CN-122022083-A - Mobile source carbon-sewage collaborative emission reduction full-flow analysis method and system

CN122022083ACN 122022083 ACN122022083 ACN 122022083ACN-122022083-A

Abstract

The invention discloses a full flow analysis method and a full flow analysis system for mobile source carbon pollution collaborative emission reduction, wherein the method comprises the following steps of S1, constructing a current situation of a vehicle conservation amount and prediction integrated coupling model, generating a localized basic emission list, S2, distributing the basic emission list to a high-resolution grid to obtain a space grid emission list, S3, quantifying an uncertainty boundary of the emission list by adopting a Monte Carlo simulation method, S4, constructing a multi-dimensional emission reduction scene, iteratively calculating the net emission reduction effect of each scene year by utilizing a nonlinear superposition formula, S5, constructing a pollutant-greenhouse gas-economic benefit three-dimensional dual collaborative evaluation system, and outputting comprehensive benefit ranking and optimal policy combination suggestion of each emission reduction scene. The system comprises an integrated coupling list construction module, a space gridding distribution module, an uncertainty quantitative analysis module, a multidimensional scene simulation and superposition calculation module, a three-dimensional double-cooperation comprehensive evaluation module and a data management and storage module.

Inventors

HU JINGNAN
ZHU RENCHENG
HUANG JIAYU
Han Quankang
LI ZHE
ZHANG YUZHE
ZHANG BEIBEI

Assignees

中国环境科学研究院

Dates

Publication Date: 20260512
Application Date: 20260415

Claims (9)

1. A total flow analysis method for the synergistic emission reduction of carbon pollutants of a mobile source is characterized by comprising the following steps: s1, constructing a current situation and prediction integrated coupling model of the vehicle maintenance quantity, namely synchronously measuring and calculating the current situation maintenance quantity and the prediction maintenance quantity of the vehicle in the planning period of a reference year in a unified calculation frame by taking historical registration data of a target area as constraint conditions and socioeconomic prediction data as driving factors, and generating a localized basic emission list with time continuity; S2, space gridding fine allocation, namely constructing a standard road length model based on road grade weight, establishing a road network activity level mapping relation, and discretizing and allocating a localized basic emission list to a high-resolution grid to obtain a gridding emission list with space attributes; s3, uncertainty quantitative analysis, namely adopting a Monte Carlo simulation method to carry out iterative simulation on the maintenance quantity, the annual average driving mileage and the emission factor of the motor vehicle, outputting a 95% confidence interval of a gridding emission list, and quantifying an uncertainty boundary of data; S4, multi-dimensional scene simulation and nonlinear superposition are carried out, namely a hierarchical emission reduction scene system comprising a reference scene, a single control scene, a combined scene and a comprehensive scene is constructed, a comprehensive emission reduction rate is calculated by adopting a multi-measure nonlinear superposition formula aiming at the combined scene and the comprehensive scene, repeatedly calculated emission reduction is removed, and an emission prediction list of each emission reduction scene is generated iteratively year by year; S5, constructing a pollutant-greenhouse gas-economic benefit three-dimensional double-cooperative evaluation system, namely respectively calculating an environment cooperative index and an economic cooperative index, forming a comprehensive cooperative index through multi-objective normalization and weighted fusion, and outputting comprehensive benefit ranking and optimal policy combination suggestion of each emission reduction scene.
2. The method for total flow analysis of carbon pollution collaborative emission reduction of a mobile source according to claim 1, wherein the step S1 comprises the following steps: s11, acquiring basic data of a target area, wherein the basic data comprise historical registration data of a motor vehicle, aged scrapping recovery data, macro economic parameters and planning period prediction data; S12, constructing a current situation of the vehicle maintenance quantity and prediction integrated coupling model, namely describing a physical attenuation rule of the vehicle by a survival curve, determining a maintenance constraint of a historical vehicle, simulating an S-shaped saturation trend of a per capita maintenance rate by a Gompertz function, determining an upper limit drive, introducing nonlinear regulation of short-term economic fluctuation on the vehicle purchasing demand by an elasticity coefficient method, and synchronously measuring and calculating the current situation of a reference year, the vehicle type of a planning period and the vehicle maintenance quantity of a discharge standard in the same calculation frame by the deep coupling of the maintenance constraint, the upper limit drive and a correction mechanism of the historical vehicle; S13, constructing a localized emission factor library, namely merging localization parameters of a target area based on a COPERT model, wherein the localization parameters comprise vehicle type structural distribution, fuel type duty ratio, environmental meteorological conditions and vehicle operation condition data, and generating localized dynamic emission factors; S14, calculating a localized basic emission list, namely calculating the annual emission total of the pollutants and the greenhouse gases of the motor vehicle in the target area based on the maintenance quantity of the motor vehicle and localized dynamic emission factors, wherein the calculation formula is as follows: ; Wherein, the The total annual emission of the motor vehicle; is of the automotive type; is the first The amount of maintenance of the class of motor vehicles; is the first A unit mileage emission factor of the motor vehicle; is the first Annual average range of motor vehicles.
3. The method for total flow analysis of carbon pollution collaborative emission reduction of a mobile source according to claim 2, wherein in the step S12, a model for integrated coupling of a vehicle maintenance quantity and prediction comprises the following three logic modules: (1) The long-term trend prediction module is used for measuring and calculating the saturation trend of the average maintenance rate of the motor vehicle based on the average GDP level and taking the saturation trend as a theoretical reference for the increase of the maintenance amount of the motor vehicle, and the calculation formula is as follows: ; Wherein V is the average conservation rate of motor vehicles, V S is the saturation value of the conservation rate of motor vehicles, V p is the conservation amount of light buses, g is the average GDP, and P is the number of people; (2) The short-term fluctuation correction module is used for calculating an elasticity coefficient based on historical data, deriving a vehicle type maintenance quantity increase rate and calculating a final maintenance quantity according to future economic increase expectations, wherein the calculation formula is as follows: calculating a historical elasticity coefficient: ; Calculating the future economic growth rate: ; Deducing the growth rate of the future motor vehicle: ; Calculating and predicting the annual vehicle maintenance amount: ; In the formula, Is the quantity of the motor vehicle which is divided according to the types; j is the vehicle type; Is the first The annual j-type motor vehicle keeps the volume increasing rate; Is the first Annual average income year growth rate; is the constant elastic coefficient of the j-th motor vehicle; The total domestic production value of the reference year; GDP values for the target year or reference year; T is the predicted target year or reference year; The method comprises the steps of predicting a reference year; (3) The inventory evolution and increment coupling module is used for calculating the old vehicle retention based on a survival curve and reversely pushing the new vehicle registration amount by combining the total retention prediction value: ; ; Wherein S r,T is the holding quantity of the emission period r in the target year T, N r,y is the number of vehicles in the emission period r registered in the y year; θ r,y is the theoretical elimination amount duty ratio of the discharge stage r in the y year; The number of vehicles in the discharge stage r which is theoretically eliminated in the y-th year.
4. The method for analyzing the total flow of carbon and pollution collaborative emission reduction of a mobile source according to claim 3, wherein in the step S13, specific calculation logic for constructing a localized emission factor library is as follows, based on a basic emission factor of a COPERT model, an average vehicle speed, an environmental parameter and an accumulated driving range of a target area are introduced for localized correction to generate a localized dynamic emission factor, and a correction formula is as follows: ; Wherein: month m of the target area Localized emission factors for motor vehicles; is the first The standard emission factor of the motor vehicle under the standard test working condition; Is a velocity correction coefficient; is an environmental correction coefficient; is a degradation correction coefficient; and correcting the coefficient for the load.
5. The method for total flow analysis of carbon pollution collaborative emission reduction of a mobile source according to claim 4, wherein the fine spatial gridding allocation in the step S2 comprises the following specific steps: s21, constructing a standard road length model based on road grade weight, namely setting weight coefficients according to traffic capacity differences of roads of different grades, and converting the actual road network length in a target area into standard road length with unified dimension; S22, establishing a space allocation mapping relation, namely analyzing the topological structure of road network data by using a geographic information system, calculating the activity level duty ratio of the motor vehicle of various roads in the grid unit, and discretizing and allocating the localized basic emission list obtained in the step S1 to a grid with the resolution of 3km multiplied by 3km to generate a grid emission list with space attributes.
6. The method for analyzing the total flow of the mobile source carbon pollution synergistic emission reduction according to claim 5, wherein the method is characterized in that uncertainty quantitative analysis in the step S3 is implemented by constructing a probability distribution model by adopting a Monte Carlo simulation method, selecting a vehicle holding quantity, an annual average driving distance and an emission factor as key uncertainty parameters for iterative simulation, setting the variation coefficient of the vehicle holding quantity to be 5%, the variation coefficient of the annual average driving distance to be 15%, the variation coefficient of the emission factor to be 10%, setting the iteration times to be not less than 10000, calculating probability density distribution of a meshed emission list by random sampling, and outputting a 95% confidence interval of the total emission.
7. The method for total flow analysis of carbon pollution collaborative emission reduction of a mobile source according to claim 6, wherein the step S4 comprises the following steps: S41, constructing a hierarchical emission reduction scene system, namely respectively constructing a reference scene, a single control scene, a combined scene and a comprehensive scene, wherein the reference scene continues the current set policy; s42, calculating nonlinear net effects of superposition of a plurality of emission reduction measures, namely defining annual emission reduction amplitude and implementation sequence of each emission reduction scene, iteratively calculating an emission prediction list of each emission reduction scene in 2025-2035 year by year, and calculating comprehensive emission reduction rate by adopting a multi-measure nonlinear superposition formula aiming at combined scenes and comprehensive scenes to avoid repeated calculation of emission reduction, wherein the calculation formula is as follows: ; Wherein, the The total annual emission reduction amount after superposition of a plurality of emission reduction measures is reduced; n is the total number of superimposed emission reduction measures; is the first Theoretical emission reduction rate when the emission reduction measures are implemented independently.
8. The method for total flow analysis of carbon pollution emission reduction by mobile sources according to claim 7, wherein the pollutant-greenhouse gas-economic benefit three-dimensional double-cooperative evaluation system in the step S5 is implemented by the following steps and calculation formulas: S51, constructing a unified evaluation dimension, namely uniformly converting the greenhouse gas emission into CO 2 equivalent, uniformly characterizing different types of pollutants into atmospheric pollutant equivalent by adopting a comprehensive equivalent normalization method, wherein the calculation formula is as follows: ; Wherein, the For the integrated equivalent value of atmospheric pollutant emissions, Is the first The discharge amount of the pollutant in the seed gas, Is the corresponding equivalent coefficient; S52, calculating an environmental emission reduction co-index, namely respectively calculating emission reduction rates of greenhouse gases and atmospheric pollutants under each emission reduction scene, wherein the emission reduction rates serve as basic data for environmental co-evaluation, and the calculation formula is as follows: ; Wherein, the And The total emission amounts of greenhouse gases and atmospheric pollutants before the emission reduction measures are implemented; And Respectively reducing the emission of greenhouse gases and atmospheric pollutants after implementation of measures; for the emission reduction rate of greenhouse gases, The emission reduction rate of the atmospheric pollutants is reduced; S53, mapping the emission reduction scene to a two-dimensional coordinate system by adopting a synergistic effect coordinate method, wherein the horizontal axis in the two-dimensional coordinate system is the emission reduction effect of the atmospheric pollutants, the vertical axis is the emission reduction effect of the greenhouse gases, identifying the optimal synergistic emission reduction scene of the first quadrant, and eliminating the negative synergistic scene of another index deterioration caused by a single emission reduction measure; S54, constructing a synergy-economy comprehensive index, and carrying out weighted calculation according to an equal weight method to output comprehensive benefit ranking and optimal policy combination suggestion of each emission reduction scene, wherein the calculation formula of the synergy-economy comprehensive index is as follows: ; ; ; ; Wherein, the As a contaminant Is reduced in displacement; is the equivalent coefficient of the pollutant; reducing the emission t of CO 2 pollutants; The comprehensive equivalent emission reduction of conventional pollutants is realized; = =0.5 means CO 2 is equally important as conventional contaminants; The maximum value of the corresponding index in the sample emission reduction scene; Is of carbon valence; As a contaminant Tax rate or unit damage value of (1); 、 the weighting coefficients of the environment coordination index and the economic benefit index are respectively, env 'is the standardized environment coordination index, econ' is the standardized economic benefit index.
9. The mobile source carbon pollution cooperative emission reduction full-flow analysis system is used for implementing the mobile source carbon pollution cooperative emission reduction full-flow analysis method according to claim 8 and is characterized by comprising a list construction module for constructing a basic emission list, a space distribution module for carrying out space gridding distribution on the basic emission list, an uncertainty analysis module for quantifying uncertainty of the space gridding emission list, a scene setting module for constructing emission reduction scenes and calculating emission prediction lists of all emission reduction scenes and emission reduction measure superposition effects, a cooperative evaluation module for calculating and outputting cooperative emission reduction effect ranking of all emission reduction scenes and optimal policy suggestions, and a data storage module for carrying out data storage, wherein the list construction module, the space distribution module, the uncertainty analysis module, the scene setting module and the cooperative evaluation module are sequentially connected and realize data transmission operation, and the list construction module, the space distribution module, the uncertainty analysis module, the scene setting module and the cooperative evaluation module are all connected with the data storage module and realize data storage operation.

Description

Mobile source carbon-sewage collaborative emission reduction full-flow analysis method and system Technical Field The invention relates to the field of intersection of atmospheric environment science and traffic transportation planning, in particular to a mobile source carbon pollution cooperative emission reduction full-flow analysis method and system based on multi-model coupling and multi-dimensional cooperative evaluation. Background With the continuous proliferation of the conservation amount of motor vehicles, the mobile source becomes a main contribution source of urban atmospheric pollution and greenhouse gas emission in China. Nitrogen oxides and volatile organic compounds emitted by motor vehicles are key precursors for causing ozone and secondary particulate matter pollution, and carbon dioxide emitted by the motor vehicles is also a great challenge facing the carbon peak in the transportation field. For this reason, there is a need to develop a scientific mobile source emission reduction list and management and control strategy. However, in the current mobile source emission inventory and policy evaluation technology, there are the following key technical bottlenecks that need to be resolved: 1. The preservation amount prediction and the current situation investigation have logic fracture, which leads to data chain faults. In the prior art, when an emission list is compiled, a segmentation processing mode is generally adopted, wherein static registration data of a vehicle management department is mainly relied on for the current situation of a reference year, and a person average GDP elastic coefficient or a linear extrapolation method is simply adopted for future prediction. The physical evolution rule of the whole life cycle of the vehicle is ignored by the processing mode, so that the current data and the predicted data are inconsistent in statistical caliber and growth logic, the phenomenon that the predicted curve jumps or breaks at the reference year often occurs, and a long-period high-precision list with time continuity is difficult to generate. 2. The dimension of the collaborative evaluation system is single, and quantitative evaluation of comprehensive benefits of environment and economy is lacking. The current cooperative control technology mainly focuses on physical synergy of pollution reduction and carbon reduction, namely, only evaluating whether a certain measure reduces pollutants and greenhouse gases at the same time. However, in practical decisions, the economic cost and benefit of policies are key factors in determining the feasibility of the policies to land. The prior art lacks a calculation model for converting environmental benefits into monetization indexes, cannot answer the core problem of highest cost performance of an emission reduction path, and has the problems that partial emission reduction effect is good but the policy of high economic cost is difficult to implement. 3. The lack of a closed loop analysis framework for the whole process has insufficient policy support capability. The existing method focuses on a single link in a multi-way mode, and lacks a complete closed loop which is constructed from a localization list, space-time refined distribution, uncertainty quantification and multi-scenario nonlinear superposition to multi-dimensional double collaborative evaluation. In particular to the problem of marginal effect decrementing when multiple emission reduction measures are overlapped, the prior art often adopts simple linear addition, so that the emission reduction is overestimated, and the scientificity of decision making is seriously influenced. In view of the foregoing, there is a need to develop a method and a system for total flow analysis of carbon pollution of a mobile source, which can open up the current situation and prediction logic, and merge the environment and economic evaluation dimension, so as to improve the fine management and control level of the mobile source in a region. Disclosure of Invention The invention aims to solve the technical problems of motor vehicle conservation quantity prediction and current situation investigation logic fracturing and lack of an environment and economy comprehensive cooperative evaluation system in the prior art, and provides a mobile source carbon pollution cooperative emission reduction full-flow analysis method and system based on an integrated coupling model and a double cooperative evaluation system. In order to achieve the above object, the technical scheme of the present invention is as follows: a total flow analysis method for the synergistic emission reduction of carbon pollutants of a mobile source comprises the following steps: S1, constructing a motor vehicle maintenance quantity current situation and prediction integrated coupling model, namely synchronously measuring and calculating a reference year current situation maintenance quantity and a planning period prediction maintenance quant