CN114139893A - Carbon emission influence factor analysis and index evaluation method and device - Google Patents
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Abstract
The invention relates to a method and a device for analyzing carbon emission influence factors and evaluating indexes, which are technically characterized in that: deducing the functional relation between the carbon emission and the influence factors in a logarithmic relation form, and testing the stationarity of each variable by using a unit root; analyzing the interaction between the carbon emission and the environmental system and the technical innovation and the quantitative relation between variables by adopting a generalized moment quantile quantitative regression analysis method aiming at the time sequence data; and constructing a carbon balance comprehensive evaluation index model according to the carbon emission source and carbon sink calculation model, evaluating the relationship between the carbon emission in the composite ecosystem of the region and the carbon absorption in the natural ecosystem, and guiding the low-carbon development transformation path of the region. According to the method, the weighting absolute number of the quantile quantitative regression model is limited through a generalized moment algorithm, the error condition is reduced to the lowest, so that different weighting is performed on the positive residual error and the negative residual error in the selected quantile, and the interaction between related variables and the quantitative influence relation between the related variables and carbon emission are clearer and more accurate.
Description
Technical Field
The invention belongs to the technical field of power systems, and particularly relates to a method and a device for analyzing carbon emission influence factors and evaluating indexes.
Background
At present, the influence of independent variables on dependent variables can only be approximately explained by considering a fixed effect model in the unit root test, and the influence of points with different degrees cannot be estimated. This deficiency can be addressed by using a quantile regression model that measures the impact of carbon dioxide emissions on other independent variables in detail, but this quantile quantitative regression model is not practical because the weighted absolute number is large and it is necessary to satisfy the restrictive assumption that the expressions of the error terms are the same.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a method and a device for analyzing carbon emission influence factors and evaluating indexes, and solves the problem that the traditional quantile quantitative regression model has larger weighted absolute number and needs to meet the restrictive assumption that the expressions of error terms are the same.
The invention solves the technical problems in the prior art by adopting the following technical scheme:
a carbon emission influence factor analysis and index evaluation method comprises the following steps:
step 1, deducing a functional relation between carbon emission and influence factors in a logarithmic relation form, and testing the stationarity of each variable by using a unit root;
step 2, analyzing the interaction between the carbon emission and the environmental system and the technical innovation and the quantitative relation between variables by adopting a generalized moment quantile quantitative regression analysis method aiming at the time sequence data;
and 3, constructing a carbon balance comprehensive evaluation index model according to the carbon emission source and carbon sink calculation model, evaluating the relationship between carbon emission caused by human production and living activities in the regional composite ecosystem and carbon absorption of the natural ecosystem, and guiding a regional low-carbon development transformation path.
Moreover, the influencing factors include economic development, energy composition, population composition, environmental systems, and technological innovation.
Moreover, the specific implementation method of the step 1 comprises the following steps:
step 1.1, deducing and obtaining the functional relations of carbon emission, economic development, energy composition, population composition, environmental system and technical innovation in a logarithmic relation mode, wherein the functional relations comprise:
CO2et＝η0+η1ln INDSt+η2ln EIt+η3ln CIt+η4ln ECGt+η5ln GCFt+εt
in the formula, CO2etIndicating carbon dioxide emissions, INDStIndicating the composition of the population, EItRepresenting the energy composition, CItPresentation technical innovation, ECGtIndicating economic development, GCFtRepresenting the environmental system formula, t is time, η0，η1，η2，η3，η4，η5Is a model coefficient, the above model coefficient being a constant, εtIs an error term;
step 1.2, the stationarity of the variables is proved by using the following unit root test:
in which t represents a time index,. DELTA.CtAnd represents a random trend error term, lambda, at time t. Represents a displacement term, T represents a linear trend, τ. Represents a linear trend correlation coefficient, Ut-1Denotes the lag term, τ1Coefficient representing lag term,. DELTA.Ct-1Represents t-1 time-of-day random trend error term, λlDenotes the autoregressive coefficient, l 1,2tRepresenting white noise.
And in the step 2, a generalized moment-fraction quantitative regression analysis method is adopted, and the quantitative relationship among the interaction and the variables of the carbon emission, the economic development, the energy composition, the population composition, the environmental system and the technical innovation is represented as follows:
in the formula, CO2etIndicating carbon dioxide emissions, INDStIndicating the composition of the population, EItRepresenting the energy composition, CItPresentation technical innovation, ECGtIndicating economic development, GCFtRepresenting an environmental system formula, t is time,
respectively is a fractal regression estimation coefficient with the value range of 0.1-0.9, epsilontIs an error term.
Moreover, the specific implementation method of step 3 includes the following steps:
step 3.1, dividing the land use types into: the method comprises the following steps of constructing a carbon sink calculation model according to different land types for the wetland, the water body, the sea area, the forest and the farmland:
wherein CSiRespectively representing the carbon absorption amount of different land types, i represents the different land types, AiAreas representing different land types, RiRespectively represent the carbon fixation rate per unit area of the unused land type;
step 3.2, constructing the following carbon balance comprehensive evaluation index model according to the carbon emission source and carbon sink calculation model:
wherein CBI is carbon balance index, CO2etIndicating carbon dioxide emissions.
The utility model provides a carbon emission influence factor analysis and index evaluation device, includes carbon emission modeling module, carbon emission model parameter quantization module and carbon emission index evaluation module:
a carbon emission modeling module: deducing the functional relationship between carbon emission and economic development, energy composition, population composition, environmental system and technical innovation in a logarithmic relationship form, and testing the stationarity of each variable by using a unit root;
a carbon emission model parameter quantification module: analyzing the interaction of carbon emission and economic development, energy composition, population composition, environmental system and technical innovation and the quantitative relation between variables by adopting a generalized moment quantile quantitative regression analysis method aiming at the carbon emission model output time sequence data;
a carbon emission index evaluation module: and constructing a carbon balance comprehensive evaluation index model based on the carbon dioxide emission quantitative derivation result and combining regional carbon sink data to evaluate the carbon emission index.
The invention has the advantages and positive effects that:
1. the method is reasonable in design, correlation coefficients and probabilities between the carbon emission and each variable are analyzed according to the generalized moment quantile quantitative regression model, the quantile quantitative regression model is limited to weight absolute numbers through the generalized moment algorithm, error conditions are reduced to the lowest, different weighting is conducted on positive residual errors and negative residual errors in the selected quantiles, and the interaction between the related variables and the quantitative influence relation between the related variables and the carbon emission are enabled to be clearer and more accurate.
2. The method is very suitable for evaluating the quantitative influence of carbon emission, is used for quantitatively evaluating the correlation between the carbon emission and economic development, energy composition, population composition, environmental carbon sink and technical innovation based on the quantitative relation, further provides a comprehensive evaluation index of carbon balance, evaluates the low-carbon development process of regions according to the dynamic change process of the carbon balance index, and guides the specific practice of carbon neutralization and carbon peak reaching and the low-carbon generation and production mode and transformation path.
Detailed Description
The present invention will be described in further detail with reference to examples.
A carbon emission influence factor analysis and index evaluation method comprises the following steps:
step 1, deducing the functional relationship of the carbon emission and the influence factors such as economic development, energy composition, population composition, environmental system and technical innovation in a logarithmic relationship form, and testing the stability of each variable by using a unit root.
The specific implementation method of the step comprises the following steps:
step 1.1, testing the stability of each variable by using a unit root, and expressing the functional relation between the carbon emission and each variable by using the following model:
CO2et＝f(INDSt，EIt，CIt，ECGt，GCFt) (1)
CO2e in formula (1)tIndicating carbon dioxide emissions, INDStIndicating the composition of the population, EItRepresenting the energy composition, CItPresentation technical innovation, ECGtIndicating economic development, GCFtPresentation environment system
Eq. (1) is further written as
CO2et＝η0+η1INDSt+η2EIt+η3CIt+η4ECGt (2)
+η5GCFt+εt
The form of a writable logarithm is as follows:
CO2et＝η0+η1ln INDSt+η2ln EIt+η3ln CIt+η4ln ECGt+η5ln GCFt+εt (3)
the formula (3) is a logarithmic relation form of variables such as carbon emission, economic development, energy composition, population composition, environmental system and technical innovation, t is time, and the model coefficient is eta0，η1，η2，η3，η4，η5Is a constant, epsilontIs an error term. (ii) a
Step 1.2, the stationarity of the variable is tested by using the unit root, and the proving form is as follows:
equation (4) represents the unit root test, t represents the time index, Δ CtRepresenting the random trend error term, λ, at time toRepresenting a displacement term, T representing a linear trend, τoRepresents a linear trend correlation coefficient, Ut-1Denotes the lag term, τ1Coefficient representing lag term,. DELTA.Ct-1Represents a random trend error term, lambda, at time t-1l(l ═ 1, 2.. times, k denotes the autoregressive coefficient), μtRepresenting white noise.
And 2, analyzing the interaction of the carbon emission and the variables such as economic development, energy composition, population composition, environmental system and technical innovation and the quantitative relation among the variables by adopting a generalized moment quantile quantitative regression analysis method aiming at the time sequence data.
The specific implementation method of the step is as follows:
and 2.1, analyzing the interaction of the carbon emission and variables such as economic development, energy composition, population composition, environmental system and technical innovation by adopting a generalized moment quantile quantitative regression analysis method aiming at the time sequence data, so that various estimations of the dependent variable are verified through the change of several quantiles of the independent variable. The advantages of introducing the generalized moment quantile quantitative regression method to analyze the carbon emission related factors are as follows: this method corrects the restrictive assumption that the expression of the error term is the same, and the mathematical model is:
yi＝x′iψθ+νθi (5)
equation (5) represents a general form of quantile quantitative regression model, where yiRepresenting dependent variable, vθiRepresenting an unknown error term, #θIs an unknown estimated vector loopAnd (h multiplied by 1) the value range of theta is 0-1. Furthermore, xiIs the (hx 1) dimensional vector regression parameter psiθIs used as the argument of (1). Similarly, Eq is rewritten (5) as a conditional quantile form, when y is presentiAnd xiIs written as:
Qθdenotes yi/xiConditional quantile of (3), regression of the vector to parameter psiθThe gradient decrease of the corresponding theta value is as follows
In which t denotes the time index, ytDenotes the dependent variable, x, at time ttRepresenting the time t argument.
And 2.2, limiting the weighted absolute number by adopting a generalized moment algorithm, and reducing the error condition to the minimum so as to carry out different weighting on the positive residual error and the negative residual error in the selected quantile. Thus, the interaction between the relevant variables can be further deduced from equation (2) to yield equation (8)
The above equation is used to estimate the quantitative relationship between carbon emissions and variables such as economic development, energy composition, population composition, environmental systems, and technological innovation. In the formula (8), CO2etThe amount of carbon dioxide emissions is expressed,
and
is a fractal regression estimation coefficient with the value range of 0.1-0.9 epsilontIs an error term;
and 3, integrating the carbon emission source and the carbon sink calculation model to construct a carbon balance comprehensive evaluation index model, evaluating the relationship between carbon emission caused by human production and living activities in the regional composite ecosystem and carbon absorption of the natural ecosystem, and guiding a regional low-carbon development transformation path.
The specific implementation method of the step is as follows:
step 3.1, mainly playing the role of carbon sink, including wetlands, water bodies, sea areas, forests and farmlands, and constructing a carbon sink calculation model according to different land types:
where CSi represents the carbon absorption amount of each land type, i represents the land type, Ai represents the area of each land type, and Ri represents the carbon fixation rate per unit area of each land type.
Step 3.2, integrating the carbon emission source in the formula (8) and the carbon sink calculation model in the formula (9) to construct a carbon balance comprehensive evaluation index model:
the carbon balance index CBI reflects the relationship between carbon emissions in urban complex ecosystems due to human production and living activities and carbon absorption in natural ecosystems. And evaluating the urban low-carbon development process by calculating the urban carbon balance index dynamic change process, wherein the urban low-carbon development process comprises trend evaluation, segmented development evaluation of different urban development stages and multi-index evaluation.
Based on the carbon emission influence factor analysis and index evaluation method, the invention also provides a carbon emission influence factor analysis and index evaluation device for realizing the method, which comprises a carbon emission modeling module, a carbon emission model parameter quantification module and a carbon emission index evaluation module. Wherein:
a carbon emission modeling module: the functional relationship between the carbon emission and the influence factors such as economic development, energy composition, population composition, environmental system and technical innovation is deduced in a logarithmic relation mode, and the unit root is used for testing the stability of each variable.
A carbon emission model parameter quantification module: based on the carbon emission model, analyzing the interaction of the carbon emission and variables such as economic development, energy composition, population composition, environmental system and technical innovation and the quantitative relation among the variables by adopting a generalized moment quantile quantitative regression analysis method aiming at the output time sequence data of the carbon emission model;
a carbon emission index evaluation module: and constructing a carbon balance comprehensive evaluation index model based on the carbon dioxide emission quantitative derivation result and combining regional carbon sink data.
The effect of the present invention is verified by a specific example. According to the example, unit root tests among variables such as carbon emission, economic development, energy composition, population composition, environmental carbon sink and technical innovation are carried out according to the statistical yearbook data of a certain region, and the test results are as follows:
the table above shows the results for the horizontal and initial differences. The overall value of the ADF test statistic is greater than the critical value at the 5% significance level. It indicates that the original hypothesis meant to deny the unit root test of all relevant variables. The results of the ADF and P-P test tests showed that the sequence was stable with 5% significance when first inconsistent.
The results of the generalized moment quantile quantitative regression model analysis on the study variables are shown in the following table.
The results presented in this table show that the energy structure has a constructive impact on CO2 emissions, with a coefficient (0.161636) and probability value (0.0000). Likewise, the environmental structure and technical innovation have positive coefficients (0.895212) and (0.442922), probability values (0.2171), (0.0004), and (0.0002), respectively. Demographic composition and economic development have negative coefficients (-0.206843) and (-0.002841), probability values (0.2171) and (0.8795), respectively.
From the coefficients and the probability values obtained by the calculation, it can be seen that the regional carbon dioxide emission and the regional energy structure have positive correlation with the technical innovation capability and the environmental structure, the higher the clean energy ratio in the regional energy structure is, the smaller the carbon dioxide emission is, the stronger the regional technical innovation capability is, the smaller the carbon dioxide emission is, the more the carbon sink is in the regional environment is, and the smaller the carbon dioxide emission is, and it can also be seen from the table that the influence of the energy structure, the technical innovation capability and the carbon sink size in the environment on the reduction of the total carbon emission of the region is in turn the energy structure, the technical innovation capability and the environmental carbon sink, and the population composition and the economic development have negative correlation with the carbon dioxide emission, that is, the more the population is, the faster the economic development is, and the carbon emission is not beneficial.
According to the calculation result, regional carbon emission reduction strategy emphasis can be obtained, namely regional energy consumption structure is optimized, the proportion of clean energy in energy consumption is improved to be the emphasis of carbon emission reduction, in addition, the low-carbon technology is used for improving and optimizing regional environment carbon sink level, carbon sink sources such as wetland and forest are increased, and the method is an effective strategy for carbon emission reduction.
According to the regional statistical yearbook data and the carbon balance comprehensive evaluation index, a formula is calculated, 1481.77 million tons of carbon dioxide emission in 2020 in a certain region, 986.53 million tons of carbon sink are obtained, the carbon balance index is 1.502, and the difference from the carbon balance index 1 is large, so that the carbon dioxide emission needs to be reduced by nearly 30% when the carbon balance is achieved in the region, and according to the result of the generalized moment quantile quantitative regression model analysis provided by the text, a low-carbon transformation path needs to be taken as follows: preferentially optimizing the regional energy structure and improving the clean energy consumption ratio; the research and development of low-carbon modification technology of traditional energy sources are enhanced, and the emission of carbon dioxide of the traditional energy sources is reduced; improve the ecological environment of the area and increase the wetland. And (5) planning carbon sink construction such as forests and the like.
It should be emphasized that the embodiments described herein are illustrative rather than restrictive, and thus the present invention is not limited to the embodiments described in the detailed description, but also includes other embodiments that can be derived from the technical solutions of the present invention by those skilled in the art.
Claims (6)
1. A carbon emission influence factor analysis and index evaluation method is characterized by comprising the following steps: the method comprises the following steps:
step 1, deducing a functional relation between carbon emission and influence factors in a logarithmic relation form, and testing the stationarity of each variable by using a unit root;
step 2, analyzing the interaction between the carbon emission and the environmental system and the technical innovation and the quantitative relation between variables by adopting a generalized moment quantile quantitative regression analysis method aiming at the time sequence data;
and 3, constructing a carbon balance comprehensive evaluation index model according to the carbon emission source and carbon sink calculation model, evaluating the relationship between carbon emission caused by human production and living activities in the regional composite ecosystem and carbon absorption of the natural ecosystem, and guiding a regional low-carbon development transformation path.
2. The method of claim 1, wherein the method comprises the steps of: the influencing factors include economic development, energy composition, population composition, environmental systems and technical innovation.
3. The method for analyzing and evaluating influence factors of carbon emission and indexes according to claim 1 or 2, wherein: the specific implementation method of the step 1 comprises the following steps:
step 1.1, deducing and obtaining the functional relations of carbon emission, economic development, energy composition, population composition, environmental system and technical innovation in a logarithmic relation mode, wherein the functional relations comprise:
CO2et＝η0+η1ln INDSt+η2ln EIt+η3ln CIt+η4ln ECGt+η5ln GCFt+εt
in the formula, CO2etIndicating carbon dioxide emissions, INDStIndicating the composition of the population, EItRepresenting the energy composition, CItPresentation technical innovation, ECGtIndicating economic development, GCFtRepresenting the environmental system formula, t is time, η0,η1,η2,η3,η4，η5Is a model coefficient, the above model coefficient being a constant, εtIs an error term;
step 1.2, the stationarity of the variables is proved by using the following unit root test:
in which t represents a time index,. DELTA.CtA random trend error term is shown at the time t,
represents a displacement term, T represents a linear trend,
represents a linear trend correlation coefficient, Ut-1Denotes the lag term, τ1Coefficient representing lag term,. DELTA.Ct-1Represents a random trend error term, lambda, at time t-1lDenotes the autoregressive coefficient, l 1,2tRepresenting white noise.
4. The method for analyzing and evaluating influence factors of carbon emission and indexes according to claim 1 or 2, wherein: step 2, a generalized moment-quantile quantitative regression analysis method is adopted, and the quantitative relations among the interaction and the variables of the carbon emission, the economic development, the energy composition, the population composition, the environmental system and the technical innovation are represented as follows:
in the formula, CO2etIndicating carbon dioxide emissions, INDStIndicating the composition of the population, EItRepresenting the energy composition, CItPresentation technical innovation, ECGtIndicating economic development, GCFtRepresenting an environmental system formula, t is time,
respectively is a fractal regression estimation coefficient with the value range of 0.1-0.9, epsilontIs an error term.
5. The method for analyzing and evaluating influence factors of carbon emission and indexes according to claim 1 or 2, wherein: the specific implementation method of the step 3 comprises the following steps:
step 3.1, dividing the land use types into: the method comprises the following steps of constructing a carbon sink calculation model according to different land types for the wetland, the water body, the sea area, the forest and the farmland:
wherein CSiRespectively representing the carbon absorption amount of different land types, i represents the different land types, AiAreas representing different land types, RiRespectively represent the carbon fixation rate per unit area of the unused land type;
step 3.2, constructing the following carbon balance comprehensive evaluation index model according to the carbon emission source and carbon sink calculation model:
wherein CBI is carbon balance index, CO2etIndicating carbon dioxide emissions.
6. An apparatus for implementing the carbon emission influencing factor analyzing and index evaluating method according to any one of claims 1 to 5, characterized in that: the carbon emission evaluation system comprises a carbon emission modeling module, a carbon emission model parameter quantification module and a carbon emission index evaluation module:
a carbon emission modeling module: deducing the functional relationship between carbon emission and economic development, energy composition, population composition, environmental system and technical innovation in a logarithmic relationship form, and testing the stationarity of each variable by using a unit root;
a carbon emission model parameter quantification module: analyzing the interaction of carbon emission and economic development, energy composition, population composition, environmental system and technical innovation and the quantitative relation between variables by adopting a generalized moment quantile quantitative regression analysis method aiming at the carbon emission model output time sequence data;
a carbon emission index evaluation module: and constructing a carbon balance comprehensive evaluation index model based on the carbon dioxide emission quantitative derivation result and combining regional carbon sink data to evaluate the carbon emission index.
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| CN115937692A (en) * | 2023-02-15 | 2023-04-07 | 山东省海洋资源与环境研究院(山东省海洋环境监测中心、山东省水产品质量检验中心) | Coastal wetland carbon sink effect evaluation method and system |
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