Mobile source carbon pollution collaborative emission reduction whole-process analysis method and system

Abstract

The invention discloses a mobile source carbon pollution collaborative emission reduction whole-process analysis method and system. The method comprises the following steps: S1, constructing a motor vehicle ownership current situation and prediction integrated coupling model, and generating a localized basic emission list; s2, distributing the basic emission list to a high-resolution grid to obtain a spatial gridding emission list; s3, quantifying the uncertainty boundary of the emission list by adopting a Monte Carlo simulation method; s4, constructing a multi-dimensional emission reduction scene, and iteratively calculating the net emission reduction effect of each scene year by year by using a nonlinear superposition formula; and S5, constructing a pollutant-greenhouse gas-economic benefit three-dimensional double-collaborative evaluation system, and outputting the comprehensive benefit ranking and the 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 multi-dimensional scene simulation and superposition calculation module, a three-dimensional double-collaborative comprehensive evaluation module and a data management and storage module.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of atmospheric environmental science and transportation planning, specifically to a method and system for analyzing the entire process of coordinated emission reduction of carbon pollution from mobile sources based on multi-model coupling and multi-dimensional collaborative evaluation. Background Technology

[0002] With the continuous surge in the number of motor vehicles, mobile sources have become a major contributor to urban air pollution and greenhouse gas emissions in my country. Nitrogen oxides and volatile organic compounds emitted by motor vehicles are key precursors to ozone and secondary particulate matter pollution, while carbon dioxide emissions pose a significant challenge to achieving carbon peaking in the transportation sector. Therefore, it is urgent for all regions to develop scientific emission reduction inventories and control strategies for mobile sources.

[0003] However, the following key technical bottlenecks still urgently need to be addressed in the current technology for compiling mobile source emissions inventories and evaluating policies: 1. There is a logical disconnect between the forecast of the number of vehicles in use and the current situation survey, resulting in a break in the data chain.

[0004] Current technologies for compiling emissions inventories typically employ a segmented processing approach: for the baseline year, they primarily rely on static registration data from vehicle management departments; while for future forecasts, they simply use the per capita GDP elasticity coefficient or linear extrapolation. This approach ignores the physical evolution of vehicles throughout their entire lifecycle, leading to inconsistencies between current and forecast data in terms of statistical caliber and growth logic. This often results in abrupt changes or breaks in the forecast curve at the baseline year, making it difficult to generate a long-term, high-precision inventory with temporal continuity.

[0005] 2. The collaborative evaluation system has only one dimension and lacks quantitative assessment of the comprehensive environmental and economic benefits.

[0006] Current synergistic control technologies primarily focus on the physical synergy of pollution and carbon reduction, assessing only whether a particular measure simultaneously reduces pollutants and greenhouse gases. However, in practical decision-making, the economic costs and benefits of policies are key factors determining their feasibility. Existing technologies lack computational models to convert environmental benefits into monetizable indicators, failing to answer the core question of which emission reduction path offers the highest cost-effectiveness. This makes it difficult to implement policies that, while effective in emission reduction, come at an excessively high economic cost.

[0007] 3. Lack of a closed-loop analysis framework covering the entire process, resulting in insufficient policy support capabilities.

[0008] Existing methods often focus on single aspects, lacking a complete closed loop encompassing localized inventory construction, refined spatiotemporal allocation, uncertainty quantification, multi-scenario nonlinear superposition, and multi-dimensional dual-coordinated evaluation. Particularly concerning is the diminishing marginal effect when multiple emission reduction measures are superimposed; existing technologies often employ simple linear addition, leading to an overestimation of emission reductions and severely impacting the scientific rigor of decision-making.

[0009] In summary, there is an urgent need to develop a method and system for analyzing the entire process of coordinated carbon emission reduction from mobile sources that can bridge the gap between current status and forecasting logic and integrate environmental and economic evaluation dimensions, so as to improve the level of refined management and control of regional mobile sources. Summary of the Invention

[0010] This invention aims to solve the technical problems of the logical separation between motor vehicle ownership prediction and current status survey in the prior art, and the lack of a comprehensive environmental and economic collaborative evaluation system. It provides a method and system for analyzing the whole process of coordinated carbon pollution reduction from mobile sources based on an integrated coupling model and a dual collaborative evaluation system.

[0011] To achieve the above objectives, the technical solution of the present invention is as follows: A method for analyzing the entire process of synergistic carbon pollution reduction from mobile sources includes the following steps: S1. Construct an integrated coupled model of current status and prediction of motor vehicle ownership: Using historical registration data of the target area as constraints and socio-economic forecast data as driving factors, the current ownership in the base year and the predicted ownership in the planning period are calculated simultaneously within a unified calculation framework to generate a localized basic emission inventory with time continuity. S2. Spatial gridded fine allocation: Construct a standard road length model based on road grade weights, establish a road network activity level mapping relationship, and discretize and allocate the basic emission inventory to a high-resolution grid to obtain a gridded emission inventory with spatial attributes. S3. Uncertainty Quantification Analysis: The Monte Carlo simulation method is used to iteratively simulate key parameters such as motor vehicle ownership, average annual mileage, and emission factors, and output the 95% confidence interval of the emission inventory to quantify the uncertainty boundary of the data. S4. Multi-dimensional scenario simulation and nonlinear superposition: Construct a tiered emission reduction scenario system including baseline scenario, single control scenario, combined scenario, and comprehensive scenario. For combined and comprehensive scenarios, a multi-measure nonlinear superposition formula is used to calculate the comprehensive emission reduction rate, eliminate duplicate emission reductions, and iteratively generate emission prediction inventories for each scenario year by year. S5. Construct a three-dimensional dual-synergistic evaluation system for pollutants, greenhouse gases, and economic benefits: Calculate the environmental synergy index and the economic synergy index separately, form a comprehensive synergy index through multi-objective normalization and weighted fusion, and output the comprehensive benefit ranking of each emission reduction scenario and the optimal policy combination recommendation.

[0012] Furthermore, step S1 includes the following steps: S11. Obtain basic data for the target area, including historical vehicle registration data, annual scrapping and recycling data, macroeconomic parameters (population, GDP) and planning period forecast data; S12. Construct an integrated coupled model for the current status and prediction of motor vehicle ownership: change the traditional segmented prediction mode, use the survival curve to depict the physical decay law of vehicles, and determine the retention constraints of historical vehicles; use the Gompertz function to simulate the S-shaped saturation trend of per capita ownership rate, and determine the growth upper limit driver; introduce the nonlinear adjustment of short-term economic fluctuations on vehicle purchase demand using the elasticity coefficient method; through the deep coupling of the above constraints, drivers and correction mechanisms, simultaneously calculate the current status of the baseline year and the motor vehicle ownership by vehicle type and emission standard in the planning period within the same calculation framework; S13. Construct a localized emission factor library: Based on the COPERT model, incorporate localized parameters of the target region, including vehicle type structure distribution, fuel type proportion, environmental meteorological conditions (temperature, humidity, altitude) and vehicle operating condition data, to generate localized dynamic emission factors. S14. Calculate the basic emission inventory: Based on the aforementioned vehicle ownership and localized emission factors, calculate the total annual emissions of vehicle pollutants and greenhouse gases in the target area. The calculation formula is as follows: ; in, This refers to the total annual emissions from motor vehicles. Motor vehicle type (distinguishing between vehicle model and emission standards); For the first The number of similar motor vehicles (output by the integrated coupling model in step S12). For the first The emission factor per unit mileage for Class A motor vehicles (calculated by step S13); For the first The average annual mileage of this type of motor vehicle.

[0013] Furthermore, in step S12, the integrated coupled model for the current status and prediction of motor vehicle ownership includes the following three logical modules in its specific calculation formula: (1) The long-term trend prediction module (Gompertz function) calculates the saturation trend of per capita motor vehicle ownership based on per capita GDP level, serving as a theoretical benchmark for ownership growth: ; In the formula, V Motor vehicle ownership per capita (vehicles per thousand people); V S The saturation value for motor vehicle ownership (vehicles per thousand people); VpThe number of light passenger vehicles (in vehicles); g GDP per capita (ten thousand yuan / person); P Population size (thousands); a For the model regression parameters, 0 < a <1; b For the model regression parameters, 0 < b <1.

[0014] (2) Short-term fluctuation correction module (elasticity coefficient method) calculates the elasticity coefficient based on historical data, combines it with future economic growth expectations, derives the growth rate of vehicle-specific ownership, and calculates the final ownership. The calculation formula is as follows: ① Calculate the historical elasticity coefficient ; ② Calculate the future economic growth rate ; ③ Deriving the future motor vehicle growth rate ; ④ Calculate and predict the annual number of motor vehicles ; In the formula, This refers to the number of motor vehicles categorized by type. It is the year; j It refers to the vehicle type; It is the first Year j Growth rate of new-type motor vehicle ownership; It is the first Annual growth rate of per capita income (annual GDP growth rate). It is the first j Elasticity coefficient of vehicle ownership; The base year's GDP; The GDP value for the target year or reference year; This represents the annual growth rate of GDP. t For predicting the target year or reference year; This serves as the base year for prediction.

[0015] (3) The stock evolution and incremental coupling module calculates the retention of old vehicles based on the survival curve and inversely calculates the number of new vehicle registrations by combining the total stock prediction value: ; ; In the formula, S r,T For emission stage r In the target year T The number of vehicles in circulation (in ten thousand vehicles). N r,y For the first y Annual registration of emission stages rNumber of vehicles (in ten thousand vehicles); For the first y Total number of vehicles actually scrapped in a year (in ten thousand vehicles). θ r,y For emission stage r In the y Percentage of theoretically eliminated annual output; For the first y The theoretically phased-out emission stage r Number of vehicles (in ten thousand).

[0016] Furthermore, in step S13, the specific calculation logic for constructing the localized emission factor library is as follows: Based on the basic emission factors of the COPERT model, the average vehicle speed, environmental parameters, and cumulative mileage of the target area are introduced for localized correction to generate dynamic emission factors; the correction formula is: ; in: For the target area m Month i Localized emission factors for similar motor vehicles; For the first i The baseline emission factor for similar motor vehicles under standard test conditions; The speed correction factor is derived by fitting a speed-emission nonlinear curve based on the measured average speed of the road network in the target area. The environmental correction factor is used to correct cold start emissions and greenhouse gas emissions based on the monthly average temperature, relative humidity and altitude data of the target area. The degradation correction factor is calculated based on the vehicle's cumulative mileage and the aging characteristic curve of the emission control device; This is a load correction factor, which is applied to the baseline factor for heavy-duty diesel trucks based on the actual freight volume and load factor.

[0017] Furthermore, the spatial gridding allocation in step S2 includes the following specific steps: S21. Construct a standard road length model based on road grade weights: Set weight coefficients according to the differences in traffic capacity of roads of different grades, and transform the actual road network length in the target area into a standard road length with unified dimensions. S22. Establish spatial allocation mapping relationship: Utilize the geographic information system to analyze the topological structure of road network data, calculate the proportion of motor vehicle activity levels of each type of road within the grid cell, and accordingly discretize and allocate the basic emission inventory obtained in step S1 to a grid with a resolution of 3km×3km to generate emission flux data with spatial attributes.

[0018] Furthermore, the uncertainty quantification analysis in step S3 is specifically carried out as follows: a probability distribution model is constructed using the Monte Carlo simulation method, and the number of motor vehicles, the average annual mileage, and the emission factor are selected as key uncertainty parameters for iterative simulation; the coefficient of variation of the number of motor vehicles is set to 5%, the coefficient of variation of the average annual mileage is set to 15%, and the coefficient of variation of the emission factor is set to 10%, and the number of iterations is set to be no less than 10,000; the probability density distribution of the emission inventory is calculated by random sampling, and the 95% confidence interval of the total emissions is output to quantify the data reliability boundary of the basic emission inventory.

[0019] Furthermore, step S4 includes the following steps: S41. Construct a tiered emission reduction scenario system: Construct four types of emission reduction scenarios: baseline scenario, single control scenario, combined scenario, and comprehensive scenario; the baseline scenario continues the current established policies; the single control scenario adjusts for a single technology or policy variable; the combined scenario and comprehensive scenario systematically superimpose multiple emission reduction measures; S42. Calculating the nonlinear net effect of superimposed measures: Clearly define the annual emission reduction magnitude and implementation sequence for each emission reduction scenario, and iteratively calculate the emission forecast inventory for each emission reduction scenario from 2025 to 2035 year by year; for combined and integrated scenarios, use a multi-measure nonlinear superposition formula to calculate the comprehensive emission reduction rate, avoiding double-counting of emission reductions. The formula is as follows: ; in, This represents the total annual emission reduction resulting from the combined effects of multiple emission reduction measures. This represents the total emissions for that year under the baseline scenario; n This represents the total number of combined emission reduction measures. For the first k The theoretical emission reduction rate when each measure is implemented alone.

[0020] Furthermore, the specific implementation steps and calculation formulas of the three-dimensional dual-synergistic evaluation system of pollutants-greenhouse gases-economic benefits in step S5 are as follows: S51. Construct a unified evaluation dimension: Convert greenhouse gas emissions into CO2 equivalents; use the comprehensive equivalent normalization method to uniformly characterize different types of pollutants such as NOx, VOCs, and PM into air pollutant equivalents, the calculation formula of which is: ; in, This represents the comprehensive equivalent value of air pollutant emissions. For the first Emissions of various air pollutants, The corresponding equivalent coefficient; S52. Calculate the environmental emission reduction synergy index: Calculate the emission reduction rates of greenhouse gases and air pollutants under each scenario, as the basic data for environmental synergy assessment. The formula is as follows: ; in, and These represent the total emissions of greenhouse gases and air pollutants before the implementation of emission reduction measures; and These represent the reductions in greenhouse gas and air pollutant emissions after the implementation of the measures; For greenhouse gas emission reduction rate, The rate of reduction of air pollutants; S53. The synergistic effect coordinate method is used to map the emission reduction scenarios to a two-dimensional coordinate system. In the two-dimensional coordinate system, the horizontal axis represents the emission reduction effect of air pollutants, and the vertical axis represents the emission reduction effect of greenhouse gases. The optimal synergistic emission reduction scenario in the first quadrant is identified, and the negative synergistic scenario in which a single emission reduction leads to the deterioration of another indicator is eliminated. S54. Construct the Synergistic-Economic Composite Index (SCCI), calculate it using the equal-weighting method, and output the overall benefit ranking of each emission reduction scenario and the optimal policy combination recommendation; the calculation formula for the Synergistic-Economic Composite Index is: ; ; ; ; in, pollutants The emission reduction (t); This refers to the pollutant equivalent coefficient. CO2 emission reduction (t); The total equivalent emission reduction for conventional pollutants is measured in tons. = =0.5 indicates that CO2 is as important as conventional pollutants; This represents the maximum value of the corresponding indicator in the sample scenario; Carbon price (yuan / tCO2); pollutants Tax rate or unit damage value (yuan / t); , These are the weighting coefficients for environmental synergy indicators and economic benefit indicators, respectively. =1, where Env' is the standardized environmental synergy indicator and Econ' is the standardized economic benefit indicator.

[0021] A mobile source carbon pollution synergistic emission reduction full-process analysis system includes an inventory construction module for building a basic emission inventory, a spatial allocation module for spatially gridded allocation of the basic emission inventory, an uncertainty analysis module for quantifying the uncertainty of the spatially gridded emission inventory, a scenario setting module for constructing emission reduction scenarios and calculating the emission prediction inventory and the superposition effect of emission reduction measures for each scenario, a collaborative evaluation module for calculating and outputting the ranking of synergistic emission reduction effects and optimal policy recommendations for each emission reduction scenario, and a data storage module for storing various data. The inventory construction module, spatial allocation module, uncertainty analysis module, scenario setting module, and collaborative evaluation module are sequentially connected to realize data transmission operations, and the inventory construction module, spatial allocation module, uncertainty analysis module, scenario setting module, and collaborative evaluation module are all connected to the data storage module to realize data storage operations.

[0022] Compared with the prior art, the present invention has the following outstanding substantive features and significant progress: 1. An innovative integrated coupled model for the current status and prediction of motor vehicle ownership was constructed, which completely solved the technical problem of data gaps in the compilation of the inventory.

[0023] Existing technologies often face inconsistencies in statistical methods and abrupt data integration when compiling vehicle inventory lists, particularly between current survey data (based on vehicle management office statistics) and future forecast data (based on macroeconomic models). This invention establishes a closed-loop calculation framework that uses historical survival curves as rigid constraints and economic Gompertz trends and elasticity coefficients as flexible drivers. The model precisely identifies the physical degradation process of existing vehicles using survival curves, determines the theoretical ceiling for inventory growth using the Gompertz model, and introduces elasticity coefficients to dynamically capture the nonlinear adjustment of short-term economic fluctuations on car purchase intentions. This mechanism ensures that, within long-term forecasts, inventory data not only conforms to the physical lifespan of vehicles but also responds sensitively to macroeconomic changes, achieving a smooth data transition and logical consistency from the baseline year to the planning target year, significantly improving the accuracy of inventory list predictions.

[0024] 2. A three-dimensional dual-coordinated evaluation system of pollutants, greenhouse gases, and economic benefits has been established, filling the gap in the quantitative assessment of the economic benefits of emission reduction policies.

[0025] Traditional coordinated control technologies are mostly limited to physical evaluations of pollution and carbon reduction, making it difficult to measure the economic costs and benefits of policy implementation. This invention pioneers a two-tiered evaluation framework that includes a first level of environmental synergy (simultaneous reduction of pollutants and greenhouse gases) and a second level of comprehensive synergy (balancing environmental benefits and economic gains). By introducing a carbon trading price mechanism and environmental tax calculation, a comprehensive synergy index (SCCI) is constructed, enabling policymakers to intuitively determine whether a particular emission reduction scenario is economically feasible while achieving environmental goals. This allows for the identification of the optimal policy combination, significantly enhancing the implementability and practicality of emission reduction schemes.

[0026] 3. A closed-loop support system based on nonlinear superposition and uncertainty quantification has been achieved for the entire process.

[0027] This invention breaks through the traditional methods' simple linear extrapolation and fragmented analysis. On the one hand, through a multi-measure nonlinear superposition algorithm, it effectively eliminates redundantly calculated emission reductions in scenario combinations, restoring the true cumulative effect of policies. On the other hand, it integrates Monte Carlo simulation to quantify the full-parameter uncertainty of vehicle ownership, mileage, and emission factors, outputting a scientific boundary with a 95% confidence interval. This closed-loop design, from micro-data cleaning to macro-decision optimization, allows the analysis results to be directly used as high-quality input data for regional air quality models, providing systematic scientific support for mobile source pollution control planning. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is the overall logic diagram of the present invention; Figure 2 A schematic diagram of the spatial gridded CO2 emission inventory for the implementation of the case study area; Figure 3 A schematic diagram of the coordinates of the carbon pollution synergistic effect; Figure 4 This is a heatmap comparing the SCCI index. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, any modifications, equivalent substitutions, improvements, etc., made by those skilled in the art to all other embodiments obtained without creative effort should be included within the protection scope of the present invention.

[0031] This invention discloses a method and system for analyzing the entire process of coordinated carbon pollution reduction from mobile sources. It can effectively solve the problems of fragmentation, weak localization, single scenario and lack of coordination in existing mobile source emission inventory technologies, and provide quantitative support for regional mobile source pollution control.

[0032] like Figure 1 As shown, the mobile source carbon pollution synergistic emission reduction full-process analysis method of the present invention includes the following five core steps: 1. Construction of localized basic emissions inventory; (1) Obtain basic data of the target area, including motor vehicle registration data, GDP and population statistics, meteorological monitoring data and road network topology data.

[0033] (2) Construct an integrated coupled model of current status and prediction of motor vehicle ownership: use survival curves to depict the evolution of vehicle stock, and combine the Gompertz function and elasticity coefficient method to predict future increments.

[0034] The Gompertz function is in the form of: ; in, V Motor vehicle ownership per capita (vehicles per thousand people); V S The saturation value for motor vehicle ownership (vehicles per thousand people); V p g represents the number of light passenger vehicles (vehicles); g represents GDP per capita (ten thousand yuan / person). P Population size (thousands); a、b For the model regression parameters (0 < a <1, 0< b <1); The stock prediction formula using the elasticity coefficient method is as follows: ; ; ; ; in, It is the first Yearj Number of motor vehicles by type (in vehicles). It is the first Year j Growth rate of new-type motor vehicle ownership; It is the first Annual growth rate of per capita income (annual GDP growth rate).

[0035] The formula for calculating the number of motor vehicles is as follows: ; ; In the formula, S r,T For emission stage r In the target year T The number of vehicles in circulation (in ten thousand vehicles). N r,y For the first y Annual registration of emission stages r Number of vehicles (in ten thousand vehicles); For the first y Total number of vehicles actually scrapped in a year (in ten thousand vehicles). θ r,y For emission stage r In the y Percentage of theoretically eliminated annual output; For the first y The theoretically phased-out emission stage r Number of vehicles (in ten thousand).

[0036] (3) Construct a localized emission factor library: Based on the COPERT model kernel, incorporate the measured annual average mileage, average vehicle speed and temperature and humidity parameters of the target area to correct the thermal emission, cold start emission and fuel evaporation emission factors.

[0037] ; in: For the target area m Month Localized emission factors for similar motor vehicles; For the first The baseline emission factor for similar motor vehicles under standard test conditions; This is a speed correction factor; This is an environmental correction factor; This is a degradation correction factor; This is the load correction factor.

[0038] (4) Calculate the basic emissions inventory: ; in, This refers to the total annual emissions from motor vehicles. Motor vehicle type; For the first The number of similar motor vehicles; For the first Emission factor per unit mileage for motor vehicles; For the first The average annual mileage of this type of motor vehicle.

[0039] 2. Spatial grid-based fine allocation; (1) The “standard road length method” is adopted, which converts the actual road network length of the target area into a standard road length with a unified dimension based on the traffic weight of roads of different grades. (2) Combining road network density data and the spatial distribution differences of motor vehicle activity levels, a spatial mapping relationship is established using a geographic information system (GIS), and the basic emission inventory is discretized and allocated to a grid with a resolution of 3km×3km to generate a spatial gridded emission inventory.

[0040] 3. Quantitative analysis of uncertainty; (1) The Monte Carlo simulation method was adopted, and the number of motor vehicles, the average annual mileage, and the emission factor were selected as key uncertain parameters; (2) Set the coefficient of variation (CV) of the stock to 5%, the average annual mileage CV to 15%, and the emission factor CV to 10%, and set the number of iterations to no less than 10,000 times to calculate the fluctuation range of the output emission inventory within the 95% confidence interval. 4. Multidimensional scenario simulation and nonlinear superposition (1) Constructing a hierarchical scenario system: Baseline Scenario (BAU): Continues the current policy path; Single Control Scenario (S1-S6): Simulates each of the following individually: emission standard upgrade (S1), elimination of old diesel trucks (S2), restriction of high-emission vehicles (S3), promotion of new energy vehicles (S4), increase in public transport share (S5), and optimization of transport structure (S6); Combined Scenario (S7-S9): Simulates the combined effects of structural adjustment (S2+S3), travel optimization (S5+S6), and technological upgrade (S1+S4); Comprehensive Scenario (S10): Overlays all measures from S1 to S6.

[0041] (2) Nonlinear superposition calculation: The comprehensive emission reduction is calculated using a multi-measure nonlinear superposition formula, eliminating duplicate calculations. ; in, This represents the total annual emission reduction resulting from the combined effects of multiple emission reduction measures. This represents the total emissions for that year under the baseline scenario; n This represents the total number of combined emission reduction measures. For the first k The theoretical emission reduction rate when each measure is implemented alone.

[0042] 5. Evaluation of the benefits of synergistic carbon and pollution reduction (dual synergistic evaluation); (1) First-level environmental synergy: The comprehensive equivalent normalization method is used to unify the measurement units, calculate the greenhouse gas emission reduction rate and the air pollutant emission reduction rate, and use the synergy effect coordinate method to identify the effective scenarios in the first quadrant (dual emission reduction).

[0043] (2) Second level of comprehensive coordination: Constructing the Coordination-Economic Composite Index (SCCI).

[0044] ; ; ; ; in, pollutants The emission reduction (t); This refers to the pollutant equivalent coefficient. CO2 emission reduction (t); The total equivalent emission reduction for conventional pollutants is measured in tons. = =0.5 indicates that CO2 is as important as conventional pollutants; This represents the maximum value of the corresponding indicator in the sample scenario; Carbon price (yuan / tCO2); pollutants Tax rate or unit damage value (yuan / t); , These are the weighting coefficients for environmental synergy indicators and economic benefit indicators, respectively. =1, Env' For standardized environmental coordination indicators, Econ' These are standardized economic benefit indicators.

[0045] The mobile source carbon pollution synergistic emission reduction full-process analysis system, such as Figure 1 As shown, it mainly includes the following seven core functional modules: 1. Integrated Coupled Emission Inventory Construction Module: This module is used to execute step S1 in claim 1. Its core function is to construct a localized, high-precision basic emission inventory. It integrates a vehicle ownership prediction unit, employing survival curves, the Gompertz function, and the elasticity coefficient method to construct an integrated coupled model. This model simultaneously calculates the current vehicle ownership in the baseline year and the predicted vehicle ownership during the planning period, resolving data gaps. Simultaneously, this module includes a COPERT model localization adaptation unit, supporting the import of vehicle model mapping tables for the target region (establishing a correspondence between regional vehicle models and COPERT standard vehicle models), meteorological data (monthly average temperature, relative humidity), and operating parameters (annual average mileage for each vehicle model, average road speed). It automatically corrects thermal emissions, cold start, and evaporative emission factors, generating a localized emission factor library.

[0046] 2. Spatial Gridded Allocation Module: This module executes step S2 of claim 1 and is responsible for the spatial discretization of emission data. Its built-in standard road length conversion algorithm transforms the physical lengths of roads of different grades into a unified dimension. By integrating a Geographic Information System (GIS) kernel and combining road network data with the horizontal distribution characteristics of motor vehicle activity, the basic emission inventory is accurately mapped to a 3km × 3km resolution grid, outputting a gridded emission inventory with spatial attributes.

[0047] 3. Uncertainty Quantification Analysis Module: This module executes step S3 in claim 1 and is responsible for assessing the reliability boundaries of the inventory data. It employs a Monte Carlo simulation algorithm to perform at least 10,000 iterative simulations on three sensitive parameters: vehicle ownership, average annual mileage, and emission factors. The system automatically calculates and outputs the emission fluctuation range under a 95% confidence interval and generates a sensitive parameter identification report, quantifying the contribution rate of each parameter to the total uncertainty.

[0048] 4. Multi-dimensional Scenario Simulation and Overlay Calculation Module: This module executes step S4 of claim 1, supporting simulation and deduction from micro-level measures to macro-level policies. The system provides a visual scenario parameter configuration interface, supporting the construction of four types of scenarios: baseline scenario, single-item control scenario, combined scenario, and comprehensive scenario. For combined and comprehensive scenarios with multiple measures in parallel, the module incorporates a nonlinear overlay algorithm to automatically eliminate duplicate emission reductions and iteratively generate emission forecast inventories and emission reduction potential analysis tables for 2025-2035 year by year.

[0049] 5. Three-Dimensional Dual-Synergistic Comprehensive Evaluation Module: This module executes step S5 in claim 1 and is the core decision support unit of the system. The module incorporates a three-dimensional evaluation system of pollutants, greenhouse gases, and economic benefits: First, it unifies pollutant dimensions using the comprehensive equivalent normalization method; second, it identifies carbon and pollution reduction scenarios in the first quadrant using the synergistic effect coordinate method (first-level environmental synergy); finally, it introduces carbon price and tax rate parameters to calculate the SCCI index, quantifying environmental dividends and economic benefits (second-level comprehensive synergy). This module includes a visualization analysis unit that can automatically generate a synergistic effect quadrant chart, an SCCI index ranking bar chart, and a spatial heat map of the S10 comprehensive scenario, supporting interactive queries by year, pollutant type, or vehicle type.

[0050] 6. Data Management and Storage Module: This module is used for unified management and interaction of underlying data. The system has a standardized database interface, compatible with multi-source data formats from regional statistical yearbooks, mobile source environmental management annual reports, and meteorological data sharing systems. It supports automatic reading of Excel and CSV files and has a built-in preprocessing algorithm to automatically perform missing value imputation and outlier removal, ensuring the quality of input data. This module is responsible for storing the basic database, intermediate data (such as emission factor databases), and final evaluation results.

[0051] 7. User Interaction Module: This module serves as the human-computer interaction window, allowing users to customize key scenario parameters (such as the promotion ratio of new energy vehicles, the retirement age of old vehicles, and the implementation time of the National VI emission standard) according to actual planning needs. The system calls the background model in real time based on user input to perform calculations and instantly generates personalized emission prediction lists and collaborative evaluation reports to assist management departments in making dynamic decisions.

[0052] The technical solution of this invention consists of five core steps in its operation process, which are combined with system modules to achieve full-process implementation, as detailed below: (1) Constructing a localized basic emissions inventory Input data: Vehicle registration data for the target area over the past 10-15 years (from the regional statistical yearbook), meteorological data for the planning base year (average monthly temperature -10℃~40℃, humidity 50%-85%, from the regional meteorological data sharing system), and average annual mileage by vehicle type (referencing regional vehicle management standards). Processing procedure: ① Integrated and coupled calculation of vehicle ownership: The vehicle retention rate of each year in the region is fitted using survival curves (e.g., 50-70% retention rate for 10-year-old vehicles) as a constraint on the existing stock; the Gompertz function and elasticity coefficient method are combined to predict the target vehicle ownership at the end of the planning year as an incremental driver; ② Emission factor correction: Based on the COPERT model, regional vehicle types are mapped to built-in categories in the model, and typical meteorological parameters of the region are substituted to correct the characteristic pollutant emission factors (adjusted by 5%-20% compared to the default value). ③ Emissions calculation: Calculate the total emissions of characteristic pollutants and greenhouse gases from motor vehicles in the planning base year according to formula ①; Output: Basic emission inventory for the target region in the planning base year, categorized by vehicle type (8-10 categories), emission standard (National I to National VI), and pollutant (5-6 categories).

[0053] (2) Fine allocation of spatial grid Input data: basic emission inventory, road network data of the target area (mileage of expressways, main roads, secondary roads, etc.); Processing procedure: The standard road length method is adopted to convert the actual road length into standard road length according to the number of lanes and traffic capacity; combined with the Geographic Information System (GIS), a spatial mapping relationship between emissions and road network density is established, the total emissions are discretized and allocated to a 3km×3km grid, and the emission intensity of characteristic pollutants in the urban grid is calculated. Output: 3km×3km spatial gridded emission inventory.

[0054] (3) Uncertainty analysis Input data: vehicle ownership (CV=5%), average annual mileage (CV=15%), emission factor (CV=10%). Processing procedure: The Monte Carlo simulation algorithm is used to perform no less than 10,000 iterations to quantify the impact of the probability distribution of each parameter on the total emissions, and the emission error range under the 95% confidence interval is output (controlled within ±5%-±10%). Output: Uncertainty analysis report (sensitive parameters sorted by annual mileage > emission factor > number of vehicles).

[0055] (4) Setting up multi-dimensional emission reduction scenarios Input data: basic emission inventory, regional mobile source governance plan (such as new energy vehicle plan, etc.); Processing procedure: ① Scenario parameter construction: In the single control scenario (new energy promotion), the proportion of new energy passenger vehicles is planned to be 15%-25% in the medium term and 30%-40% in the end year; the comprehensive scenario superimposes 6-8 individual measures; ② Nonlinear superposition calculation: For combined and integrated scenarios, a multi-measure nonlinear superposition formula is used to calculate the net emission reduction effect, eliminating redundant calculations. ③ Emissions forecast: Iterative calculations are performed year by year, predicting that the emissions of characteristic pollutants under individual scenarios will decrease by 15%-30% compared to the baseline scenario at the end of the planning year, and by 40%-50% under the comprehensive scenario.

[0056] Output: Multi-scenario emission forecast list and scenario superposition effect table for the planning period (10-15 years).

[0057] (5) Evaluation of the benefits of synergistic carbon and pollution reduction Input data: Multi-scenario emission forecast inventory, regional carbon price (yuan / tCO2), regional environmental protection tax on pollutants (yuan / pollution equivalent); Processing procedure: ① Environmental Collaborative Assessment: Calculate the emission reduction rates of greenhouse gases and pollutants, and identify the comprehensive scenario located in the first quadrant of the collaborative coordinate system (both the emission reduction rates of characteristic pollutants and greenhouse gases are >30%). ② Comprehensive benefit evaluation: Introducing economic parameters, the synergistic-economic composite index (SCCI) was calculated. The results show that the comprehensive scenario SCCI is 0.8-0.9 (Env'=0.85-0.95, Econ'=0.75-0.85), which is 40%-60% higher than the individual scenarios. Output: Scenario benefit ranking (overall scenario > combined scenario > individual scenario > baseline scenario), and optimal policy combination recommendations.

[0058] The following uses a typical provincial-level administrative region as an implementation case to explain in detail the implementation logic and operation process of this invention: 1. Data Preparation Basic data: ① Motor vehicle data: Total number of motor vehicles in the region in the planning base year (10-20 million vehicles), and the proportion of vehicle type (60%-70% for small passenger cars and 5%-10% for heavy trucks), from the Regional Statistical Yearbook (Planning Base Year); ② Meteorological data: The average monthly temperature (as shown in Table 1) and humidity (55%-80%) of the region in the planning base year are from the regional meteorological data sharing platform; ③ Road network data: regional expressways (3000-5000km), main roads (6000-10000km), and secondary roads (10000-15000km), from OpenStreetMap; ④ Policy data: Regional Motor Vehicle Pollution Prevention and Control Plan, Regional New Energy Vehicle Promotion Implementation Plan, etc.

[0059] Table 1. Average monthly temperature (°C) of the region in the planning base year. ; 2. Construction and implementation of localized basic emission inventories Step 1: Integrated Coupling Prediction of Inventory Using an integrated coupled model of current and predicted motor vehicle ownership, taking regional small passenger vehicles as an example: ① Stock Constraint (Survival Curve): Based on registration data from the past 10 years, a retention rate of 55%-65% for small passenger vehicles with a 10-year age is estimated. ② Trend-driven (Gompertz function): Set VS=500-700 vehicles / thousand people (saturation value), a=0.25-0.35, b=0.45-0.55, and the per capita GDP at the end of the planning year is predicted to be 120,000-180,000 yuan / person. Calculate the per capita ownership rate. ③ Dynamic adjustment (elasticity coefficient method): The number of small passenger vehicles in the base year is 8-12 million, the per capita GDP growth rate during the planning period is 4%-6%, the elasticity coefficient ε=0.7-0.9, and the number of vehicles in the final year of the planning period; Step 2: Localization correction of emission factors; Based on the COPERT model, taking regional National VI small passenger vehicles (gasoline) NO x For example, emission factors: ① Model default value: 0.04-0.06 g / km; ②Localization corrections: Temperature correction: The average summer temperature in the region is 25-32℃, with a temperature correction factor of 1.05-1.2. Humidity correction: Regional average humidity 70%-80%, humidity correction factor 1.02-1.1; The final emission factor was calculated to be 0.058 g / km.

[0060] Step 3: Emissions calculation; Based on the planning base year regional small passenger vehicle NO x For example, emissions: ① Parameter values: EF i =0.05-0.065g / km, P=8-12 million vehicles, VKT i =15,000-20,000 km / vehicle; ② The calculated emissions are 10,000-15,000 tons.

[0061] 3. Implementation of Space Allocation Step 1: Standard road length conversion; Taking the main roads in the core urban area of ​​the region as an example: ① Actual parameters: Length 80-120km, two-way 3-5 lanes, average speed 25-35km / h; ② Conversion factor: The conversion factor for a two-way four-lane main road is 1.0. Standard road length = actual length × conversion factor.

[0062] Step 2: Grid allocation, such as Figure 2 As shown; ① Grid division: The core urban area is divided into 3km×3km grids, with a total of 100-150 grids; ② Mileage percentage: The total standard length of main roads is 400-600km, and the length of main roads within a certain grid is 15-25km, accounting for 3%-5%; ③ Emissions Allocation: This grid's small passenger vehicles NO x Emissions = Regional small passenger vehicle NO x The result of multiplying total emissions by mileage percentage is 30-75 tons.

[0063] 4. Implementation of Uncertainty Analysis Parameter settings: Vehicle ownership (average 8-12 million vehicles, CV=4%-6%, normal distribution), average annual mileage (average 15,000-20,000 km, CV=12%-18%, normal distribution), emission factor (average 0.05-0.065 g / km, CV=8%-12%, normal distribution); Iterative simulation: 10,000 iterations were performed using Oracle Crystal Ball software, with a 95% confidence interval for the NO of small passenger vehicles. x Emissions range from 0.95 to 1.55 million tons; Sensitive parameters: The sensitivity coefficient of average annual mileage is 0.5-0.7 (with the greatest impact), the emission factor is 0.25-0.35, and the number of vehicles on the road is 0.08-0.12.

[0064] 5. Implementation of multi-dimensional emission reduction scenarios Scenario parameter settings (taking new energy promotion scenario S4, combined scenario S7, and comprehensive scenario S10 as examples): ①S4 (new energy promotion): The proportion of new energy small passenger vehicles in the medium term of the plan is 15%-25%, and 30%-40% by the end of the plan year; ②S7 (Structural Adjustment): The plan aims to phase out heavy-duty trucks that meet or fall below the National IV emission standard in the medium term and to phase out small passenger vehicles that meet or fall below the National IV emission standard by the end of the year. ③S10 (Comprehensive Measures): This includes six measures such as upgrading emission standards, promoting new energy vehicles, and phasing out old vehicles, with high emission standards implemented 1-2 years ahead of schedule.

[0065] Emissions projections (end of planning year, compared to baseline scenario BAU): ①S4 Scenario: NO x Emissions decreased by 18%-25%, and CO2 emissions decreased by 15%-22%; ②S7 Scenario: NO x Emissions will decrease by 30%-40%, and CO2 emissions will decrease by 22%-28%; ③S10 Scenario: NO xEmissions will decrease by 40%-50%, and CO2 emissions will decrease by 35%-45%.

[0066] Calculation of superposition effects (taking scenario S7 as an example): ①Single-item emission reduction: The elimination of heavy-duty trucks will reduce emissions by 0.3-0.4 million tons, the elimination of old small passenger vehicles will reduce emissions by 0.35-0.45 million tons, and the linear superposition will reduce emissions by 0.65-0.85 million tons; ② Actual emission reduction: S7 scenario actually reduces emissions by 0.6-0.8 million tons, correcting the overestimation of emission reductions that may be caused by simple linear addition.

[0067] 6. Implementation of Evaluation of the Benefits of Co-reduction of Carbon and Pollution Step 1: Indicator normalization; ① Greenhouse gases: CO2 emission reduction under the S10 scenario = CO2 emission under the BAU scenario at the end of the planning period × (35%-45%), resulting in 20-30 million tons; ② Air pollutants: S10 scenario NO x Emissions reduction of 0.8-1.0 million tons, PM2.5 2.5 Emissions reduction of 0.1-0.15 million tons, with a comprehensive equivalent (NO) x Equivalent value 0.9-1.0 kg / equivalent, PM 2.5 (Equivalent value 2.0-2.3 kg / equivalent), the result is 0.9-1.2 × 10⁻⁶. 6 Pollution equivalent.

[0068] Step 2: Evaluation of synergistic effects; ① Flexibility coefficient: S10 scenario CO2 emission reduction rate 35%-45%, NO x Emission reduction rate 40%-50%, ELS=0.8-1.1 (0.8<ELS<1.2, significant synergistic effect); ② Coordinate system: Scenario S10 is located in the first quadrant (horizontal axis NO) x The optimal synergistic scenario is a 40%-50% reduction in emissions and a 35%-45% reduction in CO2 emissions (vertical axis). Figure 3 As shown.

[0069] Step 3: Comprehensive benefit evaluation; ① Economic benefits (Econ): Carbon revenue: CO2 emission reduction × regional carbon price (70-110 yuan / tCO2), resulting in 1.4-3.3 billion yuan; Pollutant benefits: The comprehensive equivalent value multiplied by the environmental protection tax (3-7 yuan / pollution equivalent) results in 0.3-0.8 billion yuan; Total revenue: carbon revenue + pollutant revenue, resulting in RMB 1.43-3.38 billion; ② Standardization processing: Env'=0.85-0.95 (Env is the maximum value in S10 scenario), Econ'=0.75-0.85 (Econ is the maximum value in S10 scenario); ③SCCI Index: SCCI = 0.5 × Env' + 0.5 × Econ' = 0.8 - 0.9 (ranked first), such as Figure 4 As shown.

[0070] 7. Implementation Results Output Core achievements: ① High-resolution emission inventory of mobile sources in the planned base year (3km×3km, including vehicle types of 8-10 and pollutants of 5-6); ② Emission forecast reports for approximately 10 scenarios during the planning period (10-15 years); ③ Recommended optimal policy combination: "Promotion of new energy vehicles (30%-40% share) + elimination of old vehicles (complete elimination of vehicles meeting National IV emission standards and below) + upgrading of emission standards (implementation of high emission standards ahead of schedule)"; ④ Synergistic Benefits Report: The comprehensive scenario plan can reduce characteristic pollutants by 40%-50% and CO2 by 35%-45% by the end of the year, with a comprehensive benefit of RMB1.4-3.4 billion.

[0071] This invention focuses on providing comprehensive support for the coordinated reduction of carbon pollution from regional mobile sources (motor vehicles), covering three core stages: current status assessment, predictive simulation, and policy evaluation. It directly serves provincial or municipal mobile source pollution control planning. The core objective of this invention is to provide policymakers with a complete decision-making basis, including accurate inventory, scenario prediction, and synergistic benefits. It achieves a closed-loop process through five core links, ensures prediction accuracy through an integrated coupled model of motor vehicle ownership, and quantifies environmental benefits and economic gains using a three-dimensional dual-synergistic evaluation system (i.e., the first-level environmental synergistic coordinate method and the second-level SCCI index method). This forms a standard operating paradigm fully applicable to the formulation and effectiveness evaluation of regional mobile source pollution control policies. The present invention has the following beneficial effects: 1. End-to-End Closed-Loop Innovation: This pioneering approach establishes a complete technological chain encompassing localized inventory construction, high-resolution spatial allocation, Monte Carlo uncertainty analysis, multi-dimensional scenario simulation, and carbon pollution-economic collaborative evaluation. This solution provides end-to-end support from basic emission accounting to top-level policy evaluation, significantly differentiating itself from existing technologies that focus only on a single calculation stage and resolving the data fragmentation issue in traditional methods.

[0072] 2. Integrated Coupled Prediction of Motor Vehicle Ownership: This invention constructs an integrated coupled model based on survival curve constraints, Gompertz trend driving, and elasticity coefficient correction to accurately predict motor vehicle ownership by vehicle type and emission standard. By combining regional meteorological parameters and road network operation characteristics to localize the COPERT model emission factors, this method deeply adapts to the dynamic emission characteristics of motor vehicles distributed along the road network, effectively solving the problem of insufficient accuracy of general models when applied to specific regions.

[0073] 3. Three-Dimensional Dual Synergistic Evaluation of Pollutants, Greenhouse Gases, and Economic Benefits: This invention constructs a four-level scenario system—benchmark, individual, combined, and comprehensive—and utilizes a nonlinear superposition algorithm to quantify the net emission reduction effect of measures. It innovatively establishes a three-dimensional evaluation system incorporating the environmental synergistic coordinate method and the SCCI index method. While considering the physical synergy between carbon emission reduction and pollutant emission reduction, it further quantifies the economic benefits brought about by emission reduction, completely overcoming the shortcomings of existing technologies that only calculate emission reduction volume without evaluating cost-effectiveness.

[0074] 4. Quantification of Uncertainty with High Confidence: Through no fewer than 10,000 Monte Carlo simulation iterations, the fluctuations of key parameters such as vehicle ownership (coefficient of variation 5%), average annual mileage (coefficient of variation 15%), and emission factors (coefficient of variation 10%) were quantitatively analyzed, outputting the emission inventory boundaries under a 95% confidence interval. This step clarified the reliability range of the data and significantly enhanced the reference value of the inventory results in policy-making.

[0075] 5. Systemic Efficiency Enhancement Through Deep Collaboration Across All Stages: This invention forms a systematic efficiency enhancement mechanism through the organic integration of various functional modules: localized integrated forecasting provides accurate input for inventory construction, uncertainty analysis defines the credibility boundaries for scenario simulation, and collaborative evaluation quantifies comprehensive benefits based on scenario data. The policy support capabilities across the entire process are significantly superior to the simple superposition of individual stages, possessing prominent substantive characteristics and significant technological advancements.

Claims

1. A method for analyzing the entire process of synergistic carbon pollution reduction from mobile sources, characterized in that: Includes the following steps: S1. Construct an integrated coupled model of current status and prediction of motor vehicle ownership: Using historical registration data of the target area as constraints and socio-economic forecast data as driving factors, simultaneously calculate the current motor vehicle ownership in the base year and the predicted ownership in the planning period within a unified calculation framework, and generate a localized basic emission inventory with time continuity. S2. Spatial gridded fine allocation: Construct a standard road length model based on road grade weights, establish a road network activity level mapping relationship, and discretize and allocate the localized basic emission inventory to a high-resolution grid to obtain a gridded emission inventory with spatial attributes. S3. Uncertainty Quantification Analysis: The Monte Carlo simulation method is used to iteratively simulate the number of motor vehicles, the average annual mileage, and the emission factors, and output the 95% confidence interval of the gridded emission inventory to quantify the uncertainty boundary of the data. S4. Multi-dimensional scenario simulation and nonlinear superposition: Construct a hierarchical emission reduction scenario system including baseline scenario, single control scenario, combined scenario and comprehensive scenario; For combined scenario and comprehensive scenario, use multi-measure nonlinear superposition formula to calculate comprehensive emission reduction rate, eliminate the emission reduction amount calculated repeatedly, and generate emission prediction inventory for each emission reduction scenario iteratively year by year. S5. Construct a three-dimensional dual-synergistic evaluation system for pollutants, greenhouse gases, and economic benefits: Calculate the environmental synergy index and the economic synergy index separately, form a comprehensive synergy index through multi-objective normalization and weighted fusion, and output the comprehensive benefit ranking of each emission reduction scenario and the optimal policy combination recommendation.

2. The method for analyzing the entire process of synergistic carbon pollution reduction from mobile sources as described in claim 1, characterized in that: Step S1 includes the following steps: S11. Obtain basic data for the target area, including historical vehicle registration data, annual scrapping and recycling data, macroeconomic parameters, and planning period forecast data. S12. Construct an integrated coupled model for the current status and prediction of motor vehicle ownership: use survival curves to depict the physical decay law of vehicles and determine the retention constraints of historical vehicles; use the Gompertz function to simulate the S-shaped saturation trend of per capita ownership rate and determine the growth upper limit driver; use the elasticity coefficient method to introduce the nonlinear adjustment of short-term economic fluctuations on vehicle purchase demand; through the deep coupling of the retention constraints of historical vehicles, the growth upper limit driver and the correction mechanism, simultaneously calculate the current status of the base year and the motor vehicle ownership by vehicle type and emission standard in the planning period within the same calculation framework. S13. Construct a localized emission factor library: Based on the COPERT model, incorporate localized parameters of the target region, including vehicle type structure distribution, fuel type proportion, environmental meteorological conditions and vehicle operating condition data, to generate localized dynamic emission factors. S14. Calculate the localized basic emission inventory: Based on the number of motor vehicles and localized dynamic emission factors, calculate the total annual emissions of motor vehicle pollutants and greenhouse gases in the target area. The calculation formula is as follows: ； in, This refers to the total annual emissions from motor vehicles. Motor vehicle type; For the first The number of similar motor vehicles; For the first Emission factor per unit mileage for motor vehicles; For the first The average annual mileage of this type of motor vehicle.

3. The method for analyzing the entire process of synergistic carbon pollution reduction from mobile sources as described in claim 2, characterized in that: In step S12, the integrated coupling model for the current status and prediction of motor vehicle ownership specifically includes the following three logical modules: (1) Long-term trend prediction module: Calculates the saturation trend of per capita motor vehicle ownership based on per capita GDP level, serving as a theoretical benchmark for the growth of motor vehicle ownership. The calculation formula is as follows: ； In the formula, V The per capita ownership rate of motor vehicles; V S This represents the saturation point for motor vehicle ownership. V p g represents the number of light passenger vehicles in operation; g represents GDP per capita. P For population size; (2) Short-term fluctuation correction module: Based on historical data, the elasticity coefficient is calculated. Combined with future economic growth expectations, the growth rate of vehicle-specific ownership is derived and the final ownership is calculated. The calculation formula is as follows: Calculate the historical elasticity coefficient: ; Calculate the future economic growth rate: ; Deducing the future growth rate of motor vehicles: ; Calculate and predict the annual number of motor vehicles: ; In the formula, This refers to the number of motor vehicles owned, categorized by type. It is the year; j It refers to the vehicle type; It is the first Year j Growth rate of new-type motor vehicle ownership; It is the first Annual growth rate of per capita income; It is the first j Elasticity coefficient of vehicle ownership; The GDP of the base year; The GDP value for the target year or reference year; This represents the annual growth rate of GDP. t For predicting the target year or reference year; For the base year of prediction; (3) Stock Evolution and Incremental Coupling Module: Calculates the retention of old vehicles based on survival curves, and uses the total vehicle ownership prediction to infer the number of new vehicle registrations. ； ； In the formula, S r,T For emission stage r In the target year T The number of units in circulation; N r,y For the first y Annual registration of emission stages r Number of vehicles; For the first y Total number of vehicles actually scrapped each year; θ r,y For emission stage r In the y Percentage of theoretically eliminated annual output; For the first y Theoretically phased-out emission phase r Number of vehicles.

4. The method for analyzing the entire process of synergistic carbon pollution reduction from mobile sources as described in claim 3, characterized in that: In step S13, the specific calculation logic for constructing the localized emission factor library is as follows: Based on the basic emission factors of the COPERT model, the average vehicle speed, environmental parameters, and cumulative mileage of the target area are introduced for localized correction to generate localized dynamic emission factors; the correction formula is as follows: ； in: For the target area m Month Localized emission factors for similar motor vehicles; For the first Baseline emission factors for similar motor vehicles under standard test conditions; This is a speed correction factor; This is an environmental correction factor; This is a degradation correction factor; This is the load correction factor.

5. The method for analyzing the entire process of synergistic carbon pollution reduction from mobile sources as described in claim 4, characterized in that: The fine-grained spatial gridding allocation in step S2 includes the following specific steps: S21. Construct a standard road length model based on road grade weights: Set weight coefficients according to the differences in traffic capacity of roads of different grades, and transform the actual road network length in the target area into a standard road length with unified dimensions. S22. Establish spatial allocation mapping relationship: Utilize the geographic information system to analyze the topological structure of road network data, calculate the proportion of motor vehicle activity levels of each type of road within the grid unit, and accordingly discretize and allocate the localized basic emission inventory obtained in step S1 to a grid with a resolution of 3km×3km to generate a gridded emission inventory with spatial attributes.

6. The method for analyzing the entire process of synergistic carbon pollution reduction from mobile sources as described in claim 5, characterized in that: The uncertainty quantification analysis in step S3 is specifically carried out as follows: a probability distribution model is constructed using the Monte Carlo simulation method, and the number of motor vehicles, the average annual mileage, and the emission factor are selected as key uncertainty parameters for iterative simulation; the coefficient of variation of the number of motor vehicles is set to 5%, the coefficient of variation of the average annual mileage is set to 15%, and the coefficient of variation of the emission factor is set to 10%, and the number of iterations is set to be no less than 10,000; the probability density distribution of the gridded emission inventory is calculated by random sampling, and the 95% confidence interval of the total emissions is output.

7. The method for analyzing the entire process of synergistic carbon pollution reduction from mobile sources as described in claim 6, characterized in that: Step S4 includes the following steps: S41. Construct a tiered emission reduction scenario system: Construct four types of emission reduction scenarios: baseline scenario, single control scenario, combined scenario, and comprehensive scenario; the baseline scenario continues the current established policies; the single control scenario adjusts for a single technology or policy variable; the combined scenario and comprehensive scenario systematically superimpose multiple emission reduction measures; S42. Calculate the nonlinear net effect of multiple emission reduction measures: Determine the annual emission reduction magnitude and implementation sequence for each emission reduction scenario, and iteratively calculate the emission forecast inventory for each emission reduction scenario from 2025 to 2035 year by year; For combined and integrated scenarios, use a multi-measure nonlinear superposition formula to calculate the comprehensive emission reduction rate to avoid double-counting of emission reductions. The calculation formula is as follows: ； in, This represents the total annual emission reduction resulting from the combined effects of multiple emission reduction measures. This represents the total emissions for that year under the baseline scenario; n This represents the total number of combined emission reduction measures. For the first The theoretical emission reduction rate when each emission reduction measure is implemented alone.

8. The method for analyzing the entire process of synergistic carbon pollution reduction from mobile sources as described in claim 7, characterized in that: The specific implementation steps and calculation formulas of the three-dimensional dual-synergistic evaluation system of pollutants-greenhouse gases-economic benefits in step S5 are as follows: S51. Construct a unified evaluation dimension: Convert greenhouse gas emissions into CO2 equivalents; use the comprehensive equivalent normalization method to uniformly characterize different types of pollutants into air pollutant equivalents, the calculation formula of which is: ； in, This represents the comprehensive equivalent value of air pollutant emissions. For the first Emissions of various air pollutants The corresponding equivalent coefficient; S52. Calculate the environmental emission reduction synergy index: Calculate the emission reduction rates of greenhouse gases and air pollutants under each emission reduction scenario, which will serve as the basis for environmental synergy assessment. The calculation formula is as follows: ； in, and These represent the total emissions of greenhouse gases and air pollutants before the implementation of emission reduction measures; and These represent the reductions in greenhouse gas and air pollutant emissions after the implementation of the measures; For greenhouse gas emission reduction rate, The rate of reduction of air pollutants; S53. The synergistic effect coordinate method is used to map the emission reduction scenarios to a two-dimensional coordinate system. In the two-dimensional coordinate system, the horizontal axis represents the emission reduction effect of air pollutants, and the vertical axis represents the emission reduction effect of greenhouse gases. The optimal synergistic emission reduction scenario in the first quadrant is identified, and negative synergistic scenarios in which a single emission reduction measure leads to the deterioration of another indicator are eliminated. S54. Construct a synergistic-economic composite index, calculate it using the equal-weighting method, and output the comprehensive benefit ranking of each emission reduction scenario and the optimal policy combination recommendation; the calculation formula for the synergistic-economic composite index is: ； ； ； ； in, pollutants The emission reduction; This refers to the pollutant equivalent coefficient. CO2 emission reduction (t); This refers to the comprehensive equivalent emission reduction of conventional pollutants; = =0.5 indicates that CO2 is as important as conventional pollutants; This represents the maximum value of the corresponding indicator in the sample emission reduction scenario; For carbon price; pollutants The tax rate or unit damage value; , These are the weighting coefficients for environmental synergy indicators and economic benefit indicators, respectively. Env' For standardized environmental coordination indicators, Econ' These are standardized economic benefit indicators.

9. A full-process analysis system for synergistic emission reduction of carbon pollution from mobile sources, used to implement the full-process analysis method for synergistic emission reduction of carbon pollution from mobile sources as described in claim 8, characterized in that: The system includes an inventory construction module for building a basic emission inventory, a spatial allocation module for spatially gridded allocation of the basic emission inventory, an uncertainty analysis module for quantifying the uncertainty of the spatially gridded emission inventory, a scenario setting module for constructing emission reduction scenarios and calculating the emission prediction inventory and the superposition effect of emission reduction measures for each scenario, a collaborative evaluation module for calculating and outputting the ranking of the synergistic emission reduction effects of each emission reduction scenario and the optimal policy recommendation, and a data storage module for storing various data. The inventory construction module, spatial allocation module, uncertainty analysis module, scenario setting module, and collaborative evaluation module are sequentially connected to implement data transmission operations, and the inventory construction module, spatial allocation module, uncertainty analysis module, scenario setting module, and collaborative evaluation module are all connected to the data storage module to implement data storage operations.

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