Urban traffic carbon emission prediction method and device, electronic equipment and storage medium

Abstract

The application provides a kind of urban traffic carbon emission prediction method, device, electronic equipment and storage medium, the method comprises: constructing urban traffic carbon emission prediction model;Obtain historical urban traffic carbon emission related data;Determine the first type of urban traffic carbon emission influence factor based on the influence factor parameter and historical urban traffic carbon emission related data of pre-set;Determine the future urban traffic carbon emission prediction value based on the first type of urban traffic carbon emission influence factor through urban traffic carbon emission prediction model;Judge whether the future urban traffic carbon emission prediction value is consistent with the pre-set urban traffic carbon emission target value, and when not consistent, adjust influence factor parameter and urban traffic carbon emission target value, so that the future urban traffic carbon emission prediction value obtained based on the adjusted influence factor parameter is consistent with the adjusted urban traffic carbon emission target value.The application breaks the limitation of related technology, and can provide quantitative reference for future policy making.

Description

Technical Field

[0001] This application relates to the field of carbon emission technology, and more specifically to a method, apparatus, electronic device and storage medium for predicting carbon emissions from urban transportation. Background Technology

[0002] Global warming has become one of the greatest environmental threats facing humanity today. Cities, as centers of economic activity for production and daily life, are major sources of energy consumption and carbon emissions; therefore, they are also crucial for achieving carbon peaking and carbon neutrality goals. Achieving carbon neutrality in cities requires reducing greenhouse gas emissions through appropriate policies and technologies. Implementing carbon reduction policies and measures necessitates the calculation and forecasting of urban carbon dioxide emissions and future trends, thus providing a quantitative basis for formulating carbon neutrality policies and pathways.

[0003] Transportation is a significant contributor to urban energy consumption and a major source of urban carbon emissions. To better formulate carbon reduction policies and develop transportation carbon reduction technologies, it is necessary to calculate the carbon emissions of urban transportation. Among related technologies, transportation carbon emission accounting methods primarily calculate regional transportation carbon emissions at a macro level, mainly based on three parameters: average carbon emission factor of different vehicles, vehicle ownership, and average travel distance of different vehicles. Some accounting methods further consider factors such as traffic flow and vehicle driving status at a meso level, further refining the calculation methods for road traffic carbon emissions and improving the accuracy of road traffic carbon emission accounting.

[0004] However, the above methods mainly calculate based on existing carbon emission data and lack prediction of future trends in carbon emissions from transportation, thus failing to provide quantitative references for future policy formulation.

[0005] In view of the above-mentioned problems, this application proposes a new method, apparatus, electronic device and storage medium for predicting carbon emissions from urban transportation, in order to at least partially solve the above problems. Summary of the Invention

[0006] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. The summary section of this invention is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0007] This application provides a method for predicting urban traffic carbon emissions, comprising: constructing an urban traffic carbon emission prediction model; acquiring historical urban traffic carbon emission related data; determining a first type of urban traffic carbon emission influencing factor based on preset influencing factor parameters and the historical urban traffic carbon emission related data; determining a future urban traffic carbon emission prediction value based on the first type of urban traffic carbon emission influencing factor through the urban traffic carbon emission prediction model; determining whether the future urban traffic carbon emission prediction value matches a preset urban traffic carbon emission target value, and if they do not match, adjusting the influencing factor parameters and the urban traffic carbon emission target value so that the future urban traffic carbon emission prediction value obtained based on the adjusted influencing factor parameters matches the adjusted urban traffic carbon emission target value.

[0008] In one example, adjusting the influencing factor parameters and the urban traffic carbon emission target value to make the future urban traffic carbon emission forecast obtained based on the adjusted influencing factor parameters consistent with the adjusted urban traffic carbon emission target value includes: determining a first type of urban traffic carbon emission influencing factor based on the adjusted influencing factor parameters and the historical urban traffic carbon emission related data; determining the future urban traffic carbon emission forecast value based on the first type of urban traffic carbon emission influencing factor using the urban traffic carbon emission forecasting model; determining whether the future urban traffic carbon emission forecast value is consistent with the adjusted urban traffic carbon emission target value; if not, readjusting the influencing factor parameters and the urban traffic carbon emission target value, and repeating the above steps until the future urban traffic carbon emission forecast value is consistent with the adjusted urban traffic carbon emission target value.

[0009] In one example, constructing an urban traffic carbon emission prediction model includes: classifying urban traffic types; and constructing the urban traffic carbon emission prediction model based on the classification results.

[0010] In one example, the first category of urban transportation carbon emission impact factors includes the future electrification rate of motor vehicles.

[0011] In one example, the influencing factor parameter includes the final value of the proportion of motor vehicle electrification when the urban transportation carbon emission target value is reached.

[0012] In one example, determining the first type of urban traffic carbon emission influencing factor based on preset influencing factor parameters and historical urban traffic carbon emission related data includes: determining the historical motor vehicle electrification ratio based on the number of electric vehicles and non-electric vehicles in historical motor vehicles; and determining the future motor vehicle electrification ratio based on the historical motor vehicle electrification ratio and the final value of the motor vehicle electrification ratio.

[0013] In one example, determining the future electric vehicle electrification ratio based on the historical electric vehicle electrification ratio and the final value of the electric vehicle electrification ratio includes: selecting an S-curve model; and calculating the future electric vehicle electrification ratio using the S-curve model based on the historical electric vehicle electrification ratio and the final value of the electric vehicle electrification ratio.

[0014] In one example, determining the predicted value of future urban traffic carbon emissions based on the first type of urban traffic carbon emission influencing factors using the urban traffic carbon emission prediction model includes: determining a second type of urban traffic carbon emission influencing factors, wherein the second type of urban traffic carbon emission influencing factors includes at least one of the following: direct emission intensity of motor vehicles, electric emission intensity of motor vehicles, and future number of motor vehicles; and determining the predicted value of urban traffic carbon emissions based on the future electrification ratio of motor vehicles and the second type of urban traffic carbon emission influencing factors using the urban traffic carbon emission prediction model.

[0015] In one example, determining the predicted urban traffic carbon emissions based on the future electric vehicle electrification rate and the second type of urban traffic carbon emission influencing factor through the urban traffic carbon emission prediction model includes: calculating the carbon emissions of electric vehicles based on the future electric vehicle electrification rate, the electricity emission intensity of the vehicles, and the future number of vehicles; calculating the carbon emissions of non-electric vehicles based on the future electric vehicle electrification rate, the direct emission intensity of the vehicles, and the future number of vehicles; and calculating the predicted urban traffic carbon emissions based on the carbon emissions of electric vehicles and the carbon emissions of non-electric vehicles.

[0016] In one example, when the second type of urban traffic carbon emission impact factor is the direct emission intensity of motor vehicles, determining the second type of urban traffic carbon emission impact factor includes: calculating the direct emission intensity of motor vehicles based on the average driving distance of motor vehicles, the energy consumption per kilometer of motor vehicles, and the carbon emission per unit energy consumption of motor vehicles.

[0017] In one example, when the second type of urban traffic carbon emission influencing factor is the electricity consumption emission intensity of motor vehicles, determining the second type of urban traffic carbon emission influencing factor includes: determining the future unit electricity consumption carbon emission of motor vehicles based on the changing trend of unit electricity consumption carbon emission of motor vehicles and the historical unit electricity consumption carbon emission of motor vehicles; and calculating the electricity consumption emission intensity of motor vehicles based on the average driving distance of motor vehicles, the energy consumption per kilometer of motor vehicles, and the future unit electricity consumption carbon emission of motor vehicles.

[0018] In one example, when the second type of urban traffic carbon emission impact factor is the future number of motor vehicles, determining the second type of urban traffic carbon emission impact factor includes: when the type of motor vehicle is a public vehicle, the future number of motor vehicles is equal to the historical number of motor vehicles; when the type of motor vehicle is a private car, the future number of motor vehicles is determined based on the historical number of motor vehicles.

[0019] In one example, determining the future number of motor vehicles based on historical motor vehicle numbers includes: confirming future car consumption capacity values; calculating a regression constant based on the historical motor vehicle numbers and historical car consumption capacity values; and calculating the future number of motor vehicles based on the regression constant and the future car consumption capacity values.

[0020] In one example, determining the future automobile purchasing power value includes: determining the future average automobile price based on the inflation index and historical average automobile prices; determining the future GDP per capita based on the economic growth forecast rate and historical GDP per capita; and determining the automobile purchasing power value based on the future GDP per capita and the future average automobile price.

[0021] In another aspect, this invention provides an urban traffic carbon emission prediction device, comprising: a prediction model construction module for constructing an urban traffic carbon emission prediction model; a historical data acquisition module for acquiring historical urban traffic carbon emission related data; an influence factor determination module for determining a first type of urban traffic carbon emission influence factor based on preset influence factor parameters and the historical urban traffic carbon emission related data; a carbon emission prediction value determination module for determining a future urban traffic carbon emission prediction value based on the first type of urban traffic carbon emission influence factor through the urban traffic carbon emission prediction model; and a judgment module for judging whether the future urban traffic carbon emission prediction value matches a preset urban traffic carbon emission target value, and if they do not match, adjusting the influence factor parameters and the urban traffic carbon emission target value so that the future urban traffic carbon emission prediction value obtained based on the adjusted influence factor parameters matches the adjusted urban traffic carbon emission target value.

[0022] In another aspect, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the program to implement the urban traffic carbon emission prediction method described in any one of the above embodiments.

[0023] In another aspect, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the urban traffic carbon emission prediction method described in any one of the above embodiments.

[0024] The urban traffic carbon emission prediction method, apparatus, electronic device, and storage medium according to embodiments of this application introduce influencing factor parameters. During the comparison of the predicted urban traffic carbon emission value calculated by the traffic carbon emission prediction model with the target urban traffic carbon emission value, the influencing factor parameters and the target urban traffic carbon emission value can be continuously adjusted until the predicted urban traffic carbon emission value matches the target urban traffic carbon emission value. This achieves the prediction of future urban traffic carbon emissions and can be used to formulate urban traffic carbon neutrality targets and conduct traffic carbon reduction planning under these targets. This breaks the limitation of related technologies that can only calculate based on existing carbon emission data, and can provide a quantitative reference for future policy formulation. Furthermore, the approach of introducing and adjusting influencing factor parameters has higher prediction accuracy compared to the approach of predicting future urban traffic carbon emissions solely based on historical urban traffic carbon emission data. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] In the attached diagram:

[0027] Figure 1 A schematic block diagram of an electronic device according to an embodiment of this application is shown;

[0028] Figure 2 A schematic flowchart illustrating an urban traffic carbon emission prediction method according to an embodiment of this application is shown.

[0029] Figure 3 A schematic flowchart illustrating a method for predicting urban traffic carbon emissions according to another embodiment of this application is shown.

[0030] Figure 4 A schematic block diagram of an urban traffic carbon emission prediction device according to an embodiment of this application is shown;

[0031] Figure 5 A schematic block diagram of an electronic device according to an embodiment of this application is shown. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this application more apparent, exemplary embodiments according to this application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein. Based on the embodiments of this application described herein, all other embodiments obtained by those skilled in the art without inventive effort should fall within the protection scope of this application.

[0033] The following description provides numerous specific details to offer a more thorough understanding of this application. However, it will be apparent to those skilled in the art that this application can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described to avoid confusion with this application.

[0034] It should be understood that this application can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of this application to those skilled in the art.

[0035] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising” and / or “including,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.

[0036] To fully understand this application, a detailed structure will be presented in the following description to illustrate the technical solution proposed in this application. Optional embodiments of this application are described in detail below; however, in addition to these detailed descriptions, this application may have other implementation methods.

[0037] First, refer to Figure 1 This describes an example electronic device 100 for implementing the urban traffic carbon emission prediction method and apparatus of embodiments of the present invention.

[0038] like Figure 1 As shown, the electronic device 100 includes one or more processors 102, one or more memories 104, input devices 106, and output devices 108, which are interconnected via a bus system 110 and / or other forms of connection mechanisms (not shown). It should be noted that... Figure 1The components and structure of the electronic device 100 shown are merely exemplary and not limiting; the electronic device may also have other components and structures as needed.

[0039] The processor 102 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 100 to perform desired functions.

[0040] The memory 104 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 102 may execute the program instructions to implement the client functions (implemented by the processor) in the embodiments of the present invention described below, and / or other desired functions. Various applications and various data may also be stored in the computer-readable storage medium, such as various data used and / or generated by the applications.

[0041] The input device 106 may be a device used by a user to input commands, and may include one or more of the following: keyboard, mouse, microphone, and touch screen.

[0042] The output device 108 can output various information (such as images or sounds) to the outside (e.g., a user) and may include one or more of a display, a speaker, etc.

[0043] For example, an example electronic device for implementing the urban traffic carbon emission prediction method and apparatus according to embodiments of the present invention can be implemented as a terminal such as a smartphone, tablet computer, desktop computer, etc.

[0044] Below, we will refer to Figure 2 A method for predicting urban traffic carbon emissions according to an embodiment of the present invention is described. Figure 2 This is a schematic flowchart of an urban traffic carbon emission prediction method 200 according to an embodiment of this application. The urban traffic carbon emission prediction method of this application is used in an urban traffic carbon emission prediction device, which includes a processor, memory, input device, and output device, etc., and can be implemented as the electronic device 100 described above. Specifically, the urban traffic carbon emission prediction method 200 of this application includes the following steps:

[0045] First, in step S210, a prediction model for urban traffic carbon emissions is constructed.

[0046] In this embodiment of the invention, the cities that need to predict and plan transportation carbon emissions are first identified. After identifying the relevant cities, a city transportation carbon emission prediction model is constructed for each city. Based on the constructed city transportation carbon emission prediction model, the city's transportation carbon emission values ​​for a future year, month, or other time period can be predicted.

[0047] Specifically, urban traffic carbon emission prediction models can be constructed in various ways, such as based on urban traffic conditions, vehicle type and combustion type, and traffic pollution type. Taking the construction of an urban traffic carbon emission prediction model based on traffic type as an example, it is necessary to classify the city's traffic types, and then construct the urban traffic carbon emission prediction model based on the classification results. For example, urban traffic types include public transportation and non-public transportation.

[0048] In step S220, historical data on urban traffic carbon emissions are obtained.

[0049] In this embodiment of the invention, urban traffic carbon emission-related data includes a series of data related to traffic carbon emissions, such as the number of vehicles, mileage, energy consumption, and carbon emissions. By acquiring this data, future urban traffic carbon emissions can be predicted. It should be noted that "historical" here refers to any historical time period, such as a certain year or month in the past.

[0050] In step S230, the first type of urban traffic carbon emission impact factor is determined based on the preset impact factor parameters and the historical urban traffic carbon emission related data.

[0051] In this embodiment of the invention, the first type of urban traffic carbon emission influencing factor is calculated using both influencing factor parameters and historical urban traffic carbon emission data. When predicting future urban traffic carbon emissions solely based on historical data, accuracy is only guaranteed if the future trend aligns with past trends. When the future trend deviates from past trends, the predicted value often differs significantly from the actual value. Therefore, predicting future urban traffic carbon emissions based solely on historical data has significant limitations. By setting influencing factor parameters, the trend obtained from predictions based solely on historical data can be corrected, thereby ensuring that the predicted value matches the actual value as closely as possible.

[0052] The influencing factor parameters are related to the historical urban traffic carbon emission data obtained, as well as the first category of urban traffic carbon emission influencing factors that need to be determined. For example, when the historical urban traffic carbon emission data is the historical proportion of electric vehicles, and the first category of urban traffic carbon emission influencing factors is the future proportion of electric vehicles, then the influencing factor parameters can be the final value of the proportion of electric vehicles when urban traffic carbon emissions reach the target value.

[0053] The historical proportion of electric vehicles can be determined based on the number of electric vehicles and non-electric vehicles in the historical vehicle fleet, and the final value of the vehicle electrification proportion is preset. In one example, determining the first type of urban traffic carbon emission influencing factor based on preset influencing factor parameters and the historical urban traffic carbon emission related data includes: determining the historical proportion of electric vehicles based on the number of electric vehicles and non-electric vehicles in the historical vehicle fleet; and determining the future proportion of electric vehicles based on the historical proportion of electric vehicles and the final value of the vehicle electrification proportion.

[0054] Specifically, the historical rate of electric vehicle ownership can be calculated using the following formula:

[0055]

[0056] in, The percentage of electric vehicles that represents history, Q EV The number of electric vehicles among historical motor vehicles, Q FV This represents the number of non-electric vehicles in historical vehicle data. Taking the calculation of a specific type of vehicle in a past year as an example, first, obtain historical urban traffic carbon emission data, i.e., the number of electric and non-electric vehicles of that type in that year. Then, substitute the numbers of electric and non-electric vehicles into the formula above to obtain the electrification rate of that type of vehicle in that year. Similarly, the above formula can be used to calculate the electrification rate of that type of vehicle in any other time period, such as a past month. By calculating the historical electrification rate of all types of vehicles, the historical vehicle electrification rate in the city can be determined. Alternatively, one can directly obtain the number of electric and non-electric vehicles in all types of vehicles, substitute them into the above formula, and thus obtain the historical vehicle electrification rate in the city.

[0057] After calculating the historical proportion of electric vehicles Afterwards, according to The future electric vehicle electrification rate can be obtained by substituting the pre-set final value of the electric vehicle electrification rate into the corresponding calculation model. In one example, determining the future electric vehicle electrification rate based on the historical electric vehicle electrification rate and the final value of the electric vehicle electrification rate includes: selecting an S-curve model; and calculating the future electric vehicle electrification rate using the S-curve model based on the historical electric vehicle electrification rate and the final value of the electric vehicle electrification rate. In this example, an S-curve is used to simulate the electric vehicle proportion growth curve, thereby predicting the future electric vehicle electrification rate.

[0058] Several S-shaped curve models are available for selection, such as the Logistic curve model (logistic cumulative distribution curve model), the Gompertz curve model, and the Weibull curve model (Weibull cumulative distribution curve model). Taking the Logistic curve model as an example, the formula for calculating the future electrification rate of motor vehicles is as follows:

[0059]

[0060] in, Represents the future proportion of electric vehicles. The final value representing the proportion of electric vehicles, n represents the number of years since the city acquired its first electric vehicle, and γ represents the slope of the curve.

[0061] The slope γ of the curve can be determined by substituting historical figures for the electrification rate of motor vehicles. The result is obtained by fitting the data to the number of years n0 since the city acquired its first electric vehicle, i.e.:

[0062]

[0063] Subsequently, in step S240, the predicted value of future urban traffic carbon emissions is determined based on the first type of urban traffic carbon emission influencing factors through the urban traffic carbon emission prediction model.

[0064] In this embodiment of the invention, the predicted value of future urban traffic carbon emissions is obtained by substituting a first type of urban traffic carbon emission influencing factor into the urban traffic carbon emission prediction model. When the first type of urban traffic carbon emission influencing factor is the future proportion of electric vehicles, the future proportion of electric vehicles is used as a calculation factor in the urban traffic carbon emission prediction model, thereby predicting the future urban traffic carbon emissions based on the future proportion of electric vehicles.

[0065] In addition, a second type of urban traffic carbon emission influencing factor related to the first type of urban traffic carbon emission influencing factor can be determined, and the first and second types of urban traffic carbon emission influencing factors are used together as calculation factors and substituted into the urban traffic carbon emission prediction model. In one example, determining the future urban traffic carbon emission prediction value based on the first type of urban traffic carbon emission influencing factor through the urban traffic carbon emission prediction model includes: determining the second type of urban traffic carbon emission influencing factor, which includes at least one of the following: the direct emission intensity of motor vehicles, the electricity emission intensity of motor vehicles, and the future number of motor vehicles; and determining the urban traffic carbon emission prediction value based on the future electrification ratio of motor vehicles and the second type of urban traffic carbon emission influencing factor through the urban traffic carbon emission prediction model.

[0066] The second category of urban transportation carbon emission influencing factors is also related to historical urban transportation carbon emission data. Therefore, the direct emission intensity of motor vehicles, the electricity emission intensity of motor vehicles, and the future number of motor vehicles can be obtained by calculating the historical urban transportation carbon emission data obtained in step S220.

[0067] In some implementations, when the second category of urban traffic carbon emission influencing factors includes the future number of motor vehicles, historical urban traffic carbon emission-related data such as the historical number of motor vehicles, historical automobile consumption capacity value, inflation index, historical average automobile price, economic growth forecast rate, and historical per capita GDP can be obtained in step S220. The future number of motor vehicles can be calculated based on the obtained historical urban traffic carbon emission-related data, thereby determining the second category of urban traffic carbon emission influencing factors.

[0068] When the type of motor vehicle is a public vehicle such as a bus, taxi, or truck, since the number of public vehicles in the city is relatively stable, it can be assumed that the number of such vehicles remains unchanged. That is, the number of motor vehicles in the future is equal to the number of motor vehicles in the past. Here, the number of motor vehicles in the future and the number of motor vehicles in the past refer to the number of public vehicles.

[0069] When the type of motor vehicle is a private car, the number of private cars will vary depending on various influencing factors such as car prices and GDP per capita. Therefore, it is necessary to combine historical data on the number of motor vehicles and other relevant data to predict the future number of motor vehicles.

[0070] For example, determining the future number of motor vehicles based on historical vehicle numbers includes: confirming future car purchasing power; calculating a regression constant based on the historical vehicle numbers and historical car purchasing power; and calculating the future number of motor vehicles based on the regression constant and the future car purchasing power. In this example, a univariate linear regression method is used to predict the future number of motor vehicles, where the historical vehicle numbers refer to the number of private cars. In the process of confirming the future number of motor vehicles, the steps of confirming future car purchasing power and calculating the regression constant can be performed sequentially or simultaneously; no specific order is required.

[0071] First, the regression constant is obtained by fitting the historical number of motor vehicles and historical car consumption capacity values. The following calculation formula can be used:

[0072] a = Q 私家车0 / R0

[0073] Where a represents the regression constant, Q 私家车0 R0 represents the historical number of motor vehicles, and R0 represents the historical automobile purchasing power. R0 can be calculated using the historical average automobile price and the historical GDP per capita, i.e., R0 = historical GDP per capita / historical average automobile price.

[0074] After obtaining the regression constant 'a', the future number of motor vehicles is calculated using univariate linear regression based on 'a' and future car consumption capacity. The calculation formula is as follows:

[0075] Q 私家车 =aR

[0076] Among them, Q 私家车 R represents the future number of motor vehicles, and R represents the future car consumption capacity.

[0077] As for the future automobile consumption capacity value R, it can be calculated based on historical urban transportation carbon emission data such as the inflation index, historical average automobile prices, economic growth forecasts, and historical per capita GDP.

[0078] For example, determining future car purchasing power includes: determining the future average car price based on the inflation index and historical average car prices; determining the future GDP per capita based on the projected economic growth rate and historical GDP per capita; and determining car purchasing power based on the future GDP per capita and the future average car price. In this example, the average car price and GDP per capita reflect people's purchasing power for cars. In the process of determining future car purchasing power, the steps of determining the future average car price and determining the future GDP per capita can be performed sequentially or simultaneously; there is no limitation on this.

[0079] The inflation index, also known as the inflation rate, refers to the rate of increase in the general price level over a specific period (usually one year). In this example, the inflation index can be selected as the inflation forecast indicator I published by the city government. The formula for calculating the future (n years later) average car price based on the inflation forecast indicator I and historical average car prices is as follows:

[0080] The future average car price = the historical average car price × (1 + I) n

[0081] The economic growth forecast rate is a prediction of the economic growth rate over a certain period of time. It can be selected as the economic forecast indicator x released by the city government. The formula for calculating the future (n years from now) GDP per capita based on the economic forecast indicator x and historical GDP per capita is as follows:

[0082] Future GDP per capita = Historical GDP per capita × (1 + x) n

[0083] After calculating the future average car price and the future GDP per capita, the future car purchasing power value R can be further calculated using the following formula:

[0084] R = Future GDP per capita / Future average car price

[0085] In some implementations, when the motor vehicle is not an electric vehicle, the second type of urban traffic carbon emission impact factor includes the direct emission intensity of the motor vehicle. This can be achieved by obtaining historical urban traffic carbon emission data in step S220, such as the average driving distance of the motor vehicle, energy consumption per kilometer, and carbon emissions per unit of energy consumption. Based on these data, the direct emission intensity of the motor vehicle can be calculated, thus obtaining the second type of urban traffic carbon emission impact factor. Specifically, the calculation can be performed using the following formula:

[0086]

[0087] in, D represents the direct emission intensity of motor vehicles. i FC represents the average distance traveled by a motor vehicle. i CEF represents the energy consumption per kilometer of a motor vehicle, while CEF represents the carbon emissions per unit of energy consumed by the motor vehicle. When the average driving distance D... i It is the average annual mileage, from which the annual direct emission intensity of motor vehicles can be calculated; when the average mileage D... i This refers to the average monthly mileage or mileage over other durations. Accordingly, the direct emission intensity of motor vehicles over a month or other durations can be calculated. Since the energy consumption per kilometer and carbon emissions per unit of energy consumption differ among different types of motor vehicles, the direct emission intensity of each type of motor vehicle can be calculated separately using the above formula, and then summarized to obtain the direct emission intensity of motor vehicles in the city.

[0088] In some implementations, when the motor vehicle is an electric vehicle, the second type of urban traffic carbon emission impact factor includes the motor vehicle's electricity consumption emission intensity. In step S220, historical urban traffic carbon emission data such as the trend of change in carbon emissions per unit of electricity consumption of the motor vehicle, historical carbon emissions per unit of electricity consumption of the motor vehicle, average driving distance of the motor vehicle, and energy consumption per kilometer of the motor vehicle can be obtained. Based on the obtained trend of change in carbon emissions per unit of electricity consumption of the motor vehicle, historical carbon emissions per unit of electricity consumption of the motor vehicle, average driving distance of the motor vehicle, and energy consumption per kilometer of the motor vehicle, the electricity consumption emission intensity of the motor vehicle can be calculated, thereby obtaining the second type of urban traffic carbon emission impact factor.

[0089] Specifically, the future carbon emissions per unit of electricity consumption of motor vehicles are determined first based on the changing trend of carbon emissions per unit of electricity consumption of motor vehicles and the historical carbon emissions per unit of electricity consumption of motor vehicles. The carbon emissions per unit of electricity consumption change with adjustments in the energy structure. Taking China's energy structure adjustment as an example, the changing trend of carbon emissions per unit of electricity consumption of motor vehicles can be assumed to be a decrease of 2.4% annually. The formula for calculating the carbon emissions per unit of electricity consumption of motor vehicles in the future (n years from now) is as follows:

[0090] CEE = CEE' × (1 - 2.4%) n

[0091] CEE' represents the carbon emissions per unit of electricity consumption of historical motor vehicles, published by the national or local government, while CEE represents the carbon emissions per unit of electricity consumption of future motor vehicles.

[0092] Then, the energy consumption intensity of motor vehicles is calculated based on the average driving distance, energy consumption per kilometer, and future carbon emissions per unit of electricity consumption. The calculation formula is as follows:

[0093]

[0094] in, D represents the intensity of electrical emissions from motor vehicles. i EC represents the average distance traveled by a motor vehicle. i This represents the energy consumption per kilometer of a motor vehicle. When the average distance traveled is D... i The average annual mileage can be used to calculate the annual electricity emission intensity of motor vehicles; when the average mileage D... i This refers to the average monthly mileage or mileage over other durations. Accordingly, the monthly or other duration-based energy consumption emission intensity of motor vehicles can be calculated. Since the energy consumption per kilometer and carbon emissions per unit of electricity consumption differ among different types of motor vehicles, the energy consumption emission intensity of each type of motor vehicle can be calculated separately using the above formula, and then summarized to obtain the energy consumption emission intensity of motor vehicles in the city.

[0095] Once the second category of urban transportation carbon emission influencing factors is determined, the predicted value of future urban transportation carbon emissions can be calculated using an urban transportation carbon emission prediction model based on the future proportion of electric vehicles and the determined second category of urban transportation carbon emission influencing factors.

[0096] In one example, determining the predicted urban traffic carbon emissions based on the future electric vehicle electrification rate and the second type of urban traffic carbon emission influencing factor using the urban traffic carbon emission prediction model includes: calculating the carbon emissions of electric vehicles based on the future electric vehicle electrification rate, the electricity emission intensity of the vehicles, and the future number of vehicles; calculating the carbon emissions of non-electric vehicles based on the future electric vehicle electrification rate, the direct emission intensity of the vehicles, and the future number of vehicles; and calculating the predicted urban traffic carbon emissions based on the carbon emissions of electric vehicles and the carbon emissions of non-electric vehicles. In this example, the predicted urban traffic carbon emissions are obtained by calculating the future carbon emissions of electric vehicles and non-electric vehicles separately, and then summing them up. During the process of determining the predicted urban traffic carbon emissions, the steps of calculating the carbon emissions of electric vehicles and calculating the carbon emissions of non-electric vehicles can be performed sequentially or simultaneously, without limitation.

[0097] Specifically, the formula for calculating the predicted urban transportation carbon emissions (n ​​years from now) based on the carbon emissions of electric vehicles and non-electric vehicles is as follows:

[0098]

[0099] Here, GHG represents the projected future carbon emissions from urban transportation. Q represents the future proportion of electric vehicles.i The future number of motor vehicles can be obtained by summing the future number of public vehicles and the future number of private vehicles. Represents the direct emission intensity of motor vehicles. This represents the intensity of electricity emissions from motor vehicles.

[0100] Finally, in step S250, it is determined whether the predicted value of future urban traffic carbon emissions is consistent with the preset target value of urban traffic carbon emissions. If they are inconsistent, the influencing factor parameters and the target value of urban traffic carbon emissions are adjusted so that the predicted value of future urban traffic carbon emissions obtained based on the adjusted influencing factor parameters is consistent with the adjusted target value of urban traffic carbon emissions.

[0101] In this embodiment of the invention, by adjusting the influencing factor parameters and the target value of urban traffic carbon emissions until the calculated predicted value of urban traffic carbon emissions matches the target value of urban traffic carbon emissions, a quantitative target for urban traffic carbon neutrality can be specified based on the calculated predicted value of urban traffic carbon emissions, and a carbon reduction plan for urban traffic can be further subdivided based on the target value of urban traffic carbon emissions and the influencing factors.

[0102] In one example, such as Figure 3 When the predicted value of future urban traffic carbon emissions does not match the preset target value of urban traffic carbon emissions, steps S230 to S250 are repeated. That is, adjusting the influencing factor parameters and the target value of urban traffic carbon emissions to make the predicted value of future urban traffic carbon emissions based on the adjusted influencing factor parameters match the adjusted target value of urban traffic carbon emissions includes: determining a first type of urban traffic carbon emission influencing factor based on the adjusted influencing factor parameters and the historical urban traffic carbon emission related data; determining the predicted value of future urban traffic carbon emissions based on the first type of urban traffic carbon emission influencing factor through the urban traffic carbon emission prediction model; determining whether the predicted value of future urban traffic carbon emissions matches the adjusted target value of urban traffic carbon emissions; if not, readjusting the influencing factor parameters and the target value of urban traffic carbon emissions, and repeating the above steps until the predicted value of future urban traffic carbon emissions matches the adjusted target value of urban traffic carbon emissions.

[0103] Based on the above description, the urban traffic carbon emission prediction method according to embodiments of the present invention, by introducing influencing factor parameters, can continuously adjust the influencing factor parameters and the urban traffic carbon emission target value during the process of comparing the predicted urban traffic carbon emission value calculated by the traffic carbon emission prediction model with the target value of urban traffic carbon emission, until the predicted urban traffic carbon emission value matches the target value of urban traffic carbon emission. This achieves the prediction of future urban traffic carbon emissions and can be used to formulate urban traffic carbon neutrality targets and carry out traffic carbon reduction planning under these targets. This breaks the limitation of related technologies that can only calculate based on existing carbon emission data, and can provide quantitative reference for future policy formulation. Moreover, the approach of introducing and adjusting influencing factor parameters has higher prediction accuracy than the approach of predicting future urban traffic carbon emissions based solely on historical urban traffic carbon emission data.

[0104] Furthermore, this application, based primarily on macroscopic calculations using the product of the number of motor vehicles of different models and carbon emission factors, also incorporates factors such as the future number of motor vehicles, the future electrification rate of motor vehicles, the direct emission intensity of motor vehicles, and the electricity emission intensity of motor vehicles, thereby accurately predicting future trends in urban traffic carbon emissions.

[0105] The above description exemplarily illustrates a method for predicting urban traffic carbon emissions according to embodiments of the present invention. Exemplarily, the method for predicting urban traffic carbon emissions according to embodiments of the present invention can be implemented in a device, apparatus, or system having a memory and a processor.

[0106] Furthermore, the urban traffic carbon emission prediction method according to embodiments of the present invention can be easily deployed on local terminals such as smartphones, tablets, and desktop computers. Alternatively, the urban traffic carbon emission prediction method according to embodiments of the present invention can also be deployed on a server (or in the cloud). Alternatively, the urban traffic carbon emission prediction method according to embodiments of the present invention can also be distributed and deployed on a server (or in the cloud) and at local terminals.

[0107] Figure 4 A schematic block diagram of an urban transportation carbon emission prediction device according to an embodiment of the present invention is shown. Figure 4As shown, the urban traffic carbon emission prediction device 400 according to an embodiment of the present invention includes a prediction model construction module 410, a historical data acquisition module 420, an impact factor determination module 430, a carbon emission prediction value determination module 440, and a judgment module 450. The prediction model construction module 410 is used to construct an urban traffic carbon emission prediction model; the historical data acquisition module 420 is used to acquire historical urban traffic carbon emission related data; the impact factor determination module 430 is used to determine a first type of urban traffic carbon emission impact factor based on preset impact factor parameters and the historical urban traffic carbon emission related data; the carbon emission prediction value determination module 440 is used to determine a future urban traffic carbon emission prediction value based on the first type of urban traffic carbon emission impact factor through the urban traffic carbon emission prediction model; the judgment module 450 is used to determine whether the future urban traffic carbon emission prediction value matches a preset urban traffic carbon emission target value, and if they do not match, adjust the impact factor parameters and the urban traffic carbon emission target value so that the future urban traffic carbon emission prediction value obtained based on the adjusted impact factor parameters matches the adjusted urban traffic carbon emission target value.

[0108] Among them, the prediction model construction module 410, historical data acquisition module 420, impact factor determination module 430, carbon emission prediction value determination module 440, and judgment module 450 can be composed of... Figure 1 The processor 102 in the illustrated electronic device 100 executes program instructions stored in the memory 104 to implement and perform corresponding steps in the urban traffic carbon emission prediction method 200 according to an embodiment of the present invention. The following description focuses on the main functions of each module of the urban traffic carbon emission prediction device, omitting details already described above.

[0109] In this embodiment of the invention, the cities that need to predict and plan transportation carbon emissions are first identified. After identifying the relevant cities, a city transportation carbon emission prediction model is constructed for each city. Based on the constructed city transportation carbon emission prediction model, the city's transportation carbon emission values ​​for a future year, month, or other time period can be predicted.

[0110] Specifically, urban traffic carbon emission prediction models can be constructed in various ways, such as based on urban traffic conditions, vehicle type and combustion type, and traffic pollution type. Taking the construction of an urban traffic carbon emission prediction model based on traffic type as an example, it is necessary to classify the city's traffic types, and then construct the urban traffic carbon emission prediction model based on the classification results. For example, urban traffic types include public transportation and non-public transportation.

[0111] In this embodiment of the invention, urban traffic carbon emission-related data includes a series of data related to traffic carbon emissions, such as the number of vehicles, mileage, energy consumption, and carbon emissions. By acquiring this data, future urban traffic carbon emissions can be predicted. It should be noted that "historical" here refers to any historical time period, such as a certain year or month in the past.

[0112] In this embodiment of the invention, the first type of urban traffic carbon emission influencing factor is calculated using both influencing factor parameters and historical urban traffic carbon emission data. When predicting future urban traffic carbon emissions solely based on historical data, accuracy is only guaranteed if the future trend aligns with past trends. When the future trend deviates from past trends, the predicted value often differs significantly from the actual value. Therefore, predicting future urban traffic carbon emissions based solely on historical data has significant limitations. By setting influencing factor parameters, the trend obtained from predictions based solely on historical data can be corrected, thereby ensuring that the predicted value matches the actual value as closely as possible.

[0113] The influencing factor parameters are related to the historical urban traffic carbon emission data obtained, as well as the first category of urban traffic carbon emission influencing factors that need to be determined. For example, when the historical urban traffic carbon emission data is the historical proportion of electric vehicles, and the first category of urban traffic carbon emission influencing factors is the future proportion of electric vehicles, then the influencing factor parameters can be the final value of the proportion of electric vehicles when urban traffic carbon emissions reach the target value.

[0114] The historical proportion of electric vehicles can be determined based on the number of electric vehicles and non-electric vehicles in the historical vehicle fleet, and the final value of the vehicle electrification proportion is preset. In one example, determining the first type of urban traffic carbon emission influencing factor based on preset influencing factor parameters and the historical urban traffic carbon emission related data includes: determining the historical proportion of electric vehicles based on the number of electric vehicles and non-electric vehicles in the historical vehicle fleet; and determining the future proportion of electric vehicles based on the historical proportion of electric vehicles and the final value of the vehicle electrification proportion.

[0115] Specifically, the historical rate of electric vehicle ownership can be calculated using the following formula:

[0116]

[0117] in, The percentage of electric vehicles that represents history, Q EVThe number of electric vehicles among historical motor vehicles, Q FV This represents the number of non-electric vehicles in historical vehicle data. Taking the calculation of a specific type of vehicle in a past year as an example, first, obtain historical urban traffic carbon emission data, i.e., the number of electric and non-electric vehicles of that type in that year. Then, substitute the numbers of electric and non-electric vehicles into the formula above to obtain the electrification rate of that type of vehicle in that year. Similarly, the above formula can be used to calculate the electrification rate of that type of vehicle in any other time period, such as a past month. By calculating the historical electrification rate of all types of vehicles, the historical vehicle electrification rate in the city can be determined. Alternatively, one can directly obtain the number of electric and non-electric vehicles in all types of vehicles, substitute them into the above formula, and thus obtain the historical vehicle electrification rate in the city.

[0118] After calculating the historical proportion of electric vehicles Afterwards, according to The future electric vehicle electrification rate can be obtained by substituting the pre-set final value of the electric vehicle electrification rate into the corresponding calculation model. In one example, determining the future electric vehicle electrification rate based on the historical electric vehicle electrification rate and the final value of the electric vehicle electrification rate includes: selecting an S-curve model; and calculating the future electric vehicle electrification rate using the S-curve model based on the historical electric vehicle electrification rate and the final value of the electric vehicle electrification rate. In this example, an S-curve is used to simulate the electric vehicle proportion growth curve, thereby predicting the future electric vehicle electrification rate.

[0119] Several S-shaped curve models are available for selection, such as the Logistic curve model (logistic cumulative distribution curve model), the Gompertz curve model, and the Weibull curve model (Weibull cumulative distribution curve model). Taking the Logistic curve model as an example, the formula for calculating the future electrification rate of motor vehicles is as follows:

[0120]

[0121] in, Represents the future proportion of electric vehicles. The final value representing the proportion of electric vehicles, n represents the number of years since the city acquired its first electric vehicle, and γ represents the slope of the curve.

[0122] The slope γ of the curve can be determined by substituting historical figures for the electrification rate of motor vehicles. The result is obtained by fitting the data to the number of years n0 since the city acquired its first electric vehicle, i.e.:

[0123]

[0124] In this embodiment of the invention, the predicted value of future urban traffic carbon emissions is obtained by substituting a first type of urban traffic carbon emission influencing factor into the urban traffic carbon emission prediction model. When the first type of urban traffic carbon emission influencing factor is the future proportion of electric vehicles, the future proportion of electric vehicles is used as a calculation factor in the urban traffic carbon emission prediction model, thereby predicting the future urban traffic carbon emissions based on the future proportion of electric vehicles.

[0125] In addition, a second type of urban traffic carbon emission influencing factor related to the first type of urban traffic carbon emission influencing factor can be determined, and the first and second types of urban traffic carbon emission influencing factors are used together as calculation factors and substituted into the urban traffic carbon emission prediction model. In one example, determining the future urban traffic carbon emission prediction value based on the first type of urban traffic carbon emission influencing factor through the urban traffic carbon emission prediction model includes: determining the second type of urban traffic carbon emission influencing factor, which includes at least one of the following: the direct emission intensity of motor vehicles, the electricity emission intensity of motor vehicles, and the future number of motor vehicles; and determining the urban traffic carbon emission prediction value based on the future electrification ratio of motor vehicles and the second type of urban traffic carbon emission influencing factor through the urban traffic carbon emission prediction model.

[0126] The second category of urban transportation carbon emission influencing factors is also related to historical urban transportation carbon emission data. Therefore, the direct emission intensity of motor vehicles, the electricity emission intensity of motor vehicles, and the future number of motor vehicles can be obtained by calculating the historical urban transportation carbon emission data obtained from the historical data acquisition module 420.

[0127] In some implementations, when the second category of urban traffic carbon emission influencing factors includes the future number of motor vehicles, historical data on urban traffic carbon emissions, such as the historical number of motor vehicles, historical automobile consumption capacity, inflation index, historical average automobile price, economic growth forecast rate, and historical per capita GDP, can be obtained from the historical data acquisition module 420. The future number of motor vehicles can be calculated based on the obtained historical data on urban traffic carbon emissions, thereby determining the second category of urban traffic carbon emission influencing factors.

[0128] When the type of motor vehicle is a public vehicle such as a bus, taxi, or truck, since the number of public vehicles in the city is relatively stable, it can be assumed that the number of such vehicles remains unchanged. That is, the number of motor vehicles in the future is equal to the number of motor vehicles in the past. Here, the number of motor vehicles in the future and the number of motor vehicles in the past refer to the number of public vehicles.

[0129] When the type of motor vehicle is a private car, the number of private cars will vary depending on various influencing factors such as car prices and GDP per capita. Therefore, it is necessary to combine historical data on the number of motor vehicles and other relevant data to predict the future number of motor vehicles.

[0130] For example, determining the future number of motor vehicles based on historical vehicle numbers includes: confirming future car purchasing power; calculating a regression constant based on the historical vehicle numbers and historical car purchasing power; and calculating the future number of motor vehicles based on the regression constant and the future car purchasing power. In this example, a univariate linear regression method is used to predict the future number of motor vehicles, where the historical vehicle numbers refer to the number of private cars. In the process of confirming the future number of motor vehicles, the steps of confirming future car purchasing power and calculating the regression constant can be performed sequentially or simultaneously; no specific order is required.

[0131] First, the regression constant is obtained by fitting the historical number of motor vehicles and historical car consumption capacity values. The following calculation formula can be used:

[0132] a = Q 私家车0 / R0

[0133] Where a represents the regression constant, Q 私家车0 R0 represents the historical number of motor vehicles, and R0 represents the historical automobile purchasing power. R0 can be calculated using the historical average automobile price and the historical GDP per capita, i.e., R0 = historical GDP per capita / historical average automobile price.

[0134] After obtaining the regression constant 'a', the future number of motor vehicles is calculated using univariate linear regression based on 'a' and future car consumption capacity. The calculation formula is as follows:

[0135] Q 私家车 =aR

[0136] Among them, Q 私家车 R represents the future number of motor vehicles, and R represents the future car consumption capacity.

[0137] As for the future automobile consumption capacity value R, it can be calculated based on historical urban transportation carbon emission data such as the inflation index, historical average automobile prices, economic growth forecasts, and historical per capita GDP.

[0138] For example, determining future car purchasing power includes: determining the future average car price based on the inflation index and historical average car prices; determining the future GDP per capita based on the projected economic growth rate and historical GDP per capita; and determining car purchasing power based on the future GDP per capita and the future average car price. In this example, the average car price and GDP per capita reflect people's purchasing power for cars. In the process of determining future car purchasing power, the steps of determining the future average car price and determining the future GDP per capita can be performed sequentially or simultaneously; there is no limitation on this.

[0139] The inflation index, also known as the inflation rate, refers to the rate of increase in the general price level over a specific period (usually one year). In this example, the inflation index can be selected as the inflation forecast indicator I published by the city government. The formula for calculating the future (n years later) average car price based on the inflation forecast indicator I and historical average car prices is as follows:

[0140] The future average car price = the historical average car price × (1 + I) n

[0141] The economic growth forecast rate is a prediction of the economic growth rate over a certain period of time. It can be selected as the economic forecast indicator x released by the city government. The formula for calculating the future (n years from now) GDP per capita based on the economic forecast indicator x and historical GDP per capita is as follows:

[0142] Future GDP per capita = Historical GDP per capita × (1 + x) n

[0143] After calculating the future average car price and the future GDP per capita, the future car purchasing power value R can be further calculated using the following formula:

[0144] R = Future GDP per capita / Future average car price

[0145] In some implementations, when the motor vehicle is not an electric vehicle, the second category of urban traffic carbon emission impact factors includes the direct emission intensity of the motor vehicle. Historical data on urban traffic carbon emissions, such as the average driving distance, energy consumption per kilometer, and carbon emissions per unit of energy consumption, can be obtained from the historical data acquisition module 420. Based on the obtained data, the direct emission intensity of the motor vehicle can be calculated, thus obtaining the second category of urban traffic carbon emission impact factors. Specifically, the following formula can be used for calculation:

[0146]

[0147] in, D represents the direct emission intensity of motor vehicles. i FC represents the average distance traveled by a motor vehicle. i CEF represents the energy consumption per kilometer of a motor vehicle, while CEF represents the carbon emissions per unit of energy consumed by the motor vehicle. When the average driving distance D... i It is the average annual mileage, from which the annual direct emission intensity of motor vehicles can be calculated; when the average mileage D... i This refers to the average monthly mileage or mileage over other durations. Accordingly, the direct emission intensity of motor vehicles over a month or other durations can be calculated. Since the energy consumption per kilometer and carbon emissions per unit of energy consumption differ among different types of motor vehicles, the direct emission intensity of each type of motor vehicle can be calculated separately using the above formula, and then summarized to obtain the direct emission intensity of motor vehicles in the city.

[0148] In some implementations, when the motor vehicle is an electric vehicle, the second type of urban traffic carbon emission influencing factor includes the motor vehicle's electricity consumption emission intensity. Historical data related to urban traffic carbon emissions, such as the trend of changes in carbon emissions per unit of electricity consumption, historical carbon emissions per unit of electricity consumption, average driving distance, and energy consumption per kilometer of the motor vehicle, can be obtained from the historical data acquisition module 420. Based on the obtained trend of changes in carbon emissions per unit of electricity consumption, historical carbon emissions per unit of electricity consumption, average driving distance, and energy consumption per kilometer of the motor vehicle, the motor vehicle's electricity consumption emission intensity can be calculated, thereby obtaining the second type of urban traffic carbon emission influencing factor.

[0149] Specifically, the future carbon emissions per unit of electricity consumption of motor vehicles are determined first based on the changing trend of carbon emissions per unit of electricity consumption of motor vehicles and the historical carbon emissions per unit of electricity consumption of motor vehicles. The carbon emissions per unit of electricity consumption change with adjustments in the energy structure. Taking China's energy structure adjustment as an example, the changing trend of carbon emissions per unit of electricity consumption of motor vehicles can be assumed to be a decrease of 2.4% annually. The formula for calculating the carbon emissions per unit of electricity consumption of motor vehicles in the future (n years from now) is as follows:

[0150] CEE = CEE' × (1 - 2.4%) n

[0151] CEE' represents the carbon emissions per unit of electricity consumption of historical motor vehicles, published by the national or local government, while CEE represents the carbon emissions per unit of electricity consumption of future motor vehicles.

[0152] Then, the energy consumption intensity of motor vehicles is calculated based on the average driving distance, energy consumption per kilometer, and future carbon emissions per unit of electricity consumption. The calculation formula is as follows:

[0153]

[0154] in, D represents the intensity of electrical emissions from motor vehicles. i EC represents the average distance traveled by a motor vehicle. i This represents the energy consumption per kilometer of a motor vehicle. When the average distance traveled is D... i The average annual mileage can be used to calculate the annual electricity emission intensity of motor vehicles; when the average mileage D... i This refers to the average monthly mileage or mileage over other durations. Accordingly, the monthly or other duration-based energy consumption emission intensity of motor vehicles can be calculated. Since the energy consumption per kilometer and carbon emissions per unit of electricity consumption differ among different types of motor vehicles, the energy consumption emission intensity of each type of motor vehicle can be calculated separately using the above formula, and then summarized to obtain the energy consumption emission intensity of motor vehicles in the city.

[0155] Once the second category of urban transportation carbon emission influencing factors is determined, the predicted value of future urban transportation carbon emissions can be calculated using an urban transportation carbon emission prediction model based on the future proportion of electric vehicles and the determined second category of urban transportation carbon emission influencing factors.

[0156] In one example, determining the predicted urban traffic carbon emissions based on the future electric vehicle electrification rate and the second type of urban traffic carbon emission influencing factor using the urban traffic carbon emission prediction model includes: calculating the carbon emissions of electric vehicles based on the future electric vehicle electrification rate, the electricity emission intensity of the vehicles, and the future number of vehicles; calculating the carbon emissions of non-electric vehicles based on the future electric vehicle electrification rate, the direct emission intensity of the vehicles, and the future number of vehicles; and calculating the predicted urban traffic carbon emissions based on the carbon emissions of electric vehicles and the carbon emissions of non-electric vehicles. In this example, the predicted urban traffic carbon emissions are obtained by calculating the future carbon emissions of electric vehicles and non-electric vehicles separately, and then summing them up. During the process of determining the predicted urban traffic carbon emissions, the steps of calculating the carbon emissions of electric vehicles and calculating the carbon emissions of non-electric vehicles can be performed sequentially or simultaneously, without limitation.

[0157] Specifically, the formula for calculating the predicted urban transportation carbon emissions (n ​​years from now) based on the carbon emissions of electric vehicles and non-electric vehicles is as follows:

[0158]

[0159] Here, GHG represents the projected future carbon emissions from urban transportation. Q represents the future proportion of electric vehicles. i The future number of motor vehicles can be obtained by summing the future number of public vehicles and the future number of private vehicles. Represents the direct emission intensity of motor vehicles. This represents the intensity of electricity emissions from motor vehicles.

[0160] In this embodiment of the invention, by adjusting the influencing factor parameters and the target value of urban traffic carbon emissions until the calculated predicted value of urban traffic carbon emissions matches the target value of urban traffic carbon emissions, a quantitative target for urban traffic carbon neutrality can be specified based on the calculated predicted value of urban traffic carbon emissions, and a carbon reduction plan for urban traffic can be further subdivided based on the target value of urban traffic carbon emissions and the influencing factors.

[0161] In one example, when the predicted value of future urban traffic carbon emissions does not match the preset target value of urban traffic carbon emissions, the influencing factor determination module 430, the carbon emission prediction value determination module 440, and the judgment module 450 run repeatedly. That is, adjusting the influencing factor parameters and the target value of urban traffic carbon emissions to make the predicted value of future urban traffic carbon emissions based on the adjusted influencing factor parameters match the adjusted target value of urban traffic carbon emissions includes: determining a first type of urban traffic carbon emission influencing factor based on the adjusted influencing factor parameters and the historical urban traffic carbon emission related data; determining the predicted value of future urban traffic carbon emissions based on the first type of urban traffic carbon emission influencing factor through the urban traffic carbon emission prediction model; judging whether the predicted value of future urban traffic carbon emissions matches the adjusted target value of urban traffic carbon emissions; if not, readjusting the influencing factor parameters and the target value of urban traffic carbon emissions, and repeating the above steps until the predicted value of future urban traffic carbon emissions matches the adjusted target value of urban traffic carbon emissions.

[0162] Based on the above description, the urban traffic carbon emission prediction device according to embodiments of the present invention, by introducing influencing factor parameters, can continuously adjust the influencing factor parameters and the urban traffic carbon emission target value during the process of comparing the predicted urban traffic carbon emission value calculated by the traffic carbon emission prediction model with the target value of urban traffic carbon emission, until the predicted urban traffic carbon emission value matches the target value of urban traffic carbon emission. This achieves the prediction of future urban traffic carbon emissions and can be used to formulate urban traffic carbon neutrality targets and carry out traffic carbon reduction planning under these targets. This breaks the limitation of related technologies that can only calculate based on existing carbon emission data, and can provide quantitative reference for future policy formulation. Moreover, the approach of introducing and adjusting influencing factor parameters has higher prediction accuracy than the approach of predicting future urban traffic carbon emissions based solely on historical urban traffic carbon emission data.

[0163] Furthermore, this application, based primarily on macroscopic calculations using the product of the number of motor vehicles of different models and carbon emission factors, also incorporates factors such as the future number of motor vehicles, the future electrification rate of motor vehicles, the direct emission intensity of motor vehicles, and the electricity emission intensity of motor vehicles, thereby accurately predicting future trends in urban traffic carbon emissions.

[0164] Furthermore, those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0165] Figure 5 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown. The electronic device 500 includes a memory 510 and a processor 520.

[0166] The memory 510 stores programs for implementing corresponding steps in the urban traffic carbon emission prediction method according to embodiments of the present invention. The processor 520 is used to run the programs stored in the memory 510 to execute the corresponding steps in the urban traffic carbon emission prediction method according to embodiments of the present invention, and to implement corresponding modules in the urban traffic carbon emission prediction device according to embodiments of the present invention.

[0167] In one embodiment, when the program is run by the processor 520, the urban traffic carbon emission prediction device 500 performs the following steps: constructing an urban traffic carbon emission prediction model; acquiring historical urban traffic carbon emission related data; determining a first type of urban traffic carbon emission influencing factor based on preset influencing factor parameters and historical urban traffic carbon emission related data; determining future urban traffic carbon emission prediction values ​​based on the first type of urban traffic carbon emission influencing factor through the urban traffic carbon emission prediction model; determining whether the future urban traffic carbon emission prediction values ​​are consistent with preset urban traffic carbon emission target values, and if they are inconsistent, adjusting the influencing factor parameters and urban traffic carbon emission target values ​​so that the future urban traffic carbon emission prediction values ​​obtained based on the adjusted influencing factor parameters are consistent with the adjusted urban traffic carbon emission target values.

[0168] In one embodiment, adjusting the influencing factor parameters and the urban traffic carbon emission target value to make the future urban traffic carbon emission forecast obtained based on the adjusted influencing factor parameters consistent with the adjusted urban traffic carbon emission target value includes: determining a first type of urban traffic carbon emission influencing factor based on the adjusted influencing factor parameters and historical urban traffic carbon emission related data; determining the future urban traffic carbon emission forecast value based on the first type of urban traffic carbon emission influencing factor through an urban traffic carbon emission forecasting model; determining whether the future urban traffic carbon emission forecast value is consistent with the adjusted urban traffic carbon emission target value; if not, readjusting the influencing factor parameters and the urban traffic carbon emission target value, and repeating the above steps until the future urban traffic carbon emission forecast value is consistent with the adjusted urban traffic carbon emission target value.

[0169] In one embodiment, constructing an urban traffic carbon emission prediction model includes: classifying urban traffic types; and constructing an urban traffic carbon emission prediction model based on the classification results.

[0170] In one embodiment, the first category of urban transportation carbon emission impact factors includes the future electrification rate of motor vehicles.

[0171] In one embodiment, the influencing factor parameter includes the final value of the proportion of motor vehicle electrification when the urban transportation carbon emission target is met.

[0172] In one embodiment, determining the first type of urban traffic carbon emission influencing factor based on preset influencing factor parameters and historical urban traffic carbon emission related data includes: determining the historical motor vehicle electrification ratio based on the number of electric vehicles and non-electric vehicles in historical motor vehicles; and determining the future motor vehicle electrification ratio based on the historical motor vehicle electrification ratio and the final value of the motor vehicle electrification ratio.

[0173] In one embodiment, determining the future electric vehicle electrification ratio based on historical electric vehicle electrification ratios and the final value of the electric vehicle electrification ratio includes: selecting an S-curve model; and calculating the future electric vehicle electrification ratio using the S-curve model based on historical electric vehicle electrification ratios and the final value of the electric vehicle electrification ratio.

[0174] In one embodiment, determining the predicted value of future urban traffic carbon emissions based on a first type of urban traffic carbon emission influencing factor through an urban traffic carbon emission prediction model includes: determining a second type of urban traffic carbon emission influencing factor, wherein the second type of urban traffic carbon emission influencing factor includes at least one of the following: direct emission intensity of motor vehicles, electric emission intensity of motor vehicles, and future number of motor vehicles; and determining the predicted value of urban traffic carbon emissions based on the future electrification ratio of motor vehicles and the second type of urban traffic carbon emission influencing factor through an urban traffic carbon emission prediction model.

[0175] In one embodiment, the determination of the predicted urban traffic carbon emissions based on the future electrification rate of motor vehicles and the second category of urban traffic carbon emission influencing factors through an urban traffic carbon emission prediction model includes: calculating the carbon emissions of electric vehicles based on the future electrification rate of motor vehicles, the electricity emission intensity of motor vehicles, and the future number of motor vehicles; calculating the carbon emissions of non-electric vehicles based on the future electrification rate of motor vehicles, the direct emission intensity of motor vehicles, and the future number of motor vehicles; and calculating the predicted urban traffic carbon emissions based on the carbon emissions of electric vehicles and non-electric vehicles.

[0176] In one embodiment, when the second type of urban traffic carbon emission impact factor is the direct emission intensity of motor vehicles, determining the second type of urban traffic carbon emission impact factor includes: calculating the direct emission intensity of motor vehicles based on the average driving distance of motor vehicles, the energy consumption per kilometer of motor vehicles, and the carbon emission per unit energy consumption of motor vehicles.

[0177] In one embodiment, when the second type of urban traffic carbon emission influencing factor is the electricity consumption emission intensity of motor vehicles, determining the second type of urban traffic carbon emission influencing factor includes: determining the future unit electricity consumption carbon emission of motor vehicles based on the changing trend of unit electricity consumption carbon emission of motor vehicles and the historical unit electricity consumption carbon emission of motor vehicles; and calculating the electricity consumption emission intensity of motor vehicles based on the average driving distance of motor vehicles, the energy consumption per kilometer of motor vehicles, and the future unit electricity consumption carbon emission of motor vehicles.

[0178] In one embodiment, when the second type of urban traffic carbon emission impact factor is the future number of motor vehicles, determining the second type of urban traffic carbon emission impact factor includes: when the type of motor vehicle is a public vehicle, the future number of motor vehicles is equal to the historical number of motor vehicles; when the type of motor vehicle is a private car, the future number of motor vehicles is determined based on the historical number of motor vehicles.

[0179] In one embodiment, determining the future number of motor vehicles based on historical motor vehicle numbers includes: identifying future automobile consumption capacity; calculating a regression constant based on historical motor vehicle numbers and historical automobile consumption capacity; and calculating the future number of motor vehicles based on the regression constant and future automobile consumption capacity.

[0180] In one embodiment, determining future automobile purchasing power includes: determining the future average automobile price based on an inflation index and historical average automobile prices; determining future GDP per capita based on projected economic growth rates and historical GDP per capita; and determining automobile purchasing power based on future GDP per capita and future average automobile prices.

[0181] Furthermore, according to embodiments of the present invention, a storage medium is provided on which a computer program is stored. When executed by a computer or processor, the computer program is used to execute the urban traffic carbon emission prediction method of the present invention and to implement corresponding modules in the urban traffic carbon emission prediction device according to embodiments of the present invention. The storage medium may, for example, include a memory card of a smartphone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media; for example, one computer-readable storage medium may contain computer-readable program code for constructing an urban traffic carbon emission prediction model, and another computer-readable storage medium may contain computer-readable program code for determining a first type of urban traffic carbon emission influencing factor.

[0182] In one embodiment, the computer program, when run by a computer, can implement the various functional modules of the urban traffic carbon emission prediction device according to the embodiments of the present invention, and / or can execute the urban traffic carbon emission prediction method according to the embodiments of the present invention.

[0183] In one embodiment, when the computer program is run by a computer or processor, it causes the computer or processor to perform the following steps: constructing an urban traffic carbon emission prediction model; acquiring historical urban traffic carbon emission related data; determining a first type of urban traffic carbon emission influencing factor based on preset influencing factor parameters and historical urban traffic carbon emission related data; determining future urban traffic carbon emission prediction values ​​based on the first type of urban traffic carbon emission influencing factor through the urban traffic carbon emission prediction model; determining whether the future urban traffic carbon emission prediction values ​​are consistent with preset urban traffic carbon emission target values, and if they are inconsistent, adjusting the influencing factor parameters and urban traffic carbon emission target values ​​so that the future urban traffic carbon emission prediction values ​​obtained based on the adjusted influencing factor parameters are consistent with the adjusted urban traffic carbon emission target values.

[0184] In one embodiment, adjusting the influencing factor parameters and the urban traffic carbon emission target value to make the future urban traffic carbon emission forecast obtained based on the adjusted influencing factor parameters consistent with the adjusted urban traffic carbon emission target value includes: determining a first type of urban traffic carbon emission influencing factor based on the adjusted influencing factor parameters and historical urban traffic carbon emission related data; determining the future urban traffic carbon emission forecast value based on the first type of urban traffic carbon emission influencing factor through an urban traffic carbon emission forecasting model; determining whether the future urban traffic carbon emission forecast value is consistent with the adjusted urban traffic carbon emission target value; if not, readjusting the influencing factor parameters and the urban traffic carbon emission target value, and repeating the above steps until the future urban traffic carbon emission forecast value is consistent with the adjusted urban traffic carbon emission target value.

[0185] In one embodiment, constructing an urban traffic carbon emission prediction model includes: classifying urban traffic types; and constructing an urban traffic carbon emission prediction model based on the classification results.

[0186] In one embodiment, the first category of urban transportation carbon emission impact factors includes the future electrification rate of motor vehicles.

[0187] In one embodiment, the influencing factor parameter includes the final value of the proportion of motor vehicle electrification when the urban transportation carbon emission target is met.

[0188] In one embodiment, determining the first type of urban traffic carbon emission influencing factor based on preset influencing factor parameters and historical urban traffic carbon emission related data includes: determining the historical motor vehicle electrification ratio based on the number of electric vehicles and non-electric vehicles in historical motor vehicles; and determining the future motor vehicle electrification ratio based on the historical motor vehicle electrification ratio and the final value of the motor vehicle electrification ratio.

[0189] In one embodiment, determining the future electric vehicle electrification ratio based on historical electric vehicle electrification ratios and the final value of the electric vehicle electrification ratio includes: selecting an S-curve model; and calculating the future electric vehicle electrification ratio using the S-curve model based on historical electric vehicle electrification ratios and the final value of the electric vehicle electrification ratio.

[0190] In one embodiment, determining the predicted value of future urban traffic carbon emissions based on a first type of urban traffic carbon emission influencing factor through an urban traffic carbon emission prediction model includes: determining a second type of urban traffic carbon emission influencing factor, wherein the second type of urban traffic carbon emission influencing factor includes at least one of the following: direct emission intensity of motor vehicles, electric emission intensity of motor vehicles, and future number of motor vehicles; and determining the predicted value of urban traffic carbon emissions based on the future electrification ratio of motor vehicles and the second type of urban traffic carbon emission influencing factor through an urban traffic carbon emission prediction model.

[0191] In one embodiment, the determination of the predicted urban traffic carbon emissions based on the future electrification rate of motor vehicles and the second category of urban traffic carbon emission influencing factors through an urban traffic carbon emission prediction model includes: calculating the carbon emissions of electric vehicles based on the future electrification rate of motor vehicles, the electricity emission intensity of motor vehicles, and the future number of motor vehicles; calculating the carbon emissions of non-electric vehicles based on the future electrification rate of motor vehicles, the direct emission intensity of motor vehicles, and the future number of motor vehicles; and calculating the predicted urban traffic carbon emissions based on the carbon emissions of electric vehicles and non-electric vehicles.

[0192] In one embodiment, when the second type of urban traffic carbon emission impact factor is the direct emission intensity of motor vehicles, determining the second type of urban traffic carbon emission impact factor includes: calculating the direct emission intensity of motor vehicles based on the average driving distance of motor vehicles, the energy consumption per kilometer of motor vehicles, and the carbon emission per unit energy consumption of motor vehicles.

[0193] In one embodiment, when the second type of urban traffic carbon emission influencing factor is the electricity consumption emission intensity of motor vehicles, determining the second type of urban traffic carbon emission influencing factor includes: determining the future unit electricity consumption carbon emission of motor vehicles based on the changing trend of unit electricity consumption carbon emission of motor vehicles and the historical unit electricity consumption carbon emission of motor vehicles; and calculating the electricity consumption emission intensity of motor vehicles based on the average driving distance of motor vehicles, the energy consumption per kilometer of motor vehicles, and the future unit electricity consumption carbon emission of motor vehicles.

[0194] In one embodiment, when the second type of urban traffic carbon emission impact factor is the future number of motor vehicles, determining the second type of urban traffic carbon emission impact factor includes: when the type of motor vehicle is a public vehicle, the future number of motor vehicles is equal to the historical number of motor vehicles; when the type of motor vehicle is a private car, the future number of motor vehicles is determined based on the historical number of motor vehicles.

[0195] In one embodiment, determining the future number of motor vehicles based on historical motor vehicle numbers includes: identifying future automobile consumption capacity; calculating a regression constant based on historical motor vehicle numbers and historical automobile consumption capacity; and calculating the future number of motor vehicles based on the regression constant and future automobile consumption capacity.

[0196] In one embodiment, determining future automobile purchasing power includes: determining the future average automobile price based on an inflation index and historical average automobile prices; determining future GDP per capita based on projected economic growth rates and historical GDP per capita; and determining automobile purchasing power based on future GDP per capita and future average automobile prices.

[0197] Each module in the urban traffic carbon emission prediction device according to the present invention can be implemented by a computer program stored in a memory running by a processor of an electronic device according to the present invention, or by a computer program stored in a computer-readable storage medium of a computer program product according to the present invention being implemented by a computer running.

[0198] Furthermore, according to embodiments of the present invention, a computer program is also provided, which can be stored on a cloud or local storage medium. When this computer program is run by a computer or processor, it is used to perform corresponding steps of the urban traffic carbon emission prediction method according to embodiments of the present invention, and to implement corresponding modules in the urban traffic carbon emission prediction device according to embodiments of the present invention.

[0199] Based on the above description, the urban traffic carbon emission prediction method, apparatus, electronic device, and storage medium according to embodiments of the present invention, by introducing influencing factor parameters, can continuously adjust the influencing factor parameters and the urban traffic carbon emission target value during the process of comparing the predicted urban traffic carbon emission value calculated by the traffic carbon emission prediction model with the target value of urban traffic carbon emission, until the predicted urban traffic carbon emission value matches the target value of urban traffic carbon emission. This achieves the prediction of future urban traffic carbon emissions and can be used to formulate urban traffic carbon neutrality targets and carry out traffic carbon reduction planning under these targets. This breaks the limitation of related technologies that can only calculate based on existing carbon emission data, and can provide quantitative reference for future policy formulation. Moreover, the approach of introducing and adjusting influencing factor parameters has higher prediction accuracy than the approach of predicting future urban traffic carbon emissions based solely on historical urban traffic carbon emission data.

[0200] Furthermore, this application, based primarily on macroscopic calculations using the product of the number of motor vehicles of different models and carbon emission factors, also incorporates factors such as the future number of motor vehicles, the future electrification rate of motor vehicles, the direct emission intensity of motor vehicles, and the electricity emission intensity of motor vehicles, thereby accurately predicting future trends in urban traffic carbon emissions.

[0201] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of this application. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of this application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.

[0202] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0203] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0204] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0205] Similarly, it should be understood that, in order to streamline this application and aid in understanding one or more of the various inventive aspects, features of this application may sometimes be grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of this application. However, this approach should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with features fewer than all features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0206] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus so disclosed can be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0207] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0208] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules according to the embodiments of this application. This application can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0209] It should be noted that the above embodiments are illustrative of this application and not limiting of it, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0210] The above description is merely a specific embodiment or illustration of the embodiments of this application. The scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. The scope of protection of this application shall be determined by the scope of the claims.

Claims

1. A method for predicting carbon emissions from urban transportation, characterized in that, include: Construct a carbon emission prediction model for urban transportation; Obtain historical data related to urban transportation carbon emissions; The first type of urban traffic carbon emission impact factor is determined based on preset impact factor parameters and the historical urban traffic carbon emission related data; wherein, the impact factor parameters are related to the historical urban traffic carbon emission related data and the first type of urban traffic carbon emission impact factor; Based on the first type of urban transportation carbon emission influencing factors, the predicted value of future urban transportation carbon emissions is determined by the urban transportation carbon emission prediction model. Determine whether the predicted value of future urban traffic carbon emissions matches the preset target value of urban traffic carbon emissions. If they do not match, adjust the influencing factor parameters and the target value of urban traffic carbon emissions so that the predicted value of future urban traffic carbon emissions based on the adjusted influencing factor parameters matches the adjusted target value of urban traffic carbon emissions. The first category of urban traffic carbon emission influencing factors includes the future proportion of electric vehicles. The influencing factor parameters include the final value of the proportion of electric vehicles when the urban traffic carbon emission target value is reached. The determination of the first category of urban traffic carbon emission influencing factors based on preset influencing factor parameters and historical urban traffic carbon emission related data includes: determining the historical proportion of electric vehicles based on the number of electric vehicles and non-electric vehicles in historical motor vehicles; and determining the future proportion of electric vehicles based on the historical proportion of electric vehicles and the final value of the proportion of electric vehicles.

2. The urban transportation carbon emission prediction method as described in claim 1, characterized in that, The step of adjusting the influencing factor parameters and the urban transportation carbon emission target value to make the future urban transportation carbon emission forecast value obtained based on the adjusted influencing factor parameters consistent with the adjusted urban transportation carbon emission target value includes: The first category of urban transportation carbon emission impact factors is determined based on the adjusted impact factor parameters and the historical urban transportation carbon emission related data. Based on the first type of urban transportation carbon emission influencing factors, the predicted value of future urban transportation carbon emissions is determined by the urban transportation carbon emission prediction model. Determine whether the predicted future urban traffic carbon emissions match the adjusted target value for urban traffic carbon emissions. If not, readjust the influencing factor parameters and the target value for urban traffic carbon emissions, and repeat the above steps until the predicted future urban traffic carbon emissions match the adjusted target value for urban traffic carbon emissions.

3. The urban transportation carbon emission prediction method as described in claim 1, characterized in that, The construction of the urban transportation carbon emission prediction model includes: Classify the types of urban transportation; The urban transportation carbon emission prediction model is constructed based on the classification results.

4. The urban transportation carbon emission prediction method as described in claim 1, characterized in that, Determining the future electric vehicle ratio based on the historical electric vehicle ratio and the final value of the electric vehicle ratio includes: Select the S-curve model; The future electric vehicle electrification rate is calculated using the S-curve model based on the historical electric vehicle electrification rate and the final value of the electric vehicle electrification rate.

5. The urban transportation carbon emission prediction method as described in any one of claims 1 or 4, characterized in that, The step of determining the future urban traffic carbon emission forecast value based on the first type of urban traffic carbon emission influencing factors through the urban traffic carbon emission prediction model includes: The second category of urban transportation carbon emission influencing factors shall be determined, which shall include at least one of the following: direct emission intensity of motor vehicles, electricity emission intensity of motor vehicles, and future number of motor vehicles; Based on the future electrification rate of motor vehicles and the second type of urban traffic carbon emission influencing factors, the predicted value of urban traffic carbon emissions is determined by the urban traffic carbon emission prediction model.

6. The urban transportation carbon emission prediction method as described in claim 5, characterized in that, The process of determining the predicted urban traffic carbon emissions based on the future electrification rate of motor vehicles and the second type of urban traffic carbon emission influencing factors through the urban traffic carbon emission prediction model includes: The carbon emissions of electric vehicles are calculated based on the future electrification rate of motor vehicles, the electricity consumption and emission intensity of motor vehicles, and the future number of motor vehicles. The carbon emissions of non-electric vehicles are calculated based on the future electrification rate of motor vehicles, the direct emission intensity of motor vehicles, and the future number of motor vehicles. The predicted value of urban traffic carbon emissions is calculated based on the carbon emissions of electric vehicles and the carbon emissions of non-electric vehicles.

7. The urban transportation carbon emission prediction method as described in claim 5, characterized in that, When the second category of urban traffic carbon emission impact factors is the direct emission intensity of motor vehicles, determining the second category of urban traffic carbon emission impact factors includes: The direct emission intensity of the motor vehicle is calculated based on the vehicle's average driving distance, energy consumption per kilometer, and carbon emissions per unit of energy consumption.

8. The urban transportation carbon emission prediction method as described in claim 5, characterized in that, When the second category of urban traffic carbon emission impact factors is the electricity emission intensity of motor vehicles, determining the second category of urban traffic carbon emission impact factors includes: The future carbon emissions per unit of electricity consumption of motor vehicles will be determined based on the changing trend of carbon emissions per unit of electricity consumption of motor vehicles and the historical carbon emissions per unit of electricity consumption of motor vehicles. The energy consumption intensity of the motor vehicle is calculated based on the vehicle's average driving distance, energy consumption per kilometer, and the carbon emissions per unit of electricity consumption of the future motor vehicle.

9. The urban transportation carbon emission prediction method as described in claim 5, characterized in that, When the second type of urban traffic carbon emission impact factor is the future number of motor vehicles, determining the second type of urban traffic carbon emission impact factor includes: When the type of motor vehicle is a public vehicle, the future number of motor vehicles is equal to the historical number of motor vehicles; When the type of motor vehicle is a private car, the future number of motor vehicles is determined based on the historical number of motor vehicles.

10. The urban transportation carbon emission prediction method as described in claim 9, characterized in that, Determining the future number of motor vehicles based on historical motor vehicle numbers includes: Confirm future car purchasing power; The regression constant is calculated based on the historical number of motor vehicles and the historical automobile consumption capacity. The future number of motor vehicles is calculated based on the regression constant and the future automobile consumption capacity value.

11. The urban transportation carbon emission prediction method as described in claim 10, characterized in that, The confirmed future automobile purchasing power value includes: The future average car price is determined based on the inflation index and historical average car prices. Determine future GDP per capita based on the projected economic growth rate and historical GDP per capita. The automobile consumption capacity value is determined based on the future per capita GDP and the future average automobile price.

12. A device for predicting carbon emissions from urban transportation, characterized in that, include: The prediction model building module is used to build prediction models for urban traffic carbon emissions. The historical data acquisition module is used to acquire historical data related to urban traffic carbon emissions. The impact factor determination module is used to determine a first type of urban traffic carbon emission impact factor based on preset impact factor parameters and the historical urban traffic carbon emission related data; wherein the impact factor parameters are related to the historical urban traffic carbon emission related data and the first type of urban traffic carbon emission impact factor; The carbon emission prediction value determination module is used to determine the future urban traffic carbon emission prediction value based on the first type of urban traffic carbon emission influencing factors and the urban traffic carbon emission prediction model. The judgment module is used to determine whether the predicted value of future urban traffic carbon emissions is consistent with the preset target value of urban traffic carbon emissions, and if they are inconsistent, to adjust the influencing factor parameters and the target value of urban traffic carbon emissions so that the predicted value of future urban traffic carbon emissions obtained based on the adjusted influencing factor parameters is consistent with the adjusted target value of urban traffic carbon emissions. The first category of urban traffic carbon emission influencing factors includes the future proportion of electric vehicles. The influencing factor parameters include the final value of the proportion of electric vehicles when the urban traffic carbon emission target value is reached. The determination of the first category of urban traffic carbon emission influencing factors based on preset influencing factor parameters and historical urban traffic carbon emission related data includes: determining the historical proportion of electric vehicles based on the number of electric vehicles and non-electric vehicles in historical motor vehicles; and determining the future proportion of electric vehicles based on the historical proportion of electric vehicles and the final value of the proportion of electric vehicles.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the urban traffic carbon emission prediction method according to any one of claims 1 to 11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the urban traffic carbon emission prediction method according to any one of claims 1 to 11.

Images