the estimation result of carbon emissions of the economic dimension, represents the correction result of the policy dimension, represents the carbon sink offset amount of the environmental dimension, represents the prediction bias result.

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