Method and system for evaluating coordination degree of carbon peak-reaching paths among multiple regions

Abstract

The embodiment of the invention provides a method and system for evaluating the coordination degree of a carbon to peak path among multiple regions, and the method comprises the steps: employing a dynamic time warping (DTW) algorithm to analyze all influence factors of single-region carbon emission so as to screen key influence factors of the carbon emission, employing a principal component analysis (PCA) method to carry out the dimension reduction processing of the key influence factors, and obtaining the coordination degree of the carbon to peak path among the multiple regions. Comprehensive indexes are obtained; based on the comprehensive indexes, predicting the future carbon emission trend of the single region to obtain the carbon peak reaching time of the single region, based on the comprehensive indexes, predicting the carbon peak reaching time of the single region, and determining the expected value of the number of years required for carbon peak reaching from the basic year calculation of each region; and setting a scheme value of the number of years required by each region to reach the peak from the base year, and calculating the peak reaching time coordination degree among the regions by combining an expected value of the number of years required by each region to reach the peak from the base year. According to the method, by calculating the expected peak arrival time of each region, a regional carbon peak arrival carbon neutralization implementation scheme, an economic policy, an energy power plan and an air pollution prevention and control policy can be formulated, and the scientific decision-making level is improved.

Description

Technical Field

[0001] This document relates to the field of energy technology, and in particular, to a method and system for evaluating the coordination of carbon peak paths among multiple regions. Background Art

[0002] The analysis of regional carbon peak path belongs to the category of regional coordinated development. There are many evaluation objects for regional coordinated development, including industrial system coordination, innovation development level coordination, carrying capacity evaluation, etc. The regional carbon peak analysis mainly adopts econometric methods. In the existing technology, the STIRPAT model and ridge regression method are used to predict carbon emissions. Although these methods have shown effectiveness in analyzing and predicting factors affecting carbon emissions, there are still some shortcomings: First, they fail to fully reflect the combination of uniqueness and synergy, ignoring the large differences in regional development, and the significant differences in the distribution of geographical environment, resource endowment, capital and human resources among regions. There is a lack of consideration of the complementary and interdependent coupling relationship between regions to study regional coordinated carbon emission reduction; second, the relationship between development and emission reduction is not well reflected, and the differences in the development stages of various regions are not taken into account, as well as the lack of guaranteeing the equal development rights of various provinces. At the same time, the characteristics of resource endowment and energy structure are not fully considered to achieve the coordination of development and emission reduction. Summary of the invention

[0003] One or more embodiments of this specification provide a method for evaluating the coordination of carbon peak paths among multiple regions, including:

[0004] The dynamic time warping algorithm DTW is used to analyze the various influencing factors of carbon emissions in a single region to screen the key influencing factors of carbon emissions. The principal component analysis (PCA) method is used to reduce the dimension of the key influencing factors to obtain a comprehensive index.

[0005] Based on the comprehensive indicators, the carbon peak time of a single region is predicted to determine the expected number of years required for each region to reach carbon peak starting from the base year;

[0006] Set the planned number of years required for each region to reach its carbon peak starting from the base year, and calculate the degree of coordination in peak time among the regions based on the expected number of years required for each region to reach its carbon peak starting from the base year.

[0007] Furthermore, the influencing factors of carbon emissions in a single region include: population, per capita GDP, urbanization rate, industrial structure, energy intensity, energy structure, power generation structure and carbon emission intensity; wherein, the industrial structure includes the proportion of the primary industry, the proportion of the secondary industry and the proportion of the tertiary industry; the energy structure includes the terminal energy structure and the primary energy structure, the terminal energy structure includes the electrification level, the primary energy structure includes the proportion of coal, oil, natural gas and non-fossil energy in the primary energy; the power generation structure includes the proportion of thermal power generation.

[0008] Furthermore, the dynamic time warping algorithm DTW is used to analyze the various influencing factors of carbon emissions in a single region to screen the key influencing factors of carbon emissions. The specific method is:

[0009] Assume that the time series of carbon emissions in a single region is A = {a1, a2, …, a m}, the time series of a certain influencing factor is B = {b1, b2, …, b m}, m and n are the number of elements in the two time series respectively. According to the time series of carbon emissions in a single region and the time series of influencing factors, the distance matrix D of the path space composed of curved paths is constructed, as follows:

[0010]

[0011] Among them, d(a i ,b i ) is the distance between the elements in the time series of carbon emissions in a single region and the time series of influencing factors;

[0012] D(i,j)=d(a i ,b i )=(a i -b i ) 2 ;

[0013] The curved path is calculated based on the search path direction and the distance matrix as follows:

[0014] R={(i1,j1),(i2,j2),…,(i s , j s ),…,(i l , j l )};

[0015]

[0016] Where R is the curved path between elements, which is an ordered set of l binary arrays; s is the coordinate of the sth point on the dynamic curved path; l is the number of elements in the path;

[0017] For each curved path, the curved path with the smallest sum of DTW distances between all matching point pairs of the carbon emission time series and the influencing factor time series on the curved path is the optimal curved path. The specific calculation method of the DTW distance of the optimal curved path is as follows:

[0018]

[0019] The impact of each influencing factor on carbon emissions is evaluated according to the DTW distance between each influencing factor and the optimal curved path of carbon emissions, and the influencing factors with a DTW distance less than the preset threshold are selected as key influencing factors.

[0020] Furthermore, principal component analysis (PCA) was used to reduce the dimension of key influencing factors and obtain comprehensive indicators. The specific method is as follows:

[0021] The index value of the key influencing factor is standardized, and the index value of the key influencing factor X ij Transformed into standardized indicators, as follows:

[0022]

[0023] Among them, μ j and j Represent the sample mean and sample standard deviation of the jth key influencing factor index value; A ij is a standardized indicator; X ij is the index value of the key influencing factor, m and n represent the sample variables of m key influencing factors and n key influencing factor index values ​​respectively;

[0024] According to the carbon emissions and the standardized indicators of the selected key influencing factors, the correlation coefficient matrix R of the key influencing factors is calculated, and according to (R-λ j E)U j = 0 to calculate the eigenvalue λ j and the eigenvector U j , E is the unit matrix;

[0025] Select p (p≤m) principal components based on key influencing factors and calculate the eigenvalue λ j The cumulative contribution rates are as follows:

[0026]

[0027] Among them, L p is the cumulative contribution rate of the principal components; p is the number of principal components;

[0028] The principal component coefficient matrix of each key influencing factor is established according to the cumulative contribution rate of each principal component. The columns in the principal component coefficient matrix correspond to the standardized indicators in order, as shown below:

[0029]

[0030] The columns of the principal component coefficient matrix of each key influencing factor are multiplied by their corresponding standardized indexes to determine the principal component expression. The calculation formula of the principal component is as follows:

[0031]

[0032] Among them, H1,H2,…,H P are the first, second, ..., pth principal components of the key influencing factors, i.e., comprehensive indicators; u Pt is the t-dimensional eigenvector corresponding to the p-th eigenvalue.

[0033] Furthermore, based on the comprehensive indicators, the carbon peak time of a single region is predicted, and the expected number of years required for each region to reach the carbon peak from the base year is determined. The specific method is:

[0034] The STIRPAT extended model is used to predict the carbon peak time in a single region, and the expected number of years required for each region to reach carbon peak from the base year is obtained, as follows:

[0035]

[0036] Among them, C represents the expected number of years required for each region to reach carbon peak from the base year; a is the scale coefficient; T k represents k principal component variables, p is the number of principal component variables; β k represents the elasticity coefficient of each principal component; c represents the random interference term;

[0037] Taking the logarithm of the above formula, we get the STIRPAT model:

[0038] lnC=lna+β1lnH1+β2lnH2+…+β p LqCy p +lnc.

[0039] Furthermore, the specific method for evaluating the coordination degree of carbon peak time between regions under different plans is as follows:

[0040] Based on the expected number of years required to reach carbon peak in each region from the base year, the carbon peak parameters of each region are set. The carbon peak parameters include the planned number of years required to reach carbon peak from the base year. The mean square error Z of the deviation between the planned number of years required to reach carbon peak and the expected number of years required to reach carbon peak in all regions is calculated:

[0041]

[0042] in, is the annual plan value required for the carbon peak in the nth region, is the expected year for the nth region to reach its carbon peak;

[0043] The mean square error Z is inversely proportional to the regional carbon peak coordination degree.

[0044] One or more embodiments of this specification provide a system for evaluating the coordination of carbon peak paths among multiple regions, including:

[0045] Index determination module: used to analyze the influencing factors of carbon emissions in a single region using the dynamic time warping algorithm DTW to screen the key influencing factors of carbon emissions, and use the principal component analysis (PCA) method to reduce the dimension of the key influencing factors to obtain comprehensive indicators;

[0046] Carbon peak time prediction module: used to predict the carbon peak time of a single region based on the comprehensive indicators, and determine the expected number of years required for each region to reach the carbon peak from the base year;

[0047] Coordination evaluation module: used to set the planned number of years required for each region to reach carbon peak from the base year, and calculate the coordination degree of peak time between regions based on the expected number of years required for each region to reach carbon peak from the base year.

[0048] Furthermore, the influencing factors of carbon emissions in a single region include: population, per capita GDP, urbanization rate, industrial structure, energy intensity, energy structure, power generation structure and carbon emission intensity; wherein, the industrial structure includes the proportion of the primary industry, the proportion of the secondary industry and the proportion of the tertiary industry; the energy structure includes the terminal energy structure and the primary energy structure, the terminal energy structure includes the electrification level, the primary energy structure includes the proportion of coal, oil, natural gas and non-fossil energy in the primary energy; the power generation structure includes the proportion of thermal power generation.

[0049] Furthermore, the indicator determination module is specifically used for:

[0050] Assume that the time series of carbon emissions in a single region is A = {a1, a2, …, a m}, the time series of a certain influencing factor is B = {b1, b2, …, b m}, m and n are the number of elements in the two time series respectively. According to the time series of carbon emissions in a single region and the time series of influencing factors, the distance matrix D of the path space composed of curved paths is constructed, as follows:

[0051]

[0052] Among them, d(a i ,b i ) is the distance between the elements in the time series of carbon emissions in a single region and the time series of influencing factors;

[0053] D(i,j)=d(a i ,b i )=(a i -b i ) 2 ;

[0054] The curved path is calculated based on the search path direction and the distance matrix as follows:

[0055] R={(i1,j1),(i2,j2),…,(i s , j s ),…,(i l , j l )};

[0056]

[0057] Where R is the curved path between elements, which is an ordered set of l binary arrays; s is the coordinate of the sth point on the dynamic curved path; l is the number of elements in the path;

[0058] For each curved path, the curved path with the smallest sum of distances between the matching point pairs of all carbon emission time series and the time series of influencing factors on the curved path is the optimal curved path. The specific calculation method of the DTW distance of the optimal curved path is as follows:

[0059]

[0060] The impact of each influencing factor on carbon emissions is evaluated according to the DTW distance between each influencing factor and the optimal curved path of carbon emissions, and the influencing factors with a DTW distance less than the preset threshold are selected as key influencing factors.

[0061] Furthermore, the indicator determination module is further used to: perform standardization processing on the indicator value of the key influencing factor, and standardize the indicator value X of the key influencing factor. ij Transformed into standardized indicators, as follows:

[0062]

[0063] Among them, μ j and i Represent the sample mean and sample standard deviation of the jth key influencing factor index value; A ij is a standardized indicator; X ij is the index value of the key influencing factor, m and n represent the sample variables of m key influencing factors and n key influencing factor index values ​​respectively;

[0064] According to the carbon emissions and the standardized indicators of the selected key influencing factors, the correlation coefficient matrix R of the key influencing factors is calculated, and according to (R-λ j E)U j = 0 to calculate the eigenvalue λ j and the eigenvector U j , E is the unit matrix;

[0065] Select p (p≤m) principal components based on key influencing factors and calculate the eigenvalue λj The cumulative contribution rates are as follows:

[0066]

[0067] Among them, L p is the cumulative contribution rate of the principal components; p is the number of principal components;

[0068] The principal component coefficient matrix of each key influencing factor is established according to the cumulative contribution rate of each principal component. The columns in the principal component coefficient matrix correspond to the standardized indicators in order, as shown below:

[0069]

[0070] The columns of the principal component coefficient matrix of each key influencing factor are multiplied by their corresponding standardized indexes to determine the principal component. The calculation formula of the principal component is as follows:

[0071]

[0072] Among them, H1,H2,…,H P are the first, second, ..., pth principal components of the key influencing factors, i.e., comprehensive indicators; u pt is the t-dimensional eigenvector corresponding to the p-th eigenvalue.

[0073] Furthermore, the carbon peak time prediction module is specifically used for:

[0074] The STIRPAT extended model is used to predict the carbon peak time in a single region, and the expected number of years required for each region to reach carbon peak from the base year is obtained, as follows:

[0075]

[0076] Among them, C represents the expected number of years required for each region to reach carbon peak from the base year; a is the scale coefficient; T k represents k principal component variables, p is the number of principal component variables; β k represents the elasticity coefficient of each principal component; c represents the random interference term;

[0077] Taking the logarithm of the above formula, we get the STIRPAT model:

[0078] lnC=lna+β1lnH1+β2lnH2+…+β p LqCy p +lnc.

[0079] Furthermore, the coordination evaluation module is specifically used for:

[0080] Based on the expected number of years required to reach carbon peak in each region from the base year, the carbon peak parameters of each region are set. The carbon peak parameters include the planned number of years required to reach carbon peak from the base year. The mean square error Z of the deviation between the planned number of years required to reach carbon peak and the expected number of years required to reach carbon peak in all regions is calculated:

[0081]

[0082] in, is the annual plan value required for the carbon peak in the nth region, is the expected year for the nth region to reach its carbon peak;

[0083] The mean square error Z is inversely proportional to the regional carbon peak coordination degree.

[0084] One or more embodiments of the present specification provide an electronic device, including:

[0085] processor; and,

[0086] A memory arranged to store computer-executable instructions, which, when executed, enable the processor to implement the steps of the above-mentioned method for evaluating the coordination of carbon peak paths among multiple regions.

[0087] One or more embodiments of the present specification provide a storage medium for storing computer-executable instructions, which, when executed, implement the steps of the above-mentioned method for evaluating the coordination of carbon peak paths among multiple regions.

[0088] By adopting the embodiments of the present invention, it is possible to fully consider the economic development, resource endowment, energy structure, and energy efficiency level of each region, and propose the expected peak time for each region; it is helpful to formulate regional carbon peak and carbon neutrality implementation plans, economic policies, energy and power planning, and air pollution prevention and control policies, and improve the level of scientific decision-making.

[0089] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0091] Figure 1 A flowchart of a method for evaluating the coordination of carbon peak paths among multiple regions provided for one or more embodiments of this specification;

[0092] Figure 2 A schematic diagram of the composition of a multi-regional carbon peak path coordination evaluation system provided for one or more embodiments of this specification;

[0093] Figure 3 A schematic diagram of the structure of an electronic device provided for one or more embodiments of this specification. DETAILED DESCRIPTION

[0094] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0095] Method Embodiment

[0096] According to an embodiment of the present invention, a method for evaluating the coordination degree of carbon peak paths among multiple regions is provided. Figure 1 A flowchart of a method for evaluating the coordination of carbon peak paths among multiple regions provided for one or more embodiments of this specification, such as Figure 1 As shown, the method for evaluating the coordination degree of carbon peak paths among multiple regions according to an embodiment of the present invention specifically includes:

[0097] S1. The dynamic time warping algorithm DTW is used to analyze the influencing factors of carbon emissions in a single region to screen the key influencing factors of carbon emissions. The principal component analysis (PCA) method is used to reduce the dimensionality of the key influencing factors to obtain a comprehensive indicator.

[0098] The factors affecting carbon emissions in a single region can be divided into economic and social factors, including: population, per capita GDP, urbanization rate, industrial structure, energy intensity, energy structure, power generation structure and carbon emission intensity; among them, the industrial structure includes the proportion of the primary industry, the proportion of the secondary industry and the proportion of the tertiary industry; the energy structure includes the terminal energy structure and the primary energy structure, the terminal energy structure includes the electrification level, the primary energy structure includes the proportion of coal, oil, natural gas and non-fossil energy in the primary energy; the power generation structure includes the proportion of thermal power generation.

[0099] Use the DTW method for screening. The Dynamic Time Warping (DTW) algorithm is an algorithm used to compare the similarity of time data series. It sorts them according to the length of the path. The shorter the path, the closer the data series. The DTW algorithm can effectively handle data series with drastic fluctuations and inconsistent time series. It is not affected by quality problems such as missing data and the presence of outliers. It has strong robustness and adaptability. The specific calculation method is as follows:

[0100] Assume that the time series of carbon emissions in a single region is A = {a1, a2, …, a m}, the time series of a certain influencing factor is B = {b1, b2, …, b m}, m and n are the number of elements in the two time series respectively. According to the time series of carbon emissions in a single region and the time series of influencing factors, the distance matrix D of the path space composed of curved paths is constructed, as follows:

[0101]

[0102] Among them, d(a i ,b i ) is the distance between the elements in the time series of carbon emissions in a single region and the time series of influencing factors. The calculation formula is as follows:

[0103] D(i,j)=d(a i ,b i )=(a i -b i ) 2 ;

[0104] Based on the calculation rules of DTW, the curved path must be continuous and cannot cross the middle point for matching. It can only be aligned with adjacent elements, and the search path direction is specified as top, right, and top-right. That is, with D(i,j) as the starting point, the search end points can only be D(i+1,j), D(i,j+1), and D(i+1,j+1). The curved path is calculated based on the search path direction and the distance matrix as follows:

[0105] R={(i1,j1),(i2,j2),…,(i s , j s ),…,(i l , j l )};

[0106]

[0107] Where R is the curved path between elements, which is an ordered set of l binary arrays; s is the coordinate of the sth point on the dynamic curved path; l is the number of elements in the path;

[0108] For each curved path, the curved path with the smallest sum of distances between the matching point pairs of all carbon emission time series and the time series of influencing factors on the curved path is the optimal curved path. The specific calculation method of the DTW distance of the optimal curved path is as follows:

[0109]

[0110] The impact of each influencing factor on carbon emissions is evaluated according to the DTW distance between each influencing factor and the optimal curved path of carbon emissions, and the influencing factors with a DTW distance less than the preset threshold are selected as key influencing factors.

[0111] Furthermore, principal component analysis (PCA) was used to reduce the dimension of key influencing factors and obtain comprehensive indicators, specifically:

[0112] The index value of the key influencing factor is standardized, and the index value of the key influencing factor X ij Converted to [0,1] standardized index, as follows:

[0113]

[0114] Among them, μ j and j Represent the sample mean and sample standard deviation of the jth key influencing factor index value; A ij is a standardized indicator; X ij is the index value of the key influencing factor, m and n represent the sample variables of m key influencing factors and n key influencing factor index values ​​respectively;

[0115] According to the carbon emissions and the standardized indicators of the selected key influencing factors, the correlation coefficient matrix R of the key influencing factors is calculated, and according to (R-λ j E)U j = 0 to calculate the eigenvalue λ j and the eigenvector U j , E is the unit matrix;

[0116] Select p (p≤m) principal components based on key influencing factors and calculate the eigenvalue λ j The cumulative contribution rates are as follows:

[0117]

[0118] Among them, L p is the cumulative contribution rate of the principal component, ranging from 80% to 85%; p is the number of principal components;

[0119] The principal component coefficient matrix of each key influencing factor is established according to the cumulative contribution rate of each principal component. The columns in the principal component coefficient matrix correspond to the standardized indicators in order, as shown below:

[0120]

[0121] The columns of the principal component coefficient matrix of each key influencing factor are multiplied by their corresponding standardized indexes to determine the principal component. The calculation formula of the principal component is as follows:

[0122]

[0123] Among them, H1,H2,…,H P are the first, second, ..., pth principal components of the key influencing factors, i.e., comprehensive indicators; u pt is the t-dimensional eigenvector corresponding to the p-th eigenvalue.

[0124] S2. Based on the comprehensive indicators, predict the carbon peak time for a single region and determine the expected number of years required for each region to reach carbon peak starting from the base year.

[0125] The STIRPAT extended model is used to predict the carbon peak time in a single region, and the expected number of years required for each region to reach carbon peak from the base year is obtained, as follows:

[0126]

[0127] Among them, C represents the expected number of years required for each region to reach carbon peak from the base year; a is the scale coefficient; T k represents k principal component variables, p is the number of principal component variables; β k represents the elasticity coefficient of each principal component; c represents the random interference term;

[0128] Taking the logarithm of the above formula, we get the STIRPAT model:

[0129] lnC=lna+β1lnH1+β2lnH2+…+β p LqCy p +lnc.

[0130] S3. Set the planned number of years required for each region to reach its carbon peak starting from the base year, and calculate the degree of coordination in peak time among the regions based on the expected number of years required for each region to reach its carbon peak starting from the base year.

[0131] Based on the expected number of years required to reach carbon peak in each region from the base year, the carbon peak parameters of each region are set. The carbon peak parameters include the planned number of years required to reach carbon peak from the base year. The mean square error Z of the deviation between the planned number of years required to reach carbon peak and the expected number of years required to reach carbon peak in all regions is calculated:

[0132]

[0133] in, is the annual plan value required for the carbon peak in the nth region, is the expected year for the nth region to reach its carbon peak;

[0134] The mean square error Z is inversely proportional to the regional carbon peak coordination degree.

[0135] The beneficial effects of the present invention are as follows:

[0136] By adopting the embodiment of the present invention, the dynamic time warping algorithm DTW is used to analyze the various influencing factors of carbon emissions in a single region, which can effectively identify the factors that play a key role in carbon emissions; by using the principal component analysis method PCA to reduce the dimension of key influencing factors, a comprehensive index is obtained, which simplifies the complex data structure of multiple factors and more intuitively reflects the comprehensive influence of various factors; based on the comprehensive index, the future carbon emission trend of a single region can be predicted, and its carbon peak time and the expected number of years required to reach the peak from the base year can be clarified, which is helpful for the region to plan in advance, formulate reasonable carbon emission reduction strategies, and scientifically control the carbon emission process; the number of years required for peaking is set, and the coordination degree of peaking time between regions is calculated in combination with the expected value, which can measure the synergy of carbon peaking progress between regions, and provide a powerful reference for optimizing the coordinated management of carbon emissions between regions and promoting the balanced realization of overall carbon emission reduction goals. In summary, the present invention fully considers the economic development, resource endowment, energy structure, and energy efficiency level of each region, and proposes the expected peak time of each region, which is helpful to formulate regional carbon peak and carbon neutrality implementation plans, economic policies, energy and power planning, and air pollution prevention and control policies, and improve the level of scientific decision-making.

[0137] System Example

[0138] According to an embodiment of the present invention, a system for evaluating the coordination of carbon peak paths among multiple regions is provided. Figure 2 A schematic diagram of the composition of a multi-regional carbon peak path coordination evaluation system provided for one or more embodiments of this specification, such as Figure 2 As shown, the multi-regional carbon peak path coordination evaluation system according to an embodiment of the present invention specifically includes:

[0139] Index determination module 20: used to analyze the various influencing factors of carbon emissions in a single region using the dynamic time warping algorithm DTW to screen the key influencing factors of carbon emissions, and use the principal component analysis (PCA) method to reduce the dimension of the key influencing factors to obtain a comprehensive index.

[0140] The influencing factors of the carbon emissions of a single region include: population, per capita GDP, urbanization rate, industrial structure, energy intensity, energy structure, power generation structure and carbon emission intensity; wherein the industrial structure includes the proportion of the primary industry, the proportion of the secondary industry and the proportion of the tertiary industry; the energy structure includes the terminal energy structure and the primary energy structure, the terminal energy structure includes the electrification level, the primary energy structure includes the proportion of coal, oil, natural gas and non-fossil energy in the primary energy; the power generation structure includes the proportion of thermal power generation;

[0141] The indicator determination module 20 is specifically used for:

[0142] Assume that the time series of carbon emissions in a single region is A = {a1, a2, …, a m}, the time series of a certain influencing factor is B = {b1, b2, …, b m}, m and n are the number of elements in the two time series respectively. According to the time series of carbon emissions in a single region and the time series of influencing factors, the distance matrix D of the path space composed of curved paths is constructed, as follows:

[0143]

[0144] Among them, d(a i ,b i ) is the distance between the elements in the time series of carbon emissions in a single region and the time series of influencing factors;

[0145] D(i,j)=d(a i ,b i )=(a i -b i ) 2 ;

[0146] The curved path is calculated based on the search path direction and the distance matrix as follows:

[0147] R={(i1,j1),(i2,j2),…,(i s , j s ),…,(i l , j l )};

[0148]

[0149] Where R is the curved path between elements, which is an ordered set of l binary arrays; s is the coordinate of the sth point on the dynamic curved path; l is the number of elements in the path;

[0150] For each curved path, the curved path with the smallest sum of distances between the matching point pairs of all carbon emission time series and the time series of influencing factors on the curved path is the optimal curved path. The specific calculation method of the DTW distance of the optimal curved path is as follows:

[0151]

[0152] The impact of each influencing factor on carbon emissions is evaluated according to the DTW distance between each influencing factor and the optimal curved path of carbon emissions, and the influencing factors with a DTW distance less than the preset threshold are selected as key influencing factors.

[0153] Furthermore, the index value of the key influencing factor is standardized, and the index value of the key influencing factor X ij Transformed into standardized indicators, as follows:

[0154]

[0155] Among them, μ j and j Represent the sample mean and sample standard deviation of the jth key influencing factor index value; A ij is a standardized indicator; X ij is the index value of the key influencing factor, m and n represent the sample variables of m key influencing factors and n key influencing factor index values ​​respectively;

[0156] According to the carbon emissions and the standardized indicators of the selected key influencing factors, the correlation coefficient matrix R of the key influencing factors is calculated, and according to (R-λ j E)U j = 0 to calculate the eigenvalue λ j and the eigenvector U j , E is the unit matrix;

[0157] Select p (p≤m) principal components based on key influencing factors and calculate the eigenvalue λ j The cumulative contribution rates are as follows:

[0158]

[0159] Among them, L p is the cumulative contribution rate of the principal component, ranging from 80% to 85%; p is the number of principal components;

[0160] The principal component coefficient matrix of each key influencing factor is established according to the cumulative contribution rate of each principal component. The columns in the principal component coefficient matrix correspond to the standardized indicators in order, as shown below:

[0161]

[0162] The columns of the principal component coefficient matrix of each key influencing factor are multiplied by their corresponding standardized indexes to determine the principal component. The calculation formula of the principal component is as follows:

[0163]

[0164] Among them, H1,H2,…,H P are the first, second, ..., pth principal components of the key influencing factors, i.e., comprehensive indicators; u pt is the t-dimensional eigenvector corresponding to the p-th eigenvalue.

[0165] Carbon peak time prediction module 22: used to predict the carbon peak time of a single region based on the comprehensive indicators, and determine the expected number of years required for each region to reach carbon peak starting from the base year.

[0166] The carbon peak time prediction module 22 is specifically used for:

[0167] The STIRPAT extended model is used to predict the carbon peak time in a single region, and the expected number of years required for each region to reach carbon peak from the base year is obtained, as follows:

[0168]

[0169] Among them, C represents the expected number of years required for each region to reach carbon peak from the base year; a is the scale coefficient; T k represents k principal component variables, p is the number of principal component variables; β k represents the elasticity coefficient of each principal component; c represents the random interference term;

[0170] Taking the logarithm of the above formula, we get the STIRPAT model:

[0171] lnC=lna+β1lnH1+β2lnH2+…+β p LqCy p +lnc.

[0172] Coordination evaluation module 24: used to set the planned value of the number of years required for each region to reach its carbon peak from the base year, and calculate the coordination degree of peak time between regions based on the expected value of the number of years required for each region to reach its carbon peak from the base year.

[0173] The coordination evaluation module is specifically used for:

[0174] Based on the expected number of years required to reach carbon peak in each region from the base year, the carbon peak parameters of each region are set. The carbon peak parameters include the planned number of years required to reach carbon peak from the base year. The mean square error Z of the deviation between the planned number of years required to reach carbon peak and the expected number of years required to reach carbon peak in all regions is calculated:

[0175]

[0176] in, is the annual plan value required for the carbon peak in the nth region, is the expected year for the nth region to reach its carbon peak;

[0177] The mean square error Z is inversely proportional to the regional carbon peak coordination degree.

[0178] The embodiment of the present invention is a system embodiment corresponding to the above-mentioned method embodiment. The specific operations of each module can be understood by referring to the description of the method embodiment, which will not be repeated here.

[0179] Device Example 1

[0180] An embodiment of the present invention provides an electronic device, such as Figure 3 As shown, it includes: a memory 30, a processor 32, and a computer program stored in the memory 30 and executable on the processor 32. When the computer program is executed by the processor 32, the following method steps are implemented:

[0181] S1. The dynamic time warping algorithm DTW is used to analyze the various influencing factors of carbon emissions in a single region to screen the key influencing factors of carbon emissions, and the principal component analysis (PCA) method is used to reduce the dimension of the key influencing factors to obtain a comprehensive index;

[0182] S2. Based on the comprehensive indicators, predict the carbon peak time of a single region and determine the expected number of years required for each region to reach carbon peak starting from the base year;

[0183] S3. Set the planned number of years required for each region to reach its carbon peak starting from the base year, and calculate the degree of coordination in peak time among the regions based on the expected number of years required for each region to reach its carbon peak starting from the base year.

[0184] Device Example 2

[0185] An embodiment of the present invention provides a computer-readable storage medium, on which a program for implementing information transmission is stored. When the program is executed by the processor 52, the following method steps are implemented:

[0186] S1. The dynamic time warping algorithm DTW is used to analyze the various influencing factors of carbon emissions in a single region to screen the key influencing factors of carbon emissions, and the principal component analysis (PCA) method is used to reduce the dimension of the key influencing factors to obtain a comprehensive index;

[0187] S2. Based on the comprehensive indicators, predict the carbon peak time of a single region and determine the expected number of years required for each region to reach carbon peak starting from the base year;

[0188] S3. Set the planned number of years required for each region to reach its carbon peak starting from the base year, and calculate the degree of coordination in peak time among the regions based on the expected number of years required for each region to reach its carbon peak starting from the base year.

[0189] The computer-readable storage medium in this embodiment includes, but is not limited to, ROM, RAM, magnetic disk or optical disk, etc.

[0190] 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 it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the coordination of carbon peak paths among multiple regions, characterized in that: include: The dynamic time warping algorithm DTW is used to analyze the various influencing factors of carbon emissions in a single region to screen the key influencing factors of carbon emissions. The principal component analysis (PCA) method is used to reduce the dimension of the key influencing factors to obtain a comprehensive index. Based on the comprehensive indicators, the carbon peak time of a single region is predicted to determine the expected number of years required for each region to reach carbon peak starting from the base year; Set the planned number of years required for each region to reach its carbon peak starting from the base year, and calculate the degree of coordination in peak time among the regions based on the expected number of years required for each region to reach its carbon peak starting from the base year.

2. The method according to claim 1, characterized in that: The influencing factors of carbon emissions in a single region include: population, per capita GDP, urbanization rate, industrial structure, energy intensity, energy structure, power generation structure and carbon emission intensity; among them, the industrial structure includes the proportion of the primary industry, the proportion of the secondary industry and the proportion of the tertiary industry; the energy structure includes the terminal energy structure and the primary energy structure, the terminal energy structure includes the electrification level, the primary energy structure includes the proportion of coal, oil, natural gas and non-fossil energy in the primary energy; the power generation structure includes the proportion of thermal power generation.

3. The method according to claim 1, characterized in that The dynamic time warping algorithm DTW is used to analyze the various influencing factors of carbon emissions in a single region to screen the key influencing factors of carbon emissions. The specific method is: Assume that the time series of carbon emissions in a single region is A = {a1, a2, …, a m }, the time series of a certain influencing factor is B = {b1, b2, …, b m }, m and n are the number of elements in the two time series respectively. According to the time series of carbon emissions in a single region and the time series of influencing factors, the distance matrix D of the path space composed of curved paths is constructed, as follows: Among them, d(a i ,b i ) is the distance between the elements in the time series of carbon emissions in a single region and the time series of influencing factors; D(i,j)=d(a i ,b i )=(a i -b i ) 2 ; The curved path is calculated based on the search path direction and the distance matrix as follows: R={(i1,j1),(i2,j2),…,(i s ,j s ),…,(i l ,j l )}; Where R is the curved path between elements, which is an ordered set of l binary arrays; s is the coordinate of the sth point on the dynamic curved path; l is the number of elements in the path; For each curved path, the curved path with the smallest sum of distances between the matching point pairs of all carbon emission time series and the time series of influencing factors on the curved path is the optimal curved path. The specific calculation method of the DTW distance of the optimal curved path is as follows: The impact of each influencing factor on carbon emissions is evaluated according to the DTW distance between each influencing factor and the optimal curved path of carbon emissions, and the influencing factors with a DTW distance less than the preset threshold are selected as key influencing factors.

4. The method according to claim 3, characterized in that: The principal component analysis (PCA) method is used to reduce the dimension of key influencing factors and obtain comprehensive indicators. The specific method is as follows: The index value of the key influencing factor is standardized, and the index value of the key influencing factor X ij Transformed into standardized indicators, as follows: Among them, μ j and j Represent the sample mean and sample standard deviation of the jth key influencing factor index value; A ij is a standardized indicator; X ij is the index value of the key influencing factor, m and n represent the sample variables of m key influencing factors and n key influencing factor index values ​​respectively; According to the carbon emissions and the standardized indicators of the selected key influencing factors, the correlation coefficient matrix R of the key influencing factors is calculated, and according to (R-λ j E)U j = 0 to calculate the eigenvalue λ j and the eigenvector U j , E is the unit matrix; Select p (p≤m) principal components based on key influencing factors and calculate the eigenvalue λ j The cumulative contribution rates are as follows: Among them, L p is the cumulative contribution rate of the principal components; p is the number of principal components; The principal component coefficient matrix of each key influencing factor is established according to the cumulative contribution rate of each principal component. The columns in the principal component coefficient matrix correspond to the standardized indicators in order, as shown below: The columns of the principal component coefficient matrix of each key influencing factor are multiplied by their corresponding standardized indexes to determine the principal component. The calculation formula of the principal component is as follows: Among them, H1,H2,…,H P are the first, second, ..., pth principal components of the key influencing factors, i.e., comprehensive indicators; u pt is the t-dimensional eigenvector corresponding to the p-th eigenvalue.

5. The method according to claim 4, characterized in that Based on the comprehensive indicators, the carbon peak time of a single region is predicted, and the expected number of years required for each region to reach carbon peak from the base year is determined as follows: The STIRPAT extended model is used to predict the carbon peak time in a single region, and the expected number of years required for each region to reach carbon peak from the base year is obtained, as follows: Among them, C represents the expected number of years required for each region to reach carbon peak from the base year; a is the scale coefficient; T k represents k principal component variables, p is the number of principal component variables; β k represents the elasticity coefficient of each principal component; c represents the random interference term; Taking the logarithm of the above formula, we get the STIRPAT model: lnC=lna+β1lnH1+β2lnH2+…+β p lnH p +lnc.

6. The method according to claim 1, characterized in that The specific method for evaluating the coordination degree of carbon peak time between regions under different plans is as follows: Based on the expected number of years required to reach carbon peak in each region from the base year, the carbon peak parameters of each region are set. The carbon peak parameters include the planned number of years required to reach carbon peak from the base year. The mean square error Z of the deviation between the planned number of years required to reach carbon peak and the expected number of years required to reach carbon peak in all regions is calculated: in, is the annual plan value required for the carbon peak in the nth region, is the expected year for the nth region to reach its carbon peak; The mean square error Z is inversely proportional to the regional carbon peak coordination degree.

7. A multi-regional carbon peak path coordination evaluation system, characterized in that: include: Index determination module: used to analyze the influencing factors of carbon emissions in a single region using the dynamic time warping algorithm DTW to screen the key influencing factors of carbon emissions, and use the principal component analysis (PCA) method to reduce the dimension of the key influencing factors to obtain comprehensive indicators; Carbon peak time prediction module: used to predict the carbon peak time of a single region based on the comprehensive indicators, and determine the expected number of years required for each region to reach the carbon peak from the base year; Coordination evaluation module: used to set the planned number of years required for each region to reach carbon peak from the base year, and calculate the coordination degree of peak time between regions based on the expected number of years required for each region to reach carbon peak from the base year.

8. The system according to claim 7, characterized in that The influencing factors of carbon emissions in a single region include: population, per capita GDP, urbanization rate, industrial structure, energy intensity, energy structure, power generation structure and carbon emission intensity; among them, the industrial structure includes the proportion of the primary industry, the proportion of the secondary industry and the proportion of the tertiary industry; the energy structure includes the terminal energy structure and the primary energy structure, the terminal energy structure includes the electrification level, the primary energy structure includes the proportion of coal, oil, natural gas and non-fossil energy in the primary energy; the power generation structure includes the proportion of thermal power generation.

9. The system according to claim 7, characterized in that The indicator determination module is specifically used for: Assume that the time series of carbon emissions in a single region is A = {a1, a2, …, a m }, the time series of a certain influencing factor is B = {b1, b2, …, b m }, m and n are the number of elements in the two time series respectively. According to the time series of carbon emissions in a single region and the time series of influencing factors, the distance matrix D of the path space composed of curved paths is constructed, as follows: Among them, d(a i ,b i ) is the distance between the elements in the time series of carbon emissions in a single region and the time series of influencing factors; D(i,j)=d(a i ,b i )=(a i -b i ) 2 ; The curved path is calculated based on the search path direction and the distance matrix as follows: R={(i1,j1),(i2,j2),…,(i s ,j s ),…,(i l ,j l )}; Where R is the curved path between elements, which is an ordered set of l binary arrays; s is the coordinate of the sth point on the dynamic curved path; l is the number of elements in the path; For each curved path, the curved path with the smallest sum of distances between the matching point pairs of all carbon emission time series and the time series of influencing factors on the curved path is the optimal curved path. The specific calculation method of the DTW distance of the optimal curved path is as follows: The impact of each influencing factor on carbon emissions is evaluated according to the DTW distance between each influencing factor and the optimal curved path of carbon emissions, and the influencing factors with a DTW distance less than the preset threshold are selected as key influencing factors.

10. The system according to claim 9, characterized in that The indicator determination module is further used to: perform standardization on the indicator value of the key influencing factor, and convert the indicator value X of the key influencing factor into ij Transformed into standardized indicators, as follows: Among them, μ j and j Represent the sample mean and sample standard deviation of the jth key influencing factor index value; A ij is a standardized indicator; X ij is the index value of the key influencing factor, m and n represent the sample variables of m key influencing factors and n key influencing factor index values ​​respectively; According to the carbon emissions and the standardized indicators of the selected key influencing factors, the correlation coefficient matrix R of the key influencing factors is calculated, and according to (R-λ j E)U j = 0 to calculate the eigenvalue λ j and the eigenvector U j , E is the unit matrix; Select p (p≤m) principal components based on key influencing factors and calculate the eigenvalue λ j The cumulative contribution rates are as follows: Among them, L p is the cumulative contribution rate of the principal components; p is the number of principal components; The principal component coefficient matrix of each key influencing factor is established according to the cumulative contribution rate of each principal component. The columns in the principal component coefficient matrix correspond to the standardized indicators in order, as shown below: The columns of the principal component coefficient matrix of each key influencing factor are multiplied by their corresponding standardized indexes to determine the principal component. The calculation formula of the principal component is as follows: Among them, H1,H2,…,H P are the first, second, ..., pth principal components of the key influencing factors, i.e., comprehensive indicators; u pt is the t-dimensional eigenvector corresponding to the p-th eigenvalue.

11. The system according to claim 7, characterized in that The carbon peak time prediction module is specifically used for: The STIRPAT extended model is used to predict the carbon peak time in a single region, and the expected number of years required for each region to reach carbon peak from the base year is obtained, as follows: Among them, C represents the expected number of years required for each region to reach carbon peak from the base year; a is the scale coefficient; T k represents k principal component variables, p is the number of principal component variables; β k represents the elasticity coefficient of each principal component; c represents the random interference term; Taking the logarithm of the above formula, we get the STIRPAT model: lnC=lna+β1lnH1+β2lnH2+…+β p lnH p +lnc.

12. The system according to claim 7, characterized in that The coordination evaluation module is specifically used for: Based on the expected number of years required to reach carbon peak in each region from the base year, the carbon peak parameters of each region are set. The carbon peak parameters include the planned number of years required to reach carbon peak from the base year. The mean square error Z of the deviation between the planned number of years required to reach carbon peak and the expected number of years required to reach carbon peak in all regions is calculated: in, is the annual plan value required for the carbon peak in the nth region, is the expected year for the nth region to reach its carbon peak; The mean square error Z is inversely proportional to the regional carbon peak coordination degree.

13. An electronic device, characterized in that: include: processor; as well as, A memory arranged to store computer-executable instructions, which, when executed, enable the processor to implement the steps of the method for evaluating the coordination of carbon peak paths among multiple regions as described in any one of claims 1 to 6.

14. A storage medium, characterized in that: Used to store computer-executable instructions, which, when executed, implement the steps of the multi-regional carbon peak path coordination evaluation method as described in any one of claims 1 to 6.

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