CN117649132A - Pollution and carbon reduction cooperative evaluation result generation method and device - Google Patents
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Abstract
The invention provides a method and a device for generating a synergistic evaluation result of pollution reduction and carbon reduction, which can be applied to the technical field of pollution reduction and carbon reduction. The method for generating the pollution and carbon reduction cooperative evaluation result comprises the following steps: constructing a pollution and carbon reduction cooperative evaluation index model; calculating a first target weight of each of the plurality of evaluation dimensions and a second target weight of each of the plurality of influence strategies under each of the plurality of evaluation dimensions; calculating to obtain a pollution and carbon reduction synergy level value of the target city in the target year based on the index scores, the first target weight and the second target weight of each of a plurality of influence strategies in each evaluation dimension in the target year; calculating a pollution reduction and carbon reduction synergistic effect coefficient based on the first pollutant emission total amount and the first carbon emission total amount, and the second pollutant emission total amount and the second carbon emission total amount of the reference year; and generating a pollution reduction and carbon reduction synergy evaluation result aiming at the target city based on the pollution reduction and carbon reduction synergy level value and the pollution reduction and carbon reduction synergy coefficient.
Description
Technical Field
The invention relates to the technical field of pollution reduction and carbon reduction, in particular to a method and a device for generating a pollution reduction and carbon reduction cooperative evaluation result.
Background
Various environmental pollutants and carbon emissions can be generated in the fields of energy, industry, traffic, construction and the like. Environmental pollutants and carbon emission have the characteristics of same root, same source, same process characteristics and emission space-time consistency, and pollution reduction and carbon reduction can be seen to have consistent control objects and treatment paths. At present, most of the cooperative researches on pollution reduction and carbon reduction in the prior art are qualitative analysis and theoretical researches, and multi-dimensional monitoring and analysis on the cooperative synergy level of pollution reduction and carbon reduction are lacking.
Disclosure of Invention
In view of the above problems, the invention provides a method and a device for generating a synergistic evaluation result of pollution and carbon reduction.
The invention provides a method for generating a pollution and carbon reduction cooperative evaluation result, which comprises the following steps: constructing a pollution reduction and carbon reduction cooperative evaluation index model aiming at a target city, wherein the pollution reduction and carbon reduction cooperative evaluation index model comprises a target layer, a criterion layer and an index layer, the criterion layer comprises a plurality of evaluation dimensions influencing the pollution reduction and carbon reduction effect of the target city, the index layer comprises a plurality of influence strategies influencing the pollution reduction and carbon reduction effect of the target city under each evaluation dimension, and the target layer is used for representing: based on a plurality of influence strategies under each evaluation dimension, the pollution and carbon reduction collaborative evaluation result of the target city is obtained.
Based on the pollution and carbon reduction collaborative evaluation index model, calculating a first target weight of each of a plurality of evaluation dimensions, and determining a second target weight of each of a plurality of influence strategies under each evaluation dimension.
And calculating to obtain the pollution and carbon reduction synergy level value of the target city in the target year based on the index scores, the first target weight and the second target weight of each of a plurality of influence strategies in each evaluation dimension in the target year.
And calculating to obtain a pollution and carbon reduction synergistic effect coefficient of the target city in the target year based on the first pollutant emission total amount and the first carbon emission total amount of the target city in the target year and the second pollutant emission total amount and the second carbon emission total amount of the target city in the reference year, wherein the reference year is the last year of the target year.
And generating a pollution reduction and carbon reduction synergy evaluation result aiming at the target city based on the pollution reduction and carbon reduction synergy level value and the pollution reduction and carbon reduction synergy coefficient.
Optionally, calculating a first target weight for each of the plurality of evaluation dimensions, and determining a second target weight for each of the plurality of influence policies under each of the evaluation dimensions comprises: generating a first subjective weight of each of a plurality of evaluation dimensions and a second subjective weight of each of a plurality of influence strategies by using expert scoring results for each evaluation dimension and each influence strategy; generating a first objective weight for each of the plurality of evaluation dimensions and a second objective weight for each of the plurality of influence strategies using objective reference information for each of the evaluation dimensions and each of the influence strategies; calculating a first target weight based on the first subjective weight and the first objective weight; and calculating a second target weight based on the second subjective weight and the second objective weight.
Optionally, calculating the first target weight based on the first subjective weight and the first objective weight includes: calculating a first comprehensive weight based on the first subjective weight and the first objective weight; and updating the first comprehensive weight by using a particle swarm optimization algorithm to obtain a first target weight.
Optionally, updating the first comprehensive weight by using a particle swarm optimization algorithm, and obtaining the first target weight includes: the following operations S1-S4 are iteratively performed: operation S1: calculating fitness of each particle based on a position of each particle in the population of particles, wherein each particle is used for representing an optimization mode for updating and optimizing the first comprehensive weight, and the position of each particle is used for representing: updating and optimizing the first comprehensive weight to obtain a weight updating value, wherein the initial position of the particle is a random initialization value of the first comprehensive weight; operation S2: according to the fitness of each particle, determining the optimal position of an individual corresponding to each particle, and determining the optimal position of the population of the particle population; operation S3: calculating an objective function value based on the individual optimal position and the population optimal position; operation S4: updating the position of each particle according to the objective function value; until the objective function value meets a preset termination condition, taking the optimal population position of the particle population as a first primary selection weight for preliminarily updating the first comprehensive weight; and adjusting the first initial selection weight to obtain a first target weight.
Optionally, adjusting the first preliminary selection weight to obtain a first target weight includes: determining an average value and a minimum value of an objective function in multiple iterative computations; according to the first initial selection weight and the individual optimal position corresponding to each particle when iteration is terminated, a plurality of groups of reference positions are obtained through calculation; and obtaining the first target weight according to the maximum value and the minimum value in the multiple groups of reference positions, the average value of the target function and the minimum value of the target function.
Optionally, generating the first objective weight for each of the plurality of evaluation dimensions includes: generating a reference information matrix by using objective reference information for each evaluation dimension; carrying out standardization processing on the reference information matrix to generate a standardized information matrix; converting the standardized information matrix into an information specific gravity matrix, wherein single element values in the information specific gravity matrix are used for representing: a ratio of the unit information quantity represented by the single evaluation dimension to the total information quantity represented by the plurality of evaluation dimensions; calculating information entropy of each element in the information proportion matrix, wherein the information entropy is used for representing the uncertainty degree of objective reference information of each evaluation dimension; based on the information entropy of each element in the information weight matrix, a respective first objective weight for a plurality of evaluation dimensions is generated.
Optionally, generating the first subjective weight for each of the plurality of evaluation dimensions includes: generating a scoring matrix by using expert scoring results for each evaluation dimension; and performing matrix transformation processing on the scoring matrix to generate first subjective weights of each of the multiple evaluation dimensions.
Optionally, the method for generating the synergistic evaluation result of pollution reduction and carbon reduction further comprises the following steps: acquiring energy consumption values of a target city in a target year aiming at a plurality of statistical energy consumption items and pollution discharge coefficients corresponding to the statistical energy consumption items, wherein the statistical energy consumption items at least comprise industrial energy consumption items, power generation energy consumption items, heat supply energy consumption items and transportation energy consumption items; and calculating to obtain the first pollutant emission total amount of the target city in the target year according to the energy consumption values of the plurality of statistical energy consumption items and the pollution discharge coefficients corresponding to the statistical energy consumption items.
Optionally, the method for generating the synergistic evaluation result of pollution reduction and carbon reduction further comprises the following steps: obtaining energy consumption values of a target city in a target year aiming at a plurality of statistical energy consumption items and carbon emission coefficients corresponding to the statistical energy consumption items, wherein the statistical energy consumption items at least comprise industrial energy consumption items, power generation energy consumption items, heat supply energy consumption items and transportation energy consumption items; and calculating to obtain the first carbon emission total of the target city in the target year according to the energy consumption values of the plurality of statistical energy consumption items and the carbon emission coefficients corresponding to the statistical energy consumption items.
Another aspect of the present invention provides a pollution-reducing and carbon-reducing cooperative evaluation result generating device, including:
the construction module is used for constructing a pollution reduction and carbon reduction cooperative evaluation index model aiming at a target city, wherein the pollution reduction and carbon reduction cooperative evaluation index model comprises a target layer, a criterion layer and an index layer, the criterion layer comprises a plurality of evaluation dimensions influencing the pollution reduction and carbon reduction effect of the target city, the index layer comprises a plurality of influence strategies influencing the pollution reduction and carbon reduction effect of the target city under each evaluation dimension, and the target layer is used for representing: based on a plurality of influence strategies under each evaluation dimension, the pollution and carbon reduction collaborative evaluation result of the target city is obtained.
The first calculation module is used for calculating first target weights of the evaluation dimensions respectively based on the pollution reduction and carbon reduction cooperative evaluation index model, and determining second target weights of the influence strategies under the evaluation dimensions respectively.
The second calculation module is used for calculating and obtaining the pollution and carbon reduction synergy level value of the target city in the target year based on the index scores, the first target weight and the second target weight of each of a plurality of influence strategies in each evaluation dimension in the target year.
And the third calculation module is used for calculating and obtaining the pollution and carbon reduction synergistic effect coefficient of the target city in the target year based on the first pollutant emission total amount and the first carbon emission total amount of the target city in the target year, and the second pollutant emission total amount and the second carbon emission total amount of the target city in the reference year, wherein the reference year is the last year of the target year.
The generation module is used for generating a pollution reduction and carbon reduction cooperative evaluation result aiming at the target city based on the pollution reduction and carbon reduction cooperative synergistic level value and the pollution reduction and carbon reduction cooperative effect coefficient.
According to the embodiment of the invention, the pollution and carbon reduction synergy evaluation index model is constructed by adopting the analytic hierarchy process, a plurality of evaluation dimensions under different hierarchies are combined, and the pollution and carbon reduction synergy result analysis is carried out according to different corresponding influence strategies, so that the variety of index data is enriched, the data base and the dimension of the pollution and carbon reduction synergy result analysis are increased, objective monitoring and analysis on the pollution and carbon reduction synergy level are realized, and further, the pollutant emission and carbon emission actual emission synergy result is combined through the pollution emission and carbon emission actual emission synergy result obtained based on the analytic hierarchy process, so that the final pollution and carbon reduction synergy evaluation result is generated, and the result is more objective and accurate, has more references, and is beneficial to improving the pollution and carbon reduction synergy treatment efficiency.
Drawings
The foregoing and other objects, features and advantages of the invention will be apparent from the following description of embodiments of the invention with reference to the accompanying drawings, in which:
FIG. 1 shows a flowchart of a method for generating a synergistic evaluation result of pollution and carbon reduction according to an embodiment of the invention;
FIG. 2 shows a block diagram of a pollution and carbon reduction collaborative evaluation index model according to an embodiment of the invention;
FIG. 3 illustrates a flow chart of a method of calculating a first target weight and a second target weight according to an embodiment of the invention;
FIG. 4 shows a flow chart for optimizing the composite weights using a particle swarm optimization algorithm;
FIG. 5 illustrates a flow chart of a method of calculating a carbon reduction synergy level value for a pollution reduction according to an embodiment of the invention;
FIG. 6 shows a flowchart of a method for generating a synergistic evaluation result of pollution and carbon reduction according to another embodiment of the present invention;
fig. 7 shows a block diagram of a configuration of a pollution reduction and carbon reduction cooperative evaluation result generating device according to an embodiment of the present invention.
Detailed Description
Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. It should be understood that the description is only illustrative and is not intended to limit the scope of the invention. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the present invention.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
As the cooperative research on pollution reduction and carbon reduction in the prior art is mostly qualitative analysis and theoretical research, the multi-dimensional detection and analysis on the cooperative synergy level and the cooperative degree of pollution reduction and carbon reduction are lacking. According to the embodiment of the invention, the monitoring and analysis of the synergistic level of pollution reduction and carbon reduction are realized by combining the industrial structure data, the emission data, the activity data, the double carbon data and the like in all dimensions of the main source, the main field, the key industry, the key links and the like of the environmental pollutants and the carbon emission, so that the synergistic treatment efficiency of pollution reduction and carbon reduction is improved.
The embodiment of the invention provides a method for generating a pollution and carbon reduction cooperative evaluation result.
Fig. 1 shows a flowchart of a method for generating a synergistic evaluation result of pollution and carbon reduction according to an embodiment of the present invention. FIG. 2 shows a block diagram of a pollution reduction and carbon reduction collaborative evaluation index model according to an embodiment of the invention. The following is a detailed description with reference to fig. 1 and 2.
As shown in FIG. 1, the method for generating the collaborative evaluation result of reducing pollution and reducing carbon in the embodiment includes operations S110 to S150.
In operation S110, a pollution reduction and carbon reduction cooperative evaluation index model is constructed for the target city, wherein the pollution reduction and carbon reduction cooperative evaluation index model includes a target layer, a criterion layer, and an index layer.
According to an embodiment of the invention, the criterion layer comprises a plurality of evaluation dimensions affecting the pollution and carbon reduction effect of the target city; the index layer comprises a plurality of influence strategies for influencing the pollution and carbon reduction effects of the target city under each evaluation dimension; the target layer is used for representing the pollution and carbon reduction cooperative evaluation result of the target city based on a plurality of influence strategies under each evaluation dimension.
As shown in fig. 2, the multiple evaluation dimensions of the criteria layer may be: carbon reduction effect, environmental quality, ecological quality, economic growth, system, energy field, industrial field, traffic field, building field and the like. The evaluation dimension may be any combination of one or more of the above, depending on the different conditions of the different target urban pollutants and carbon emissions.
As shown in fig. 2, the index layer includes a plurality of influence strategies that influence the pollution abatement and carbon reduction effects of the target city in each evaluation dimension. Illustratively, in the case where the criterion layer is a carbon reduction effect, the criterion layer may be a unit area production total carbon dioxide emission and a unit area production total carbon dioxide emission reduction; in the case that the criterion layer is environmental quality, the index layer can be air quality and water environment quality; in the case of the criterion layer being ecological quality, the index layer may be an ecological quality index; under the condition that the criterion layer is economic growth, the index layer can be the ratio of the increased value of the strategic emerging industry to the GDP; under the condition that the criterion layer is a system, the index layer can be the perfect condition of an ecological environment sub-control, pollution reduction and carbon reduction cooperative synergistic management mechanism; in the case that the criterion layer is in the energy field, the index layer can be fossil energy proportion and new energy proportion; in the case that the criterion layer is in the industrial field, the index layer can be steel duty ratio and cement duty ratio; the index layer can be the fuel automobile proportion and the new energy automobile proportion under the condition that the criterion layer is in the traffic field; in the case that the criterion layer is in the building field, the index layer can be the heating ratio of fire coal and the heating ratio of new energy.
In operation S120, a first target weight w for each of the plurality of evaluation dimensions is calculated based on the pollution-reducing and carbon-reducing collaborative evaluation index model i And determining each of a plurality of impact policies under each evaluation dimensionSelf second target weight w ij 。
According to an embodiment of the invention, the first target weight w i The method is used for representing corresponding target weights under different evaluation dimensions in the criterion layer; second target weight w ij And the target weights are used for representing target weights corresponding to different influence strategies in the index layer.
In operation S130, a respective index score S based on a plurality of impact policies in respective evaluation dimensions in the target year ij First target weight w i Second target weight w ij And calculating to obtain the pollution and carbon reduction synergy level value T of the target city in the target year.
According to an embodiment of the invention, the index score S ij Can be obtained directly from expert scores.
According to the embodiment of the invention, the pollution-reducing and carbon-reducing synergy level value T is calculated as shown in the formula (1).
(1)
Wherein T is a synergy level value; s is S ij Scoring each factor of the index layer; w (w) i Is a first target weight; w (w) ij Is a second target weight; a, a i The number of corresponding influence strategies under each evaluation dimension; i is the number of evaluation dimensions in the criteria layer.
In operation S140, the total amount of first pollutant emissions PM and the total amount of first carbon emissions CM of the target city at the target year, and the total amount of second pollutant emissions PM of the target city at the reference year are based on 0 And a second total carbon emission amount CM 0 And calculating to obtain a pollution and carbon reduction synergistic effect coefficient I of the target city in the target year, wherein the reference year is the last year of the target year.
According to an embodiment of the invention, the first total pollutant emission PM is used to characterize a target annual total pollutant emission; total second pollutant emission amount PM 0 For characterizing the total amount of pollutant emissions in the reference year. The first total carbon emissions CM is used to characterize a target annual total carbon emissions; second total carbon emissions CM 0 The reference year is used to characterize the total carbon emissions.
According to the embodiment of the invention, the pollution-reducing and carbon-reducing synergistic effect coefficient I is shown as the formula (2).
(2)
Wherein,the calculation method is referred to as the following formula (3).
(3)
The calculation method is referred to as the following formula (4).
(4)
According to an embodiment of the invention, if presentAnd->And if the total amount of the carbon dioxide and the atmospheric pollutant is smaller than 0, the emission of the atmospheric pollutant and the carbon dioxide is reduced simultaneously, namely the conditions for entering the synergistic stage of pollution reduction and carbon reduction are met.
In operation S150, a carbon reduction synergy evaluation result for the target city is generated based on the carbon reduction synergy level value T and the carbon reduction synergy coefficient I. According to the embodiment of the invention, the pollution-reducing and carbon-reducing synergy evaluation result can be calculated according to the pollution-reducing and carbon-reducing synergy level value T and the pollution-reducing and carbon-reducing synergy effect coefficient I, and the specific calculation is shown as the formula (5). (5)
Wherein TI representsAnd (3) a pollution-reducing and carbon-reducing cooperative evaluation result, wherein T is a pollution-reducing and carbon-reducing cooperative synergistic level value, and I is a pollution-reducing and carbon-reducing cooperative effect coefficient.Weight value being the synergy level value T, +.>Is the weight value of the synergistic effect coefficient I.
According to the embodiment of the invention, the synergy level value and the synergy coefficient are combined and comprehensively evaluated to reduce pollution and carbon so as to obtain more objective and real results, thereby strictly controlling the total carbon pollution emission.
According to the embodiment of the invention, the pollution and carbon reduction synergy evaluation index model is constructed by adopting the analytic hierarchy process, a plurality of evaluation dimensions under different hierarchies are combined, and the pollution and carbon reduction synergy result analysis is carried out according to different corresponding influence strategies, so that the variety of index data is enriched, the data base and the dimension of the pollution and carbon reduction synergy result analysis are increased, objective monitoring and analysis on the pollution and carbon reduction synergy level are realized, and further, the pollutant emission and carbon emission actual emission synergy result is combined through the pollution emission and carbon emission actual emission synergy result obtained based on the analytic hierarchy process, so that the final pollution and carbon reduction synergy evaluation result is generated, and the result is more objective and accurate, has more references, and is beneficial to improving the pollution and carbon reduction synergy treatment efficiency.
Fig. 3 shows a flowchart of a method of calculating a first target weight and a second target weight according to an embodiment of the invention.
As shown in fig. 3, the method for calculating the first target weight and the second target weight in this embodiment includes operations S310 to S340.
In operation S310, a first subjective weight for each of the plurality of evaluation dimensions and a second subjective weight for each of the plurality of influence strategies are generated using expert scoring results for each of the evaluation dimensions and each of the influence strategies.
According to the embodiment of the invention, the first subjective weight is used for representing the subjective weight obtained directly by scoring each evaluation dimension according to an expert; the second subjective weight is used for representing subjective weight obtained directly by scoring corresponding different influence strategies under each evaluation dimension according to the expert.
According to the embodiment of the invention, the first subjective weight w is used ri To give a specific explanation for an example, a first subjective weight w of each of a plurality of evaluation dimensions is generated ri Specifically, the method comprises the steps 11 and 12.
In step 11, a scoring matrix is generated using expert scoring results for each evaluation dimension.
According to an embodiment of the invention, expert scoring result d ij For characterizing the scores of the expert on the relative importance of the different evaluation dimensions. The expert can rank the importance degree of each index, score according to national emission standards and historical experience, give out a judgment matrix factor and construct a judgment matrix D. Wherein, the judgment matrix factor and the meaning thereof refer to table 1.
Table 1 judges matrix factors and meanings thereof
Each judgment matrix D is shown in formula (6).
(6)
The order n is the number of indexes to be compared in each dimension.
The judgment matrix D satisfies the expression (7).
(7)
In step 12, the judgment matrix D is subjected to matrix transformation to generate first subjective weights w of each of a plurality of evaluation dimensions ri 。
According to an embodiment of the present invention, performing a matrix transformation process includes: will judgeAfter normalizing each column of the broken matrix D, each term factor in the matrix isAs in formula (8).
(8)
The judgment matrix is further added according to each row to obtain onenX 1 column vectors, each of which is defined asAs in formula (9).
(9)
Column vectorNormalization processing to obtain new productnX 1 column vectors, each term in the vector being defined as w ri As in formula (10).
(10)
Wherein w is ri The first subjective weight is the subjective weight of the criterion layer relative to the target layer; the relative weight vector is shown as formula (11).
(11)
Second subjective weight w rij For subjective weight of the index layer relative to the criterion layer, refer to the following formula (12).
(12)
The physical meaning of each parameter in the specific calculation method and formula refers to the first subjective weight calculation mode, and is not described herein.
In operation S320, a first objective weight for each of the plurality of evaluation dimensions and a second objective weight for each of the plurality of influence policies is generated using the objective reference information for each of the evaluation dimensions and each of the influence policies.
According to an embodiment of the invention, the first objective weight w oi To illustrate, a first objective weight w is generated for each of a plurality of evaluation dimensions oi Comprises the steps 21 to 25.
In step 21, objective reference information a for each evaluation dimension is used ij Generating a reference information matrix; for example, normalization processing is performed on the objective reference information to obtain a normalized information matrix X.
According to an embodiment of the present invention, objective reference information a ij Objective factors used for representing influence on the evaluation result, such as the improvement (reduction) degree of the actual water environment quality, the improvement (reduction) degree of the actual air quality, the improvement (reduction) degree of the new energy utilization rate and the like.
In step 22, the reference information matrix is normalized to generate a normalized information matrix X.
According to the embodiment of the invention, the standard information matrix is standardized by adopting the polar error method to obtain the standardized information matrix X, wherein the standardized information matrix X comprises elements X ij As in formula (13).
(13)
Wherein x is ij For normalizing elements in the information matrix X, a max A) maximum value of objective factors for influencing the evaluation result min Is the minimum value of objective factors that affect the evaluation result.
In step 23, the normalized information matrix X is converted into an information specific gravity matrix P, in which the individual element values P are present ij For characterization of: unit information quantity x represented by single evaluation dimension ij And multiple functions ofThe ratio of the total information amount represented by the individual evaluation dimensions; calculating the value P of a single element in the information weight matrix P ij As in formula (14).
(14)
In step 24, the information entropy e of each element in the information specific gravity matrix P is calculated ij Wherein the information entropy e ij The degree of uncertainty of the objective reference information used to characterize the individual evaluation dimensions.
According to an embodiment of the invention, the information weight matrix is calculatedjInformation entropy e of item index ij As in formula (15).
(15)
In step 25, a respective first objective weight w for the plurality of evaluation dimensions is generated based on the information entropy of each element in the information weight matrix oi 。
According to an embodiment of the invention, a first objective weight w oi Is an objective weight of the criterion layer relative to the target layer, as in equation (16).
(16)
Wherein e f And e h Are all constant.
Second objective weight w oij For objective weight of the index layer relative to the criterion layer, reference is made to (14), and the specific calculation method and the physical meaning of each parameter in the formula refer to the first subjective weight calculation mode, which is not described herein.
In operation S330, the first subjective weight w is based on ri And a first objective weight w oi Calculating to obtain a first target weight w i 。
According to an embodiment of the invention, the first target weight w i Can be weighted by a first subjective weight w ri And a first objective weight w oi Directly and directlyThe combination weighting can be obtained, and the weighting can also be obtained by optimizing other optimizing algorithms.
In operation S340, a second target weight is calculated based on the second subjective weight and the second objective weight.
According to the embodiment of the invention, the second target weight can be obtained by directly combining the second subjective weight and the second objective weight, and can also be obtained by optimizing the weight by utilizing other optimization algorithms.
According to the embodiment of the invention, the error caused by subjective factors existing in expert scoring is corrected by adopting the entropy weighting method, so that the weighting coefficient is more accurate and objective.
According to an embodiment of the present invention, in operation S230, the first subjective weight w is based on ri And a first objective weight w oi Calculating to obtain a first target weight w i Includes steps 31-32.
In step 31, based on the first subjective weight w ri And a first objective weight w oi Calculating to obtain a first comprehensive weight w qi 。
According to an embodiment of the invention, the first subjective weight w ri And a first objective weight w oi Combining weights to obtain a first comprehensive weight w qi First comprehensive weight w qi Is the composite weight of the criterion layer relative to the target layer, as in equation (17).
(17)
Second comprehensive weight w qij The overall weight of the index layer relative to the criteria layer is as in equation (18).
(18)
Further, the first comprehensive weight w can be weighted by a particle swarm optimization algorithm qi Updating to obtain a first target weight w i For the second comprehensive weight w qij Updating to obtain a first target weight w ij 。
Hereinafter, to the first comprehensive weight w qi For example, update is performed, and the second comprehensive weight w is exemplified qij The method for updating is the same as above and will not be described in detail here.
Specifically, in step 32, the first comprehensive weight w is weighted using a particle swarm optimization algorithm qi Updating to obtain a first target weight w i 。
According to the embodiment of the invention, since the determination of the comprehensive weight depends on the adjustment of the forward feedback mechanism, it is difficult to readjust the design index weight according to the standard evaluation result so as to establish a perfect feedback mechanism. In the embodiment of the invention, the first comprehensive weight and the second comprehensive weight are further optimized by utilizing an improved particle swarm algorithm.
According to an embodiment of the invention, the first comprehensive weight w is weighted by a particle swarm optimization algorithm qi Updating to obtain a first target weight w i The method specifically comprises the following steps: the fitness of each particle is calculated based on the position of each particle in the population of particles. Wherein each particle is used for representing an optimization mode for updating and optimizing the first comprehensive weight, and the position of the particle is used for representing: updating and optimizing the first comprehensive weight to obtain a weight updating value, wherein the initial position of the particle is a random initialization value of the first comprehensive weight; according to the fitness of each particle, determining the optimal position of an individual corresponding to each particle, and determining the optimal position of the population of the particle population; calculating an objective function value based on the individual optimal position and the population optimal position; updating the position of each particle according to the objective function value; until the objective function value meets a preset termination condition, taking the optimal population position of the particle population as a first primary selection weight for preliminarily updating the first comprehensive weight; and adjusting the first initial selection weight to obtain a first target weight.
Fig. 4 shows a flow chart for optimizing the composite weights using a particle swarm optimization algorithm.
As shown in fig. 4, the first comprehensive weight w is given to the first comprehensive weight w by using a particle swarm optimization algorithm qi Updating to obtain a first target weight w i The method specifically comprises the following steps: according to an embodiment of the invention, the particles are randomly initialized, and the random initialization results in the position x of each particle id And velocity v id . The fitness of each particle is calculated from the position of each particle in the population of particles. And comparing the fitness of each particle with the best passing position, and taking the current fitness as an individual optimal position pbest if the current fitness is better. And comparing the fitness of each particle with the best passing position, and taking the current fitness as the population optimal position gbest if the current fitness is better. And updating the current position according to the obtained individual optimal position and population optimal position and judging whether the objective function meets the preset termination condition. If the preset termination condition is met, outputting the first initial selection weight; if the preset termination condition is not met, updating the position and the speed of each particle, and carrying out the k+1st iteration until the objective function value meets the preset termination condition, and then outputting the first primary selection weight.
Wherein the objective function value is calculated based on the individual optimum position and the population optimum position as in formula (19).
(19)
Wherein Z represents a group of generated evaluation results, and the evaluation results Z are calculated according to the optimal positions of the individuals and the optimal positions of the population in iterative updating.
Further, the particle position is updated as in equation (20).
(20)
The particle velocity update method is shown in formula (21).
(21)
Wherein,indicating particlesiAt the position ofkThe first iterationdThe speed of the individual dimensions; />Indicating particlesiAt the position ofkThe first iterationdThe position of the individual dimensions;uis inertial weight (super parameter);p id is a particleiIs the optimal position of (a);pgbestis the optimal position of the particle population;c 1 、c 2 is an acceleration factor;ris [0,1]Random numbers in between.
According to the embodiment of the invention, as the particle swarm optimization algorithm has the problem of local optimization, a dynamic inertia weight can be introduced on the basis of the first primary selection weight and the second primary selection weight to help a particle individual jump out of a local optimal solution, and finally a first target weight and a second target weight are obtained.
Specifically, the first preliminary selection weight is exemplified.
According to the embodiment of the invention, the first initial selection weight is adjusted to obtain a first target weight w i Comprising the following steps: determining an average value and a minimum value of an objective function in multiple iterative computations; according to the first initial selection weight and the individual optimal position corresponding to each particle when iteration is terminated, a plurality of groups of reference positions are obtained through calculation; wherein, a plurality of groups of reference positions w id The calculation method of (2) is as shown in the formula (22).
(22)
w id For one of the reference positions,for the first initial selection weight, i.e. the population optimal position of the particle population at the end of the iteration, +.>For the individual optimal position corresponding to each particle at the end of the iteration, N is the population number,ris [0,1]Random numbers in between.
According to the maximum value w in the multiple groups of reference positions max And a minimum value w min Average value F of objective function avg Minimum value F of objective function min Obtaining a first target weight w i As in formula (23).
(23)
Wherein w is i For the first target weight, w max Is the maximum value of a plurality of reference positions, w min For the minimum of the plurality of reference positions, F is the current objective function value of the particle. FIG. 5 illustrates a flow chart of a method of calculating a carbon reduction synergy level value for a pollution reduction according to an embodiment of the invention. Fig. 6 shows a flowchart of a method for generating a synergistic evaluation result of pollution reduction and carbon reduction according to another embodiment of the present invention. The following is a detailed description with reference to fig. 5 and 6.
According to the embodiment of the invention, a pollution and carbon reduction cooperative evaluation index model is constructed aiming at a target city, wherein in fig. 5, the constructed hierarchical model comprises a target layer, a criterion layer and an index layer. Establishing respective judgment matrixes of different evaluation dimensions and different influence strategies according to expert scores by adopting an analytic hierarchy process, and calculating to obtain respective subjective weights; and meanwhile, carrying out standardization processing on the judgment matrix by adopting an entropy weight method to obtain an information proportion matrix, and calculating to obtain objective weights according to the information entropy in the information proportion matrix. And combining and weighting the subjective weight and the objective weight, and calculating to obtain the comprehensive weight. Since the determination of the preliminary selection weight depends on the adjustment of the feed-forward mechanism, it is difficult to readjust the design preliminary selection weight according to the evaluation result of the criterion. Therefore, the particle swarm optimization algorithm is added to further optimize the comprehensive weight, so that the primary selection weight is obtained. Further, a dynamic inertia weight factor is introduced into the particle swarm optimization algorithm, so that the obtained target weight is embodied as a global optimal weight.
According to the embodiment of the invention, as shown in fig. 6, the pollution and carbon reduction synergy level value T of the target city in the target year is calculated according to the index score obtained by the expert score and the target weight obtained by the calculation.
According to the embodiment of the invention, the pollution reduction and carbon reduction synergistic effect coefficient I of the target city in the target year is calculated based on the pollutant emission total and the carbon emission total of the target city in the target year and the pollutant emission total and the carbon emission total of the target city in the reference year.
According to the embodiment of the invention, the pollution-reducing and carbon-reducing synergy evaluation result is generated based on the pollution-reducing and carbon-reducing synergy level value T and the pollution-reducing and carbon-reducing synergy effect coefficient I.
According to the embodiment of the invention, the method for generating the pollution reduction and carbon reduction cooperative evaluation result further comprises the following steps: and obtaining energy consumption values of the target city in the target year aiming at a plurality of statistical energy consumption items and pollution discharge coefficients corresponding to the statistical energy consumption items.
According to an embodiment of the invention, the plurality of statistical energy consumption terms comprises at least: industry energy consumption term E i，t I represents a major industry, including the steel, chemical, building and other coal industries. Power generation energy consumption item F j，t J represents a power generation mode, including thermal power, nuclear power, hydroelectric power, wind power and solar energy. Heat supply energy consumption item H m，t， m represents a heating mode, including coal-fired heating and non-coal-fired heating. Transportation energy consumption term T n，t N represents a traffic mode, including a gasoline car and a new energy car.
And calculating to obtain the first pollutant emission total amount of the target city in the target year according to the energy consumption values of the plurality of statistical energy consumption items and the pollution discharge coefficients corresponding to the statistical energy consumption items.
According to an embodiment of the invention, the first total pollutant emission amount is used to characterize a total pollutant emission amount of a plurality of statistical energy consumption terms. The first total amount of pollutant emissions PM is as in formula (24).
(24)
Wherein, the blowdown coefficient that each statistics energy consumption item corresponds includes: pollution discharge coefficient per unit of industrial productionPollution discharge coefficient per unit power generation>Pollution discharge coefficient of unit heat supply>Pollution discharge coefficient per unit traffic volume-> 。
And calculating to obtain the first carbon emission total of the target city in the target year according to the energy consumption values of the plurality of statistical energy consumption items and the carbon emission coefficients corresponding to the statistical energy consumption items.
According to an embodiment of the invention, the first total carbon emission is used to characterize a total carbon emission of a plurality of statistical energy consumption terms. The first total carbon emissions are as in formula (25).
(25)
Wherein, carbon removal coefficient that each statistics energy consumption item corresponds includes: carbon emission coefficient per unit of industrial production Carbon rejection coefficient per unit power generation>Carbon rejection coefficient per unit heat supply>Carbon number per unit traffic volume +.> 。
According to an embodiment of the present invention, the first total pollutant emission amount upper limit PM is set according to a pollutant emission standard, a carbon emission standard or historical experience lim And a first carbon emission total amount upper limit CM lim . According to the preset discrimination condition pairThe total amount of first pollutant emissions and/or the total amount of first carbon emissions for a target year for the target region is regulated. Specifically, the predetermined criterion may be that the total amount of first pollutant emissions PM is less than or equal to the first upper limit of first pollutant emissions PM lim Or the first carbon emission total amount CM is less than or equal to the first carbon emission upper limit CM lim 。
Further, whether the pollutant discharge amounts corresponding to different energy consumption items meet the preset judging sub-conditions can be judged respectively. And controlling pollutant emission and/or carbon emission of each energy consumption item of the target year of the target area according to the preset judging sub-condition. Specifically, industrial atmospheric pollutant emission PME is less than or equal to industrial atmospheric pollutant emission upper limit PME lim The method comprises the steps of carrying out a first treatment on the surface of the Industry carbon emission amount CME is less than or equal to industry carbon emission upper limit CME lim . PMF (particulate matters) emission amount of atmospheric pollutants of power system is less than or equal to PMF (particulate matters) emission upper limit of atmospheric pollutants of power system lim The method comprises the steps of carrying out a first treatment on the surface of the The carbon emission quantity CMF of the electric power system is less than or equal to the carbon emission upper limit CMF of the electric power system lim . The discharge amount PMH of the air pollutants of the heating system is less than or equal to the discharge upper limit PMH of the air pollutants of the heating system lim The method comprises the steps of carrying out a first treatment on the surface of the The carbon emission amount CMH of the heating system is less than or equal to the carbon emission upper limit CMH of the heating system lim . The emission PMT of the atmospheric pollutants of the traffic system is less than or equal to the emission upper limit PMT of the atmospheric pollutants of the traffic system lim The method comprises the steps of carrying out a first treatment on the surface of the Traffic system carbon emission CMT is less than or equal to traffic system carbon emission upper limit CMT lim Etc.
Based on the pollution-reducing and carbon-reducing cooperative evaluation result generation method, the invention also provides a pollution-reducing and carbon-reducing cooperative evaluation result generation device. The device will be described in detail below in connection with fig. 7.
Fig. 7 shows a block diagram of a configuration of a pollution reduction and carbon reduction cooperative evaluation result generating device according to an embodiment of the present invention.
As shown in fig. 7, the pollution reduction and carbon reduction cooperative evaluation result generating apparatus 700 of this embodiment includes a construction module 710, a first calculation module 720, a second calculation module 730, a third calculation module 740, and a generation module 750.
The construction module 710 is configured to construct a pollution reduction and carbon reduction collaborative evaluation index model for a target city, where the pollution reduction and carbon reduction collaborative evaluation index model includes a target layer, a criterion layer, and an index layer, the criterion layer includes a plurality of evaluation dimensions that affect a pollution reduction and carbon reduction effect of the target city, the index layer includes a plurality of influence policies that affect the pollution reduction and carbon reduction effect of the target city in each evaluation dimension, and the target layer is configured to characterize: based on a plurality of influence strategies under each evaluation dimension, the pollution and carbon reduction collaborative evaluation result of the target city is obtained. In an embodiment, the construction module 710 may be configured to perform the operation S110 described above, which is not described herein.
The first calculation module 720 is configured to calculate a first target weight of each of the plurality of evaluation dimensions based on the pollution-reducing and carbon-reducing collaborative evaluation index model, and determine a second target weight of each of the plurality of influence strategies under each of the evaluation dimensions. In an embodiment, the first calculating module 720 may be configured to perform the operation S120 described above, which is not described herein.
The second calculating module 730 is configured to calculate, based on the index scores of the plurality of impact policies in each evaluation dimension in the target year, the first target weight, and the second target weight, a pollution and carbon reduction synergy level value of the target city in the target year. In an embodiment, the second computing module 630 may be configured to perform the operation S230 described above, which is not described herein.
The third calculation module 740 is configured to calculate a synergistic effect coefficient of reducing pollution and reducing carbon of the target city in the target year based on the total first pollutant emission and the total first carbon emission of the target city in the target year, and the total second pollutant emission and the total second carbon emission of the target city in the reference year, where the reference year is the last year of the target year. In an embodiment, the third calculation module 740 may be used to perform the operation S140 described above, which is not described herein.
The generating module 750 is configured to generate a synergistic evaluation result of carbon reduction for the target city based on the synergistic level value of carbon reduction and the synergistic effect coefficient of carbon reduction. In an embodiment, the generating module 750 may be configured to perform the operation S150 described above, which is not described herein.
Any of the building module 710, the first computing module 720, the second computing module 730, the third computing module 740, and the generating module 750 may be combined in one module to be implemented, or any of the modules may be split into a plurality of modules, according to an embodiment of the present invention. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the invention, at least one of the building block 710, the first computing block 720, the second computing block 730, the third computing block 740, and the generating block 750 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or as hardware or firmware in any other reasonable manner of integrating or packaging the circuitry, or as any one of or a suitable combination of three of software, hardware, and firmware. Alternatively, at least one of the construction module 710, the first calculation module 720, the second calculation module 730, the third calculation module 740, and the generation module 750 may be at least partially implemented as a computer program module, which when executed, may perform the corresponding functions.
Those skilled in the art will appreciate that the features recited in the various embodiments of the invention and/or in the claims may be combined in various combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the invention. In particular, the features recited in the various embodiments of the invention and/or in the claims can be combined in various combinations and/or combinations without departing from the spirit and teachings of the invention. All such combinations and/or combinations fall within the scope of the invention.
The embodiments of the present invention are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the invention is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the invention, and such alternatives and modifications are intended to fall within the scope of the invention.
Claims (10)
1. The method for generating the pollution and carbon reduction cooperative evaluation result is characterized by comprising the following steps of:
constructing a pollution and carbon reduction cooperative evaluation index model aiming at a target city, wherein the pollution and carbon reduction cooperative evaluation index model comprises a target layer, a criterion layer and an index layer, the criterion layer comprises a plurality of evaluation dimensions influencing the pollution and carbon reduction effect of the target city, the index layer comprises a plurality of influence strategies influencing the pollution and carbon reduction effect of the target city under each evaluation dimension, and the target layer is used for representing: based on a plurality of influence strategies under each evaluation dimension, the pollution and carbon reduction collaborative evaluation result of the target city is obtained;
Calculating a first target weight of each of a plurality of evaluation dimensions based on the pollution reduction and carbon reduction cooperative evaluation index model, and determining a second target weight of each of a plurality of influence strategies under each evaluation dimension;
calculating to obtain a pollution and carbon reduction synergy level value of the target city in the target year based on the index scores of the influence strategies in each evaluation dimension in the target year, the first target weight and the second target weight;
calculating a pollution reduction and carbon reduction synergistic effect coefficient of the target city in the target year based on the first pollutant emission total amount and the first carbon emission total amount of the target city in the target year and the second pollutant emission total amount and the second carbon emission total amount of the target city in a reference year, wherein the reference year is the last year of the target year;
and generating a pollution reduction and carbon reduction synergy evaluation result aiming at the target city based on the pollution reduction and carbon reduction synergy level value and the pollution reduction and carbon reduction synergy effect coefficient.
2. The method of claim 1, wherein calculating a first target weight for each of a plurality of the evaluation dimensions, and determining a second target weight for each of a plurality of impact policies at each of the evaluation dimensions comprises:
Generating a first subjective weight of each of a plurality of evaluation dimensions and a second subjective weight of each of a plurality of influence strategies by using expert scoring results for each of the evaluation dimensions and each of the influence strategies;
generating a first objective weight for each of the plurality of evaluation dimensions and each of the influence policies using objective reference information for each of the evaluation dimensions and each of the influence policies, and generating a second objective weight for each of the plurality of influence policies;
calculating the first target weight based on the first subjective weight and the first objective weight;
and calculating the second target weight based on the second subjective weight and the second objective weight.
3. The method of claim 2, wherein calculating the first target weight based on the first subjective weight and the first objective weight comprises:
calculating a first comprehensive weight based on the first subjective weight and the first objective weight;
and updating the first comprehensive weight by using a particle swarm optimization algorithm to obtain the first target weight.
4. The method of claim 3, wherein updating the first integrated weight using a particle swarm optimization algorithm to obtain the first target weight comprises:
The following operations S1-S4 are iteratively performed:
operation S1: calculating fitness of each particle based on a position of each particle in the population of particles, wherein each particle is used for representing an optimization mode for updating and optimizing the first comprehensive weight, and the position of each particle is used for representing: updating and optimizing the first comprehensive weight to obtain a weight updating value, wherein the initial position of particles is a random initializing value of the first comprehensive weight;
operation S2: according to the fitness of each particle, determining the optimal position of an individual corresponding to each particle, and determining the optimal position of the population of the particle population;
operation S3: calculating an objective function value based on the individual optimal position and the population optimal position;
operation S4: updating the position of each particle according to the objective function value;
until the objective function value meets a preset termination condition, taking the optimal population position of the particle population as a first initial selection weight for initially updating the first comprehensive weight;
and adjusting the first initial selection weight to obtain the first target weight.
5. The method of claim 4, wherein adjusting the first preliminary selection weight to obtain the first target weight comprises:
Determining an average value and a minimum value of an objective function in multiple iterative computations;
according to the first initial selection weight and the individual optimal position corresponding to each particle when iteration is terminated, a plurality of groups of reference positions are obtained through calculation;
and obtaining the first target weight according to the maximum value and the minimum value in the multiple groups of reference positions, the average value of the target function and the minimum value of the target function.
6. The method of claim 2, wherein generating a first objective weight for each of a plurality of the evaluation dimensions comprises:
generating a reference information matrix by using objective reference information for each evaluation dimension;
carrying out standardization processing on the reference information matrix to generate a standardized information matrix;
converting the normalized information matrix into an information specific gravity matrix, wherein single element values in the information specific gravity matrix are used for representing: a ratio of the amount of unit information represented by a single evaluation dimension to the total amount of information represented by the plurality of evaluation dimensions;
calculating information entropy of each element in the information proportion matrix, wherein the information entropy is used for representing the uncertainty degree of objective reference information of each evaluation dimension;
Generating a first objective weight for each of the plurality of evaluation dimensions based on the information entropy of each element in the information weight matrix.
7. The method of claim 2, wherein generating the first subjective weight for each of the plurality of evaluation dimensions comprises:
generating a scoring matrix by using expert scoring results for each of the scoring dimensions;
and performing matrix transformation processing on the scoring matrix to generate first subjective weights of each of the plurality of evaluation dimensions.
8. The method as recited in claim 1, further comprising:
obtaining energy consumption values of the target city in the target year aiming at a plurality of statistical energy consumption items and pollution discharge coefficients corresponding to the statistical energy consumption items, wherein the statistical energy consumption items at least comprise industrial energy consumption items, power generation energy consumption items, heat supply energy consumption items and transportation energy consumption items;
and calculating to obtain the total emission amount of the first pollutant of the target city in the target year according to the energy consumption values of a plurality of statistical energy consumption items and the pollution discharge coefficient corresponding to each statistical energy consumption item.
9. The method as recited in claim 1, further comprising:
Obtaining energy consumption values of the target city in the target year aiming at a plurality of statistical energy consumption items and carbon emission coefficients corresponding to the statistical energy consumption items, wherein the statistical energy consumption items at least comprise industrial energy consumption items, power generation energy consumption items, heat supply energy consumption items and transportation energy consumption items;
and calculating to obtain the first carbon emission total of the target city in the target year according to the energy consumption values of a plurality of statistical energy consumption items and the carbon emission coefficients corresponding to the statistical energy consumption items.
10. The pollution and carbon reduction cooperative evaluation result generating device is characterized by comprising:
the construction module is used for constructing a pollution reduction and carbon reduction cooperative evaluation index model aiming at a target city, wherein the pollution reduction and carbon reduction cooperative evaluation index model comprises a target layer, a criterion layer and an index layer, the criterion layer comprises a plurality of evaluation dimensions for influencing the pollution reduction and carbon reduction effect of the target city, the index layer comprises a plurality of influence strategies for influencing the pollution reduction and carbon reduction effect of the target city under each evaluation dimension, and the target layer is used for representing: based on a plurality of influence strategies under each evaluation dimension, the pollution and carbon reduction collaborative evaluation result of the target city is obtained;
The first calculation module is used for calculating first target weights of each of a plurality of evaluation dimensions based on the pollution and carbon reduction cooperative evaluation index model, and determining second target weights of each of a plurality of influence strategies under each evaluation dimension;
the second calculation module is used for calculating and obtaining a pollution and carbon reduction synergy level value of the target city in the target year based on the index scores of the influence strategies in each evaluation dimension in the target year, the first target weight and the second target weight;
a third calculation module, configured to calculate, based on a first total pollutant emission amount and a first total carbon emission amount of the target city in the target year, and a second total pollutant emission amount and a second total carbon emission amount of the target city in a reference year, a pollution-reducing and carbon-reducing synergistic effect coefficient of the target city in the target year, where the reference year is a year previous to the target year;
the generation module is used for generating a pollution reduction and carbon reduction cooperative evaluation result aiming at the target city based on the pollution reduction and carbon reduction cooperative synergistic level value and the pollution reduction and carbon reduction cooperative effect coefficient.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106651211A (en) * | 2016-12-30 | 2017-05-10 | 吉林师范大学 | Different-scale regional flood damage risk evaluation method |
| CN107480856A (en) * | 2017-07-06 | 2017-12-15 | 浙江大学 | Based on the sale of electricity company power customer appraisal procedure for improving similarity to ideal solution ranking method |
| CN108876131A (en) * | 2018-06-06 | 2018-11-23 | 国家电网有限公司 | Deposit project preferred method based on power grid effect contribution degree |
-
2024
- 2024-01-30 CN CN202410121477.9A patent/CN117649132A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106651211A (en) * | 2016-12-30 | 2017-05-10 | 吉林师范大学 | Different-scale regional flood damage risk evaluation method |
| CN107480856A (en) * | 2017-07-06 | 2017-12-15 | 浙江大学 | Based on the sale of electricity company power customer appraisal procedure for improving similarity to ideal solution ranking method |
| CN108876131A (en) * | 2018-06-06 | 2018-11-23 | 国家电网有限公司 | Deposit project preferred method based on power grid effect contribution degree |
Non-Patent Citations (3)
| Title |
|---|
| 李中杰 等: "昆明市减污降碳绿色发展协同效应及驱动因素研究", 《环境生物学》, vol. 5, no. 12, 31 December 2023 (2023-12-31) * |
| 李飞 等: "我国省域CO2-PM2.5-O3时空关联效应与协同管控对策", 《中国环境科学》, vol. 43, no. 12, 20 December 2023 (2023-12-20), pages 1 - 2 * |
| 王敏 等: "城市减污降碳协同创新评价技术体系构建及应用研究", 《中国环境科学学会2023年科学技术年会论文集（一）》, 22 April 2023 (2023-04-22), pages 1 - 2 * |
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