Quantitative analysis method for influence factors of carbon emission and related equipment

Abstract

The invention provides a carbon emission influence factor quantitative analysis method and related equipment. The method comprises the following steps: adding related indexes of a green electricity market, a green evidence market and a carbon market on the basis of traditional carbon emission influence factor indexes, obtaining a sequence matrix according to the indexes, selecting a maximum value and a minimum value of two-stage range from the sequence matrix, determining a target resolution coefficient according to the sequence matrix and the maximum value, and determining the target resolution coefficient according to the target resolution coefficient. And obtaining a target correlation coefficient of the carbon emission and the target carbon emission influence factor index according to the target resolution coefficient, the maximum value and the minimum value, and calculating a target grey correlation degree according to the target correlation coefficient to obtain a quantitative analysis result of the influence factors of the carbon emission. According to the method provided by the invention, quantitative analysis is carried out by combining related indexes under the background of'electricity-certificate-carbon 'and adopting the improved grey correlation degree, so that the carbon emission trend can be mastered more accurately, the theoretical support is improved, and substantial support is provided for formulating the optimal carbon peak reaching path.

Description

Technical Field

[0001] This application relates to the technical field of carbon emission analysis, and particularly to a method for quantitatively analyzing influencing factors of carbon emissions and related equipment. Background Art

[0002] Green certificates are the only proof of the environmental attributes of renewable energy electricity. The full coverage of green certificates for renewable energy electricity further plays the role of green certificates in constructing a green and low-carbon environmental value system for renewable energy electricity, promoting the development and utilization of renewable energy, guiding the whole society's green consumption, etc., and provides strong support for ensuring the safe and reliable supply of energy, achieving the goals of carbon peak and carbon neutrality, and promoting the green and low-carbon transformation and high-quality development of the economic society.

[0003] In the context of the increasingly serious global climate change and environmental problems, achieving carbon peak and carbon neutrality has become an important topic. However, with the continuous change of the policy environment, the uncertainty of the peak path has gradually increased, and the coordinated linkage of the "electricity-certificate-carbon" market will become a trend. In this context, the carbon peak prediction model and related peak achievement paths that do not consider the impact of new policies are no longer clear, and it is urgent to establish a carbon peak prediction model and peak achievement path under the influence of new policies. Summary of the Invention

[0004] In view of this, the purpose of this application is to propose a method for quantitatively analyzing influencing factors of carbon emissions and related equipment to solve or partially solve the above problems.

[0005] Based on the above purpose, in the first aspect of this application, a method for quantitatively analyzing influencing factors of carbon emissions is provided, including:

[0006] Obtain target carbon emission influencing factor indicators, where the target carbon emission influencing factor indicators include first key indicators related to the green electricity market, green certificate market, and carbon market, and second key indicators related to regional carbon emissions;

[0007] Obtain a sequence matrix according to the target carbon emission influencing factor indicators;

[0008] Select the maximum and minimum values of the two-level range from the sequence matrix;

[0009] Determine a target resolution coefficient according to the sequence matrix and the maximum value;

[0010] Obtain a target correlation coefficient between the carbon emissions and the target carbon emission influencing factor indicators according to the target resolution coefficient, the maximum value, and the minimum value;

[0011] Calculate a target grey correlation degree according to the target correlation coefficient to obtain the quantitative analysis result of the influencing factors of carbon emissions.

[0012] In the second aspect of the present application, a device for quantitatively analyzing influencing factors of carbon emissions is provided, including:

[0013] An acquisition module, configured to acquire target carbon emission influencing factor indicators, where the target carbon emission influencing factor indicators include first key indicators related to the green power market, the green certificate market, and the carbon market, and second key indicators related to regional carbon emissions;

[0014] A first calculation module, configured to obtain a sequence matrix according to the target carbon emission influencing factor indicators;

[0015] A selection module, configured to select the maximum and minimum values of the two-level range from the sequence matrix;

[0016] A determination module, configured to determine a target resolution coefficient according to the sequence matrix and the maximum value;

[0017] A second calculation module, configured to obtain a target correlation coefficient between the carbon emissions and the target carbon emission influencing factor indicators according to the target resolution coefficient, the maximum value, and the minimum value;

[0018] A third calculation module, configured to calculate a target grey correlation degree according to the target correlation coefficient to obtain a quantitative analysis result of the influencing factors of the carbon emissions.

[0019] In the third aspect of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where when the processor executes the program, the method described in the first aspect is implemented.

[0020] In the fourth aspect of the present application, a non-transitory computer-readable storage medium is provided, where the non-transitory computer-readable storage medium stores computer instructions for causing a computer to execute the method described in the first aspect.

[0021] As can be seen from the above, the present application provides a method and related devices for quantitatively analyzing the influencing factors of carbon emissions. The method includes: adding indicators related to the green power market, green certificate market, and carbon market on the basis of traditional carbon emission influencing factor indicators, obtaining a sequence matrix according to these indicators, selecting the maximum and minimum values of the two-level range from the sequence matrix, determining the target resolution coefficient according to the sequence matrix and the maximum value, obtaining the target correlation coefficient between carbon emissions and the target carbon emission influencing factor indicators according to the target resolution coefficient, the maximum value, and the minimum value, and calculating the target grey correlation degree according to the target correlation coefficient to obtain the quantitative analysis result of the influencing factors of carbon emissions. The method provided by the present application combines the relevant indicators in the context of "electricity-certificate-carbon", and uses the improved grey correlation degree for quantitative analysis, which can more accurately grasp the trend of carbon emissions, improve the theoretical support, and provide substantial support for formulating the optimal carbon peak path. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the present application or related technologies, the following will briefly introduce the drawings required for use in the embodiments or related technology descriptions. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0023] Figure 1 FIG. shows a schematic flowchart of an exemplary method for quantitatively analyzing the influencing factors of carbon emissions according to an embodiment of the present application.

[0024] Figure 2 FIG. shows a schematic diagram of an exemplary device for quantitatively analyzing the influencing factors of carbon emissions according to an embodiment of the present application.

[0025] Figure 3 FIG. shows a schematic diagram of an exemplary electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] In order to make the objectives, technical solutions, and advantages of the present application clearer, the following further describes the present application in detail with reference to specific embodiments and the accompanying drawings.

[0027] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of this application should have the ordinary meanings understood by those of ordinary skill in the field to which this application belongs. The "first", "second" and similar terms used in the embodiments of this application do not indicate any order, quantity or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connected" or "linked" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to represent relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0028] The green certificate is the only proof of the environmental attributes of renewable energy electricity. The full coverage of green certificates for renewable energy electricity further plays the role of green certificates in constructing a green and low-carbon environmental value system for renewable energy electricity, promoting the development and utilization of renewable energy, guiding the whole society to consume green, etc., and provides strong support for ensuring the safe and reliable supply of energy, achieving the goals of carbon peak and carbon neutrality, and promoting the green and low-carbon transformation and high-quality development of the economic society.

[0029] In the context of the increasingly serious global climate change and environmental problems, achieving carbon peak and carbon neutrality has become an important topic. However, with the continuous change of the policy environment, the uncertainty of the peak path has gradually increased, and the coordinated linkage of the "electricity-certificate-carbon" market will become a trend. In this context, the carbon peak prediction model and the related peak achievement path that do not consider the impact of the new policy are no longer clear, and it is urgent to establish a carbon peak prediction model and a peak achievement path under the influence of the new policy.

[0030] In this context, a quantitative analysis of the influencing factors of carbon emissions in the context of "electricity-certificate-carbon" not only helps to deeply understand the driving mechanism of carbon emissions, helps to establish a carbon peak prediction model, but also provides a scientific basis for formulating effective emission reduction policies.

[0031] Currently, the model methods used for carbon peak prediction at home and abroad are different, and the analysis of the influencing factors of carbon emission peak prediction is not complete. There are few studies on carbon peak prediction in the context of the coordinated linkage of the "electricity-certificate-carbon" market.

[0032] At present, the LMDI (Logarithmic Mean Divisia Index) decomposition method is mostly used for the quantitative analysis of carbon emission influencing factors, but there are many deficiencies. First of all, the accuracy of the LMDI model highly depends on the quality and integrity of the input data. If the data is inaccurate or incomplete, the analysis results may be biased, thus affecting the accuracy of decision-making. Secondly, although the LMDI model can clearly identify and quantify the contribution degree of each factor, the mechanism behind it still needs further analysis and explanation. This means that even if the LMDI model can point out that a certain factor has a significant impact on the target variable, other methods are still needed to deeply understand how this factor works.

[0033] Grey relational analysis is a multi-factor statistical analysis method. Its basic idea is to judge whether the connection between different sequences is close according to the similarity degree of the geometric shapes of the sequence curves, find the relevance in the characteristic data sequence reflecting the system behavior and the effective factor data sequence affecting the system behavior, calculate its correlation degree through a certain amount of data processing based on the existing partial information, determine the main factors affecting the system behavior and the differences in the influence of each factor on the system behavior, and quantitatively describe the influence degrees of various influencing factors to obtain the specific influence value of each factor. This method can effectively make up for the deficiencies of system analysis methods such as regression analysis, variance analysis, and principal component analysis in mathematical statistics, such as requiring a large amount of data, requiring samples to follow a certain typical probability distribution, requiring the factors to be independent of each other, and having a large amount of calculation.

[0034] In the traditional grey relational analysis model, the grey relational degree is obtained by taking the mean of the correlation coefficients of each historical period. In fact, the influence degrees of historical data on the current situation are inconsistent. Usually, under the same conditions, the closer the historical time of the data is, the higher the influence degree on the current situation. For example, if the economy has entered a new normal in recent years, the data in recent years is significantly more important than the previous data.

[0035] In addition, the correlation degree calculation formula in the traditional grey relational analysis model uses equal weight processing for each influencing factor, which has two problems: one is that the average value is easy to cover up the individuality of each influencing factor, and the experience or opinions of experts are not taken into account, making it difficult to cope with the changes in future situations; the other is that when the correlation coefficients are relatively discrete, the overall correlation degree will be determined by the points with large correlation coefficients, resulting in a local correlation tendency and causing deviation in the analysis results.

[0036] To at least solve the above problems, the present application provides a method and related equipment for quantitatively analyzing the influencing factors of carbon emissions. The method includes: adding indicators related to the green power market, green certificate market, and carbon market on the basis of traditional carbon emission influencing factor indicators, obtaining a sequence matrix according to these indicators, selecting the maximum and minimum values of the two-level range from the sequence matrix, determining the target resolution coefficient according to the sequence matrix and the maximum value, obtaining the target correlation coefficient between carbon emissions and the target carbon emission influencing factor indicators according to the target resolution coefficient, the maximum value, and the minimum value, and calculating the target grey correlation degree according to the target correlation coefficient to obtain the quantitative analysis result of the influencing factors of carbon emissions. The method provided by the present application combines the relevant indicators in the context of "electricity-certificate-carbon", and uses the improved grey correlation degree for quantitative analysis, which can more accurately grasp the carbon emission trend, improve the theoretical support, and provide substantial support for formulating the optimal carbon peak path.

[0037] In the embodiments of the present application, starting from the green power, green certificate, and carbon markets, relevant data and data sources are collected and integrated. Considering the authority of data sources, data availability, and data time period, in terms of the green power market, key indicators such as the proportion of renewable energy power generation and green power trading volume are extracted; in terms of the green certificate market, key indicators such as green certificate price and green certificate quantity are extracted; in terms of the carbon market, key indicators such as the benchmark price of carbon quotas and the auction ratio of carbon quotas are extracted, a total of 17 first key indicators of "electricity-certificate-carbon" are extracted (specifically see the first key indicators of "electricity-certificate-carbon" shown in Table 1), and 15 second key indicators related to regional carbon emissions in five dimensions including industrial structure, energy consumption structure, and carbon dioxide emissions are extracted (specifically see the second key indicators related to regional carbon emissions shown in Table 2). Among them, the second key indicators are conventional carbon emission influencing factor indicators, while the first key indicators are influencing factors closely related to "electricity-certificate-carbon". Taking the first key indicators and the second key indicators as the target carbon emission influencing factor indicators, the indicator data not only covers multiple aspects such as economy, energy, and environment, but also has the characteristics of time series, providing a solid data foundation for subsequent quantitative analysis. Based on the key indicators extracted from the "electricity-certificate-carbon" three markets, combined with various indicator constraint conditions and thresholds, indicator quantification research is carried out to construct an "electricity-certificate-carbon" indicator system.

[0038] Table 1: The first key indicators of "electricity-certificate-carbon".

[0039]

[0040] Table 2: The second key indicators related to regional carbon emissions.

[0041]

[0042] Among them, GDP (Gross Domestic Product) in Table 2 represents the gross domestic product.

[0043] In the embodiments of the present application, an improved grey relational analysis method is adopted to determine the influence degree of each influencing factor on carbon emissions. The basic principle of grey relational analysis is as follows:

[0044] After selecting the carbon emission influencing factor indicators in the context of "electricity-certificate-carbon", in some embodiments, an analysis matrix can be formed:

[0045]

[0046] Among them, t represents the year of the selected sample data, t = 1, 2,..., T, and the national data from 2005 to 2020 is adopted here; y represents the carbon emission sequence from 2005 to 2020, and y(t) represents the national carbon emissions in the t-th year; x represents the influencing factor index sequence, and the indicators here refer to the set of 17 "electricity-certificate-carbon" key indicators selected in Table 1 and 15 national relevant indicators selected in Table 2; x i (t) represents the observed data of the i-th influencing factor indicator in the t-th year; m is the total number of influencing factor indicators, and m = 32 in the embodiments of the present application.

[0047] After forming the analysis matrix, in some embodiments, the sequences in the analysis matrix can be dimensionless processed through the following formula to form an initial value image matrix:

[0048]

[0049] Among them, i = 1, 2,..., m; t = 1, 2,..., T; x′ i (t) and y′(t) represent the dimensionless variables.

[0050] In some embodiments, the initial value image matrix is expressed as:

[0051]

[0052] After obtaining the initial value image matrix, in some embodiments, the elements in the initial value image matrix can be subjected to difference operation according to the following formula to calculate the difference between the carbon emissions and the influencing factor indicator i, so as to obtain the sequence matrix Δ(t).

[0053] Δ i (t) = ∣y′(t) - x i ′(t)∣

[0054] Among them, y′(t) represents the carbon emissions; Δ i (t) represents the difference between the carbon emissions and the influencing factor indicator.

[0055] In some embodiments, the sequence matrix is represented as:

[0056]

[0057] In some embodiments, the maximum value Δ of the two-level range can be selected from the sequence matrix max and the minimum value Δ min , where the maximum and minimum values of the two-level range are used to adjust the correlation coefficient:

[0058]

[0059] where max represents the maximum and min represents the minimum.

[0060] After obtaining the maximum and minimum values of the two-level range, in some embodiments, the correlation coefficient λ i (t) between the carbon emission and the value of the influencing factor index i at time t can be calculated by the following formula, and their correlation coefficient matrix λ(t) is formed.

[0061]

[0062] where ρ represents the resolution coefficient, ρ ∈ (0, 1), usually taking 0.5, and the smaller its value, the more significant the difference between the obtained correlation coefficients.

[0063] In some embodiments, the correlation coefficient matrix is represented as:

[0064]

[0065] In some embodiments, by taking the mean of each column in the correlation coefficient matrix λ(t), the grey correlation degree r i :

[0066]

[0067] The embodiments of the present application improve the traditional grey correlation analysis model from two aspects of sample weight (weight value) and resolution coefficient to make up for the deficiencies of the above traditional grey correlation analysis model.

[0068] In order to distinguish the importance of different time samples, in some embodiments, the weight ω(t) of the correlation coefficient λ i (t) at different times can be calculated, and the calculation method is as follows:

[0069] According to the principle of "near is large and far is small" for historical time, in some embodiments, a fuzzy complementary preference relation matrix of the first historical time t 1 and the second historical time t 2 can be formed Among them, represents the comparison relationship of the importance of the data at the first historical time t 1 with respect to a certain influencing factor index, and the data at the second historical time t 2 ; t 1 , t 2 = 1, 2, …, T.

[0070] When t 1 > t 2 , it means that the data at the first historical time t 1 is more important than the data at the second historical time t 2 . In some embodiments, it can be set that On the contrary, when t 1 < t 2 , in some embodiments, it can be set that When t 1 = t 2 , it means that the data at the first historical time t 1 is equally important as the data at the second historical time t 2 . In some embodiments, it can be set that

[0071] Improve the fuzzy complementary preference relationship matrix to a fuzzy consistent matrix

[0072]

[0073] Among them, represents the complementary preference relationship between time t 1 and time k, where k = 1, 2, …, T. The advantages of doing this are, firstly, the step of consistency test can be omitted, thus simplifying the whole operation process; secondly, the number of iterations of test and correction can be reduced, improving the calculation accuracy and convergence speed.

[0074] After obtaining the fuzzy consistent matrix, in some embodiments, the weight ω(t) can be solved:

[0075]

[0076] Among them, a is a parameter that satisfies , and generally takes s tk represents the fuzzy consistent relationship between time t and time k.

[0077] In this way, by using the improved grey relational analysis method to solve the influence weights of various influencing factors on carbon peak, the correlation between each index and carbon emissions can be quantified. The historical data closer to the current will be assigned a greater weight value, thus highlighting its importance more. Based on the calculated weights, the grey relational degree r i between the above-mentioned influencing factor index i and carbon emissions can be updated to the grey relational degree r i ′ considering weights, and the calculation formula is as follows:

[0078]

[0079] Furthermore, based on the calculation formula of the grey relational degree r i ′, the target grey relational degree r i ″ can be calculated.

[0080] When calculating the grey relational coefficient, the distinguishing coefficient in the formula is directly related to the relational coefficient, and its value determines the distribution of the grey relational coefficient. However, currently, there is no exact standard for the distinguishing coefficient ρ used in the calculation of the grey relational coefficient (i.e., the traditional grey relational analysis model), and usually 0.5 is taken. Different distinguishing coefficients ρ will produce different correlation coefficients, which leads to different relational degree rankings. The size of the distinguishing coefficient ρ in the grey relational coefficient determines the contribution of Δ max to the relational coefficient, and the relational coefficient also affects the size of the relational degree. Therefore, the distinguishing coefficient will affect the size of the grey relational degree interval, thus affecting the correlation analysis.

[0081] In view of this, in some embodiments, a new method for quantitatively determining the value of the distinguishing coefficient can be adopted. Let:

[0082]

[0083] where Δ v (k) represents the average value of the difference between each influencing factor and carbon emissions at time k; ε(k) represents the reference coefficient for determining the distinguishing coefficient ρ.

[0084] If 3ε(k) < 1, the distinguishing coefficient ρ can take values in [ε(k), 1.5ε(k)]; if the distinguishing coefficient ρ can take values in (1.5ε(k), 2ε(k)]; if the distinguishing coefficient ρ can take any value in [0.8, 1]; if ε(k) = 0, the distinguishing coefficient ρ can be arbitrarily selected within the range (0, 1]; combining the above theory, the following treatment can be done for the improvement of the distinguishing coefficient ρ. Take the value of the distinguishing coefficient ρ at the maximum value of the interval: when 3ε(k) < 1, in some embodiments, the target distinguishing coefficient ρ′ = 1.5ε(k); when When, in some embodiments, the target resolution coefficient ρ′ = 2ε(k); when When, in some embodiments, the target resolution coefficient ρ′ = 1.

[0085] In summary, in some embodiments, the target grey correlation degree r i ″ between the influencing factor index i and the carbon emissions is calculated by the following formula:

[0086]

[0087] where λ′ i (t) represents the target correlation coefficient.

[0088] The embodiments of the present application study the influencing factors of carbon emissions in the context of "electricity-certificate-carbon", construct an influencing factor index set, and use the improved grey correlation degree for quantitative analysis. Based on the background of the "electricity-certificate-carbon" market coordination, the impact of "electricity-certificate-carbon" on carbon peak is quantified through system modeling and data analysis. It can provide theoretical support for constructing carbon peak prediction models for different regions and key industries, and more accurately grasp the trend of carbon emissions, and provide substantial support for formulating the optimal carbon peak path. The basis for realizing the optimal peak path decision lies in ensuring the credibility and reliability of the data source, and adopting scientific and effective analysis methods to ensure the scientificity and effectiveness of the decision.

[0089] Figure 1 FIG. shows a schematic flow chart of an exemplary method 100 for quantitative analysis of influencing factors of carbon emissions according to an embodiment of the present application. The method 100 may include the following steps.

[0090] In step 102, obtain the target carbon emission influencing factor index, where the target carbon emission influencing factor index includes a first key index related to the green electricity market, the green certificate market, and the carbon market, and a second key index related to regional carbon emissions.

[0091] In some embodiments, the first key index includes the installed capacity of renewable energy, the power generation of renewable energy, the proportion of renewable energy power generation, the national green electricity trading volume, and the green electricity trading situation in different regions related to the green electricity market; and / or the first key index further includes the national green certificate issuance volume, the national green certificate trading volume, the green certificate price in different regions, and the green electricity trading rate in different regions related to the green certificate market; and / or the first key index further includes the carbon quota benchmark price, the carbon quota auction ratio, the national carbon quota trading volume, the carbon emissions, the carbon emission intensity, the installed capacity of conventional energy, the power generation of conventional energy, and the proportion of conventional energy power generation related to the carbon market.

[0092] In some embodiments, the second key indicators include industrial structure, energy consumption structure, economic openness, green innovation level, energy consumption, per capita GDP, population size, carbon dioxide emissions, R & D investment intensity, electricity consumption, energy intensity, urbanization rate, carbon dioxide emission intensity, investment intensity, and GDP.

[0093] In step 104, a sequence matrix is obtained according to the target carbon emission influencing factor indicators.

[0094] In some embodiments, obtaining the sequence matrix according to the target carbon emission influencing factor indicators further includes: obtaining an analysis matrix according to the target carbon emission influencing factor indicators; performing dimensionless processing on the analysis matrix to obtain an initial value image matrix; performing difference operations on the elements in the initial value image matrix to obtain the sequence matrix.

[0095] In step 106, the maximum and minimum values of the two - level range are selected from the sequence matrix.

[0096] In step 108, a target resolution coefficient is determined according to the sequence matrix and the maximum value.

[0097] In some embodiments, determining the target resolution coefficient according to the sequence matrix and the maximum value further includes: calculating the average change amount of the target carbon emission influencing factor indicators according to the sequence matrix; calculating the normalized change amount according to the average change amount and the maximum value; determining the target resolution coefficient according to the normalized change amount.

[0098] In some embodiments, determining the target resolution coefficient according to the normalized change amount further includes: in response to three times the normalized change amount being less than 1, determining the target resolution coefficient to be 1.5 times the normalized change amount; in response to the normalized change amount being greater than or equal to and less than or equal to determining the resolution to be 2 times the normalized change amount; in response to the normalized change amount being greater than determining the target resolution coefficient to be 1.

[0099] In step 110, a target correlation coefficient between the carbon emissions and the target carbon emission influencing factor indicators is obtained according to the target resolution coefficient, the maximum value, and the minimum value.

[0100] In step 112, a target grey correlation degree is calculated according to the target correlation coefficient to obtain a quantitative analysis result of the influencing factors of carbon emissions.

[0101] In some embodiments, the calculation of the grey relational grade based on the correlation coefficient to obtain the quantitative analysis result of the influencing factors of the carbon emissions further includes: obtaining the first historical time and the second historical time of the target carbon emission influencing factor index; obtaining a fuzzy complementary preference relation matrix according to the first historical time and the second historical time; obtaining a fuzzy consistent matrix according to the fuzzy complementary preference relation matrix; obtaining the weight of the target carbon emission influencing factor index according to the fuzzy consistent matrix; calculating the grey relational grade according to the correlation coefficient and the weight to obtain the quantitative analysis result of the influencing factors of the carbon emissions.

[0102] A method and related equipment for quantitative analysis of influencing factors of carbon emissions provided by the present application. The method includes: adding indicators related to the green power market, the green certificate market, and the carbon market on the basis of traditional carbon emission influencing factor indicators, obtaining a sequence matrix according to these indicators, selecting the maximum and minimum values of the two-level extreme differences from the sequence matrix, determining a target resolution coefficient according to the sequence matrix and the maximum value, obtaining a target correlation coefficient between the carbon emissions and the target carbon emission influencing factor index according to the target resolution coefficient, the maximum value, and the minimum value, and calculating a target grey relational grade according to the target correlation coefficient to obtain the quantitative analysis result of the influencing factors of the carbon emissions. The method provided by the present application combines the related indicators in the context of "electricity-certificate-carbon", and uses the improved grey relational grade for quantitative analysis, which can more accurately grasp the carbon emission trend, improve the theoretical support, and provide substantial support for formulating the optimal carbon peak path.

[0103] It should be noted that the method of the embodiments of the present application can be executed by a single device, such as a computer or a server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In this case of a distributed scenario, one of the multiple devices can only execute one or more steps of the method of the embodiments of the present application, and these multiple devices will interact with each other to complete the described method.

[0104] It should be noted that some embodiments of the present application have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in a different order from that in the above embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0105] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application also provides a device for quantitative analysis of influencing factors of carbon emissions.

[0106] ReferenceFigure 2 , the quantitative analysis device for influencing factors of carbon emissions includes:

[0107] An acquisition module 201, configured to acquire target carbon emission influencing factor indicators, where the target carbon emission influencing factor indicators include first key indicators related to the green power market, the green certificate market, and the carbon market, and second key indicators related to regional carbon emissions.

[0108] Among them, the first key indicators include the installed capacity of renewable energy, the power generation of renewable energy, the proportion of renewable energy in power generation, the national green power trading volume, and the green power trading situation by region related to the green power market; and / or the first key indicators further include the national green certificate issuance volume, the national green certificate trading volume, the green certificate price by region, and the green power trading rate by region related to the green certificate market; and / or the first key indicators further include the carbon quota benchmark price, the carbon quota auction ratio, the national carbon quota trading volume, the carbon emissions, the carbon emission intensity, the installed capacity of conventional energy, the power generation of conventional energy, and the proportion of conventional energy in power generation related to the carbon market.

[0109] Among them, the second key indicators include industrial structure, energy consumption structure, economic openness, green innovation level, energy consumption, per capita GDP, population size, carbon dioxide emissions, R & D investment intensity, electricity consumption, energy intensity, urbanization rate, carbon dioxide emission intensity, investment intensity, and GDP.

[0110] A first calculation module 202, configured to obtain a sequence matrix according to the target carbon emission influencing factor indicators.

[0111] The first calculation module 202 is further configured to obtain an analysis matrix according to the target carbon emission influencing factor indicators; perform dimensionless processing on the analysis matrix to obtain an initial value image matrix; perform difference operations on the elements in the initial value image matrix to obtain the sequence matrix.

[0112] A selection module 203, configured to select the maximum and minimum values of the two - level range from the sequence matrix.

[0113] A determination module 204, configured to determine a target resolution coefficient according to the sequence matrix and the maximum value.

[0114] The determination module 204 is further configured to calculate the average change amount of the target carbon emission influencing factor indicators according to the sequence matrix; calculate the normalized change amount according to the average change amount and the maximum value; determine the target resolution coefficient according to the normalized change amount.

[0115] The determination module 204 is further configured to determine that the target resolution coefficient is 1.5 times the normalized change amount in response to 3 times the normalized change amount being less than 1; in response to the normalized change amount being greater than or equal to and less than or equal to determine that the resolution is 2 times the normalized change amount; in response to the normalized change amount being greater than determine that the target resolution coefficient is 1.

[0116] The second calculation module 205 is configured to obtain the target correlation coefficient between the carbon emission and the target carbon emission influencing factor index according to the target resolution coefficient, the maximum value, and the minimum value.

[0117] The third calculation module 206 is configured to calculate the target grey correlation degree according to the target correlation coefficient to obtain the quantitative analysis result of the influencing factors of the carbon emission.

[0118] The third calculation module 206 is further configured to obtain the first historical time and the second historical time of the target carbon emission influencing factor index; obtain a fuzzy complementary preference relation matrix according to the first historical time and the second historical time; obtain a fuzzy consistent matrix according to the fuzzy complementary preference relation matrix; obtain the weight value of the target carbon emission influencing factor index according to the fuzzy consistent matrix; calculate the grey correlation degree according to the correlation coefficient and the weight value to obtain the quantitative analysis result of the influencing factors of the carbon emission.

[0119] For the convenience of description, when describing the above device, various modules are described separately according to their functions. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0120] The device in the above embodiment is used to implement the corresponding method 100 in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be described in detail here.

[0121] Based on the same technical concept, corresponding to the method in any of the above embodiments, the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the method 100 described in any of the above embodiments.

[0122] Figure 3A schematic diagram of an exemplary electronic device according to an embodiment of the present application is shown. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. Among them, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other inside the device through the bus 1050.

[0123] The processor 1010 may be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0124] The memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0125] The input / output interface 1030 is used to connect to an input / output module to implement information input and output. The input / output module may be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0126] The communication interface 1040 is used to connect to a communication module (not shown in the figure) to implement communication and interaction between this device and other devices. Among them, the communication module may implement communication in a wired manner (such as USB, network cable, etc.) or in a wireless manner (such as mobile network, WIFI, Bluetooth, etc.).

[0127] The bus 1050 includes a path for transmitting information between various components of the device (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0128] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary to implement the solution of the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0129] The electronic device of the above embodiment is used to implement the corresponding method 100 in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0130] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the method 100 described in any of the foregoing embodiments.

[0131] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0132] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute the method 100 described in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0133] Those of ordinary skill in the art should understand that: the discussion of any of the above embodiments is only exemplary, and is not intended to imply that the scope of the present application (including the claims) is limited to these examples; under the concept of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present application as described above, and they are not provided in detail for the sake of brevity.

[0134] In addition, for simplicity of explanation and discussion, and in order not to make the embodiments of the present application difficult to understand, well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Further, the devices may be shown in block diagram form in order to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present application are to be implemented (i.e., these details should be entirely within the understanding of those skilled in the art). In cases where specific details (such as circuits) are set forth to describe exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application may be practiced without these specific details or with variations of these specific details. Accordingly, these descriptions should be considered illustrative rather than restrictive.

[0135] Although the present application has been described in connection with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (such as dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0136] Embodiments of the present application are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of the present application shall be included within the protection scope of the present application.

Claims

1. A quantitative analysis method for factors affecting carbon emissions, comprising: Obtaining target carbon emission influencing factor indicators, wherein the target carbon emission influencing factor indicators include a first key indicator related to the green electricity market, the green certificate market, and the carbon market, and a second key indicator related to regional carbon emissions; According to the target carbon emission influencing factor indicators, a sequence matrix is ​​obtained; Selecting the maximum and minimum values ​​of the two-level range from the sequence matrix; Determining a target resolution coefficient according to the sequence matrix and the maximum value; Obtaining a target correlation coefficient between the carbon emission amount and the target carbon emission influencing factor index according to the target resolution coefficient, the maximum value and the minimum value; The target grey correlation degree is calculated according to the target correlation coefficient to obtain the quantitative analysis results of the influencing factors of the carbon emissions.

2. The method of claim 1, wherein: The step of obtaining a sequence matrix according to the target carbon emission influencing factor index further includes: According to the target carbon emission influencing factor indicators, an analysis matrix is ​​obtained; Performing dimensionless processing on the analysis matrix to obtain an initial value image matrix; A difference operation is performed on the elements in the initial image matrix to obtain the sequence matrix.

3. The method of claim 1, wherein: The step of calculating the grey correlation degree according to the correlation coefficient to obtain the quantitative analysis result of the influencing factors of the carbon emissions further includes: Obtaining a first historical time and a second historical time of the target carbon emission influencing factor indicator; According to the first historical time and the second historical time, a fuzzy complementary priority relationship matrix is ​​obtained; According to the fuzzy complementary priority relationship matrix, a fuzzy consistency matrix is ​​obtained; Obtaining the weight of the target carbon emission influencing factor index according to the fuzzy consistency matrix; The grey correlation degree is calculated according to the correlation coefficient and the weight to obtain the quantitative analysis result of the influencing factors of the carbon emission.

4. The method of claim 1, wherein: The first key indicator includes renewable energy installed capacity, renewable energy power generation, renewable energy power generation ratio, national green power trading volume and regional green power trading situation related to the green power market; and / or The first key indicator also includes the national green certificate issuance volume, national green certificate trading volume, regional green certificate prices and regional green electricity trading rates related to the green certificate market; and / or The first key indicator also includes the carbon quota benchmark price, carbon quota auction ratio, national carbon quota trading volume, carbon emissions, carbon emission intensity, conventional energy installed capacity, conventional energy power generation and conventional energy power generation ratio related to the carbon market.

5. The method of claim 1, wherein: The second key indicators include industrial structure, energy consumption structure, economic opening-up process, green innovation level, energy consumption, GDP per capita, population size, carbon dioxide emissions, R&D investment intensity, electricity consumption, energy intensity, urbanization rate, carbon dioxide emission intensity, investment intensity and GDP.

6. The method of claim 1, wherein: Determining the target resolution coefficient according to the sequence matrix and the maximum value further comprises: Calculating the average change of the target carbon emission influencing factor index according to the sequence matrix; Calculating a normalized variation according to the average variation and the maximum value; The target resolution coefficient is determined according to the normalized variation.

7. The method of claim 6, wherein: Determining the target resolution coefficient according to the normalized variation further comprises: In response to 3 times the normalized variation being less than 1, determining the target resolution coefficient to be 1.5 times the normalized variation; In response to the normalized change being greater than or equal to and less than or equal to Determining the resolution as twice the normalized variation; In response to the normalized change being greater than The target resolution factor is determined to be 1.

8. A device for quantitatively analyzing factors affecting carbon emissions, comprising: An acquisition module is configured to acquire a target carbon emission influencing factor index, wherein the target carbon emission influencing factor index includes a first key index related to a green electricity market, a green certificate market, and a carbon market, and a second key index related to regional carbon emissions; A first calculation module is configured to obtain a sequence matrix according to the target carbon emission influencing factor index; A selection module is configured to select the maximum value and the minimum value of the two-level range from the sequence matrix; A determination module, configured to determine a target resolution coefficient according to the sequence matrix and the maximum value; A second calculation module is configured to obtain a target correlation coefficient between the carbon emission amount and the target carbon emission influencing factor index according to the target resolution coefficient, the maximum value and the minimum value; The third calculation module is configured to calculate the target grey correlation degree according to the target correlation coefficient to obtain the quantitative analysis result of the influencing factors of the carbon emissions.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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