Passenger car fleet life cycle carbon emission prediction and carbon emission reduction path evaluation method and device

Abstract

The application discloses a passenger vehicle fleet full life cycle carbon emission prediction and carbon emission reduction path evaluation method and device, determines the fleet full life cycle carbon emission and the fleet carbon emission reduction caused by the integral constraint under the integral constraint scenario and the integral constraint scenario based on the fleet scale, the fuel type market structure, the vehicle energy consumption and technical index, the carbon emission factor and the integral constraint parameter; the emission reduction benefit, the emission reduction efficiency and the comprehensive score of each path are determined by changing the parameters corresponding to different carbon emission reduction paths, combining the carbon emission change and the path cost, and generating the carbon emission reduction policy guidance result accordingly. The embodiment of the application reflects the conduction influence of the integral constraint on the fleet structure and the full life cycle carbon emission, improves the accuracy, consistency and comparability of the prediction result, and provides a quantitative basis for the carbon emission reduction path selection and policy making.

Description

Technical Field

[0001] This application relates to, but is not limited to, the field of new energy technology, and in particular to a method and apparatus for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire life cycle of a passenger vehicle fleet. Background Technology

[0002] With the continuous growth of vehicle ownership and the parallel development of various power types such as pure electric vehicles, plug-in hybrid electric vehicles, hybrid electric vehicles, and traditional gasoline vehicles, the sources and mechanisms of carbon emissions in the automotive industry are becoming increasingly complex. Different power types of vehicles differ in technical parameters such as average fuel consumption, average energy consumption, curb weight, battery capacity, driving range, and energy type, and these technical parameters will change with the development of vehicle technology and changes in energy structure.

[0003] Automobile carbon emissions include not only the carbon emissions generated from fuel or electricity consumption during vehicle operation, but also those generated during the production of raw materials and components, vehicle manufacturing, and fuel or electricity production. Therefore, when predicting carbon emissions from a vehicle fleet, it is necessary to comprehensively consider the changes in the number of vehicles with different power types, vehicle energy consumption and technical parameters, and emission factors at different stages of the vehicle's life cycle.

[0004] Determining the lifecycle carbon emissions of a vehicle fleet for each forecast year under different scenarios and improving the accuracy and consistency of lifecycle carbon emission forecasts is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] This application provides a method and apparatus for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire life cycle of a passenger vehicle fleet. This method avoids using static fleet structures or fragmented models for prediction, thereby improving the accuracy, consistency, and comparability of medium- and long-term fleet life cycle carbon emission prediction results.

[0006] This invention provides a method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire lifecycle of a passenger vehicle fleet, including: Based on passenger vehicle fleet size, market structure of vehicles with different fuel types, vehicle energy consumption and technical indicators, carbon emission factors and credit constraint parameters, the total life cycle carbon emissions of the fleet are determined for each forecast year under both the no-credit-constraint scenario and the credit-constraint scenario. The carbon emission reduction of the fleet caused by the credit constraints is determined based on the total life cycle carbon emissions of the fleet under the two scenarios. By changing the parameters corresponding to different carbon emission reduction paths, and based on the changes in the fleet's carbon emissions over the entire life cycle caused by the parameter changes and the costs of the corresponding carbon emission reduction paths, the emission reduction benefits and efficiency of different carbon emission reduction paths are determined, and the comprehensive score of different carbon emission reduction paths is determined based on the emission reduction benefits and efficiency. The results of carbon reduction policy guidance are determined based on a comprehensive score of the fleet's carbon emission reduction and different carbon emission reduction pathways.

[0007] In one exemplary instance, determining the fleet carbon emission reduction caused by the integral constraint includes: Obtain basic data for predicting carbon emissions throughout the entire life cycle of the passenger vehicle; the basic data includes historical passenger vehicle ownership data, economic parameters, historical passenger vehicle sales, historical sales percentage by fuel type, historical energy consumption and technical indicators of vehicles of different fuel types, prediction parameters for energy consumption and technical indicators, the carbon emission factor, and the integral constraint parameters. The predicted passenger vehicle ownership for each forecast year within the forecast period is determined based on historical passenger vehicle ownership data and economic parameters. The total sales volume of passenger vehicles for each forecast year is determined based on the predicted value of passenger vehicle ownership and the number of vehicles scrapped. Based on the probability of consumers choosing different fuel types of vehicles and the predicted total sales of passenger vehicles, the first-year sales of different fuel types of vehicles in each predicted year under the scenario without credit constraints are determined; wherein, when determining the first-year sales under the scenario without credit constraints, corporate average fuel consumption credit constraints, new energy vehicle credit constraints and credit trading rules are not introduced. Based on the enterprise type of passenger vehicle enterprises, the sales volume of vehicles by fuel type in each forecast year is determined for market-driven enterprises and compliance-impact enterprises respectively. Based on the sales volume of vehicles by fuel type of the two types of enterprises, the second-year sales volume of vehicles of different fuel types in each forecast year under the integral constraint scenario is determined. Determine the number of vehicles of different fuel types in each predicted year under the scenarios without integral constraints and under the scenarios with integral constraints, respectively. Based on the historical energy consumption and technical indicators in the basic data, as well as the predicted parameters of energy consumption and technical indicators, the energy consumption and technical indicators of vehicles with different fuel types in each predicted year within the prediction period are determined. Based on the energy consumption and technical specifications of vehicles with different fuel types, determine the carbon emissions of a single vehicle throughout its entire life cycle for each fuel type. Based on the total lifecycle carbon emissions of a single vehicle with different fuel types and the number of vehicles with different fuel types in the corresponding scenarios, the total lifecycle carbon emissions of the fleet in each forecast year are determined. Based on the scenario without integral constraints and the fleet's carbon emissions over its entire lifecycle under the scenario with integral constraints, determine the fleet carbon emission reduction caused by the integral constraints.

[0008] In one exemplary instance, determining the comprehensive score for different carbon reduction pathways includes: The parameters corresponding to the carbon emission reduction path are changed by using the controlled variable method, and the marginal emission reduction corresponding to the carbon emission reduction path is determined based on the difference between the carbon emissions of the fleet throughout its entire life cycle before and after the parameter change. Based on the marginal emission reduction and marginal cost of different carbon emission reduction paths, the emission reduction efficiency of different carbon emission reduction paths is determined; the marginal emission reduction corresponding to different carbon emission reduction paths is determined as the emission reduction benefit of the corresponding carbon emission reduction path. The emission reduction efficiency and emission reduction benefit of different carbon emission reduction paths are normalized respectively. Then, the normalized emission reduction efficiency and emission reduction benefit are weighted according to the first preference value corresponding to the emission reduction efficiency and the second preference value corresponding to the emission reduction benefit to obtain the comprehensive score of the different carbon emission reduction paths.

[0009] In one exemplary instance, determining the guiding outcome of carbon reduction policies includes: Based on the comprehensive scores of the different carbon emission reduction paths and the corresponding emission reduction benefits and efficiency, the different carbon emission reduction paths are ranked to determine the implementation priority of the different carbon emission reduction paths. Based on the fleet carbon emission reduction and the policy evaluation index corresponding to the integral constraint scenario, multiple candidate values ​​for the integral constraint parameter are set, and the fleet carbon emission reduction and policy evaluation index corresponding to each candidate value are determined respectively. Based on the fleet carbon emission reduction and policy evaluation index corresponding to each candidate value, the policy strength evaluation result is obtained. Based on the evaluation results of the implementation priorities and policy intensity of different carbon emission reduction pathways, generate carbon emission reduction policy guidance results that include at least one of the evaluation results of implementation priorities and policy intensity. The policy evaluation indicators include at least one of the following: points-based prediction indicators, fuel savings, emission reductions of typical pollutants, total socio-economic benefits, and net social benefits.

[0010] In one exemplary instance, generating a carbon reduction policy guidance result that includes at least one of the evaluation results of implementation priorities and policy intensity includes: Based on the aforementioned policy evaluation indicators, the carbon emission reduction effect and economic effect corresponding to different policy intensities are determined respectively; Based on the implementation priorities of the different carbon emission reduction paths, the carbon emission reduction effects and economic effects corresponding to different policy intensities, the carbon emission reduction policy guidance results are generated, including at least one of the following: recommended carbon emission reduction paths, the implementation order of carbon emission reduction paths, recommended values ​​of policy constraint parameters, and policy implementation years.

[0011] In one exemplary instance, it also includes: The system receives natural language questions input by users, parses the received natural language questions, and determines at least one of the following: the forecast year, forecast scenario, fuel type, carbon emission reduction path, and policy evaluation indicators involved in the natural language questions. Based on the analysis results, extract at least one of the following: fleet size prediction data, vehicle sales data, vehicle ownership data, fleet life cycle carbon emission data, carbon emission reduction path evaluation data, and policy evaluation indicator data. The extracted data is processed to generate an answer corresponding to the natural language question.

[0012] In one exemplary instance, determining the predicted passenger vehicle ownership for each forecast year within the forecast period based on historical passenger vehicle ownership data and economic parameters includes: The historical passenger vehicle ownership data, the economic parameters, and the Gompertz model fitting parameters are input into the Gompertz model. The output of the Gompertz model is the predicted passenger vehicle ownership value for each prediction year within the prediction period. The predicted passenger vehicle ownership values ​​are categorized and stored according to at least one of fuel type, vehicle age, and usage area.

[0013] In one exemplary instance, determining the predicted total sales volume of passenger vehicles for each predicted year based on the predicted passenger vehicle ownership and the number of vehicles scrapped includes: The number of vehicles to be scrapped in each projected year is determined based on the age of the vehicles. Based on the current forecast year's passenger vehicle ownership, the previous year's passenger vehicle ownership, and the current forecast year's vehicle scrapping volume, the current forecast year's total passenger vehicle sales volume is determined according to the vehicle inventory balance.

[0014] In one exemplary instance, determining the first-year sales volume of vehicles of different fuel types for each forecast year under a scenario without integral constraints includes: Using a multivariate discrete consumer choice model, the probability of consumers choosing different fuel types of vehicles in each forecast year is predicted. The selection probability corresponding to different fuel types of vehicles is determined as the annual sales percentage of the corresponding fuel type of vehicle. The first-year sales volume of each fuel type of vehicle is determined based on the annual sales volume percentage and the predicted total sales volume of passenger vehicles for the corresponding forecast year.

[0015] In one exemplary instance, determining the sales volume of vehicles by fuel type for market-driven companies and compliance-impact companies in each forecast year includes: Based on the total passenger vehicle sales forecast, the sales proportion of vehicles of different fuel types, and the market share of the market-driven enterprise by fuel type, the sales volume of vehicles of different fuel types of the market-driven enterprise in each forecast year is determined. Using the sales volume of vehicles by fuel type, the improvement in vehicle energy consumption, and the amount of credits purchased by the compliance-influenced enterprises as decision variables, and under the conditions of satisfying fuel consumption credit constraints and new energy vehicle credit constraints, with the goal of minimizing the compliance cost of the credit policy, the sales volume of vehicles by fuel type, the improvement in vehicle energy consumption, and the amount of credits purchased by the compliance-influenced enterprises in each forecast year are determined.

[0016] In one exemplary instance, determining the number of vehicles of different fuel types in each predicted year under both the no-integral-constraint scenario and the integral-constraint scenario includes: Based on the number of vehicles of the corresponding fuel type in the previous year, the annual sales volume of vehicles of the corresponding fuel type in the current forecast year, and the number of vehicles of the corresponding fuel type scrapped in the current forecast year, the number of vehicles of the corresponding fuel type in each forecast year is determined year by year according to the vehicle stock balance relationship. Wherein, the annual sales volume under the no-credit-constraint scenario is the first year's sales volume, and the annual sales volume under the credit-constraint scenario is the second year's sales volume; the vehicle inventory balance relationship is: current year's inventory = previous year's inventory + current year's sales volume - current year's scrap volume.

[0017] In one exemplary instance, determining the energy consumption and technical specifications of vehicles of different fuel types for each forecast year within the forecast period includes: Based on historical vehicle data, the energy consumption and technical indicators of vehicles are statistically analyzed according to fuel type and historical year, and the historical energy consumption and technical indicators of vehicles of different fuel types in each historical year are obtained. Obtain the annual rate of change for each energy consumption and technical indicator; Based on the energy consumption and technical indicators corresponding to historical years, and according to the annual change rate of the corresponding indicators, the energy consumption and technical indicators of vehicles with different fuel types in each forecast year within the forecast period are determined year by year in chronological order of the forecast years. The energy consumption and technical indicators may include at least one of average fuel consumption, average electricity consumption, average energy consumption, average curb weight, average driving range, and average battery capacity; the energy consumption and technical indicator prediction parameters may include at least one of the annual change rate corresponding to each energy consumption and technical indicator, and future annual indicator values ​​obtained through enterprise surveys or user settings.

[0018] In one exemplary instance, the total lifecycle carbon emissions of a single vehicle with different fuel types include the sum of carbon emissions from raw materials and components, carbon emissions from vehicle production, carbon emissions from fuel production, and carbon emissions from fuel use.

[0019] In one exemplary instance, determining the fleet carbon emission reduction caused by the integral constraint includes: Obtain the fleet's total lifecycle carbon emissions for each predicted year under the scenario without integral constraints, and the fleet's total lifecycle carbon emissions for each predicted year under the scenario with integral constraints. For the same forecast year, the difference between the fleet's total lifecycle carbon emissions under the no-integral-constraint scenario and the fleet's total lifecycle carbon emissions under the integral-constraint scenario is used to obtain the fleet carbon emission reduction caused by the integral constraint.

[0020] In one exemplary instance, the carbon reduction pathway reduces at least one of vehicle fuel consumption, vehicle electricity consumption, carbon emission factors in battery production, and the promotion of new energy vehicles. Among them, the carbon emission reduction path for reducing vehicle fuel consumption involves changing the average fuel consumption of at least one of the traditional fuel vehicles and hybrid vehicles. To reduce carbon emissions through lower vehicle energy consumption, the average energy consumption of at least one of the following vehicles is altered: pure electric vehicles and plug-in hybrid electric vehicles. To reduce carbon emissions through pathways that lower the carbon emission factor in battery production, the carbon emission factor in power battery production is altered. To promote carbon emission reduction through the promotion of new energy vehicles, the sales share of new energy vehicles should be increased, while the sales share of gasoline vehicles should be reduced accordingly.

[0021] In one exemplary instance, the typical pollutant emissions under the two scenarios are determined based on the number of vehicles of different fuel types under the no integral constraint scenario and the integral constraint scenario, as well as the emission standards, vehicle age, mileage and pollutant emission factors of the corresponding vehicles. The emission reduction of typical pollutants caused by the integral constraint is determined based on the difference in typical pollutant emissions under the two scenarios.

[0022] This application also provides a computer-readable storage medium storing computer-executable instructions, which are used to execute the passenger vehicle fleet life-cycle carbon emission prediction and carbon reduction path evaluation method described in any of the above embodiments.

[0023] This application embodiment provides another electronic device, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the steps of the passenger vehicle fleet life-cycle carbon emission prediction and carbon reduction path evaluation method described in any of the above claims.

[0024] This application embodiment further provides a device for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire lifecycle of a passenger vehicle fleet, including: a carbon emission reduction prediction module, a carbon emission reduction pathway evaluation module, and a policy guidance processing module; wherein... The carbon emission reduction prediction module is used to determine the fleet's life-cycle carbon emissions for each forecast year under both the no-integral-constraint scenario and the integral-constraint scenario, based on the passenger car fleet size, market structure of vehicles with different fuel types, vehicle energy consumption and technical indicators, carbon emission factors, and integral constraint parameters. It also determines the fleet carbon emission reduction caused by integral constraints based on the fleet's life-cycle carbon emissions under the two scenarios. The carbon emission reduction path evaluation module is used to change the parameters corresponding to different carbon emission reduction paths, determine the emission reduction benefits and efficiency of different carbon emission reduction paths based on the changes in the fleet's carbon emissions throughout its entire life cycle caused by the parameter changes and the costs of the corresponding carbon emission reduction paths, and determine the comprehensive score of different carbon emission reduction paths based on the emission reduction benefits and the emission reduction efficiency. The policy guidance processing module is used to determine the carbon reduction policy guidance results based on the comprehensive score of the fleet's carbon emission reduction and different carbon emission reduction paths.

[0025] In one exemplary instance, the system further includes: an intelligent decision-making assistant module, configured to receive a natural language question input by a user, parse the natural language question, determine at least one of the following: prediction year, prediction scenario, fuel type, carbon emission reduction path, and policy evaluation indicators involved in the natural language question; extract relevant data from the carbon emission reduction prediction module, carbon emission reduction path evaluation module, and policy guidance processing module based on the parsing results, perform calculations on the extracted data, and generate the answer content corresponding to the natural language question. The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the lifecycle of a passenger vehicle fleet, provided in this application, determines the fleet's lifecycle carbon emissions and carbon reduction caused by the integral constraints under both scenarios with and without integral constraints, based on fleet size, market structure by fuel type, vehicle energy consumption and technical indicators, carbon emission factors, and integral constraint parameters. By changing the parameters corresponding to different carbon reduction pathways, and combining changes in carbon emissions and pathway costs, the method determines the emission reduction benefits, emission reduction efficiency, and comprehensive score of each pathway, and generates carbon reduction policy guidance results accordingly. This application reflects the transmission impact of integral constraints on fleet structure and lifecycle carbon emissions, improves the accuracy, consistency, and comparability of prediction results, and provides a quantitative basis for carbon reduction pathway selection and policy formulation.

[0026] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0027] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0028] Figure 1 This is a flowchart illustrating the method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire lifecycle of a passenger vehicle fleet, as described in this application. Figure 2 This is a schematic diagram of the method for predicting the carbon emissions of a passenger vehicle fleet throughout its entire lifecycle, as described in this application. Figure 3 This is a schematic diagram of the composition of the passenger vehicle fleet life-cycle carbon emission prediction and carbon reduction path evaluation device in the embodiments of this application. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be arbitrarily combined with each other.

[0030] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0032] It is understood that the terms "first" and "second" used in this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0033] It is understood that the term "connection" in the following embodiments should be understood as "electrical connection," "communication connection," etc., if the connected circuits, modules, units, etc., have electrical signal or data transmission with each other.

[0034] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising / including” or “having,” etc., specify the presence of the stated features, wholes, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0035] The steps illustrated in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases the steps shown or described may be performed in a different order than that presented here.

[0036] In related technologies, one type of method typically calculates carbon emissions during vehicle use based on vehicle ownership, average mileage, average fuel consumption or average electricity consumption, and corresponding emission factors. This type of method primarily focuses on the vehicle's usage phase and cannot simultaneously reflect carbon emissions across multiple lifecycle stages, including materials and components, vehicle production, energy production, and vehicle use.

[0037] Another approach focuses on individual vehicles, calculating the life-cycle carbon emissions of different powertrain types based on vehicle curb weight, battery capacity, fuel consumption, electricity consumption, and emission factors. While this method yields carbon emission results at the individual vehicle level, when extended to the fleet level, it typically multiplies the individual vehicle carbon emissions by a pre-set number of vehicles, failing to update the fleet composition annually based on changes in new sales, scrapping, and overall vehicle ownership for different powertrain types during the forecast period.

[0038] In the actual evolution of a fleet, vehicles of different power types will continuously enter or leave the fleet, causing the number and proportion of vehicles of each power type to change continuously. If a fixed number of vehicles, a fixed proportion of vehicle types, or a fleet structure for a single year are used in the forecasting process, then the fleet carbon emissions for subsequent years will still be calculated based on the initial or static fleet composition, and will not accurately reflect the changes in the fleet's technical structure during the forecast period.

[0039] Furthermore, while some related technologies can predict total passenger vehicle sales, the sales share of different powertrain types, or vehicle ownership, the sales forecasts, ownership updates, and the full lifecycle carbon emission accounting process are typically independent of each other. After changes in sales or ownership, it is still necessary to reorganize the quantity data for different powertrain types and then input them into the carbon emission accounting model, making it difficult to establish a continuous calculation relationship from changes in vehicle sales and fleet composition to changes in carbon emissions.

[0040] If different data calibers, vehicle quantity benchmarks, or calculation ranges are used for carbon emission prediction under different scenarios, the carbon emission results corresponding to each scenario will lack direct comparability, which will affect the accuracy of determining the carbon emission reduction of the fleet based on the scenario difference.

[0041] In summary, it is currently difficult to dynamically update fleet composition based on the annual changes in sales volume, scrapping volume, and ownership of vehicles of different power types, and to update the fleet's full life cycle carbon emissions accordingly, resulting in insufficient accuracy of medium- and long-term carbon emission predictions. Therefore, this application provides a method for predicting vehicle full life cycle carbon emissions. This method is based on dynamic updates to fleet structure, aiming to update fleet composition annually according to changes in sales volume, scrapping volume, and ownership of vehicles of different power types during the prediction period. The updated fleet composition is then correlated with vehicle fuel consumption, electricity consumption, curb weight, battery capacity, and emission factors to determine the fleet's full life cycle carbon emissions for each prediction year under different scenarios within a unified accounting boundary, thereby improving the accuracy and consistency of vehicle fleet full life cycle carbon emission prediction results.

[0042] Figure 1 This is a flowchart illustrating the method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire lifecycle of a passenger vehicle fleet, as described in the embodiments of this application. Figure 1 As shown, it may include: Step 100: Based on the passenger vehicle fleet size, market structure of vehicles with different fuel types, vehicle energy consumption and technical indicators, carbon emission factors and credit constraint parameters, determine the fleet's total life cycle carbon emissions for each forecast year under both the no-credit-constraint scenario and the credit-constraint scenario, and determine the fleet carbon emission reduction caused by the credit constraints based on the fleet's total life cycle carbon emissions under the two scenarios.

[0043] In one exemplary instance, step 100 may include: Obtain basic data for predicting carbon emissions throughout the entire life cycle of passenger vehicles; the basic data includes historical passenger vehicle ownership data, economic parameters, historical passenger vehicle sales, historical sales share by fuel type, historical energy consumption and technical indicators of vehicles of different fuel types, prediction parameters for energy consumption and technical indicators, carbon emission factors, and credit constraint parameters. The predicted passenger vehicle ownership for each forecast year within the forecast period is determined based on historical passenger vehicle ownership data and economic parameters. The total sales volume of passenger vehicles for each forecast year is determined based on the predicted value of passenger vehicle ownership and the number of vehicles scrapped. Based on the probability of consumers choosing different fuel types of vehicles and the predicted total sales of passenger vehicles, the first-year sales of different fuel types of vehicles in each predicted year under the scenario without credit constraints are determined; where, when determining the first-year sales under the scenario without credit constraints, corporate average fuel consumption credit constraints, new energy vehicle credit constraints and credit trading rules are not introduced. Based on the enterprise type of passenger vehicle enterprises, the sales volume of vehicles by fuel type in each forecast year is determined for market-driven enterprises and compliance-impact enterprises respectively. Based on the sales volume of vehicles by fuel type of the two types of enterprises, the second-year sales volume of vehicles of different fuel types in each forecast year under the credit constraint scenario is determined. Determine the number of vehicles of different fuel types in each forecast year under both the no integral constraint scenario and the integral constraint scenario. Based on the historical energy consumption and technical indicators in the basic data, as well as the predicted parameters of energy consumption and technical indicators, the energy consumption and technical indicators of vehicles with different fuel types in each predicted year within the prediction period are determined. Based on the energy consumption and technical specifications of vehicles with different fuel types, determine the carbon emissions of a single vehicle throughout its entire life cycle for each fuel type. Based on the total lifecycle carbon emissions of a single vehicle with different fuel types and the number of vehicles with different fuel types in the corresponding scenarios, the total lifecycle carbon emissions of the fleet in each forecast year are determined. Based on the fleet's carbon emissions throughout its entire lifecycle under both the no-integral-constraint and integral-constraint scenarios, determine the fleet's carbon emission reduction caused by integral constraints.

[0044] Step 101: Change the parameters corresponding to different carbon emission reduction paths respectively. Based on the changes in the fleet's carbon emissions over the entire life cycle caused by the parameter changes and the costs of the corresponding carbon emission reduction paths, determine the emission reduction benefits and efficiency of different carbon emission reduction paths, and determine the comprehensive score of different carbon emission reduction paths based on the emission reduction benefits and efficiency.

[0045] In one exemplary instance, step 101 may include: The parameters corresponding to the carbon emission reduction path are changed by using the controlled variable method, and the marginal emission reduction corresponding to the carbon emission reduction path is determined based on the difference between the carbon emissions of the fleet throughout its entire life cycle before and after the parameter change. Based on the marginal emission reduction and marginal cost of different carbon emission reduction paths, the emission reduction efficiency of different carbon emission reduction paths is determined; the marginal emission reduction corresponding to different carbon emission reduction paths is determined as the emission reduction benefit of the corresponding carbon emission reduction path. The emission reduction efficiency and emission reduction benefits of different carbon emission reduction paths are normalized respectively. Then, based on the first preference value corresponding to the emission reduction efficiency and the second preference value corresponding to the emission reduction benefits, the normalized emission reduction efficiency and emission reduction benefits are weighted and calculated to obtain the comprehensive score of different carbon emission reduction paths.

[0046] Step 102: Determine the carbon reduction policy guidance results based on the comprehensive score of the fleet's carbon emission reduction and different carbon emission reduction paths.

[0047] In one exemplary instance, step 102 may include: Based on the comprehensive scores of different carbon emission reduction paths and the corresponding emission reduction benefits and efficiency, the different carbon emission reduction paths are ranked to determine the implementation priority of different carbon emission reduction paths. Based on the fleet carbon emission reduction and the policy evaluation indicators corresponding to the credit constraint scenario, multiple candidate values ​​for the credit constraint parameters are set, and the fleet carbon emission reduction and policy evaluation indicators corresponding to each candidate value are determined respectively. Based on the fleet carbon emission reduction and policy evaluation indicators corresponding to each candidate value, the policy strength evaluation results are obtained. Based on the evaluation results of the implementation priorities and policy intensity of different carbon emission reduction pathways, generate carbon emission reduction policy guidance results that include at least one of the evaluation results of implementation priorities and policy intensity. The policy evaluation indicators include at least one of the following: points-based prediction indicators, fuel savings, emission reductions of typical pollutants, total socio-economic benefits, and net social benefits.

[0048] In one embodiment, generating carbon reduction policy guidance results that include at least one of the evaluation results of implementation priority and policy intensity may include: Based on policy evaluation indicators, the carbon emission reduction effect and economic effect corresponding to different policy intensities are determined respectively; Based on the implementation priorities of different carbon emission reduction paths, the carbon emission reduction effects and economic effects corresponding to different policy intensities, a carbon emission reduction policy guidance result is generated, which includes at least one of the following: recommended carbon emission reduction paths, the order of implementation of carbon emission reduction paths, recommended values ​​of policy constraint parameters, and the policy implementation year.

[0049] The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the lifecycle of a passenger vehicle fleet, provided in this application, determines the fleet's lifecycle carbon emissions and carbon reduction caused by the integral constraints under both scenarios with and without integral constraints, based on fleet size, market structure by fuel type, vehicle energy consumption and technical indicators, carbon emission factors, and integral constraint parameters. By changing the parameters corresponding to different carbon reduction pathways, and combining changes in carbon emissions and pathway costs, the method determines the emission reduction benefits, emission reduction efficiency, and comprehensive score of each pathway, and generates carbon reduction policy guidance results accordingly. This application reflects the transmission impact of integral constraints on fleet structure and lifecycle carbon emissions, improves the accuracy, consistency, and comparability of prediction results, and provides a quantitative basis for carbon reduction pathway selection and policy formulation.

[0050] In one exemplary instance, the embodiments of this application may further include: The system receives natural language questions input by users, parses the received natural language questions, and determines at least one of the following: the forecast year, forecast scenario, fuel type, carbon emission reduction path, and policy evaluation indicators involved in the natural language questions. Based on the analysis results, extract at least one of the following: fleet size prediction data, vehicle sales data, vehicle ownership data, fleet life cycle carbon emission data, carbon emission reduction path evaluation data, and policy evaluation indicator data. The extracted data is processed to generate an answer corresponding to the natural language question.

[0051] In this embodiment, by receiving and parsing the natural language questions input by the user, the system retrieves the data and calculation processes corresponding to the predicted year, predicted scenario, fuel type, carbon emission reduction path, or policy evaluation indicators based on the parsing results, and generates the answer. This transforms the user's natural language query into a targeted retrieval and calculation of fleet prediction, carbon emission accounting, emission reduction path evaluation, and policy evaluation data. It reduces the user's manual data filtering, query setting, and calculation process organization, improving the query efficiency, interactive convenience, and output relevance of carbon emission prediction and policy evaluation results.

[0052] Figure 2 This is a schematic diagram of the method for predicting the carbon emissions of a passenger vehicle fleet throughout its entire lifecycle, as described in the embodiments of this application. Figure 2 As shown, it may include: Step 200: Obtain basic data for predicting carbon emissions throughout the entire life cycle of automobiles; the basic data includes historical passenger vehicle ownership data, economic parameters, historical passenger vehicle sales, historical sales share by fuel type, historical energy consumption and technical indicators of vehicles of different fuel types, prediction parameters for energy consumption and technical indicators, carbon emission factors, and integral constraint parameters.

[0053] In one embodiment, the historical passenger vehicle ownership data may include at least one of the following: year, vehicle age, fuel type, and usage area. The fuel type may include conventional gasoline-powered vehicles (ICE), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), and battery electric vehicles (BEV).

[0054] In one embodiment, economic parameters may include parameters that reflect the changing trend of passenger vehicle ownership, such as per capita GDP and population size for each historical and forecast year.

[0055] In one embodiment, the historical energy consumption and technical indicators of vehicles with different fuel types may include at least one of average fuel consumption, average electricity consumption, average energy consumption, average curb weight, average driving range, and average battery capacity. Historical energy consumption and technical indicators can be determined statistically based on collected historical vehicle data, categorized by fuel type and historical year.

[0056] In one embodiment, the energy consumption and technical indicator prediction parameters may include at least one of the annual change rate corresponding to each energy consumption and technical indicator, and future annual indicator values ​​obtained through enterprise surveys or user settings. The energy consumption and technical indicators may include at least one of average fuel consumption, average electricity consumption, average energy consumption, average curb weight, average driving range, and average battery capacity.

[0057] In this embodiment of the application, for the forecast year, the energy consumption and technical indicators of each forecast year can be determined year by year based on the historical energy consumption and technical indicators of vehicles of different fuel types and the annual change rate of the corresponding indicators; or, the indicator values ​​of future years obtained through enterprise surveys or user settings can be determined as the energy consumption and technical indicators of the corresponding forecast year.

[0058] In one embodiment, the carbon emission factor may include at least one of the following: carbon emission factor for fuel production, carbon emission factor for fuel use, carbon emission factor for electricity production, carbon emission factor for battery production, carbon emission factor for vehicle materials, and carbon emission factor for complete vehicle production.

[0059] In one embodiment, the credit constraint parameters may include: fuel consumption credit constraint parameters, new energy vehicle credit constraint parameters, and credit trading rule parameters. Specifically, the fuel consumption credit constraint parameters may include the enterprise's average fuel consumption target value and fuel consumption credit calculation rules; the new energy vehicle credit constraint parameters may include new energy vehicle credit ratio requirements and credit scores corresponding to different new energy vehicle models; and the credit trading rule parameters may include parameters such as credit transfer, trading, carry-over ratios, and validity periods. The original specification already describes the input and configuration methods for vehicle parameters of different fuel types, credit constraint parameters, and credit trading rules.

[0060] The credits referred to in this application are the corporate average fuel consumption credits and new energy vehicle credits calculated in accordance with the parallel management rules for passenger vehicle corporate average fuel consumption and new energy vehicle credits. They are used to characterize the difference between passenger vehicle companies and the prescribed compliance requirements in terms of average fuel consumption and the production or import of new energy vehicles.

[0061] The credit constraint scenario in this paper refers to a forecasting scenario that considers corporate average fuel consumption credit constraints, new energy vehicle credit constraints, and credit trading rules, and adjusts the sales volume of different fuel types of vehicles in each forecast year based on the response methods of different types of passenger vehicle companies to the credit constraints. The no-credit-constraint scenario (also known as the zero-credit scenario) refers to a forecasting scenario where corporate average fuel consumption credit constraints, new energy vehicle credit constraints, and credit trading rules are not imposed, and the sales volume of different fuel types of vehicles in each forecast year is determined based on the probability of consumers choosing different fuel types of vehicles and the forecast value of total passenger vehicle sales. Specifically, the corporate average fuel consumption credit constraint requires companies to offset negative average fuel consumption credits to zero through prescribed methods, thus constraining the sales structure of high-fuel-consumption and low-fuel-consumption models; the new energy vehicle credit constraint requires companies that meet the applicable conditions to satisfy the annual new energy vehicle credit ratio requirements and offset negative new energy vehicle credits to zero through positive new energy vehicle credits, thus constraining new energy vehicle sales.

[0062] Step 201: Determine the predicted value of passenger vehicle ownership for each forecast year within the forecast period based on historical passenger vehicle ownership data and economic parameters.

[0063] In one exemplary instance, historical passenger vehicle ownership data, economic parameters, and Gompertz model fitting parameters can be input into the Gompertz model. The output of the Gompertz model is the predicted passenger vehicle ownership for each forecast year within the forecast period. This step uses the Gompertz model to predict the medium- to long-term annual ownership based on historical passenger vehicle ownership data, historical and future annual per capita GDP, population, and other parameters, and outputs predicted passenger vehicle ownership for future years by fuel type, vehicle age, and province. Specifically, in this embodiment, the predicted passenger vehicle ownership for each forecast year within the forecast period is obtained through the Gompertz model; based on at least one of the fuel type, vehicle age, and usage area (such as a province field) included in the historical ownership data, the predicted passenger vehicle ownership values ​​are classified and stored to obtain ownership prediction results by fuel type, vehicle age, and province. In other words, the output may not be a single value, but a set of data with categorical dimensions, such as: in 2030, in Province A, the number of BEVs with a vehicle age of 0-1 years is a certain value.

[0064] The Gompertz model is an S-shaped growth model, typically used to describe the process of an object growing from a low level, then slowing down and gradually approaching a saturation point. The growth of car ownership with per capita GDP usually exhibits similar characteristics; therefore, the Gompertz model can be used in this embodiment to predict medium- to long-term car ownership.

[0065] In one embodiment, the forecast period can be set according to actual forecasting needs, such as extending from the current year to the target year.

[0066] Step 201 yields a predicted passenger vehicle ownership value for each forecast year. Furthermore, these predicted values ​​can be categorized and stored according to fuel type, vehicle age, or usage area to facilitate subsequent determination of annual sales and ownership figures for vehicles of different fuel types.

[0067] In some implementations, different economic parameters can be set for different forecast scenarios and input into the Gompertz model respectively to obtain the predicted value of passenger vehicle ownership for different forecast scenarios.

[0068] Step 202: Determine the total passenger vehicle sales forecast for each forecast year based on the predicted passenger vehicle ownership and vehicle scrapping volume.

[0069] In one exemplary instance, step 202 may include: The number of vehicles scrapped in each projected year is determined based on the age of the vehicles. Based on the current forecast year's passenger vehicle ownership, the previous year's passenger vehicle ownership, and the current forecast year's vehicle scrapping volume, the current forecast year's total passenger vehicle sales volume is determined according to the vehicle inventory balance relationship. Specifically, the current forecast year's total passenger vehicle sales volume is equal to the current forecast year's passenger vehicle ownership volume minus the previous year's passenger vehicle ownership volume, and the difference is then added to the current forecast year's vehicle scrapping volume.

[0070] Step 203: Based on the probability of consumers choosing different fuel types of vehicles and the predicted total sales volume of passenger vehicles, determine the first-year sales volume of different fuel types for each forecast year under the scenario without credit constraints. When determining the first-year sales volume under the scenario without credit constraints, corporate average fuel consumption credit constraints, new energy vehicle credit constraints, and credit trading rules are not introduced.

[0071] Among them, the scenario without credit constraints refers to the prediction scenario in which the sales volume of vehicles by fuel type is determined based on the total sales volume forecast of passenger vehicles and the probability of consumers choosing vehicles of different fuel types, and the prediction scenario does not introduce corporate average fuel consumption credit constraints, new energy vehicle credit constraints and credit trading rules in the process of determining the sales volume.

[0072] In one exemplary instance, step 203 may include: By using a multivariate discrete consumer choice model, the probability of consumers (or consumer groups) choosing different fuel types of vehicles in each forecast year is predicted. The probability of choosing a vehicle with different fuel types is determined as the annual sales percentage of the corresponding fuel type. Based on the total passenger vehicle sales forecast for each forecast year and the annual sales proportion of vehicles of different fuel types, the first-year sales of vehicles of different fuel types in each forecast year under the scenario without credit constraints are determined.

[0073] In one embodiment, a multinomial discrete consumer choice model is used to simulate consumer choice behavior among multiple mutually exclusive vehicle options, such as traditional gasoline vehicles, hybrid vehicles, plug-in hybrid vehicles, and pure electric vehicles. The multinomial discrete consumer choice model can output the probability of a consumer choosing each fuel type vehicle based on input parameters related to each fuel type (such as vehicle price, usage cost, driving range, energy consumption level, and vehicle availability for different fuel types). This probability is used to characterize the proportion of different fuel types in total passenger vehicle sales for the corresponding forecast year. The multinomial discrete consumer choice model, often simply referred to as a discrete choice model (DCM), is a core econometric model used in economics, marketing, and transportation engineering to analyze and predict how consumers make decisions when faced with multiple mutually exclusive options.

[0074] In one embodiment, when determining the first-year sales volume of vehicles of each fuel type, the sales percentage of the corresponding fuel type can be multiplied by the predicted total passenger vehicle sales volume for that predicted year to obtain the first-year sales volume of that fuel type. The first-year sales volumes of vehicles of each fuel type collectively constitute the annual sales volume results by fuel type under the scenario without integral constraints.

[0075] For example, if the total passenger vehicle sales forecast for a certain year is 10 million vehicles, and the sales share of traditional fuel vehicles, hybrid vehicles, plug-in hybrid vehicles, and pure electric vehicles are 40%, 15%, 15%, and 30%, respectively, then the first-year sales of each fuel type of vehicle can be determined by multiplying each sales share by 10 million vehicles.

[0076] In one exemplary instance, the embodiments of this application may further include: Forecast the monthly sales volume of passenger vehicles and the monthly sales volume by fuel type during the forecast period.

[0077] The monthly sales forecasting process is not necessarily executed sequentially with the annual forecasting process in steps 201 to 209. It can be executed independently or in parallel with the annual forecasting process.

[0078] Specifically, historical monthly sales data for the passenger vehicle market can be obtained and input into a Winters additive exponential smoothing model for training. This model learns the patterns of horizontal, trend, and seasonal changes in the historical sales data to obtain a forecast of the total monthly passenger vehicle sales within a preset range of future months, starting from the current month. For example, it can obtain the forecast of the total monthly passenger vehicle sales for each of the next 12 months.

[0079] Historical sales data for the passenger vehicle market can be maintained manually via a web interface, imported in batches via a data interface, or using an Excel template. This historical sales data may include fields such as year, month, fuel type, and province of use.

[0080] In one exemplary instance, a model for predicting the monthly sales share of different fuel types can be selected from the Winters additive exponential smoothing model and the seasonal differential autoregressive moving average (SARIMA) model, based on the volatility of historical monthly sales share of different fuel types.

[0081] It should be noted that the Winters additive exponential smoothing model and the SARIMA model are time series prediction models, and the relevant training process and structure are well-known techniques to those skilled in the art, and will not be elaborated here.

[0082] In one embodiment, when the historical monthly sales share of different fuel types is highly volatile, a Winters additive exponential smoothing model can be used; when the historical monthly sales share of different fuel types is less volatile, a seasonally differentiated autoregressive moving average model can be used. By inputting the historical monthly sales share of different fuel types into the selected model, the predicted monthly sales share of different fuel types for each month within the forecast period can be obtained.

[0083] Subsequently, the predicted monthly sales share of each fuel type of vehicle is multiplied by the predicted total monthly sales of passenger vehicles for the corresponding month to obtain the predicted monthly sales value of the corresponding fuel type of vehicle for that month.

[0084] For example, if the predicted sales percentages of traditional gasoline vehicles, hybrid vehicles, pure electric vehicles, and plug-in hybrid vehicles for a certain month are 10%, 20%, 0%, and 70%, respectively, and the predicted total monthly sales of passenger vehicles for that month are 100,000 units, then the predicted monthly sales percentages for the corresponding fuel type vehicles are 10,000 units, 20,000 units, 0 units, and 70,000 units, respectively.

[0085] Monthly sales forecast results can be stored and displayed in time series, and users can adjust the forecast parameters to redetermine the corresponding monthly sales trend.

[0086] Step 204: Based on the enterprise type of passenger vehicle enterprises, determine the sales volume of vehicles by fuel type for market-driven enterprises and compliance-impact enterprises in each forecast year, and determine the second-year sales volume of different fuel types of vehicles in each forecast year under the credit constraint scenario based on the sales volume of vehicles by fuel type for the two types of enterprises.

[0087] In one embodiment, constraints can be applied to the sales volume of vehicles of different fuel types, the degree of energy consumption improvement of vehicle models, and the amount of credits purchased by enterprises with compliance impact based on the enterprise's average fuel consumption credit parameters, new energy vehicle credit parameters, and credit trading rule parameters.

[0088] In this embodiment, to determine the second-year sales volume of vehicles of different fuel types in each forecast year under the credit constraint scenario, passenger vehicle companies can be divided into market-driven companies and compliance-impacting companies based on the speed of their electrification transformation. The sales volume of vehicles by fuel type for each type of company in each forecast year is then determined, and the sales volume of the two types of companies is aggregated according to the same forecast year and the same fuel type. Specifically, for market-driven companies, their sales volume of vehicles by fuel type can be determined based on the forecast value of total passenger vehicle sales, the sales proportion of vehicles of different fuel types, and the corresponding company market share. For compliance-impacting companies, their sales volume of vehicles by fuel type, the improvement in vehicle energy consumption, and the amount of credits purchased are used as decision variables. Constraints are solved under the conditions of satisfying the company's average fuel consumption credit constraints and new energy vehicle credit constraints to obtain their sales volume of vehicles by fuel type.

[0089] In one exemplary instance, step 204 may include: First, based on the total passenger vehicle sales forecast, the sales proportion of vehicles of different fuel types, and the market share of market-driven companies by fuel type, the sales volume of vehicles of different fuel types for each forecast year is determined.

[0090] For market-driven enterprises, the first step can be determined according to formula (1). Future sales of vehicles of this fuel type: (1) In formula (1), This indicates that the market-driven enterprise is the first Future sales of vehicles of this fuel type This represents the projected total annual passenger vehicle sales. Indicates the first The proportion of passenger cars of different fuel types The predicted enterprise number Market share of vehicles using different fuel types.

[0091] Therefore, the annual sales volume of market-driven companies can be determined separately for different fuel types, such as traditional fuel vehicles, hybrid vehicles, plug-in hybrid vehicles, and pure electric vehicles.

[0092] Secondly, using the sales volume of vehicles by fuel type, the improvement in vehicle energy consumption, and the amount of credits purchased by compliance-influenced enterprises as decision variables, and under the condition of satisfying fuel consumption credit constraints and new energy vehicle credit constraints, a technical path solution is performed for compliance-influenced enterprises to obtain the sales volume of vehicles by fuel type for each forecast year. In other words, with the goal of minimizing the compliance cost of the credit policy, the sales volume of vehicles by fuel type, the improvement in vehicle energy consumption, and the amount of credits purchased by the aforementioned compliance-influenced enterprises in each forecast year are determined.

[0093] For enterprises with compliance impact, the technical path with the lowest compliance cost for the points policy can be determined according to formula (2): (2) In formula (2), This indicates the compliance costs of a points-based policy for companies with compliance impact. This indicates an increase in material costs resulting from the replacement of gasoline vehicles with new energy vehicles. This refers to the cost of technological upgrades made to reduce vehicle fuel consumption. This indicates the cost of purchasing points.

[0094] When solving for constraints, the corporate average fuel consumption credit prediction results for compliance-affected enterprises can be determined based on the sales volume of vehicles of each fuel type and the energy consumption parameters of the corresponding models; the new energy vehicle credit prediction results can be determined based on the sales volume of new energy vehicles, the credit scores corresponding to new energy vehicle models, and the new energy vehicle credit ratio requirements; and the credit purchase volume and corresponding credit purchase cost can be determined according to the credit trading rules.

[0095] When the forecast results of corporate average fuel consumption credits and new energy vehicle credits meet the corresponding credit requirements, the goal is to minimize the compliance cost of the credit policy. The sales volume of vehicles by fuel type, the improvement of vehicle energy consumption, and the amount of credits purchased are jointly solved to obtain the sales volume of vehicles by fuel type for compliance-affected enterprises in each forecast year.

[0096] Among them, the improvement in vehicle energy consumption is used to characterize the extent to which compliance-impacting enterprises improve the fuel or electricity consumption of corresponding vehicle models in order to meet the corporate average fuel consumption credit requirements; the amount of credits purchased is used to characterize the number of credits that compliance-impacting enterprises need to make up for through credit trading when there is still a credit shortfall after adjusting the sales volume of vehicles of different fuel types and vehicle energy consumption.

[0097] Finally, based on the vehicle sales of market-driven enterprises and compliance-impact enterprises under the same forecast year and the same fuel type, the second-year sales of vehicles of different fuel types in each forecast year under the credit constraint scenario are determined.

[0098] In one embodiment, vehicle sales for the two types of companies can be aggregated according to the forecast year and fuel type. For any forecast year, the sales of traditional gasoline vehicles for market-driven companies and the sales of traditional gasoline vehicles for compliance-impact companies are aggregated to obtain the second-year sales of traditional gasoline vehicles for that forecast year; similarly, the second-year sales of hybrid vehicles, plug-in hybrid vehicles, and pure electric vehicles are obtained respectively.

[0099] Therefore, the sales volume in the second year under the credit constraint scenario is not directly converted from the credit parameters, but is the result of solving the constraints on the sales volume of passenger car companies by fuel type, the improvement of vehicle energy consumption, and the amount of credits purchased, based on the credit requirements.

[0100] In step 204, the corporate average fuel consumption credit parameters, new energy vehicle credit parameters, and credit trading rule parameters first affect the vehicle sales and energy consumption improvement processes of different types of enterprises, thereby changing the sales structure of vehicles of different fuel types under the credit constraint scenario. The second-year sales obtained in step 104 will be used as input for determining the number of vehicles of different fuel types under the credit constraint scenario in subsequent steps, and will be further used to calculate the fleet's total life-cycle carbon emissions under the credit constraint scenario.

[0101] Step 205: Determine the number of vehicles of different fuel types in each forecast year under both the no integral constraint scenario and the integral constraint scenario.

[0102] In one exemplary instance, step 205 may include: Based on the first-year sales volume of different fuel types of vehicles in each forecast year under the unconstrained credit scenario, determine the number of vehicles of different fuel types in each forecast year under the unconstrained credit scenario. Based on the second-year sales volume of vehicles of different fuel types in each forecast year under the credit constraint scenario, determine the number of vehicles of different fuel types in each forecast year under the credit constraint scenario.

[0103] In one embodiment, for a scenario without integral constraints, for any type of vehicle, the number of vehicles of that fuel type in the current forecast year can be determined based on the number of vehicles of that fuel type in the previous year, the first-year sales volume of vehicles of that fuel type in the current forecast year, and the number of vehicles of that fuel type scrapped in the current forecast year.

[0104] In one embodiment, for an integral constraint scenario, the number of vehicles of the corresponding fuel type in the current forecast year can be determined based on the number of vehicles of the corresponding fuel type in the previous year, the second-year sales volume of vehicles of the corresponding fuel type in the current forecast year, and the number of vehicles of the corresponding fuel type scrapped in the current forecast year.

[0105] In one embodiment, the process of determining the vehicle ownership can be based on the following vehicle inventory balance relationship: Current year ownership = Previous year ownership + Current year sales - Current year scrapping. For each forecast year within the forecast period, the actual ownership of different fuel types of vehicles acquired in the year preceding the start of the forecast period can be used as the initial ownership for the no-constraint scenario and the integral-constraint scenario, respectively. After determining the ownership for the current forecast year, the ownership for the current forecast year can be used as the previous year's ownership in the process of determining the ownership for the next forecast year. For example, when forecasting the vehicle ownership from 2026 to 2030, the ownership for a certain fuel type of vehicle in 2026 can be determined first based on the actual ownership of a certain fuel type of vehicle in 2025, the annual sales and scrapping of that fuel type of vehicle in 2026; then, based on the ownership in 2026, the annual sales and scrapping of that fuel type of vehicle in 2027 can be determined, and so on up to 2030. In the scenario without integral constraints, the sales volume of the first year obtained in step 103 is used; in the scenario with integral constraints, the sales volume of the second year obtained in step 104 is used.

[0106] Since the annual sales of vehicles of different fuel types differ under the scenarios without credit constraints and those with credit constraints, the number of vehicles of different fuel types and their proportion in the fleet will gradually differ between the two scenarios as the forecast year progresses.

[0107] Step 205 outputs the number of vehicles of different fuel types in each predicted year under the two scenarios, and uses the number of vehicles as input for step 108 to determine the carbon emissions of the fleet throughout its entire life cycle.

[0108] Step 206: Based on the historical energy consumption and technical indicators in the basic data, as well as the predicted parameters of energy consumption and technical indicators, determine the energy consumption and technical indicators of vehicles with different fuel types for each predicted year within the prediction period.

[0109] The energy consumption and technical indicators may include at least one of the following: average fuel consumption, average electricity consumption, average energy consumption, average curb weight, average driving range, and average battery capacity; the predicted parameters for energy consumption and technical indicators may include at least one of the annual change rates corresponding to each energy consumption and technical indicator, as well as the indicator values ​​for future years obtained through enterprise surveys or user settings.

[0110] In one exemplary instance, step 206 may include: Based on historical vehicle data, the energy consumption and technical indicators of vehicles are statistically analyzed according to fuel type and historical year, and the historical energy consumption and technical indicators of vehicles of different fuel types in each historical year are obtained. Obtain the annual rate of change for each energy consumption and technical indicator; Based on the energy consumption and technical indicators corresponding to historical years, and according to the annual change rate of the corresponding indicators, the energy consumption and technical indicators of vehicles with different fuel types in each forecast year within the forecast period are determined year by year in chronological order of the forecast years.

[0111] For example, the average energy consumption of pure electric vehicles in each of the historical years can be determined year by year based on the average energy consumption of pure electric vehicles and the annual change rate corresponding to the average energy consumption; the average battery capacity of vehicles of the corresponding fuel type in each of the future forecast years can also be determined year by year based on the average battery capacity of pure electric vehicles or plug-in hybrid electric vehicles in the historical years and the annual change rate corresponding to the average battery capacity.

[0112] In another exemplary instance, the energy consumption and technical indicators of vehicles with different fuel types in each forecast year can be obtained through enterprise surveys or user settings, and the obtained future year indicator values ​​can be directly determined as the energy consumption and technical indicators of the corresponding fuel type vehicles in the corresponding forecast year, instead of recursively extrapolating year by year based on the annual rate of change.

[0113] In one embodiment, different determination methods can be used for different energy consumption and technical indicators. For example, average fuel consumption and average electricity consumption can be determined year by year based on historical values ​​and corresponding annual change rates; average driving range and average battery capacity can be determined using future annual indicator values ​​obtained through enterprise surveys or user settings.

[0114] Step 206 provides the energy consumption and technical indicators for each forecast year and fuel type vehicle within the forecast period, and uses these indicators as inputs to determine the carbon emissions of a single vehicle over its entire life cycle for different fuel types.

[0115] Step 207: Determine the total life cycle carbon emissions of a single vehicle based on the energy consumption and technical specifications of vehicles with different fuel types.

[0116] In one exemplary instance, based on the energy consumption and technical indicators of vehicles with different fuel types obtained in step 206, as well as the corresponding carbon emission factors, a standardized carbon footprint model can be used to determine the carbon emissions of a single vehicle with different fuel types in the material and component stage, the vehicle production stage, the fuel production stage, and the fuel use stage.

[0117] In one embodiment, the standardized carbon footprint model can be expressed as: ,in, This indicates the total carbon emissions of a single vehicle over its entire lifecycle for a particular fuel type. This indicates the carbon emissions per vehicle for this fuel type in terms of raw materials and components. This indicates the carbon emissions per vehicle during the entire vehicle production process for that fuel type. This indicates the carbon emissions per vehicle during the fuel production process for that fuel type. This indicates the carbon emissions per vehicle during the fuel usage phase for that fuel type.

[0118] In one embodiment, the carbon emissions from raw materials and components can be determined based on the vehicle's average curb weight, the material parameters used in the vehicle, and the corresponding carbon emission factors of the materials. For pure electric vehicles and plug-in hybrid electric vehicles, the carbon emissions corresponding to the power battery can also be determined based on the average battery capacity and the battery production emission factor.

[0119] In one embodiment, the carbon emissions of the vehicle production process can be determined based on the energy consumption and energy carbon emission factor corresponding to the vehicle production process, or based on pre-set emission parameters for the vehicle production process.

[0120] In one embodiment, the carbon emissions from the fuel production process can be determined based on the fuel or electricity consumed by the vehicle during a predetermined usage period, and the corresponding fuel production carbon emission factor or electricity production carbon emission factor.

[0121] In one embodiment, carbon emissions from the fuel consumption phase can be determined based on the vehicle's average fuel consumption or average electricity consumption, average mileage, and the corresponding carbon emission factor. More specifically, for conventional gasoline vehicles and hybrid vehicles, carbon emissions from the fuel consumption phase can be determined based on average fuel consumption; for pure electric vehicles, carbon emissions corresponding to electricity consumption can be determined based on average electricity consumption and grid emission factors; for plug-in hybrid electric vehicles, comprehensive fuel consumption parameters can be used to calculate fuel consumption, and the carbon emissions corresponding to electricity consumption can also be determined by combining comprehensive electricity consumption and grid emission factors.

[0122] In one embodiment, for each forecast year within the forecast period, the life-cycle carbon emissions of a single vehicle of each fuel type can be determined using the vehicle energy consumption and technical indicators corresponding to that forecast year. Through step 207, the life-cycle carbon emissions of single vehicles of different fuel types, such as traditional fuel vehicles, hybrid vehicles, plug-in hybrid vehicles, and pure electric vehicles, can be obtained for each forecast year.

[0123] In one exemplary instance, in addition to determining the total lifecycle carbon emissions of individual vehicles of different fuel types, it may also include: separately determining the carbon emissions of the fleet during each forecast year.

[0124] In one embodiment, the number of vehicles, the average mileage of the corresponding vehicles, the fuel consumption, and the carbon emission factor can be obtained for each forecast year according to fuel type and vehicle type, and the carbon emission of the fleet usage in the corresponding forecast year can be determined by a standardized carbon emission model.

[0125] In one embodiment, the standardized carbon emission model can be expressed as: ,in, Indicates the first Annual carbon emissions during fleet usage; Indicates the first Annual fuel type Vehicle type The corresponding number of vehicles in operation; Indicates the first Annual fuel type Vehicle type The corresponding average mileage; Indicates the first Annual fuel type Vehicle type The corresponding fuel consumption per 100 kilometers; Indicates the first Annual fuel type The corresponding carbon emission factor.

[0126] In one embodiment, for vehicles powered by electricity, average electricity consumption and electricity carbon emission factor can be used instead of the aforementioned average fuel consumption and fuel carbon emission factor. In another embodiment, for plug-in hybrid electric vehicles that consume both fuel and electricity, the carbon emissions corresponding to fuel consumption and electricity consumption during the usage phase can be determined separately and then aggregated.

[0127] In one embodiment, carbon emissions during fleet use can be stored separately according to the predicted year and fuel type, and used to subsequently determine fuel savings caused by credit constraints, carbon emission reductions during fleet use, and emission reductions of typical pollutants.

[0128] Step 208: Determine the fleet's total lifecycle carbon emissions for each forecast year based on the total lifecycle carbon emissions of individual vehicles with different fuel types and the number of vehicles with different fuel types in the corresponding scenarios.

[0129] In one exemplary instance, step 208 may include: Based on the total life cycle carbon emissions of individual vehicles of different fuel types obtained in step 207, and the number of vehicles of different fuel types in the scenario without integral constraints obtained in step 205, determine the total life cycle carbon emissions of the fleet in each predicted year under the scenario without integral constraints. Based on the life-cycle carbon emissions of individual vehicles of different fuel types obtained in step 207, and the number of vehicles of different fuel types under the integral constraint scenario obtained in step 205, the life-cycle carbon emissions of the fleet in each predicted year under the integral constraint scenario are determined.

[0130] In one embodiment, for any forecast year and any type of vehicle, the total lifecycle carbon emissions of a single vehicle of that fuel type can be multiplied by the number of vehicles of that fuel type in operation under the corresponding scenario to obtain the total lifecycle carbon emissions of the fleet of that fuel type for that forecast year.

[0131] In one embodiment, the annual lifecycle carbon emissions for a fuel type = Σ (lifecycle carbon emissions per vehicle × number of vehicles of the corresponding fuel type). Here, the number of vehicles of the corresponding fuel type refers to the number of vehicles of that fuel type in operation under the corresponding forecast year and the corresponding forecast scenario.

[0132] In step 208, the above processing is performed on different fuel types, such as traditional gasoline vehicles, hybrid vehicles, plug-in hybrid vehicles, and pure electric vehicles, to obtain the life-cycle carbon emissions of the fleet for each fuel type in the corresponding predicted year. Then, the annual life-cycle carbon emissions of the fleet for each fuel type are summarized to obtain the fleet's life-cycle carbon emissions for the predicted year under the corresponding scenario. In one embodiment, the summarization relationship can be expressed as: annual life-cycle carbon emissions of the fleet = Σ (annual life-cycle carbon emissions of the fleet for all fuel types). For the scenario without integral constraints, the above calculation is performed using the number of vehicles of each fuel type under the scenario without integral constraints obtained in step 205; for the scenario with integral constraints, the above calculation is performed using the number of vehicles of each fuel type under the scenario with integral constraints obtained in step 205.

[0133] In one embodiment, within the same forecast year, the no-integral-constraint scenario and the integral-constraint scenario can use the same fuel type classification, the same lifecycle accounting scope, and the same carbon emission factor. The difference in the fleet's total lifecycle carbon emissions between the two scenarios mainly stems from the different sales volumes and ownership of vehicles of different fuel types under the two scenarios.

[0134] By performing the above calculations for each forecast year within the forecast period, time series of fleet carbon emissions throughout their entire life cycle can be generated for each of the two scenarios.

[0135] It should be noted that there is no necessary sequential relationship between step 206 and steps 201 to 205. Step 206 can be executed in parallel with steps 201 to 205, or it can be executed before or after steps 201 to 205, as long as it is completed before step 207 determines the carbon emissions of a single vehicle over its entire life cycle. Steps 201 to 205 are used to determine the number of vehicles of different fuel types in each forecast year under the two scenarios. Step 206 is used to determine the energy consumption and technical indicators of vehicles of different fuel types in each forecast year. The two steps can be executed independently. Step 207 determines the carbon emissions of a single vehicle over its entire life cycle based on the output of step 206. Step 208 then combines the carbon emissions of a single vehicle over its entire life cycle obtained in step 207 with the number of vehicles in the corresponding scenario obtained in step 205.

[0136] Step 209: Determine the carbon emission reduction of the fleet caused by the integral constraint based on the fleet's carbon emissions over the entire life cycle under the scenarios without integral constraints and with integral constraints.

[0137] In one exemplary instance, step 209 may include: Obtain the fleet's total lifecycle carbon emissions for each forecast year under the no-credit-constraint scenario, and the fleet's total lifecycle carbon emissions for each forecast year under the credit-constraint scenario. For the same forecast year, the difference between the fleet's total lifecycle carbon emissions under the no-credit-constraint scenario and the fleet's total lifecycle carbon emissions under the credit-constraint scenario is used to obtain the fleet's carbon emission reduction caused by the credit constraints.

[0138] Specifically, when the fleet's total lifecycle carbon emissions under a scenario without credit constraints in a certain forecast year are higher than those under a scenario with credit constraints, the difference between the two can be used as the fleet's carbon emission reduction caused by the credit constraints in that forecast year.

[0139] By performing the above difference operation for each forecast year within the forecast period, the result of the change in fleet carbon emission reduction caused by integral constraints with the forecast year can be obtained.

[0140] For example, the difference between the fleet's total lifecycle carbon emissions under the two scenarios in 2026 can be used to obtain the fleet's carbon emission reduction in 2026; the difference between the fleet's total lifecycle carbon emissions under the two scenarios in 2027 can be used to obtain the fleet's carbon emission reduction in 2027, and the subsequent forecast years can be processed in the same way.

[0141] Furthermore, the life-cycle carbon emissions of fleets of different fuel types under the two scenarios can be compared according to fuel type to determine the impact of changes in the number of conventional gasoline vehicles, hybrid vehicles, plug-in hybrid vehicles, and pure electric vehicles on fleet carbon emission reductions.

[0142] Step 209 yields the fleet carbon emission reduction caused by the integral constraints. This carbon emission reduction is not directly calculated from the enterprise average fuel consumption integral parameters, new energy vehicle integral parameters, or integral trading rule parameters. Instead, it is indirectly determined through a data processing procedure involving integral parameters and integral trading rules, constraint solving for vehicle sales by fuel type, updating the fleet's total lifecycle carbon emissions, redetermining the fleet's total lifecycle carbon emissions under two scenarios, and comparing the fleet's total lifecycle carbon emissions under two scenarios. Therefore, the fleet carbon emission reduction caused by the integral constraints obtained in this embodiment reflects the impact of the integral constraints on the sales structure of different fuel types and the fleet's total lifecycle carbon emissions, as well as the changes in the fleet's total lifecycle carbon emissions caused by this impact.

[0143] The passenger vehicle fleet lifecycle carbon emission prediction method in this application involves correlating historical passenger vehicle ownership, economic parameters, sales by fuel type, vehicle energy consumption and technical indicators, carbon emission factors, and credit constraint parameters. First, it predicts the annual passenger vehicle ownership and total sales. Then, it determines the annual sales and ownership of vehicles of different fuel types under both credit constraint and no-credit-constraint scenarios. Combining this with technical parameters such as fuel consumption, electricity consumption, curb weight, and battery capacity of different fuel types for each predicted year, it determines the lifecycle carbon emissions of individual vehicles and the entire fleet. Finally, it determines the fleet carbon emission reduction caused by credit constraints by comparing the emission results of the two scenarios under the same accounting boundary. This allows the impact of credit constraints on the sales structure of vehicles by fuel type to be further transferred to the fleet ownership structure and lifecycle carbon emission results, avoiding predictions using static fleet structures or fragmented models, and improving the accuracy, consistency, and comparability of medium- and long-term fleet lifecycle carbon emission prediction results.

[0144] In one exemplary instance, the method for predicting carbon emissions throughout the entire lifecycle of a passenger vehicle fleet and evaluating carbon reduction pathways provided in this application includes determining a comprehensive score for different carbon reduction pathways, including: Step 210 ( Figure 2(Not shown in the image): By changing the parameters corresponding to the carbon emission reduction path, the marginal emission reduction amount of different carbon emission reduction paths can be determined. The carbon emission reduction path can include one or any combination of the following: reducing vehicle fuel consumption, reducing vehicle electricity consumption, reducing the carbon emission factor of battery production, and promoting new energy vehicles.

[0145] First, for any carbon emission reduction path, the controlled variable method is used to change only the parameters corresponding to that carbon emission reduction path, while keeping the parameters corresponding to other carbon emission reduction paths unchanged, and steps 206-208 are repeated to determine the fleet's life-cycle carbon emissions for each predicted year after changing the parameters.

[0146] For example, when evaluating pathways to reduce vehicle fuel consumption, the average fuel consumption of traditional gasoline-powered vehicles or hybrid vehicles can be altered while keeping the average electricity consumption, carbon emission factor from battery production, and sales or ownership of vehicles by fuel type constant. When evaluating pathways to reduce vehicle electricity consumption, the average electricity consumption of pure electric vehicles or plug-in hybrid electric vehicles can be altered while keeping other parameters constant. When evaluating pathways to reduce carbon emission factor from battery production, the carbon emission factor from power battery production can be altered. When evaluating pathways to promote new energy vehicles, the sales share of new energy vehicles can be increased while the sales share of gasoline-powered vehicles can be reduced accordingly.

[0147] Then, the fleet's life-cycle carbon emissions before and after changing the parameters corresponding to the carbon emission reduction path are compared. The difference between the two can be used as the marginal emission reduction corresponding to the carbon emission reduction path. The marginal emission reduction is used to characterize the emission reduction efficiency of the carbon emission reduction path.

[0148] By performing the above processing separately for different forecast years and different carbon emission reduction paths, we can obtain the marginal emission reduction amounts corresponding to various carbon emission reduction paths in each forecast year.

[0149] In step 210, for different carbon reduction paths such as reducing vehicle fuel consumption, reducing vehicle electricity consumption, reducing carbon emission factors in battery production, and promoting new energy vehicles, only the corresponding path parameters are changed while keeping other parameters unchanged. This allows the changes in fleet carbon emissions throughout the entire life cycle caused by each path to be identified individually. Then, by comparing the fleet's total life cycle carbon emissions before and after the parameter changes, the marginal emission reduction amount of each carbon reduction path in different forecast years is obtained. Step 210 reduces the interference of the coupling between different path parameters on the emission reduction results, clarifies the independent contribution of each carbon reduction path to the fleet's total life cycle carbon emissions, improves the comparability and accuracy of the emission reduction benefit calculation results of different emission reduction paths, and provides a unified data foundation for subsequently determining the marginal cost, emission reduction efficiency, and implementation priority of each carbon reduction path.

[0150] Step 211 ( Figure 2(Not shown in the image): Based on the marginal emission reduction and marginal cost of different carbon emission reduction paths, determine the emission reduction efficiency and comprehensive score of different carbon emission reduction paths.

[0151] In one exemplary instance, step 211 may include: Based on the marginal emission reduction amounts corresponding to different carbon emission reduction paths determined in step 210 for each forecast year, construct the correspondence between marginal costs and marginal emission reduction amounts under different carbon emission reduction paths. The emission reduction efficiency of a carbon emission reduction path is determined by the ratio of marginal emission reduction to marginal cost. The emission reduction efficiency and emission reduction benefits corresponding to different carbon emission reduction pathways were normalized respectively. Obtain the first preference value for the corresponding emission reduction efficiency and the second preference value for the corresponding emission reduction benefit. Based on the first preference value and the second preference value, perform a weighted calculation on the normalized emission reduction efficiency and emission reduction benefit to obtain a comprehensive score for different carbon emission reduction paths. Among them, emission reduction benefit is the marginal emission reduction determined in step 210; emission reduction efficiency is used to characterize the marginal emission reduction corresponding to unit marginal cost, and its unit can be tons of carbon dioxide equivalent / ten thousand yuan.

[0152] By applying the above processing to each forecast year, a comprehensive score can be obtained for each forecast year for different carbon emission reduction pathways.

[0153] In one embodiment, both the first preference value and the second preference value range from 0 to 1, and the sum of the first preference value and the second preference value is 1. When the first preference value is larger, the overall score focuses more on emission reduction efficiency; when the second preference value is larger, the overall score focuses more on emission reduction benefits.

[0154] In one embodiment, for the promotion of new energy vehicles, the corresponding marginal cost can be determined based on the incremental cost of the entire life cycle of new energy vehicles replacing fuel vehicles of the same level. The incremental cost of the entire life cycle can include the material cost difference between the battery motor system and the engine transmission system, and can be adjusted annually according to the progress of vehicle technology and changes in production scale.

[0155] In one embodiment, for the paths to reduce vehicle fuel consumption and the paths to reduce vehicle electricity consumption, a corresponding cost function can be fitted based on the technical envelope to characterize the increased cost required for each predetermined reduction in fuel consumption or electricity consumption per vehicle, and the cost can increase as the reduction in fuel consumption or electricity consumption increases.

[0156] In one embodiment, for a path to reduce the carbon emission factor of battery production, the marginal cost corresponding to the path can be determined based on the technological transformation cost corresponding to the reduction in carbon footprint per unit of battery, and the marginal cost can increase as the reduction in carbon emission factor of battery production increases.

[0157] In the embodiments of this application, the marginal cost function and the marginal cost change pattern corresponding to each carbon emission reduction path can be determined by fitting the path parameter change range and corresponding cost data obtained from actual surveys. The fitting method used can be selected from data fitting methods in related technologies. Those skilled in the art can select the appropriate fitting method based on the distribution characteristics of the survey data. The embodiments of this application do not limit the specific form of the fitting method and the marginal cost function.

[0158] In one exemplary instance, the method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire lifecycle of a passenger vehicle fleet provided in this application embodiment may further include: Step 212 ( Figure 2 (Not shown in the text): Based on the sales volume of vehicles by fuel type, vehicle energy consumption parameters, and credit constraint parameters of market-driven enterprises and compliance-impacted enterprises, the credit prediction results under the credit constraint scenario are determined.

[0159] In one exemplary instance, based on the fuel-type vehicle sales figures for market-driven and compliance-influenced enterprises obtained in step 204, the fuel consumption and electricity consumption of the corresponding vehicle types for each forecast year can be obtained. These fuel consumption and electricity consumption can be determined based on the energy consumption parameters determined in step 106, enterprise vehicle planning data, or enterprise survey data. For compliance-influenced enterprises, the fuel consumption or electricity consumption of the corresponding vehicle types can be adjusted based on the energy consumption improvement rate obtained in step 204 to obtain the fuel consumption and electricity consumption of the vehicles used for performance evaluation.

[0160] After obtaining the sales volume of vehicles by fuel type and the corresponding fuel consumption and electricity consumption of the two types of enterprises, the predicted amount of corporate average fuel consumption credits and the predicted amount of new energy vehicle credits for each forecast year can be determined based on the sales volume of vehicles by fuel type, the fuel consumption and electricity consumption of the vehicles used for credit calculation, as well as the low-carbon target parameters, new energy vehicle credit parameters and credit trading rule parameters.

[0161] In one embodiment, low-carbon target parameters may include an annual new energy vehicle penetration rate target, a corporate average carbon dioxide target curve, and an electricity consumption conversion factor.

[0162] In one embodiment, the new energy vehicle credit parameters may include at least one of the following: total sales growth rate, sales proportion of new energy vehicles, credit score of pure electric vehicles, credit score of plug-in hybrid electric vehicles, off-cycle technology preferential exemption amount, power consumption adjustment coefficient, low temperature attenuation rate, power consumption guidance index under power consumption mode, and fuel consumption guidance index under power retention mode.

[0163] In one embodiment, the points trading rule parameters may include at least one of points trading rules, points transfer rules, points carry-over ratio, and points validity period.

[0164] In one exemplary instance, it may also include: Based on the projected average fuel consumption credits and new energy vehicle credits, determine the credit gap or tradable credits for each enterprise.

[0165] In one exemplary instance, it may also include: The industry credit supply-demand ratio is determined based on each company's positive new energy vehicle credits generated in the current year, carry-over positive new energy vehicle credits, fuel consumption negative credit gap, and negative new energy vehicle credits.

[0166] The industry credit supply-demand ratio can be determined based on the ratio between the positive new energy vehicle credits available for trading in the current year and the negative credits that need to be offset in the current year. The positive credits available for trading can include the positive new energy vehicle credits generated by each enterprise in the current year and the positive new energy vehicle credits carried over. The negative credits that need to be offset can include the fuel consumption negative credit gap after full offset between each enterprise and its affiliated enterprises, as well as the negative new energy vehicle credits.

[0167] In one exemplary instance, the determination of carbon reduction policy guidance results in the passenger vehicle fleet lifecycle carbon emission prediction and carbon reduction path evaluation method provided in this application embodiment may include: Step 213 ( Figure 2 (Not shown in the text): Based on the vehicle energy consumption and emission results under both the unconstrained and integral-constrained scenarios, determine the fuel savings, typical pollutant emission reductions, total socio-economic benefits, and net social benefits.

[0168] In one exemplary instance, the fuel consumption of the fleet during use can be determined separately under the scenarios without integral constraints and the scenarios with integral constraints, and the difference between the fuel consumption of the fleet during use under the two scenarios in the same forecast year can be taken as the fuel saving caused by the integral constraints.

[0169] Fuel consumption during fleet operation can include fuel consumption of traditional gasoline vehicles and hybrid vehicles, as well as fuel consumption calculated by converting the electricity consumption of new energy vehicles according to the electricity consumption conversion relationship.

[0170] Furthermore, it may also include: Based on the fleet carbon emission reduction obtained in step 209, and the vehicle fuel type, emission standards, vehicle age, mileage, and pollutant emission factors, determine the typical pollutant emission reduction caused by the integral constraints. Typical pollutants may include motor vehicle pollutants such as nitrogen oxides.

[0171] In one embodiment, the total socio-economic benefits can be determined based on fuel savings, carbon emission reductions, and typical pollutant emission reductions.

[0172] In one embodiment, the economic benefits of fuel saving can be determined by multiplying the amount of fuel saved by the current fuel price.

[0173] In one embodiment, climate economic benefits can be determined based on the product of carbon emission reductions, carbon emission trading prices, and long-term discount factors.

[0174] In one embodiment, health economic benefits can be determined by multiplying the emission reduction of a typical pollutant by a health damage cost coefficient.

[0175] In one embodiment, the total socioeconomic benefits can be obtained by adding up the economic benefits of fuel saving, climate, and health.

[0176] Furthermore, it may also include: The annual total cost increment for each carbon emission reduction pathway is determined based on the marginal cost of each pathway and the annual sales volume of each industry by fuel type.

[0177] For example, for the path of reducing fuel consumption, the annual cost increment of this path can be determined based on the sales volume of fuel vehicles that adopt fuel-saving technologies and the corresponding increase in cost per vehicle; for the path of reducing electricity consumption, the annual cost increment of this path can be determined based on the sales volume of pure electric vehicles that adopt electricity-saving technologies and the corresponding increase in cost per vehicle; for the path of promoting new energy vehicles, the annual cost increment of the corresponding path can be determined based on the net increase in sales volume of new energy vehicles replacing fuel vehicles and the increase in cost per vehicle replacement.

[0178] Assuming that 700,000 gasoline vehicles are sold in a certain year, and each vehicle reduces fuel consumption costs by 5,000 yuan, the incremental cost of reducing fuel consumption is 3.5 billion yuan; 550,000 BEVs are sold, and each vehicle reduces electricity consumption costs by 3,000 yuan, the incremental cost of reducing electricity consumption is 1.65 billion yuan; 350,000 BEVs replace ICE vehicles, and each replacement costs 12,000 yuan, the incremental cost of the replacement path is 4.2 billion yuan; adding these three together, the total incremental cost is 9.35 billion yuan.

[0179] In one embodiment, the annual cost increments corresponding to different carbon emission reduction paths can be aggregated to obtain the total annual cost increment for the industry.

[0180] In one embodiment, the net social benefit is obtained by subtracting the annual increase in total industry costs from the total socioeconomic benefit. Performing this process separately for each forecast year within the policy cycle yields the result of how the net social benefit changes over the forecast years. Rolling calculations are supported year by year, and the output is a net benefit curve for the policy cycle. Combined with sensitivity testing (such as carbon price fluctuations ±20%), the long-term economic sustainability of the policy is quantified.

[0181] In one embodiment, sensitivity analysis can also be performed by changing external parameters such as carbon emission trading prices, fuel prices, or health damage cost coefficients to determine the impact of changes in external parameters on net social benefits.

[0182] In one exemplary instance, the method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire lifecycle of a passenger vehicle fleet provided in this application embodiment may further include: Step 214 ( Figure 2 (Not shown in the image): Receives a natural language question input by the user, and calls the corresponding data and calculation process based on the natural language question to generate an answer content corresponding to the natural language question.

[0183] In one exemplary instance, step 214 may include: The system receives natural language questions input by users, parses the natural language questions, and determines at least one of the following: the prediction year, prediction scenario, fuel type, carbon emission reduction pathway, and evaluation indicator involved in the natural language questions. Based on the analysis results, corresponding data is extracted from the data generated in steps 201 to 213, and the extracted data is processed according to the calculation relationship required by the natural language question to generate the answer content.

[0184] In one embodiment, the natural language question can be a policy objective-driven question. For example, a user might input: "If the national new energy vehicle penetration rate is required to reach 55% by 2030, how many additional pure electric vehicle sales are needed under the current baseline scenario, and what corresponding carbon emission reductions will result?" The system can extract the 2030 total passenger vehicle sales forecast and the new energy vehicle sales percentage under the baseline scenario from the passenger vehicle market forecast results. Based on the difference between the target new energy vehicle penetration rate and the baseline scenario's new energy vehicle penetration rate, it determines the required additional new energy vehicle sales. Then, based on the change in vehicle ownership resulting from the additional new energy vehicle sales, it re-executes the fleet's full lifecycle carbon emission accounting to determine the corresponding fleet carbon emission reductions. The generated answer can include additional pure electric vehicle sales, the corresponding annual carbon emission reductions, the carbon emission reduction percentages at each stage of the lifecycle, and vehicle distribution results categorized by province or vehicle age. Specifically, inputting the question "If the penetration rate of new energy passenger vehicles is required to reach 55% by 2030, how many additional BEV sales are needed under the current baseline scenario? How many tons of CO2 emission reduction will this bring?" will output "Required additional BEV sales in 2030: XXX million vehicles (heat map of distribution by province / vehicle age); corresponding annual carbon emission reduction: XX million tons of CO2 (of which the usage stage accounts for XX%, and the LCA full life cycle accounts for XX%)". For example, from the "Technology and Market Forecast Assessment Module", the sales forecast results for 2030 under the baseline scenario (without additional policy intervention) can be retrieved, then the target penetration rate can be set, and the required BEV sales can be calculated (for example, if the total sales in 2030 are 20 million, and the new energy penetration rate requirements for the two scenarios are 40% and 50% respectively, then the required additional BEV sales are 20 million × 10%).

[0185] In another embodiment, the natural language question can be a cost-benefit diagnostic question about a technology path. For example, a user inputs: "Comparing two paths, reducing electricity consumption and reducing fuel consumption, which path has a higher unit cost emission reduction efficiency between 2025 and 2035?" The system can extract the marginal cost, marginal emission reduction, and emission reduction efficiency of the electricity reduction and fuel consumption reduction paths for each forecast year from the calculation results of steps 210 and 211, and determine the cumulative emission reduction and emission reduction efficiency changes of the two paths during the corresponding period. When the emission reduction efficiency advantage of the two paths undergoes a continuous shift, the corresponding forecast year can be identified as the cost-benefit inflection point. The generated answer can include the annual comparison results, cumulative emission reduction, and the year of the cost-benefit inflection point. Specifically, inputting "Comparing two paths, 'reducing electricity consumption' and 'reducing fuel consumption,' which path has a higher unit emission reduction efficiency of 10,000 yuan between 2025 and 2035?", the output is "Generate a dual-path annual comparison table, including: marginal emission reduction cost (10,000 yuan / ton) for each year; cumulative total emission reduction over 10 years (10,000 tons); and the year of the cost-benefit inflection point." For example, firstly, the marginal cost function (RMB 10,000 / vehicle) and the annual emission reduction function per vehicle (tons CO2 / vehicle·year) for the two paths are obtained from the "Emission Reduction Path Marginal Benefit Assessment Unit". Dividing these two functions yields the marginal emission reduction cost (RMB 10,000 / ton CO2) for each year. The annual emission reduction per vehicle is calculated by multiplying the reduction in electricity consumption by the annual mileage by the grid carbon emission factor, and the reduction in fuel consumption by the annual mileage by the gasoline carbon emission factor. Then, the sales volume (or ownership) of BEVs and gasoline vehicles for each year is obtained from the "Technology and Market Forecasting Module", multiplied by the annual emission reduction per vehicle, and summed to obtain the total emission reduction over 10 years (tons of fuel). Finally, the marginal emission reduction costs of the two paths are compared year by year. When the efficiency advantage permanently reverses, that year is recorded as the cost-benefit inflection point (e.g., 2028). The output includes an annual comparison table, cumulative emission reductions, and the inflection point year.

[0186] It should be noted that step 214 does not change the prediction and calculation results in steps 201 to 213. Instead, it selects the corresponding data based on the user's question, calls the corresponding calculation process, and organizes the calculation results into an answer that matches the user's question.

[0187] In one exemplary instance, the method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire lifecycle of a passenger vehicle fleet provided in this application embodiment may further include: The calculation results generated in steps 201 to 214 are visualized in the form of charts.

[0188] In one embodiment, the chart may include at least one of bar charts, line charts, pie charts, time series charts, distribution heatmaps, and cost efficiency curves.

[0189] For example, line charts can be used to display the changing trends of total passenger vehicle sales, sales of vehicles of different fuel types, vehicle ownership of different fuel types, and the total lifecycle carbon emissions of the fleet over time in each forecast year; bar charts can be used to compare carbon emissions, fuel savings, and net social benefits in the same forecast year under scenarios without integral constraints and scenarios with integral constraints; pie charts can be used to display the proportion of each fuel type or each stage of the lifecycle in the total lifecycle carbon emissions of the fleet; and heat maps can be used to display the distribution of vehicle ownership in different provinces, vehicle ages, and fuel types.

[0190] In one embodiment, the system can also store the vehicle ownership dataset, sales dataset, vehicle technical parameter dataset, carbon emission dataset, and policy evaluation dataset generated by multiple independent calculations, and store them in association according to the prediction scenario identifier.

[0191] In one embodiment, users can switch between different forecast scenarios and compare passenger vehicle sales, vehicle ownership, fleet carbon emissions, carbon emission reductions, and policy evaluation results under different scenarios.

[0192] This application also provides a computer-readable storage medium storing computer-executable instructions for executing the passenger vehicle fleet lifecycle carbon emission prediction and carbon reduction path evaluation method described in any of the above claims.

[0193] This application further provides a computer device, including a memory and a processor, wherein the memory stores the following instructions executable by the processor: steps for performing the passenger vehicle fleet life-cycle carbon emission prediction and carbon reduction path evaluation method described in any of the preceding claims.

[0194] Figure 3 This is a schematic diagram of the composition and structure of the vehicle fleet lifecycle carbon emission prediction and carbon reduction path evaluation device in the embodiments of this application, as shown below. Figure 3 As shown, it may include: a carbon emission reduction prediction module, a carbon emission reduction pathway evaluation module, and a policy guidance processing module; among which, The carbon emission reduction prediction module is used to determine the fleet's life-cycle carbon emissions for each forecast year under both the no-integral-constraint scenario and the integral-constraint scenario, based on the passenger car fleet size, market structure of vehicles with different fuel types, vehicle energy consumption and technical indicators, carbon emission factors, and integral constraint parameters. It also determines the fleet carbon emission reduction caused by integral constraints based on the fleet's life-cycle carbon emissions under the two scenarios. The carbon emission reduction path evaluation module is used to change the parameters corresponding to different carbon emission reduction paths, determine the emission reduction benefits and efficiency of different carbon emission reduction paths based on the changes in the fleet's carbon emissions throughout its entire life cycle caused by the parameter changes and the costs of the corresponding carbon emission reduction paths, and determine the comprehensive score of different carbon emission reduction paths based on the emission reduction benefits and the emission reduction efficiency. The policy guidance processing module is used to determine the carbon reduction policy guidance results based on the comprehensive score of the fleet's carbon emission reduction and different carbon emission reduction paths.

[0195] The passenger vehicle fleet lifecycle carbon emission prediction and carbon reduction path evaluation device provided in this application determines the fleet's lifecycle carbon emissions and carbon reduction caused by the credit constraints under both scenarios with and without credit constraints, based on fleet size, market structure by fuel type, vehicle energy consumption and technical indicators, carbon emission factors, and credit constraint parameters. By changing the parameters corresponding to different carbon reduction paths, and combining changes in carbon emissions and path costs, the device determines the emission reduction benefits, emission reduction efficiency, and comprehensive score of each path, and generates carbon reduction policy guidance results accordingly. This application reflects the transmission impact of credit constraints on fleet structure and lifecycle carbon emissions, improves the accuracy, consistency, and comparability of prediction results, and provides a quantitative basis for carbon reduction path selection and policy formulation.

[0196] In one exemplary instance, it may further include: an intelligent decision assistant module, used to receive natural language questions input by users, parse the natural language questions, determine at least one of the predicted year, predicted scenario, fuel type, carbon emission reduction path and policy evaluation indicators involved in the natural language questions; extract relevant data from the carbon emission reduction prediction module, carbon emission reduction path evaluation module and policy guidance processing module according to the parsing results, perform calculations on the extracted data, and generate the answer content corresponding to the natural language questions.

[0197] Although the embodiments disclosed in this application are as described above, the content described is merely for the purpose of understanding this application and is not intended to limit this application. Any person skilled in the art to which this application pertains may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in this application; however, the scope of patent protection of this application shall still be determined by the scope defined in the appended claims.

Claims

1. A method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire lifecycle of a passenger vehicle fleet, characterized in that, include: Based on passenger vehicle fleet size, market structure of vehicles with different fuel types, vehicle energy consumption and technical indicators, carbon emission factors and credit constraint parameters, the total life cycle carbon emissions of the fleet are determined for each forecast year under both the no-credit-constraint scenario and the credit-constraint scenario. The carbon emission reduction of the fleet caused by the credit constraints is determined based on the total life cycle carbon emissions of the fleet under the two scenarios. By changing the parameters corresponding to different carbon emission reduction paths, and based on the changes in the fleet's carbon emissions over the entire life cycle caused by the parameter changes and the costs of the corresponding carbon emission reduction paths, the emission reduction benefits and efficiency of different carbon emission reduction paths are determined, and the comprehensive score of different carbon emission reduction paths is determined based on the emission reduction benefits and efficiency. The results of carbon reduction policy guidance are determined based on a comprehensive score of the fleet's carbon emission reduction and different carbon emission reduction pathways.

2. The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire lifecycle of a vehicle fleet as described in claim 1, wherein, The determination of the fleet carbon emission reduction caused by the integral constraint includes: Obtain basic data for predicting carbon emissions throughout the entire life cycle of the passenger vehicle; the basic data includes historical passenger vehicle ownership data, economic parameters, historical passenger vehicle sales, historical sales percentage by fuel type, historical energy consumption and technical indicators of vehicles of different fuel types, prediction parameters for energy consumption and technical indicators, the carbon emission factor, and the integral constraint parameters. The predicted passenger vehicle ownership for each forecast year within the forecast period is determined based on historical passenger vehicle ownership data and economic parameters. The total sales volume of passenger vehicles for each forecast year is determined based on the predicted value of passenger vehicle ownership and the number of vehicles scrapped. Based on the probability of consumers choosing different fuel types of vehicles and the predicted total sales of passenger vehicles, the first-year sales of different fuel types of vehicles in each predicted year under the scenario without credit constraints are determined; wherein, when determining the first-year sales under the scenario without credit constraints, corporate average fuel consumption credit constraints, new energy vehicle credit constraints and credit trading rules are not introduced. Based on the enterprise type of passenger vehicle enterprises, the sales volume of vehicles by fuel type in each forecast year is determined for market-driven enterprises and compliance-impact enterprises respectively. Based on the sales volume of vehicles by fuel type of the two types of enterprises, the second-year sales volume of vehicles of different fuel types in each forecast year under the integral constraint scenario is determined. Determine the number of vehicles of different fuel types in each predicted year under the scenarios without integral constraints and under the scenarios with integral constraints, respectively. Based on the historical energy consumption and technical indicators in the basic data, as well as the predicted parameters of energy consumption and technical indicators, the energy consumption and technical indicators of vehicles with different fuel types in each predicted year within the prediction period are determined. Based on the energy consumption and technical specifications of vehicles with different fuel types, determine the carbon emissions of a single vehicle throughout its entire life cycle for each fuel type. Based on the total lifecycle carbon emissions of a single vehicle with different fuel types and the number of vehicles with different fuel types in the corresponding scenarios, the total lifecycle carbon emissions of the fleet in each forecast year are determined. Based on the scenario without integral constraints and the fleet's carbon emissions over its entire lifecycle under the scenario with integral constraints, determine the fleet carbon emission reduction caused by the integral constraints.

3. The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire lifecycle of a vehicle fleet as described in claim 1, wherein, The comprehensive score for determining different carbon emission reduction pathways includes: The parameters corresponding to the carbon emission reduction path are changed by using the controlled variable method, and the marginal emission reduction corresponding to the carbon emission reduction path is determined based on the difference between the carbon emissions of the fleet throughout its entire life cycle before and after the parameter change. Based on the marginal emission reduction and marginal cost of different carbon emission reduction paths, the emission reduction efficiency of different carbon emission reduction paths is determined; the marginal emission reduction corresponding to different carbon emission reduction paths is determined as the emission reduction benefit of the corresponding carbon emission reduction path. The emission reduction efficiency and emission reduction benefit of different carbon emission reduction paths are normalized respectively. Then, the normalized emission reduction efficiency and emission reduction benefit are weighted according to the first preference value corresponding to the emission reduction efficiency and the second preference value corresponding to the emission reduction benefit to obtain the comprehensive score of the different carbon emission reduction paths.

4. The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire lifecycle of a vehicle fleet as described in claim 1, wherein, The determination of the guiding results of carbon emission reduction policies includes: Based on the comprehensive scores of the different carbon emission reduction paths and the corresponding emission reduction benefits and efficiency, the different carbon emission reduction paths are ranked to determine the implementation priority of the different carbon emission reduction paths. Based on the fleet carbon emission reduction and the policy evaluation index corresponding to the integral constraint scenario, multiple candidate values ​​for the integral constraint parameter are set, and the fleet carbon emission reduction and policy evaluation index corresponding to each candidate value are determined respectively. Based on the fleet carbon emission reduction and policy evaluation index corresponding to each candidate value, the policy strength evaluation result is obtained. Based on the evaluation results of the implementation priorities and policy intensity of different carbon emission reduction pathways, generate carbon emission reduction policy guidance results that include at least one of the evaluation results of implementation priorities and policy intensity. The policy evaluation indicators include at least one of the following: points-based prediction indicators, fuel savings, emission reductions of typical pollutants, total socio-economic benefits, and net social benefits.

5. The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire lifecycle of a vehicle fleet as described in claim 4, wherein, The generation of carbon emission reduction policy guidance results, including at least one of the evaluation results of implementation priorities and policy intensity, includes: Based on the aforementioned policy evaluation indicators, the carbon emission reduction effect and economic effect corresponding to different policy intensities are determined respectively; Based on the implementation priorities of the different carbon emission reduction paths, the carbon emission reduction effects and economic effects corresponding to different policy intensities, the carbon emission reduction policy guidance results are generated, including at least one of the following: recommended carbon emission reduction paths, the implementation order of carbon emission reduction paths, recommended values ​​of policy constraint parameters, and policy implementation years.

6. The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire life cycle of a vehicle fleet as described in claim 1, characterized in that, Also includes: The system receives natural language questions input by users, parses the received natural language questions, and determines at least one of the following: the forecast year, forecast scenario, fuel type, carbon emission reduction path, and policy evaluation indicators involved in the natural language questions. Based on the analysis results, extract at least one of the following: fleet size prediction data, vehicle sales data, vehicle ownership data, fleet life cycle carbon emission data, carbon emission reduction path evaluation data, and policy evaluation indicator data. The extracted data is processed to generate an answer corresponding to the natural language question.

7. The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire life cycle of a passenger vehicle fleet according to claim 2, wherein, The process of determining the predicted passenger vehicle ownership for each forecast year within the forecast period based on historical passenger vehicle ownership data and economic parameters includes: The historical passenger vehicle ownership data, the economic parameters, and the Gompertz model fitting parameters are input into the Gompertz model. The output of the Gompertz model is the predicted passenger vehicle ownership value for each prediction year within the prediction period. The predicted passenger vehicle ownership values ​​are categorized and stored according to at least one of fuel type, vehicle age, and usage area.

8. The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire life cycle of a passenger vehicle fleet according to claim 2, wherein, The process of determining the total passenger vehicle sales forecast for each forecast year based on the predicted passenger vehicle ownership and vehicle scrapping volume includes: The number of vehicles to be scrapped in each projected year is determined based on the age of the vehicles. Based on the current forecast year's passenger vehicle ownership, the previous year's passenger vehicle ownership, and the current forecast year's vehicle scrapping volume, the current forecast year's total passenger vehicle sales volume is determined according to the vehicle inventory balance.

9. The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire life cycle of a passenger vehicle fleet according to claim 2, wherein, The determination of the first-year sales volume of vehicles of different fuel types in each forecast year under the scenario without integral constraints includes: Using a multivariate discrete consumer choice model, the probability of consumers choosing different fuel types of vehicles in each forecast year is predicted. The selection probability corresponding to different fuel types of vehicles is determined as the annual sales percentage of the corresponding fuel type of vehicle. The first-year sales volume of each fuel type of vehicle is determined based on the annual sales volume percentage and the predicted total sales volume of passenger vehicles for the corresponding forecast year.

10. The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire life cycle of a passenger vehicle fleet according to claim 2, wherein, The determination of vehicle sales by fuel type for market-driven and compliance-impact companies in each forecast year includes: Based on the total passenger vehicle sales forecast, the sales proportion of vehicles of different fuel types, and the market share of the market-driven enterprise by fuel type, the sales volume of vehicles of different fuel types of the market-driven enterprise in each forecast year is determined. Using the sales volume of vehicles by fuel type, the improvement in vehicle energy consumption, and the amount of credits purchased by the compliance-influenced enterprises as decision variables, and under the conditions of satisfying fuel consumption credit constraints and new energy vehicle credit constraints, with the goal of minimizing the compliance cost of the credit policy, the sales volume of vehicles by fuel type, the improvement in vehicle energy consumption, and the amount of credits purchased by the compliance-influenced enterprises in each forecast year are determined.

11. The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire life cycle of a passenger vehicle fleet according to claim 2, wherein, The determination of the number of vehicles of different fuel types in each forecast year under both the no-integral-constraint scenario and the integral-constraint scenario includes: Based on the number of vehicles of the corresponding fuel type in the previous year, the annual sales volume of vehicles of the corresponding fuel type in the current forecast year, and the number of vehicles of the corresponding fuel type scrapped in the current forecast year, the number of vehicles of the corresponding fuel type in each forecast year is determined year by year according to the vehicle stock balance relationship. Wherein, the annual sales volume under the no-credit-constraint scenario is the first year's sales volume, and the annual sales volume under the credit-constraint scenario is the second year's sales volume; the vehicle inventory balance relationship is: current year's inventory = previous year's inventory + current year's sales volume - current year's scrap volume.

12. The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire life cycle of a passenger vehicle fleet according to claim 2, wherein, The determination of energy consumption and technical specifications for vehicles of different fuel types for each forecast year within the forecast period includes: Based on historical vehicle data, the energy consumption and technical indicators of vehicles are statistically analyzed according to fuel type and historical year, and the historical energy consumption and technical indicators of vehicles of different fuel types in each historical year are obtained. Obtain the annual rate of change for each energy consumption and technical indicator; Based on the energy consumption and technical indicators corresponding to historical years, and according to the annual change rate of the corresponding indicators, the energy consumption and technical indicators of vehicles with different fuel types in each forecast year within the forecast period are determined year by year in chronological order of the forecast years. The energy consumption and technical indicators may include at least one of average fuel consumption, average electricity consumption, average energy consumption, average curb weight, average driving range, and average battery capacity; the energy consumption and technical indicator prediction parameters may include at least one of the annual change rate corresponding to each energy consumption and technical indicator, and future annual indicator values ​​obtained through enterprise surveys or user settings.

13. The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire life cycle of a passenger vehicle fleet according to claim 2, wherein, The total lifecycle carbon emissions of a single vehicle with different fuel types include the sum of carbon emissions from raw materials and components, vehicle production, fuel production, and fuel use.

14. The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire life cycle of a passenger vehicle fleet according to claim 2, wherein, The determination of the fleet carbon emission reduction caused by the integral constraint includes: Obtain the fleet's total lifecycle carbon emissions for each predicted year under the scenario without integral constraints, and the fleet's total lifecycle carbon emissions for each predicted year under the scenario with integral constraints. For the same forecast year, the difference between the fleet's total lifecycle carbon emissions under the no-integral-constraint scenario and the fleet's total lifecycle carbon emissions under the integral-constraint scenario is used to obtain the fleet carbon emission reduction caused by the integral constraint.

15. The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire life cycle of a passenger vehicle fleet according to claim 3, wherein, The carbon reduction pathways include at least one of reducing vehicle fuel consumption, reducing vehicle electricity consumption, reducing carbon emission factors in battery production, and promoting new energy vehicles. Among them, the carbon emission reduction path for reducing vehicle fuel consumption involves changing the average fuel consumption of at least one of the traditional fuel vehicles and hybrid vehicles. To reduce carbon emissions through lower vehicle energy consumption, the average energy consumption of at least one of the following vehicles is altered: pure electric vehicles and plug-in hybrid electric vehicles. To reduce carbon emissions through pathways that lower the carbon emission factor in battery production, the carbon emission factor in power battery production is altered. To promote carbon emission reduction through the promotion of new energy vehicles, the sales share of new energy vehicles should be increased, while the sales share of gasoline vehicles should be reduced accordingly.

16. The method for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire lifecycle of a vehicle fleet as described in claim 4, wherein, Based on the number of vehicles of different fuel types under the unconstrained and integral-constrained scenarios, as well as the corresponding vehicle emission standards, vehicle age, mileage and pollutant emission factors, the typical pollutant emissions under the two scenarios are determined respectively. The emission reduction of typical pollutants caused by the integral constraint is determined based on the difference in typical pollutant emissions under the two scenarios.

17. A computer-readable storage medium storing computer-executable instructions for performing the method for predicting carbon emissions and evaluating carbon reduction pathways throughout the life cycle of a passenger vehicle fleet as described in any one of claims 1-16.

18. An electronic device comprising a processor and a memory, the memory storing a computer program that, when executed by the processor, implements the steps of the method for predicting and evaluating carbon emission reduction pathways for a passenger vehicle fleet throughout its entire life cycle, as described in any one of claims 1-16.

19. A device for predicting carbon emissions and evaluating carbon reduction pathways throughout the entire life cycle of a passenger vehicle fleet, characterized in that, include: The system includes a carbon emission reduction prediction module, a carbon emission reduction pathway evaluation module, and a policy guidance processing module; among which, The carbon emission reduction prediction module is used to determine the fleet's life-cycle carbon emissions for each forecast year under both the no-integral-constraint scenario and the integral-constraint scenario, based on the passenger car fleet size, market structure of vehicles with different fuel types, vehicle energy consumption and technical indicators, carbon emission factors, and integral constraint parameters. It also determines the fleet carbon emission reduction caused by integral constraints based on the fleet's life-cycle carbon emissions under the two scenarios. The carbon emission reduction path evaluation module is used to change the parameters corresponding to different carbon emission reduction paths, determine the emission reduction benefits and efficiency of different carbon emission reduction paths based on the changes in the fleet's carbon emissions throughout its entire life cycle caused by the parameter changes and the costs of the corresponding carbon emission reduction paths, and determine the comprehensive score of different carbon emission reduction paths based on the emission reduction benefits and the emission reduction efficiency. The policy guidance processing module is used to determine the carbon reduction policy guidance results based on the comprehensive score of the fleet's carbon emission reduction and different carbon emission reduction paths.

20. The vehicle fleet lifecycle carbon emission prediction and carbon reduction path evaluation device according to claim 19 further includes: The intelligent decision assistant module receives natural language questions input by users, parses the natural language questions, and determines at least one of the following: the predicted year, the predicted scenario, the fuel type, the carbon emission reduction path, and the policy evaluation indicators involved in the natural language questions. Based on the parsing results, it extracts relevant data from the carbon emission reduction prediction module, the carbon emission reduction path evaluation module, and the policy guidance processing module, performs calculations on the extracted data, and generates the corresponding answer content for the natural language questions.

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