Statistical management method and system for carbon emission reduction of personal green travel

Abstract

The present application belongs to the technical field of carbon emission reduction, and provides a statistical management method and system for personal green travel carbon emission reduction, comprising the following steps: S1, defining the definition of green travel, defining the boundary and caliber of green travel behavior, and determining the data source; S2, constructing a data category list based on the MECE principle; S3, establishing a preliminary personal green travel carbon emission reduction model based on the emission factor method and the data category list; S4, obtaining green travel data, and inputting data conversion model; S5, comparing with observation data based on the Monte Carlo simulation method; the method focuses on the statistical management of personal green travel carbon emission reduction, and fills the gap of current market personal carbon emission reduction measurement by defining the definition, behavior boundary and data source of green travel, providing a new idea for improving social carbon emission reduction statistics.

Description

Technical Field

[0001] This invention belongs to the field of carbon emission reduction technology, specifically a statistical management method and system for personal green travel carbon emission reduction. Background Technology

[0002] Faced with the severe challenge of global warming, governments around the world are actively seeking solutions to reduce greenhouse gas emissions. From a social perspective, with economic development, the iterative transformation of public transportation, and the emergence and vigorous development of the sharing economy, individuals are promoting carbon reduction and emission reduction in their travel.

[0003] Currently, there is not much public attention paid to the calculation of individual carbon emission reductions. Under the premise of ensuring the credibility of the data, how to calculate the carbon emission reduction of individual green travel, or the carbon emission reduction of individual green travel in a certain region or area, in order to further improve the statistics of social carbon emission reductions, is a gap in the current statistics and a challenge we are facing.

[0004] To address the problems raised in the background art, those skilled in the art have proposed a statistical management method and system for personal green travel carbon emission reduction. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a statistical management method and system for individual green travel carbon emission reduction. Taking the individual, the smallest unit of society, as the statistical object, this method and system provide insights for further improving the statistical management of social carbon emission reductions and lay a foundation for carbon trading and utilization based on these reductions. The specific solution is as follows:

[0006] Firstly, this application provides a statistical management method for carbon emission reduction of personal green travel, including the following steps:

[0007] S1. Define green travel, define the boundaries and scope of green travel behaviors, and determine the data sources;

[0008] S2. Based on the MECE principle, construct a list of data categories. The MECE principle is a basic principle for classification and categorization, ensuring that each category is independent of the others, has no overlap, and includes all possible categories without omission.

[0009] S3. Based on the emission factor method and data category list, establish a preliminary personal green travel carbon reduction model; the emission factor method is a commonly used carbon emission estimation method. Its basic principle is to estimate the carbon emissions generated by a specific activity by multiplying the amount of activity (such as driving distance, fuel consumption, etc.) by the corresponding emission factor; the emission factor refers to the amount of pollutants emitted per unit of activity under specific conditions.

[0010] S4. Obtain green travel data, input it into the data conversion model, convert the green travel data into personal travel carbon emissions, and compare it with the carbon emissions of regular travel to obtain the personal green travel carbon emission reduction.

[0011] S5. Based on the Monte Carlo simulation method, the model error is compared with the observed data to calculate the model error, and the model is corrected accordingly to determine the final personal green travel carbon emission reduction model. The Monte Carlo simulation method can be used to simulate uncertainties such as carbon emission factors from different data sources, thereby assessing the impact of these uncertainties on the model results.

[0012] S6. Based on the data collection list, obtain monthly, quarterly, or annual personal carbon emission reduction reports;

[0013] S7. Perform aggregated processing and analysis on the data to generate personal green travel carbon emission reduction data and reports based on different dimensions, including but not limited to gender, region, and time period.

[0014] Preferably, in step S1, the definition of green travel is first clarified as adopting a mode of travel with less environmental impact, aiming to save energy, improve energy efficiency, reduce pollution, and also benefit health and take efficiency into account; according to the above definition, the boundaries of green travel behavior include walking, urban public transport, and shared mobility, and the data source includes devices or software carrying personal travel data.

[0015] Preferably, in step S2, based on the MECE principle and according to the green travel behavior boundaries and data sources in S1, a data collection list is constructed, including the following:

[0016] S21. Logical Judgment Module: Establish a logical judgment module to distinguish and refine green travel categories, forming a preliminary list of data categories;

[0017] S22. Verify the logic judgment module, check and correct the output results, and form the final list of applicable data categories.

[0018] Preferably, in step S3, based on the emission factor method and the data category list of the trip in step S2, a preliminary personal green travel carbon emission reduction model is established by constructing a data conversion unit, a regular travel carbon emission unit, and a data comparison unit. This realizes the conversion of collected data into carbon emission data, constructs the carbon emission amount of equidistant regular travel, obtains the difference between the two, and presents the personal green travel carbon emission reduction result. The specific operation method is as follows:

[0019] S31. Constructing a Data Transformation Unit: Based on the fundamental carbon emission calculation formula of the emission factor method, "CO2 emissions = activity data × emission factor", the carbon dioxide emission calculation formulas for different data sources are defined as follows:

[0020] For pedestrian and shared bicycle data sources, the emission factor can be set to 0, and the carbon dioxide emission is 0. The formula is expressed as:

[0021] C W =0, C SNB =0

[0022] Among them, C W It is the carbon dioxide emissions from walking, C SNB This refers to the carbon dioxide emissions from shared bicycles. For data sources related to shared electric bicycles, the calculation formula is as follows:

[0023] C SEB =FC SEB ×β SEB ×DisT

[0024] Among them, C SEB This refers to the carbon dioxide emissions of shared electric bicycles, measured in kgCO2e; FC SEB This is electricity consumption, measured in kWh / 100km; β SEB It is the carbon emission factor of electricity, and the unit is kgCO2e / kWh; DisT is the driving distance, and the unit is km.

[0025] For data sources from shared clean energy vehicles, the calculation formula is as follows:

[0026] C SEC =FC i ×β i ×DisT

[0027] Among them, C SEC This refers to the carbon dioxide emissions of shared clean energy vehicles, measured in kgCO2e; fuel types include electricity, natural gas, hydrogen, etc., FC i This refers to fuel consumption, measured in L / 100km or kWh / 100km; β i It is the carbon emission factor of fuel, measured in kgCO2e / L or kgCO2e / kWh; DisT is the driving range, measured in km.

[0028] For carpooling and ridesharing data sources, taking M1 category vehicles as the standard, the calculation formula is as follows:

[0029]

[0030] Among them, C Scarpool This refers to the carbon dioxide emissions from carpooling / ridesharing trips, measured in kgCO2e; fuel types include gasoline, diesel, natural gas, hydrogen, and electricity, etc. (FC) iThis refers to fuel consumption, measured in L / 100km or kWh / 100km; β i This refers to the carbon emission factor of fuel, measured in kgCO2e / L or kgCO2e / kWh. When using carpooling / ridesharing services, passenger number changes during the journey are recorded in real-time by the corresponding software. Therefore, in a personal trip, the trip is segmented when the number of passengers changes to more accurately calculate individual carbon emissions (per capita). That is, the trip is divided into n segments, DisT... i It represents the mileage of the i-th segment, in km; 1 represents the vehicle driver, and N represents the mileage of the i-th segment. i It represents the number of passengers in the i-th segment of the journey;

[0031] For bus data sources, the calculation formula is as follows:

[0032]

[0033] Among them, C Bus This refers to the carbon dioxide emissions of buses, measured in kgCO2e; fuel types include gasoline, diesel, natural gas, hydrogen, and electricity, etc. (FC) i This refers to fuel consumption, measured in L / 100km or kWh / 100km; β Bus It refers to the carbon emission factor of fuel, measured in kgCO2e / L or kgCO2e / kWh; when traveling by bus, passenger number changes at different stops are recorded in real time in the corresponding software backend. Therefore, in a personal trip, the distance between every two stops is considered a segment of the trip (DisT). i The unit is km; 1 is the vehicle driver, N i It represents the number of passengers in the i-th segment of the journey.

[0034] For urban rail transit data sources, the calculation formula is as follows:

[0035]

[0036] Among them, C RailT This refers to the carbon dioxide emissions of urban rail transit, measured in kgCO2e. Fuel types include electricity, natural gas, and gasoline. Different urban rail lines use different energy structures; for the k-th rail line, the proportion of type t energy in the total energy is w. linek-t The energy consumption of type t on the k-th track line is FC. linek-t The unit is L / 100km or kWh / 100km; the carbon emission factor of energy type t for the k-th rail line is β. linek-tThe unit is kgCO2e / L or kgCO2e / kWh; when using urban rail transit, passenger number changes are recorded in real time in the corresponding software or central control system. Therefore, in the k-th rail line of an individual's journey, the distance between every two stations is taken as a segment of the journey, Dist. i The unit is km; N i It represents the total number of passengers in the carriage during the i-th segment of the journey.

[0037] S32. Constructing a Carbon Emission Module for Regular Travel: Based on the "Technical Specification for Carbon Emission Accounting of Passenger Vehicle Lifecycle" (hereinafter referred to as the "Specification") issued by China Automotive Technology and Research Center Co., Ltd. (hereinafter referred to as "CATARC") on July 29, 2021, and combined with the actual situation of the main types of vehicles used for daily personal travel, a carbon emission calculation formula for regular travel is constructed as follows:

[0038] The carbon emission calculation formula for gasoline and diesel M1 category vehicles, non-plug-in hybrid passenger vehicles, and pure electric passenger vehicles is as follows:

[0039] C Pure =FC i ×β i ×DisT

[0040] Among them, C Pure It is the carbon dioxide emissions from fuel production, measured in kgCO2e; FC i This refers to fuel consumption, expressed in L / 100km or kWh / 100km; β i It is the carbon emission factor, with units of kgCO2e / L or kgCO2e / kWh; DisT is the driving distance, with units of km. It should be noted that DisT here refers to personal green travel distance.

[0041] S33. Construct a data comparison unit: Calculate the difference between the carbon dioxide emissions obtained from the data conversion unit in step S31 and the carbon dioxide emissions obtained from the regular travel carbon emission unit in step S32. If the result is less than zero, carbon emissions have decreased; if the result is greater than or equal to zero, carbon emissions have not decreased. The calculation formula is as follows:

[0042] ΔC=C i -C Pure

[0043] Among them, C i The carbon dioxide emissions calculated for the i-th data source.

[0044] Preferably, in step S4, a data source is accessed through a preset interface or medium to obtain the activity data corresponding to each data source, and then transmitted to the data conversion model to obtain the emission reduction of personal green travel. The data source includes online data sources and offline data sources, and the activity data includes online data and offline data. Accessing the data source: Various data sources are accessed through a preset interface or medium. These data sources include devices or software carrying personal travel data, such as online data sources like city bus APP, shared mobility APP, map APP, fitness APP, etc., and offline data sources like manual information filling ports. Activity data collection: Corresponding activity data is obtained from the accessed data sources. This activity data includes online data and offline data. Online data includes mobile phone step count data, shared mobility data, map route navigation data, payment APP data, etc.; offline data includes offline exercise information filled in, etc.

[0045] Secondly, this application provides a statistical management system for carbon emission reduction of personal green travel, applicable to the aforementioned statistical management method for carbon emission reduction of personal green travel, including:

[0046] The statistical analysis module is used to perform statistical analysis on the acquired data according to different data sources and obtain statistical results.

[0047] The report generation module is used to generate personal green travel carbon emission reduction data and reports based on statistical results, including but not limited to data on gender, region, time period, and other dimensions.

[0048] Thirdly, this application provides an electronic device applicable to the statistical management method for carbon emission reduction of personal green travel described above, including:

[0049] Memory is used to protect computer programs and data;

[0050] A processor is used to run system programs.

[0051] Fourthly, this application provides a computer storage medium applicable to the aforementioned statistical management method for carbon emission reduction in personal green travel.

[0052] Preferably, the system is suitable for data statistics in various ways and dimensions, and can be continuously optimized according to social development and technological advancements to ensure data accuracy.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. This invention focuses on personal green travel, clarifies the methods of personal green travel, defines the data sources and data lists for green travel, accurately acquires and transforms data, calculates the carbon emission reduction of personal green travel by comparing with conventional travel methods, presents a useful report, and fills a market gap.

[0055] 2. This invention focuses on individual green travel, constructing a statistical management method and system for carbon emission reduction of individual green travel. Combining existing data and technology, it forms a complete and independent system, showcasing the effectiveness of social carbon emission reduction from the perspective of individual green travel, helping to improve the statistics of social carbon emission reduction, and providing a certain foundation for future carbon trading caused by carbon emission reduction.

[0056] 3. As technology advances and advancements occur, carbon emission factors may change. The statistical management method and system of this invention employ dynamic data and include a carbon emission factor correction system, which will dynamically adjust accordingly to maintain the accuracy and stability of the statistical management method and system of this invention. Attached Figure Description

[0057] Figure 1 This is a flowchart of the statistical management method for personal green travel carbon emission reduction according to the present invention;

[0058] Figure 2 This is a data category list structure diagram of the present invention;

[0059] Figure 3 This is a flowchart of the logic judgment module of the present invention;

[0060] Figure 4 This is a flowchart of the personal green travel carbon reduction model of the present invention;

[0061] Figure 5 This is a flowchart of the statistical management system for personal green travel carbon reduction according to the present invention. Detailed Implementation

[0062] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0063] like Figure 1 As shown:

[0064] Example 1: This invention provides a statistical management method for carbon emission reduction of personal green travel, including the following steps:

[0065] S1. Define green travel clearly, define the boundaries and scope of green travel behaviors, and determine the data sources;

[0066] S2. Based on the MECE principle, construct a list of data categories;

[0067] S3. Based on the emission factor method and data category list, establish a preliminary carbon emission reduction model for personal green travel;

[0068] S4. Obtain green travel data, input it into the data conversion model, convert the green travel data into personal travel carbon emissions, and compare it with the carbon emissions of regular travel to obtain the personal green travel carbon emission reduction.

[0069] S5. Based on the Monte Carlo simulation method, compare with the observation data, calculate the model error, correct the model accordingly, and determine the final personal green travel carbon emission reduction model;

[0070] S6. Based on the data collection list, obtain monthly, quarterly, or annual personal carbon emission reduction reports.

[0071] Also includes:

[0072] S7. Perform aggregated processing and analysis on the data to generate personal green travel carbon emission reduction data and reports based on various dimensions, including but not limited to gender, region, and time period. The aggregated processing and analysis includes cleaning the collected raw data to remove duplicate, erroneous, or invalid data, ensuring the accuracy and reliability of the data. The cleaning process uses zero-mean normalization to process the raw data, and the formula is as follows:

[0073]

[0074] in, This represents the standardized data, where X represents the original data. δ is the mean of the original data, and δ is the standard deviation of the original data.

[0075] In one embodiment of the present invention, in step S1, the definition of green travel is first clarified as adopting a mode of transportation with minimal environmental impact, aiming to save energy, improve energy efficiency, reduce pollution, and also benefit health and efficiency. According to the above definition, the boundaries of green travel behavior include walking, urban public transportation, and shared mobility, such as... Figure 2 As shown, the data source includes devices or software that carry personal travel data.

[0076] In one embodiment of the present invention, in step S2, based on the MECE principle, and according to the green travel behavior boundaries and data source information in S1, a data collection list is constructed, including the following:

[0077] S21. Logical Judgment Module: Establish a logical judgment module to differentiate and refine green travel categories, forming a preliminary list of data categories, such as... Figure 3 As shown;

[0078] S22. Verify the logic judgment module, check and correct the output results, and form the final list of applicable data categories.

[0079] In one embodiment of the present invention, in step S3, based on the emission factor method, and based on the data category list of the journey in step S2, such as... Figure 4 As shown, by constructing a data conversion unit, a regular travel carbon emission unit, and a data comparison unit, a preliminary personal green travel carbon reduction model is established. This model converts collected data into carbon emission data, constructs equidistant regular travel carbon emission data, obtains the difference between the two, and presents the personal green travel carbon reduction results. The specific operation method is as follows:

[0080] S31. Constructing a Data Transformation Unit: Based on the fundamental carbon emission calculation formula of the emission factor method, "CO2 emissions = activity data × emission factor," this document defines carbon dioxide emission calculation formulas for different data sources. Using the fundamental formula of the emission factor method, "CO2 emissions = activity data × emission factor," the algorithm in this document can accurately convert individual travel activities into corresponding carbon emissions, providing a scientific basis for calculating emission reductions. Details are as follows:

[0081] For pedestrian and shared bicycle data sources, the emission factor can be set to 0, and the carbon dioxide emission is 0. The formula is expressed as:

[0082] C W =0, C SNB =0

[0083] Among them, C W It is the carbon dioxide emissions from walking, C SNB This refers to the carbon dioxide emissions from shared bicycles. For data sources related to shared electric bicycles, the calculation formula is as follows:

[0084] C SEB =FC SEB ×β SEB ×DisT

[0085] Among them, C SEB This refers to the carbon dioxide emissions of shared electric bicycles, measured in kgCO2e; FC SEB This is electricity consumption, measured in kWh / 100km; β SEB It is the carbon emission factor of electricity, and the unit is kgCO2e / kWh; DisT is the driving distance, and the unit is km.

[0086] For data sources from shared clean energy vehicles, the calculation formula is as follows:

[0087] C SEC =FC i ×β i ×DisT

[0088] Among them, C SECThis refers to the carbon dioxide emissions of shared clean energy vehicles, measured in kgCO2e; fuel types include electricity, natural gas, hydrogen, etc., FC i This refers to fuel consumption, measured in L / 100km or kWh / 100km; β i It is the carbon emission factor of fuel, measured in kgCO2e / L or kgCO2e / kWh; DisT is the driving range, measured in km.

[0089] For carpooling and ridesharing data sources, taking M1 category vehicles as the standard, the calculation formula is as follows:

[0090]

[0091] Among them, C Scarpool This refers to the carbon dioxide emissions from carpooling / ridesharing trips, measured in kgCO2e; fuel types include gasoline, diesel, natural gas, hydrogen, and electricity, etc. (FC) i This refers to fuel consumption, measured in L / 100km or kWh / 100km; β i This refers to the carbon emission factor of fuel, measured in kgCO2e / L or kgCO2e / kWh. When using carpooling / ridesharing services, passenger number changes during the journey are recorded in real-time by the corresponding software. Therefore, in a personal trip, the trip is segmented when the number of passengers changes to more accurately calculate individual carbon emissions (per capita). That is, the trip is divided into n segments, DisT... i It represents the mileage of the i-th segment, in km; 1 represents the vehicle driver, and N represents the mileage of the i-th segment. i It represents the number of passengers in the i-th segment of the journey;

[0092] For bus data sources, the calculation formula is as follows:

[0093]

[0094] Among them, C Bus This refers to the carbon dioxide emissions of buses, measured in kgCO2e; fuel types include gasoline, diesel, natural gas, hydrogen, and electricity, etc. (FC) i This refers to fuel consumption, measured in L / 100km or kWh / 100km; β Bus It refers to the carbon emission factor of fuel, measured in kgCO2e / L or kgCO2e / kWh; when traveling by bus, passenger number changes at different stops are recorded in real time in the corresponding software backend. Therefore, in a personal trip, the distance between every two stops is considered a segment of the trip (DisT). i The unit is km; 1 is the vehicle driver, N i It represents the number of passengers in the i-th segment of the journey.

[0095] For urban rail transit data sources, the calculation formula is as follows:

[0096]

[0097] Among them, C RailT This refers to the carbon dioxide emissions of urban rail transit, expressed in kgCO2e. Fuel types include electricity, natural gas, and gasoline. Different urban rail lines use different energy structures; for the k-th rail line, the proportion of type t energy in the total energy consumption is... The energy consumption of type t on the k-th track line is The unit is L / 100km or kWh / 100km; the carbon emission factor of energy type t for the k-th rail line is... The unit is kgCO2e / L or kgCO2e / kWh; when using urban rail transit, passenger number changes are recorded in real time in the corresponding software or central control system. Therefore, in the k-th rail line of an individual's journey, the distance between every two stations is taken as a segment of the journey, Dist. i The unit is km; N i It represents the total number of passengers in the carriage during the i-th segment of the journey.

[0098] S32. Constructing a Carbon Emission Unit for Regular Travel: Based on the "Specification" issued by the China Automotive Technology and Research Center (CATARC) on July 29, 2021, and considering the actual situation of daily personal travel primarily using automobiles, a carbon emission calculation formula for regular travel is constructed as follows:

[0099] The carbon emission calculation formula for gasoline and diesel M1 category vehicles, non-plug-in hybrid passenger vehicles, and pure electric passenger vehicles is as follows:

[0100] C Pure =FC i ×β i ×DisT

[0101] Among them, C Pure It is the carbon dioxide emissions from fuel production, measured in kgCO2e; FC i This refers to fuel consumption, expressed in L / 100km or kWh / 100km; β i It is the carbon emission factor, with units of kgCO2e / L or kgCO2e / kWh; DisT is the driving distance, with units of km. It should be noted that DisT here refers to personal green travel distance.

[0102] S33. Construct a data comparison unit: Calculate the difference between the carbon dioxide emissions obtained from the data conversion unit in step S31 and the carbon dioxide emissions obtained from the regular travel carbon emission unit in step S32. If the result is less than zero, carbon emissions have decreased; if the result is greater than or equal to zero, carbon emissions have not decreased. The calculation formula is as follows:

[0103] ΔC=C i -C Pure

[0104] Among them, C i The carbon dioxide emissions calculated for the i-th data source.

[0105] In one embodiment of the present invention, in step S4, a data source is accessed through a preset interface or medium to obtain the activity data corresponding to each data source, and the data is transmitted to the data conversion model to obtain the emission reduction of personal green travel; the data source includes online data source and offline data source, such as urban public transport APP, shared travel APP, map APP, fitness APP, etc., and offline data source such as manually filled information portal, etc.; the activity data includes online data and offline data, such as mobile phone step count data, shared travel data, map route navigation data, payment APP data, etc., and offline data such as offline exercise information filled in, etc.

[0106] In one embodiment of the present invention, in step S5, the Monte Carlo simulation method is used to simulate the carbon emission factor β of different data sources to obtain the carbon emission factor β of the i-th data source at a 99% confidence level. 模拟i , will β 模拟i Replace the corresponding carbon emission factor in the model in step S3 and perform calculations, then compare it with the monitoring data to calculate the model error, and correct the model based on this error to determine the final personal green travel carbon emission reduction model; the operation of comparing the calculated data with the monitoring data, calculating the model error, and correcting the model based on this error is as follows:

[0107] S51. First, construct the generalized risk function, whose formula is as follows:

[0108]

[0109] Where S represents the anomaly risk probability of the model, R(h) represents the generalization risk, and R S (h) represents empirical risk, d represents model dimension, m represents the number of samples, e represents generalization error, and δ represents any constant.

[0110] S52. Next, determine the error range, using the following formula:

[0111]

[0112] S53. Based on the above, obtain the spatial geometric coordinates of the model data. Assume that the abnormal feature vectors in the model data are represented by (w q1 ,w q2 ,...w qj ) represents the number of outlier clusters in the model data, j represents the number of outlier clusters, and w represents the number of outlier clusters in the model data. q1 The similarity expression between anomaly clusters in the vector space model represents the proportion of the j-th anomaly cluster in the model's data database.

[0113]

[0114] Among them, w ij Indicates the frequency of outlier clusters in the model data;

[0115] S54. Finally, the model is modified by setting probabilities to determine the final personal green travel carbon reduction model.

[0116] As can be seen from the above, the Monte Carlo simulation method can be used to estimate carbon emission factors more accurately, and the model error can be calculated and corrected by combining observation data, thereby significantly improving the accuracy of personal green travel carbon emission reduction models.

[0117] In summary, the present invention has the following beneficial effects:

[0118] (1) Filling the market gap: This method focuses on the statistical management of carbon emission reduction of personal green travel. By clarifying the definition, behavioral boundaries and data sources of green travel, it fills the current market gap in the measurement of personal carbon emission reduction and provides a new idea for improving the statistics of social carbon emission reduction.

[0119] (2) Contribute to social carbon emission reduction: By constructing a personal green travel carbon emission reduction model and generating a personal carbon emission reduction report, this method helps to demonstrate the emission reduction effect of individuals in green travel, thereby encouraging more people to adopt green travel methods and contributing to the achievement of the overall social carbon emission reduction goal.

[0120] (3) Providing a data foundation: This method constructs a data category list based on the MECE principle to ensure the comprehensiveness and completeness of the data, providing a solid data foundation for subsequent data analysis and report generation. At the same time, the design of dynamically adjusting the carbon emission factor ensures the accuracy and stability of the data.

[0121] (4) Multi-dimensional display of emission reduction effect: By collecting and analyzing the data, personal green travel carbon emission reduction data and reports are generated in different dimensions such as gender, region, and time period. This method can display the emission reduction effect of personal green travel in a multi-dimensional and comprehensive manner, providing strong support for policy formulation and public education.

[0122] (5) Promote the foundation of carbon trading: With the continuous development of the carbon market, the carbon emission reduction data generated by this method can also provide a certain foundation for future carbon trading, and promote the prosperity and development of the carbon market.

[0123] (6) Dynamic adaptability and stability: Considering that carbon emission factors may change due to technological development and advancement, this method uses dynamic data and includes a carbon emission factor correction system, which can be dynamically adjusted to maintain the accuracy and stability of the method.

[0124] like Figure 5 As shown:

[0125] Example 2: This application provides a statistical management system for carbon emission reduction of personal green travel, applicable to the statistical management method for carbon emission reduction of personal green travel described above, including a statistical analysis module and a report generation module; the statistical analysis module and the report generation module are electrically connected in sequence;

[0126] The statistical analysis module is used to perform statistical analysis on the acquired data according to different data sources and obtain statistical results.

[0127] The report generation module is used to generate personal green travel carbon emission reduction data and reports based on statistical results, including but not limited to data on gender, region, time period, and other dimensions.

[0128] This system is suitable for data statistics in various ways and dimensions, and can be continuously optimized according to social development and technological advancements to ensure data accuracy.

[0129] This application provides an electronic device applicable to the statistical management method for personal green travel carbon emission reduction described above, including:

[0130] Memory is used to protect computer programs and data;

[0131] A processor is used to run system programs.

[0132] This application provides a computer storage medium applicable to the above-mentioned statistical management method for carbon emission reduction of personal green travel.

[0133] Those skilled in the art will understand that embodiments of this application can be provided as a system or a computer program product. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0134] This application is described with reference to flowchart illustrations and / or block diagrams of devices (systems) and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0135] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0136] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0137] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0138] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0139] Computer-readable media include both permanent and non-permanent, removable and non-removable media, which can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0140] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, article, or apparatus that includes that element.

[0141] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A statistical management method for carbon emission reduction of personal green travel, characterized in that: Includes the following steps: S1. Define green travel, define the boundaries and scope of green travel behaviors, and determine the data sources; S2. Based on the MECE principle, construct a list of data categories; S3. Based on the emission factor method and data category list, establish a preliminary carbon emission reduction model for personal green travel; S4. Obtain green travel data, input it into the data conversion model, convert the green travel data into personal travel carbon emissions, and compare it with the carbon emissions of regular travel to obtain the personal green travel carbon emission reduction. S5. Based on the Monte Carlo simulation method, compare with the observation data, calculate the model error, correct the model accordingly, and determine the final personal green travel carbon emission reduction model. S6. Based on the data collection list, obtain monthly, quarterly, or annual personal carbon emission reduction reports; S7. Perform aggregated processing and analysis on the data to generate personal green travel carbon emission reduction data and reports based on different dimensions, including but not limited to gender, region, and time period.

2. The statistical management method for carbon emission reduction of personal green travel as described in claim 1, characterized in that: In step S1, the definition of green travel is first clarified as adopting a mode of travel that has a smaller impact on the environment. According to the above definition, the boundaries of green travel behavior include walking, urban public transportation, and shared mobility, and the data source includes devices or software that carry personal travel data.

3. The statistical management method for personal green travel carbon emission reduction as described in claim 1, characterized in that: In step S2, based on the MECE principle and according to the green travel behavior boundaries and data sources in S1, a data collection list is constructed, including the following sub-steps: S21. Logical Judgment Module: Establish a logical judgment module to distinguish and refine green travel categories, forming a preliminary list of data categories; S22. Verify the logic judgment module, check and correct the output results, and form the final list of applicable data categories.

4. The statistical management method for carbon emission reduction of personal green travel as described in claim 1, characterized in that: In step S3, based on the emission factor method and the data category list of the trip in step S2, a preliminary personal green travel carbon reduction model is established by constructing a data conversion unit, a regular travel carbon emission unit, and a data comparison unit. This model converts collected data into carbon emission data, constructs equidistant regular travel carbon emission data, obtains the difference between the two, and presents the personal green travel carbon reduction result. The specific operation method is as follows: S31. Construct a data transformation unit: Based on the carbon emission calculation formula of the emission factor method, "CO2 emissions = activity data × emission factor", define carbon dioxide emission calculation formulas for different data sources; S32. Constructing a carbon emission unit for regular travel: Based on the "Technical Specifications for Carbon Emission Accounting of Passenger Vehicles Life Cycle" and combined with the actual situation of the main types of cars used for daily personal travel, construct a carbon emission calculation formula for regular travel; S33. Construct a data comparison unit: Calculate the difference between the carbon dioxide emissions obtained from the data conversion unit in step S31 and the carbon dioxide emissions obtained from the regular travel carbon emission unit in step S32. If the result is less than zero, the carbon emissions have decreased; if the result is greater than or equal to zero, the carbon emissions have not decreased.

5. The statistical management method for personal green travel carbon emission reduction as described in claim 1, characterized in that: In step S4, the data source is accessed through a preset interface or medium to obtain the activity data corresponding to each data source, and then transmitted to the data conversion model to obtain the emission reduction of personal green travel; the data source includes online data source and offline data source, and the activity data includes online data and offline data.

6. A statistical management system for personal green travel carbon emission reduction, applicable to the statistical management method for personal green travel carbon emission reduction as described in any one of claims 1-5, characterized in that: Includes a statistical analysis module and a report generation module; The statistical analysis module is used to perform statistical analysis on the acquired data according to different data sources and obtain statistical results. The report generation module is used to generate personal green travel carbon emission reduction data and reports based on statistical results, including but not limited to data on gender, region, time period, and other dimensions.

7. An electronic device, characterized in that, include: Memory is used to protect computer programs and data; A processor for running the statistical management method for carbon reduction of personal green travel as described in any one of claims 1-5.

8. A computer storage medium, characterized in that, The statistical management method for carbon emission reduction of personal green travel as described in any one of claims 1-5 is applicable to the aforementioned statistical management method for carbon emission reduction of personal green travel.

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