Method and device for estimating carbon emission and economic cost caused by express logistics in city

Abstract

The invention provides a method and device for estimating carbon emission and economic cost caused by express logistics in a city, and the method comprises the steps: obtaining the logistics basic data of a target city, and the logistics basic data comprise express information, vehicle information, delivery path information, and express information; calculating carbon emission and economic cost based on the logistics basic data; the carbon emission is the carbon emission generated by the vehicle in the logistics distribution process, and the economic cost is the vehicle cost, the energy consumption cost of the vehicle and the labor cost in the logistics distribution process. The invention provides a comprehensive evaluation method and a comprehensive evaluation system, which are used for estimating carbon emission and economic cost generated based on express logistics operation in a city and is used for more intuitively quantifying the carbon emission and the economic cost.

Description

Technical Field

[0001] The present invention belongs to the field of material transportation, and in particular relates to a method and a device for estimating carbon emissions and economic costs caused by express logistics within a city. Background Art

[0002] With the explosive growth of e-commerce and urban logistics demand in the past decade, intra-city express logistics has become an indispensable part of modern urban life. The related carbon emission issues are also becoming increasingly prominent. Existing carbon emission inventories mainly focus on emissions from industries such as industry, power generation, construction and transportation, among which heavy freight vehicles and urban passenger cars are often the focus of research. However, carbon emissions from intra-city express logistics, especially carbon emissions in the "last mile" delivery link, have been completely ignored. This has led to a lack of targeted policy making and emission management, and has also caused serious deviations in the estimation of the carbon footprint of intra-city logistics.

[0003] In the urban logistics system, most express delivery vehicles still use traditional internal combustion engine vehicles, which have relatively high carbon emissions. In addition, the high frequency and timeliness of express delivery services further increase carbon emissions. Especially in congested urban areas, frequent start-stop driving patterns are an important reason for increased carbon emissions. However, due to the lack of detailed classification of internal urban logistics in the existing carbon emission inventory, these high-emission links have not been fully quantified, resulting in their role in carbon reduction strategies not receiving due attention.

[0004] In recent years, with the development of technology, the application of electric vehicles and driverless vehicles in urban logistics has gradually been promoted. Electric vehicles are very suitable for short-distance transportation with their low carbon and low noise advantages; driverless technology can significantly reduce labor costs in terminal distribution. However, the current research on these new modes of transportation in the market mainly focuses on their technical feasibility and market promotion, and lacks systematic evaluation tools for their actual performance in reducing carbon emissions and improving economic benefits. Therefore, how to scientifically and systematically evaluate the potential carbon reduction effects and economic benefits of these new technologies in urban express delivery has become an urgent problem to be solved.

[0005] Existing emission assessment tools and management strategies fail to effectively cover carbon emissions from express logistics within cities. With the explosive growth of the logistics industry and the application of innovative technologies such as unmanned transportation, changes in related emissions and economic costs may have a significant impact on greenhouse gases and the social economy. Therefore, how to evaluate and optimize the emissions and costs of express logistics within cities through scientific methods is a gap in the current technical field. Summary of the invention

[0006] In view of the shortcomings of the prior art mentioned above, the purpose of the present invention is to provide a method and system for estimating the carbon emissions and economic costs caused by urban express logistics, so as to solve the problem of deviation in the carbon emission assessment and carbon emission optimization measures caused by urban express logistics in the prior art, and to provide a scientific evaluation method for urban express logistics to fill this gap and promote the green and low-carbon development of urban logistics systems.

[0007] To achieve the above-mentioned purpose and other related purposes, the present invention is implemented by including the following technical solutions.

[0008] In view of the fact that the existing carbon emission inventory fails to fully consider the carbon emissions of express logistics within the city, this invention constructs a comprehensive evaluation model to systematically evaluate the performance of different types of vehicles in terms of carbon emissions and economic costs throughout the entire process from warehousing to consumers, including traditional gasoline vehicles, electric vehicles and driverless vehicles.

[0009] The first aspect of the present invention provides a method for estimating carbon emissions and economic costs caused by express logistics within a city, comprising:

[0010] Obtaining basic logistics data of the target city, wherein the basic logistics data includes express delivery information, vehicle information, delivery route information, and express delivery information;

[0011] The carbon emissions and economic costs are calculated based on the basic logistics data; the carbon emissions are the carbon emissions generated by vehicles during the logistics distribution process, and the economic costs are the vehicle costs, energy consumption costs and labor costs during the logistics distribution process.

[0012] In the technical solution of the present invention, the estimation of carbon emissions and economic costs caused by express logistics within the city can be within a unit time, such as half a year, 1 year, 1 month, 1 day, etc. In this way, the carbon emissions and economic costs in different cities in the same time period can be compared, such as the comparison of carbon emissions and economic costs in the first-tier cities across the country in one year. It is also possible to compare carbon emissions and economic costs in different time periods in the same city, such as the comparison of carbon emissions and economic costs in each month of a city in one year. It is also possible to compare the carbon emission results of urban express logistics across the country for several years, such as the total carbon emissions, or proportion, and the main carbon emission cities, which can also be used to analyze the comparison of carbon emissions of different delivery methods.

[0013] In the present invention, the basic logistics data of the target city is derived from public data or data input or set according to needs, or can be an average value or set value estimated based on various commercial or official disclosures, depending on needs and actual conditions.

[0014] In one embodiment of the present invention, the logistics basic data also includes distribution method information. In a further embodiment of the present invention; the distribution method information includes any one of the following four types: method 1 is door-to-door delivery, that is, direct delivery from the warehouse to the consumer's door, mainly large and weighed packages; method 2 is self-collection cabinet combined with consumer self-collection, that is, from the warehouse to the self-collection cabinet, and then the consumer picks up the package at the self-collection cabinet, mainly for small and light packages; method 3 is post station combined with consumer self-collection, that is, from the warehouse to the post station, and then the consumer picks up the package at the post station, for a wide range of weight types of packages; method 4 is post station combined with door-to-door delivery, that is, the post station staff delivers the package to the customer's door.

[0015] In one embodiment of the present invention, the express delivery information includes the quantity information of the express delivery within a unit time period in the city, the weight information of the express delivery, and the volume information of the express delivery. The weight information and volume information of the express delivery often affect the delivery method, thereby determining the carbon emissions and economic costs under the specific formula method.

[0016] In one embodiment of the present invention, the data of the express information is derived from the China Statistical Yearbook or the express volume data input as needed. The quantity information of the express items includes the total express quantity in the city within a unit time period, the number of express items with specific weight information classified by the express logistics industry, the number of express items with specific volume information classified by the express logistics industry, etc.

[0017] In one embodiment of the present invention, the basic information of the vehicle includes the type of vehicle and the corresponding parameters. The vehicle in the present application is a type of vehicle for logistics transportation. In one embodiment of the present invention, the type of vehicle includes a traditional gasoline vehicle and an electric vehicle. In one embodiment, the type of vehicle is also an unmanned vehicle. In one embodiment of the present invention, the parameters of the vehicle include energy consumption characteristics, load capacity and service life.

[0018] In one embodiment of the present invention, the path information includes the distance of the delivery path (such as the route and kilometers). In one embodiment of the present invention, the path information may also include the urban road type of each section in the delivery path. In a preferred embodiment of the present invention, the urban road type includes one or more of main roads, secondary roads, and residential roads. The urban road type will affect the energy consumption of a specific vehicle, thereby affecting the final carbon emissions and economic costs.

[0019] In one embodiment of the present invention, the basic information further includes real-time traffic data, and the real-time traffic data includes one or more of road congestion, traffic light cycle and speed limit. Traffic data information will affect the energy consumption of a specific vehicle, thereby affecting the final carbon emissions and economic costs.

[0020] It should be further explained that the carbon emissions in this application refer to CO2 emissions.

[0021] For traditional gasoline vehicles, the present invention uses the carbon emission factor method recommended by the International Panel on Climate Change (IPCC) to calculate their emissions and makes corrections based on specific road conditions. For new electric vehicles and driverless vehicles, the present invention uses big data analysis methods, combined with real-time traffic information and energy consumption characteristics, to evaluate their carbon emissions and economic costs in different delivery scenarios.

[0022] In one embodiment of the present invention, the carbon emissions include carbon emissions from gasoline vehicles and carbon emissions from electric vehicles.

[0023] In one embodiment of the present invention, the method for calculating the carbon emissions of gasoline vehicles per unit time in the target city is:

[0024]

[0025] Among them, NDP refers to the number of packages that the oil truck needs to transport, in pieces; L refers to the load that the vehicle can carry, in kilograms; FC refers to the speed at which the engine uses fuel, in liters / kilometer; GD refers to the density of gasoline, in kilograms / liter; GEF is the carbon dioxide emission factor of gasoline, which refers to the carbon dioxide emissions corresponding to a unit weight of gasoline, in thousands of tons of carbon emissions / kilogram of gasoline; AW k It represents the average weight of a single package in a specific delivery method, in kilograms per piece. It is an empirical parameter, a parameter obtained from public data, or a set value. k is 1, 2, 3, or 4. AD is the average transportation distance, in kilometers. C G The unit is kilotonnes.

[0026] In one embodiment of the present invention, the carbon emissions of electric vehicles in a target city per unit time are calculated as follows:

[0027]

[0028] Among them, NDP refers to the number of packages that the tram needs to transport, in pieces; L refers to the load that the tram can carry, in kilograms; EEF is the carbon dioxide emission factor corresponding to electricity, which refers to the carbon dioxide emissions corresponding to unit kilowatt of electricity, in thousands of tons of carbon emissions / kilowatt-hour; AW k represents the average weight of a single package in a specific delivery method, in kilograms per package, which is an empirical parameter, a parameter obtained from public data, or a set value, and k is 1, 2, 3, or 4; AD is the average transportation distance, in kilometers; DR is the distance that the electric vehicle can travel before needing to be recharged, in kilometers; BC is the battery capacity, in kilowatt-hours; C E The unit is kilotonnes.

[0029] In one embodiment of the present invention, the carbon emissions per unit time of the target city is the sum of the carbon emissions of various delivery methods.

[0030] In an embodiment of the present invention, the economic cost includes vehicle cost, energy cost and labor cost.

[0031] In one embodiment of the present invention, the vehicle cost at least includes the vehicle purchase cost. The vehicle purchase cost includes the purchase cost of the vehicle itself and the insurance cost. In another embodiment, the vehicle cost may also include maintenance costs, and the maintenance costs include regular inspection costs and parts replacement costs.

[0032] In one embodiment of the present invention, the energy cost of a conventional gasoline vehicle depends on the fuel price and consumption. In one embodiment of the present invention, the energy cost of an electric vehicle is related to the electricity price and the electricity consumption.

[0033] In one embodiment of the present invention, the energy cost EC includes the energy cost EC of the gasoline vehicle 油 And the energy cost of trams EC electricity.

[0034] In a more specific embodiment of the present invention, the energy cost of the gasoline vehicle per unit time in the target city is EC 油 The calculation method is:

[0035]

[0036] Among them, NDP refers to the number of packages that the gasoline truck needs to transport, in pieces; L refers to the load that the vehicle can carry, in kilograms; FC refers to the speed at which the engine uses fuel, in liters / kilometer; AD is the average transportation distance, in kilometers; GP refers to the price of gasoline, in US$100,000 / kilogram.

[0037] In a more specific embodiment of the present invention, the energy cost of the electric vehicle per unit time in the target city is EC 电 The calculation method is:

[0038]

[0039] NDP refers to the number of packages that the tram needs to transport, in pieces; L refers to the load that the tram can carry, in kilograms; AW kIt represents the average weight of a single package in a specific delivery method, in kilograms per piece. It is an empirical parameter, a parameter obtained from public data, or a set value, and k is 1, 2, 3, or 4. AD is the average transportation distance, in kilometers. DR is the distance that the electric vehicle can travel before needing to be recharged, in kilometers. BC is the battery capacity, in kilowatt-hours. EP is the price of electricity, in US$100,000 per kilowatt-hour.

[0040] In one embodiment of the present invention, the purchase cost includes the purchase cost of the vehicle itself and the insurance cost. In one embodiment of the present invention, the maintenance cost includes the regular inspection cost and the parts replacement cost.

[0041] In a more specific embodiment of the present invention, the vehicle cost only includes the purchase cost.

[0042] In a specific embodiment, the vehicle cost VC is calculated as follows:

[0043]

[0044] Where n is the average daily delivery frequency. The delivery frequency of the vehicle from the warehouse to the consumer's door in delivery method 1 is 2; in delivery methods 2, 3 and 4, n is 4. PVC (Per vehicle cost) refers to the cost of purchasing a vehicle, in units of $100,000 per vehicle. k It represents the average weight of a single package in a specific delivery method, in kilograms per piece. It is an empirical parameter, a parameter obtained from public data, or a set value. k is 1, 2, 3, or 4. L is the load that the vehicle can carry, in kilograms. j refers to the number of target cities. If it is nationwide, x = 31, corresponding to the provincial data of 31 provinces and municipalities. If it is 1 city, x = 1. NDP refers to the number of packages that need to be delivered in the target city, in pieces. In addition, the cost loss is discounted at the annual average cost.

[0045] In one embodiment of the present invention, the labor cost is the courier salary. Specifically, it is the number of couriers multiplied by the average salary. The number of couriers and the average salary in the specific city or province can be obtained based on public data.

[0046] A second aspect of the present invention provides a device for estimating carbon emissions and economic costs of express logistics within a city, comprising:

[0047] A logistics basic data acquisition module for a target city, used to acquire the logistics basic data of the target city, wherein the logistics basic data includes express information, vehicle information, delivery route information and express information;

[0048] The estimation module is used to calculate the carbon emissions and economic costs based on the logistics basic data; the carbon emissions are the carbon emissions generated by vehicles during the logistics distribution process, and the economic costs are the vehicle costs, energy consumption costs and labor costs during the logistics distribution process.

[0049] In one embodiment of the present invention, the estimation module includes a fuel vehicle carbon emission calculation module, an electric vehicle carbon emission calculation module and an economic cost calculation module.

[0050] In one embodiment of the present invention, the carbon emissions calculation module of the oil vehicle is used to calculate and obtain the carbon emissions through the method described in the first aspect of the present application.

[0051] In one embodiment of the present invention, the electric vehicle carbon emissions calculation module is used to calculate and obtain the carbon emissions through the method described in the first aspect of the present application.

[0052] In one embodiment of the present invention, the economic cost calculation module is used to calculate the sum of vehicle cost, energy cost and labor cost. In one embodiment of the invention, the vehicle cost is calculated in the same manner as in the method described in the first aspect of the present application.

[0053] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the estimation methods described above.

[0054] A fourth aspect of the present invention provides a terminal, comprising: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal performs any estimation method as described above.

[0055] As described above, the method and system for estimating carbon emissions and economic costs of intra-city express logistics according to the present invention have the following beneficial effects:

[0056] This application provides a comprehensive evaluation method and system for estimating the carbon emissions and economic costs generated by express logistics operations within a city, so as to more intuitively and quantify the carbon emissions and economic costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 shows the carbon emissions and economic cost assessment framework for urban express logistics.

[0058] Figure 2 Displays shipping distance, average package weight, and ratio for different shipping methods.

[0059] Figure 3 A shows the carbon emissions of urban express delivery and its proportion in the transportation industry.

[0060] Figure 3B shows the spatial distribution of carbon emissions from express delivery in cities across provinces in China in 2021.

[0061] Figure 3 C shows the contribution of different transport modes to total emissions.

[0062] Figure 4 A shows the total carbon emissions under different pathways.

[0063] Figure 4 B shows the total economic cost under different paths.

[0064] Figure 4 C shows the carbon emissions of a single package under different pathways.

[0065] Figure 4 D shows the economic cost of a single package under different routes.

[0066] Figure 5 A shows the total carbon emissions of the four major cities under different pathways.

[0067] Figure 5 B shows the carbon emissions of a single package along different routes in four major cities.

[0068] Figure 5 C shows the contribution of stage carbon emissions under different pathways for the four major cities.

[0069] Figure 5 D shows the total economic costs of the four major cities under different paths.

[0070] Figure 5 E shows the economic cost of a single package along different routes in four major cities.

[0071] Figure 5 F shows the economic cost structure of the four major cities under different paths.

[0072] Figure 6 Shown is a schematic structural diagram of the estimation device of the present invention.

[0073] Figure 7 A schematic diagram of the structure of the terminal provided in this application. DETAILED DESCRIPTION

[0074] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0075] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0076] Figure 1A The framework diagram of the method for estimating carbon emissions and economic costs of urban express logistics in this application is shown. The method for estimating carbon emissions and economic costs of urban internal logistics in this application is shown by using basic vehicle information, express delivery volume data, and route information.

[0077] Figure 1B A schematic diagram showing a specific method for estimating carbon emissions and economic costs of intra-city express logistics in this application, including:

[0078] S1: Obtaining basic logistics data of the target city, wherein the basic logistics data includes express information, vehicle information, delivery route information and express information;

[0079] S2: Calculate the carbon emissions and economic costs based on the logistics basic data, the carbon emissions are the carbon emissions generated by vehicles during the logistics distribution process, and the economic costs are the vehicle costs, energy consumption costs and labor costs during the logistics distribution process.

[0080] In a specific embodiment, the above-mentioned specific technical solution of the present invention can estimate the carbon emissions and economic costs of express logistics within the city within a unit time, such as half a year, 1 year, 1 month, 1 day, etc. In this way, the carbon emissions and economic costs in different cities in the same time period can be compared, such as the comparison of carbon emissions and economic costs in the first-tier cities in the country in 1 year. It is also possible to compare the carbon emissions and economic costs in different time periods in the same city, such as the comparison of carbon emissions and economic costs in each month of a city in 1 year.

[0081] Specifically, the basic logistics data of the target city is derived from public data or data input or set as needed, or may be an average value or set value estimated based on various commercial or official disclosures, depending on needs and actual conditions.

[0082] In one embodiment of the present invention, the logistics basic data also includes delivery method information. Complete intra-city logistics express delivery refers to the process of delivering packages from local warehouses to consumer endpoints. Figure 2 As shown, depending on the size and weight of the package, this is mainly achieved in the following four ways:

[0083] Delivery method 1: Home delivery: Delivery directly from the warehouse to the consumer's doorstep, mainly large and heavy packages.

[0084] Delivery method 2: Self-pickup cabinet + consumer self-pickup: From the warehouse to the self-pickup cabinet, consumers can pick up the items at the self-pickup cabinet, mainly used for small and light packages.

[0085] Delivery method 3: Post office + consumer self-pickup: Delivery from the warehouse to the post office, and then the consumer picks up the package at the post office. This is the most commonly used option and is used for a wide range of packaging types. Consumers usually receive a notification via SMS indicating the exact location of their package.

[0086] Delivery method 4: Post station + door-to-door delivery: Similar to route 3, but the post station delivers the package to the customer's door.

[0087] Delivery Method 3 and Delivery Method 4 are similar, except that Delivery Method 4 uses a small vehicle (such as an electric three-wheeler) to transport the package from the delivery station to the doorstep, which is known in the delivery industry as the "last mile" or "last kilometer."

[0088] In a specific embodiment, in a city, the above delivery method parameters include average package weight, average delivery distance and package delivery percentage. Figure 2In the more specific scenario shown, when the city is Nanjing, the above delivery mode parameters are based on the estimates of Nanjing Express Station staff and Cainiao Logistics Company. The average delivery distance from the warehouse to the self-collection cabinet / station is 20 kilometers. Therefore, the average distance of mode 1 is 20+1 kilometers (last kilometer). The proportions and average package weights of these modes are as follows: Mode 1 accounts for 2% of the total express delivery volume and the average weight is 20kg; Mode 2 accounts for 18% of the total express delivery volume and the average weight input parameter is 1kg; Modes 3 and 4 account for 80% of the total and the average weight input parameter is 2kg. The last mile or last kilometer is further divided into door-to-door delivery (2%, accounting for 1.6% of the total) and consumer self-collection (98%, accounting for 78.4% of the total). Since the self-collection cabinets and stations are usually located within 1 kilometer of the customer, self-collection is usually completed on foot, and no emissions or costs are generated in the process. Therefore, the entire process of urban express delivery includes: Method 1, the distance for door-to-door delivery is 21 kilometers; Method 2, the distance for delivery to the self-service cabinet is 20 kilometers; Method 3, the distance for delivery to the post station is 20 kilometers; Method 4, the distance for delivery to the post station is 20 kilometers, and the last kilometer is 1 kilometer.

[0089] At Figure 1B In the embodiment shown, the express delivery information includes the quantity information of the express delivery within the unit time period of the city, the weight information of the express delivery and the volume information of the express delivery. The weight information and the volume information of the express delivery often affect the distribution method, thereby determining the carbon emissions and economic costs under the specific formula method. Specifically, the express delivery volume data comes from the "China Statistical Yearbook" or the express delivery volume data input as needed.

[0090] In the present invention Figure 1B In the illustrated embodiment, the basic information of the vehicle includes the type of vehicle and corresponding parameters. Specifically, the type of the vehicle includes a traditional gasoline vehicle and an electric vehicle. Specifically, the parameters of the vehicle include energy consumption characteristics, load capacity, and service life. In this specific embodiment, the basic information of the vehicle is derived from the official website of the vehicle.

[0091] In the present invention Figure 1B In the illustrated embodiment, the path information includes the distance of the delivery path (such as the route and kilometers). In one embodiment of the present invention, the path information may also include the urban road type of each section in the delivery path. In a preferred embodiment of the present invention, the urban road type includes one or more of a main road, a secondary road, and a residential road. The urban road type will affect the energy consumption of a specific vehicle, thereby affecting the final carbon emissions and economic costs.

[0092] In the present invention Figure 1BIn the illustrated embodiment, the basic information also includes real-time traffic data, which includes one or more of road congestion, traffic light cycles, and speed limits. Traffic data information affects the energy consumption of specific vehicles, thereby affecting the final carbon emissions and economic costs.

[0093] In order to better compare and evaluate carbon emissions and economic costs, this application will set the average distances of each delivery link of each vehicle in the logistics distribution between specific cities or provinces according to the four delivery methods in the delivery method information as described above.

[0094] like Figure 1B Specifically, the carbon emissions include carbon emissions from traditional vehicles and carbon emissions from electric vehicles.

[0095] In a specific calculation method of carbon emissions, based on the actual situation of urban express delivery, gasoline vehicles are used as the main mode of transportation, namely door-to-door delivery (mode 1), self-collection cabinets (mode 2) and post stations (modes 3 and 4). For the last mile or last kilometer of mode 4, electric tricycles are used for delivery.

[0096] In one embodiment of the present invention, the method for calculating the carbon emissions of gasoline vehicles per unit time in the target city is:

[0097]

[0098] Among them, NDP refers to the number of packages that the oil truck needs to transport, in pieces; L refers to the load that the vehicle can carry, in kilograms; FC refers to the speed at which the engine uses fuel, in liters / kilometer; GD refers to the density of gasoline, in kilograms / liter; GEF is the carbon dioxide emission factor of gasoline, which refers to the carbon dioxide emissions corresponding to a unit weight of gasoline, in thousands of tons of carbon emissions / kilogram of gasoline; AW k It represents the average weight of a single package in a specific delivery method, in kilograms per piece. It is an empirical parameter, a parameter obtained from public data, or a set value. k is 1, 2, 3, or 4, and the value of k corresponds to the delivery method. For example, k is 1, which means delivery method 1, k is 2, which means delivery method 2, k is 3, which means delivery method 3, and k is 4, which means delivery method 4. AD is the average transportation distance, in kilometers. C G The unit is kilotonnes.

[0099] In one embodiment of the present invention, the method for calculating the emission of electric vehicles per unit time in the target city is:

[0100]

[0101] Among them, NDP refers to the number of packages that the tram needs to transport, in pieces; L refers to the load that the tram can carry, in kilograms; EEF is the carbon dioxide emission factor corresponding to electricity, which refers to the carbon dioxide emissions corresponding to unit kilowatt of electricity, in thousands of tons of carbon emissions / kilowatt-hour; AW k represents the average weight of a single package in a specific delivery method, in kilograms per package, which is an empirical parameter, a parameter obtained from public data, or a set value, and k is 1, 2, 3, or 4; AD is the average transportation distance, in kilometers; DR is the distance that the electric vehicle can travel before needing to be recharged, in kilometers; BC is the battery capacity, in kilowatt-hours; C E The unit is kilotonnes.

[0102] It should be noted that the target city in the present invention can be a city such as Nanjing, etc., or a province such as Jiangsu Province. Specifically, based on the technical solution disclosed in the present invention, the appropriate information of the object can be selected according to the different objects to be estimated.

[0103] In the present invention, Figure 1B In one embodiment shown, the carbon emissions per unit time in the target city is the sum of the carbon emissions of various delivery methods.

[0104] Specific as Figure 2 As shown, the carbon emissions and economic costs of each delivery method can be calculated based on Nanjing’s annual express delivery volume and the proportion of each delivery method.

[0105] In the present invention Figure 1B In the illustrated embodiment, the economic cost includes vehicle purchase cost, maintenance cost, energy cost and labor cost.

[0106] In the present invention Figure 1B In the embodiment shown, the energy cost of a conventional gasoline vehicle depends on the fuel price and consumption, and the fuel cost is the product of the fuel price and consumption. In one embodiment of the present invention, the energy cost of an electric vehicle is related to the electricity price and the electricity consumption, and the electricity cost is the product of the electricity price and the electricity consumption.

[0107] In the present invention Figure 1B In the embodiment shown, the purchase cost includes the purchase cost of the vehicle itself and the insurance cost. In one embodiment of the present invention, the maintenance cost includes the regular inspection cost and the component replacement cost.

[0108] In the present invention Figure 1B In the embodiment shown, the labor cost is the courier salary, which is related to the specific salary in the target city and can be obtained based on official public and commercial data.

[0109] like Figure 3 Based on the above Figure 1B The method shown evaluates the results of carbon emissions from urban express delivery in China from 2013 to 2021. The express delivery volume data comes from the China Statistical Yearbook. The transport vehicle models are selected from the more common models on the market. In method 1, a typical light gasoline truck - Foton Xiangling Giant Panda V version is selected, and the data comes from the official website of the vehicle; in methods 2, 3, and 4, a typical micro gasoline train - BAK pure electric logistics vehicle Q503 is selected for the stage of transporting from the warehouse to the post station / express cabinet, and the data comes from the official website of the vehicle; in method 4, an electric tricycle is selected for the stage of delivering goods from the post station to the door, and the data comes from the National Standard of the People's Republic of China for Technical Requirements for Electric Tricycles for Express Delivery (Draft for Comments) and the National Mandatory Standard Technical Requirements for Electric Tricycles for Express Delivery (Draft for Comments). The load that the vehicle can carry and the speed at which the engine uses fuel vary according to the above models. The gasoline density data and the carbon dioxide emission factor of gasoline are derived from gasoline for motor vehicles (GB 17930-2013). The average weight and average transportation distance of a single package vary according to different methods, and the specific parameters are the same as the previous one. Figure 2 The corresponding instances are the same.

[0110] Specific results include:

[0111] Trend Description Figure 3 A, used to show the carbon emissions of urban express delivery and its proportion in the transportation industry.

[0112] Regional differences Figure 3 B, is used to describe the spatial distribution of carbon emissions from express delivery in cities across provinces in China in 2021.

[0113] Composition structure Figure 3 C, used to analyze the contribution of different transport modes to total emissions.

[0114] In a Figure 1B In the specific embodiment shown, the economic cost also includes energy cost, vehicle cost and labor cost. Losses during transportation are not considered. Among them, the energy cost includes the energy cost of the oil truck EC 油 and the energy cost of the tram EC 电 .

[0115] In a more specific embodiment of the present invention, the energy cost of the gasoline vehicle per unit time in the target city is EC 油 The calculation method is:

[0116]

[0117] Among them, NDP refers to the number of packages that the gasoline truck needs to transport, in pieces; L refers to the load that the vehicle can carry, in kilograms; FC refers to the speed at which the engine uses fuel, in liters / kilometer; AD is the average transportation distance, in kilometers; GP refers to the price of gasoline, in US$100,000 / kilogram.

[0118] In a more specific embodiment of the present invention, the energy cost of the electric vehicle EC 电 The calculation method is:

[0119]

[0120] NDP refers to the number of packages that the tram needs to transport, in pieces; L refers to the load that the tram can carry, in kilograms; AW k It represents the average weight of a single package in a specific delivery method, in kilograms per piece. It is an empirical parameter, a parameter obtained from public data, or a set value, and k is 1, 2, 3, or 4. AD is the average transportation distance, in kilometers. DR is the distance that the electric vehicle can travel before needing to be recharged, in kilometers. BC is the battery capacity, in kilowatt-hours. EP is the price of electricity, in US$100,000 per kilowatt-hour.

[0121] In a Figure 1B In the specific embodiment shown, only the purchase cost is considered for the vehicle cost.

[0122] In a Figure 1B In the specific embodiment shown, the vehicle cost VC is calculated as follows:

[0123]

[0124] Where n is the average daily delivery frequency. The delivery frequency of the vehicle from the warehouse to the consumer's door in delivery method 1 is 2; in delivery method 2, delivery method 3 and formula method 4, n is 4. PVC (Per vehicle cost) refers to the cost of purchasing a vehicle, in units of $100,000 per vehicle. k It indicates the average weight of a single package in a specific delivery method, in kilograms per piece. It is an empirical parameter, a parameter obtained from public data, or a set value. k is 1, 2, 3, or 4. L is the load that the vehicle can carry, in kilograms. j refers to the number of target cities. If it is nationwide, the number of express deliveries required to be delivered to the 31 provinces and municipalities in the country is selected. If it is a city, j = 1. NDP refers to the number of packages that need to be delivered in the target city, in pieces. In addition, the cost loss is discounted at the annual average cost.

[0125] In a Figure 1BIn the specific embodiment shown, the labor cost is the salary of the couriers. The labor cost of the target city = the number of couriers × the average salary.

[0126] Based on the above estimation method as shown in Figure 1, based on the progress of new transportation technologies and the future trend of express delivery, a solution for reducing emissions and costs is proposed, taking into account the application of electric vehicles and driverless cars. Based on this method and device, five specific optimization paths can be further proposed to explore potential measures to reduce carbon emissions and costs of express delivery.

[0127] The five optimized paths include: replacing gasoline vehicles with electric vehicles for home delivery (P1); replacing trucks with electric vehicles in self-collection cabinets (P2); replacing trucks with electric vehicles at post stations (P3); replacing manned electric vehicles (electric tricycles) with driverless electric vehicles in the last mile (P4); and merging P1, P2, and P3 into one path (P5). See Table 1 for details.

[0128] Table 1 Technical paths for urban express delivery

[0129]

[0130] a.GV is a gasoline vehicle;

[0131] b.EV stands for electric vehicle.

[0132] If electric vehicles are used, the energy cost is the cost of consuming electricity, and carbon emissions are the carbon dioxide emissions generated by electricity consumption.

[0133] like Figure 4This is the evaluation result of carbon emissions and costs of urban express delivery in 2021 under different development paths and their unit values. Typical electric vehicles on the market are also selected to replace gasoline vehicles under different modes of transportation. The specific operation is: Wuling Rongguang V [1.5L Practical Type with Sealed Windows] is used to replace Foton Xiangling Panda V version for mode 1 transportation; JAC Motors-Shuailing i6 replaces BAK pure electric logistics vehicle Q503 for modes 2, 3, and 4 transportation; the unmanned electric vehicle that replaces the electric tricycle is selected. The Xiaomanlu launched by Alibaba Group is used for the last mile transportation of mode 4. The data are all from the official website of the vehicle. The load that the tram can carry, the distance that the tram can travel before charging, and the battery capacity vary with different car models. The carbon dioxide emission factor corresponding to electricity is derived from academic literature (Lv Chen et al., 2021, China Environmental Science). In terms of economic cost, gasoline price data is derived from the official website of the National Development and Reform Commission; electricity price data is derived from the State Grid Corporation of China. The average cost of vehicles also varies with the vehicles used in different modes, all from the official website of the vehicle. The number of couriers and per capita wages are from the National Postal Management Work Conference. The average weight and average transportation distance of a single package vary according to the mode of transportation. The specific parameters are the same as the previous one. Figure 2 The corresponding instances are the same.

[0134] The results show that P5 shows that replacing gasoline vehicles with electric vehicles can reduce carbon emissions by 65%; P4 shows that replacing electric tricycles with driverless electric vehicles can reduce costs by 77%.

[0135] In another specific embodiment, given the huge differences in population and transportation development in various sub-regions of China, four typical cities are selected to study regional differences in emissions, costs and potential benefits. Beijing, Shanghai, Guangzhou and Shenzhen are selected as target cities in 2021 to adopt this application. Figure 1B According to the above estimation method shown, the four cities rank among the top four in China in terms of express delivery volume and business revenue. Figure 5 The results show that carbon emissions are ranked from high to low in Beijing, Shanghai, Shenzhen, and Guangzhou, which is positively correlated with the volume of express delivery. It is worth noting that Shenzhen has the lowest economic cost for urban express delivery, while Beijing has the highest economic cost. P5 can achieve a significant reduction in carbon emissions; P4 can achieve a significant reduction in costs.

[0136] This application Figure 6 As shown, a device for estimating carbon emissions and economic costs of express logistics within a city is also provided, including:

[0137] A logistics basic data acquisition module for a target city, used to acquire the logistics basic data of the target city, wherein the logistics basic data includes express information, vehicle information, delivery route information and express information;

[0138] The estimation module is used to calculate the carbon emissions and economic costs based on the logistics basic data.

[0139] In the present invention, Figure 6 In one embodiment shown, the estimation module includes a fuel vehicle carbon emission calculation module, an electric vehicle carbon emission calculation module and an economic cost calculation module.

[0140] In the present invention, Figure 6 In one embodiment shown, the carbon emission calculation module of the oil vehicle is used to obtain the carbon emission through calculation as described above.

[0141] In the present invention, Figure 6 In one embodiment shown, the electric vehicle carbon emissions calculation module is used to calculate and obtain the carbon emissions through the method shown above.

[0142] In the present invention, Figure 6 In the embodiment shown, the economic cost calculation module includes a vehicle cost calculation module, an energy cost calculation module and a labor cost calculation module. Figure 6 In one embodiment shown, the vehicle cost calculation module is used to calculate the vehicle cost, and the calculation method is as described in the method described above. The energy cost calculation module is used to obtain the energy cost according to the calculation method described in the method described above. The labor cost calculation module is used to calculate the labor cost, and is used to obtain the labor cost according to the calculation method described in the method described above.

[0143] It should be noted that it should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software called by processing elements, or they can all be implemented in the form of hardware, or some modules can be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. For example: the estimation module can be a separately established processing element, or it can be integrated in a certain chip of the above device. In addition, the estimation module can also be stored in the memory of the above device in the form of program code, and called and executed by a certain processing element of the above device. The implementation of other modules is similar. These modules can be integrated together in whole or in part, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software. The above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), one or more digital singnal processors (DSP), one or more field programmable gate arrays (FPGA), etc. When a module is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. These modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0144] Figure 7 A schematic diagram of the structure of a terminal provided in an embodiment of the present application. The terminal provided in this example includes: a processor, a memory, a transceiver, a communication interface and / or a system bus; the memory and the communication interface are connected to the processor and the transceiver through the system bus and complete the communication between them, the memory is used to store computer programs, the communication interface is used to communicate with other devices, and the processor and the transceiver are used to run computer programs, so that the estimation device performs each step of the above estimation method.

[0145] The system bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The communication interface is used to realize the communication between the database access device and other devices (such as clients, read-write libraries, and read-only libraries). The memory may include random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage.

[0146] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0147] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0148] The protection scope of the estimation method described in the present invention is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present invention are included in the protection scope of the present invention.

[0149] The present invention also provides an estimation device, which can implement the estimation method described in the present invention. However, the implementation device of the estimation method described in the present invention includes but is not limited to the structure of the estimation device listed in this embodiment. All structural deformations and replacements of the prior art made according to the principles of the present invention are included in the protection scope of the present invention.

[0150] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for estimating carbon emissions and economic costs caused by express logistics within a city, characterized in that: include: Obtaining basic logistics data of the target city, wherein the basic logistics data includes express delivery information, vehicle information, delivery route information, and express delivery information; The carbon emissions and economic costs are calculated based on the basic logistics data; the carbon emissions are the carbon emissions generated by vehicles during the logistics distribution process, and the economic costs are the vehicle costs, energy consumption costs and labor costs during the logistics distribution process.

2. The estimation method according to claim 1, characterized in that: The logistics basic data also includes delivery method information; the delivery method information includes any one of the following four types: method 1 is door-to-door delivery, that is, direct delivery from the warehouse to the consumer's door, mainly large and weighed packages; method 2 is self-collection cabinet combined with consumer self-collection, that is, from the warehouse to the self-collection cabinet, and then the consumer picks up the package at the self-collection cabinet, mainly for small and light packages; method 3 is post station combined with consumer self-collection, that is, from the warehouse to the post station, and then the consumer picks up the package at the post station, which is used for packages of a wide range of weight types; method 4 is post station combined with door-to-door delivery, that is, the post station staff delivers the package to the customer's door; And / or, the express delivery information includes the quantity information of the express delivery within a unit time period of the city, the weight classification information of the express delivery and the volume information of the express delivery; And / or, the delivery route information includes the distance of the delivery route; And / or, the vehicle information includes the type of vehicle and corresponding parameters, the vehicle includes a gasoline vehicle and an electric vehicle, and the parameters include energy consumption characteristics, load capacity and service life.

3. The estimation method according to claim 1, characterized in that: The vehicle cost includes the vehicle purchase cost; the vehicle purchase cost includes the purchase cost of the vehicle itself and the insurance cost; the energy cost includes the fuel cost of a gasoline vehicle and the electricity cost of an electric vehicle, the fuel cost is the product of the fuel price and the consumption, and the electricity cost is the product of the electricity price and the electricity consumption; the labor cost is the salary of the couriers.

4. The estimation method according to claim 2, characterized in that: The calculation method for carbon emissions of gasoline vehicles per unit time in the target city is: Among them, NDP refers to the number of packages that the truck needs to transport, in pieces; L refers to the load that the vehicle can carry, in kilograms; FC refers to the speed at which the engine uses fuel, in liters per kilometer; GD refers to the density of gasoline, in kilograms per liter; GEF is the carbon dioxide emission factor of gasoline, which refers to the carbon dioxide emissions corresponding to a unit weight of gasoline, in thousands of tons of carbon emissions per kilogram of gasoline; AW k It represents the average weight of a single package in a specific delivery method, in kilograms per package. It is an empirical parameter, a parameter obtained from public data, or a set value. k is 1, 2, 3, or 4, corresponding to delivery method 1, delivery method 2, delivery method 3, and delivery method 4, respectively. AD is the average transportation distance, in kilometers. C G The unit is kilotonnes; And / or, the energy cost EC of the gasoline vehicle per unit time in the target city 油 The calculation method is: Here, GP refers to the price of gasoline, in US$100,000 per kilogram.

5. The estimation method according to claim 2, characterized in that: The calculation method for the emission of trams per unit time in the target city is: Among them, NDP refers to the number of packages that the tram needs to transport, in pieces; L refers to the load that the tram can carry, in kilograms; EEF is the carbon dioxide emission factor corresponding to electricity, which refers to the carbon dioxide emissions corresponding to unit kilowatt of electricity, in thousands of tons of carbon emissions / kilowatt-hour; AW k It represents the average weight of a single package in a specific delivery method, in kilograms per package. It is an empirical parameter, a parameter obtained from public data, or a set value. k is 1, 2, 3, or 4. AD is the average transport distance in kilometers; DR is the distance the electric vehicle can travel before needing to be recharged in kilometers; BC is the battery capacity in kilowatt-hours; C E The unit is kilotonnes; and / or, the energy cost of the tram per unit time in the target city, EC 电 The calculation method is: Among them, EP is the price of electricity, the unit is US$100,000 / kWh.

6. The estimation method according to claim 1, characterized in that: The vehicle cost VC is calculated as follows: Where n is the average daily delivery frequency. The delivery frequency of the vehicle from the warehouse to the consumer's door in delivery method 1 is 2; n is 4 in delivery method 2, delivery method 3 and formula method 4; PVC refers to the cost of purchasing a vehicle, in US$100,000 per vehicle; AW k It represents the average weight of a single package in a specific delivery method, in kilograms per piece; j refers to the number of target cities. If it is nationwide, x=31, corresponding to the provincial data of 31 provinces and municipalities; if it is 1 city, x=1; NDP refers to the number of packages that need to be delivered in the target city, in pieces.

7. The estimation method according to claim 1, characterized in that: The energy cost EC includes the energy cost EC of the oil vehicle 油 and the energy cost of the tram EC 电 ; The energy cost EC of the oil vehicle per unit time in the target city 油 The calculation method is: Among them, NDP refers to the number of packages that the oil truck needs to transport, in pieces; L refers to the load that the vehicle can carry, in kilograms; FC refers to the speed at which the engine uses fuel, in liters / kilometer; AD is the average transportation distance, in kilometers; GP refers to the price of gasoline, in US$100,000 / kilogram; The energy cost of the tram per unit time in the target city is EC 电 The calculation method is: NDP refers to the number of packages that the tram needs to transport, in pieces; L refers to the load that the tram can carry, in kilograms; AW k It represents the average weight of a single package in a specific delivery method, in kilograms per piece. It is an empirical parameter, a parameter obtained from public data, or a set value, and k is 1, 2, 3, or 4. AD is the average transportation distance, in kilometers. DR is the distance that the electric vehicle can travel before needing to be recharged, in kilometers. BC is the battery capacity, in kilowatt-hours. EP is the price of electricity, in US$100,000 per kilowatt-hour.

8. A device for estimating carbon emissions and economic costs caused by express logistics within a city, characterized in that: include: A logistics basic data acquisition module for a target city, used to acquire the logistics basic data of the target city, wherein the logistics basic data includes express information, vehicle information, delivery route information and express information; The estimation module is used to calculate the carbon emissions and economic costs based on the logistics basic data. The carbon emissions are the carbon emissions generated by vehicles during the logistics distribution process, and the economic costs are the vehicle costs, energy consumption costs and labor costs during the logistics distribution process.

9. The estimation device according to claim 8, characterized in that The estimation module includes a fuel vehicle carbon emission calculation module, an electric vehicle carbon emission calculation module and an economic cost calculation module.

10. The estimation device according to claim 9, characterized in that The economic cost calculation module includes a vehicle cost calculation module, an energy cost calculation module and a labor cost calculation module.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the estimation method according to any one of claims 1 to 7 is implemented.

12. A terminal, characterized in that: include: Processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal performs the estimation method according to any one of claims 1 to 7.

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