Container multimodal transport network carbon emission accounting method

Abstract

The invention discloses a carbon emission accounting method for a container multimodal transport network, and the method comprises the steps: firstly, determining a modeling boundary and a data set of the container multimodal transport network, and enabling the modeling boundary to comprise a space range, a time range and a modeling object; the data set comprises node attributes, connection edge attributes and node traffic volume data of data required for constructing the multimodal transport network model; then, establishing a data-driven multimodal transport network model, identifying a network topology structure through the model, and estimating network side point traffic; and finally, calculating the network carbon emission by combining the energy consumption characteristics of different transportation modes and different combined transportation transfer modes. The scheme not only can serve carbon emission evaluation of a single hub transportation network to realize hub enterprise freight network carbon emission footprint tracking and driving factor identification, but also can serve carbon emission evaluation of a regional multimodal transportation network to realize regional freight network carbon emission management level evaluation and driving factor identification, so that the economic benefit is improved. And a theoretical tool is provided for measuring the carbon emission reduction development level of the multimodal transport network with different dimensions.

Description

Technical Field

[0001] The present invention belongs to the field of transportation carbon emission accounting, and specifically relates to a method for calculating carbon emissions in a container multimodal transport network. Background Art

[0002] How to scientifically and systematically quantify carbon emissions from intermodal transport networks has become a current research focus.

[0003] Under the dual pressures of the global energy crisis and carbon neutrality goals, the transportation industry, as a high-carbon emission sector, has garnered widespread attention. Statistics show that transportation emissions account for approximately 24% of global emissions, with road transport contributing approximately 72% of these emissions. Traditional single modes of transportation, such as diesel-dependent road transport, are inefficient and emit high carbon emissions, severely hindering the development of green transportation.

[0004] As a logistics solution that combines multiple modes of transportation, such as road, rail, and inland waterway transport, intermodal transport is considered an important way to promote low-carbon transportation. By optimizing the transportation structure and improving the energy efficiency of transportation tools, intermodal transport can significantly reduce carbon emissions during cargo transportation. However, existing intermodal transport research mainly focuses on carbon emission accounting within a single transportation process, a single chain, or a single hub. For example, the Chinese invention patent application with publication number CN 119397128 A discloses a method for calculating carbon emissions throughout the life cycle of typical port infrastructure. It only reflects the carbon emissions of the port's internal infrastructure, but does not involve the social carbon emissions caused by the port's collection and distribution activities, which are included in the carbon emissions of the intermodal transport network between the port and the hinterland.

[0005] The quantitative assessment of the comprehensive carbon emissions of the port's intermodal transport network is still in the exploratory stage. Existing technologies are unable to quantitatively track the carbon emissions caused by hub freight collection and distribution. There is a lack of a carbon emission accounting system for the intermodal transport network that conforms to the characteristics of my country's transportation structure, resulting in the inability to assess the comprehensive carbon emission reduction benefits of internal and external activities of the intermodal transport hub. Especially under the premise that there are many transportation participants and information is difficult to share, there is a lack of scientific methods to define the carbon emission boundaries of the intermodal transport network of a certain hub or the entire region in order to quantitatively assess the carbon emission level within the network.

[0006] Currently, carbon emission assessment in intermodal transport networks faces the following challenges:

[0007] 1. The accounting of transport carbon emissions is currently focused on carbon emissions accounting at transport nodes or local links along the transport route, and lacks carbon footprint tracking from "door to door". The main reason is that multimodal transport involves many modes of transport, and the transit links of different intermodal transport modes are different. As a result, it is difficult to fully obtain data on carbon emission assessment parameters for different transport modes and different intermodal transport links.

[0008] 2. Imperfect methods for measuring carbon emissions from intermodal transport networks: Most studies focus on the carbon emissions of a single mode of transport or a single intermodal transport route, lacking carbon emissions accounting at the intermodal transport network level. This is primarily because network-level carbon emissions accounting requires network-level freight flow data, which is difficult to obtain comprehensively.

[0009] 3. High model complexity: Multimodal transport involves multiple modes of transportation and different nodes. How to accurately model and ensure data reliability is difficult. Summary of the Invention

[0010] The technical problem to be solved by the present invention is to provide a method for calculating carbon emissions in a container multimodal transport network, which solves the problems in the existing technology of complex multimodal transport routes, insufficient granularity of network carbon emissions accounting, inconsistent models, and difficulty in supporting refined carbon management and decision-making.

[0011] The present invention adopts the following technical solutions to solve the above technical problems:

[0012] The carbon emission accounting method for container intermodal transport network includes the following steps:

[0013] Step S1: Determine the container intermodal transport network modeling boundary and data set, wherein the modeling boundary includes the spatial scope, time scope, and modeling objects, and the data set includes the node attributes, edge attributes, and flow data required to construct the intermodal transport network model;

[0014] Step S2: establishing a data-driven multimodal transport network model;

[0015] Step S3: Calculate carbon emissions based on the multimodal transport network model in step S2 and in combination with the energy consumption characteristics of different transport modes and different intermodal transit modes.

[0016] Step S1 specifically includes the following steps:

[0017] Step S1-1: Determine the spatial scope of the multimodal transport network modeling, covering key facilities such as ports, railway hubs, highway nodes, and inland waterways involved in container multimodal transport;

[0018] Step S1-2: Define the time range for network modeling and estimate network traffic based on annual, quarterly, or monthly data;

[0019] Step S1-3: determining a modeling object of a multimodal transport network including nodes, edges, and flows;

[0020] Step S1-4: Collect data required for multimodal transport network modeling, including node attributes, edge attributes, and flow data.

[0021] Step S2 specifically includes the following steps:

[0022] Step S2-1: Construct a hub node identification model based on random forests, using machine learning technology to identify key hub nodes. Node connectivity, economic environment, infrastructure, and freight volume levels are used as regional logistics evaluation indicators to construct feature data for machine learning. With the identified key hub nodes as the core, the cargo collection and distribution network of key nodes is tracked to form a physical network framework for container multimodal transport.

[0023] Step S2-2: Construct a traffic estimation model based on the improved gravity model, select indicators that can reflect the freight attraction between network nodes, use the gravity model to obtain the freight attraction value that can reflect the container volume distribution, and then obtain the traffic quota of each route through the attraction distribution ratio value.

[0024] Step S3 specifically includes the following steps:

[0025] Step S3-1: Clarify the carbon emission accounting boundary and carbon emissions, and calculate the carbon emissions during the transportation and transit processes;

[0026] Step S3-2: Calculate the carbon emissions of three different modes of transportation: road, rail, and water transport;

[0027] Step S3-3, calculating the carbon emissions of the transshipment process in intermodal transport;

[0028] Step S3-4: Calculate the total carbon emissions of the multimodal transport network.

[0029] Carbon emissions from container road transport are calculated using the following formula:

[0030]

[0031] in, , ( ) represent the given empty and full load energy consumption of the truck respectively; Indicates highway mileage; Indicates the truck load factor, which is the ratio of actual load to payload The ratio of is the empty weight mileage coefficient, ,in represents the empty mileage factor, i.e. The ratio of the truck's empty mileage to the loaded mileage. is the road condition coefficient, ,in It represents the road condition factor, which is composed of the ratio of ordinary road mileage to highway mileage and the resistance coefficient of different roads. Indicates that the same batch of containers are at the transport node and The fuel required The number of trucks.

[0032] Carbon emissions from container rail transport are calculated using the following formula:

[0033]

[0034] in, The historical statistics of energy consumption per net ton-kilometer of diesel or electric locomotives in the railway sector, in units of , The historical statistics of energy consumption per gross ton-kilometer of diesel or electric locomotives in the railway sector, in units of , and is the empty mileage coefficient, , is the empty vehicle mileage factor, is the railway operating coefficient, , is the road condition factor, is the net relationship factor, is the empty weight of the truck, is the train's payload capacity, is the capacity utilization of the train.

[0035] Carbon emissions from container inland waterway transport are calculated using the following formula:

[0036]

[0037] in, is the energy consumption model of container inland waterway transport, and They represent the average power of the main engine and energy consumption per kilowatt-hour during the ship's cruising period, It represents the function of ship specific energy consumption and engine load ratio. Generally speaking, Indicates the specific fuel consumption of different engines and fuels at 80% MCR load rate. Indicates the load rate, the value is 0-1, Indicates the energy consumption factor of auxiliary engines and boilers during navigation, is the drag coefficient, , is the energy consumption resistance coefficient considering load, flow and wind, is the average speed of the ship, The number of containers in a given batch Energy consumption of shipping, Refers to the total number of containers loaded on board within the permitted deadweight tonnage.

[0038] The carbon emissions from the transshipment process in the intermodal transport are calculated based on the unit energy consumption of the node transshipment operation, the unit transport energy consumption of the transport vehicle, and the number of containers transshipped at the node.

[0039] The total carbon emissions of the multimodal transport network are calculated based on the carbon emissions of a single multimodal transport network hub. The specific calculation method is as follows:

[0040] Based on the determined network nodes and by assigning values ​​to the container freight volume on the nodes and the path flow of different transport modes between nodes, the container volume of the transportation process on each multimodal transport chain path and the transshipment process on the node are obtained; for each chain, the carbon emissions of different transport modes on this chain are calculated in combination with the operating box volume of different transportation processes, and the carbon emissions of the transportation modes are added to form the total carbon emissions of the transportation process of this chain; the carbon emissions of node transshipment are calculated in combination with the container volume of the transshipment process, and the carbon emissions of the nodes on the chain are added; the carbon emissions of each chain are obtained by adding the carbon emissions of the transportation process and the transshipment process; finally, the carbon emissions of all chains in the network are added to estimate the carbon emissions of the entire network.

[0041] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, call all or part of the steps of the method.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. This proposal comprehensively considers the transportation and transit links, accurately assesses the carbon emissions of the intermodal transport network, and proposes a multimodal transport network carbon emissions assessment method that integrates mathematical models, which has important academic value and practical significance.

[0044] 2. This solution is aimed at the carbon emission assessment of intermodal transport networks involving hub freight activities. It can not only serve the carbon emission assessment of a single hub collection and distribution network, and realize the carbon emission footprint tracking and driving factor identification of hub enterprise freight networks, but also serve the carbon emission assessment of regional intermodal transport networks, and realize the carbon emission governance level assessment and driving factor identification of regional freight networks, providing theoretical tools for enterprises and governments to measure the carbon emission reduction development level of intermodal transport networks in different dimensions. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is the carbon emission estimation framework for the container intermodal transport network of the present invention.

[0046] Figure 2 This is a diagram of the container multimodal transport process of the present invention.

[0047] Figure 3 It is composed of the elements of the container multimodal transport network of the present invention.

[0048] Figure 4 This is a framework diagram of the multimodal transport network construction method of the present invention.

[0049] Figure 5 This is a hub node selection model diagram based on random forest in the present invention.

[0050] Figure 6 This is the container multimodal transport port transshipment analysis diagram of the present invention. DETAILED DESCRIPTION

[0051] To more clearly understand the above-mentioned objectives, features, and advantages of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features therein may be combined with each other unless there is a conflict. Furthermore, the present invention may also be implemented in other ways than those described herein. Therefore, the scope of protection of the present invention is not limited by the specific embodiments disclosed below.

[0052] This method calculates carbon emissions from container intermodal transport networks, including a certain amount of carbon emissions from the intermodal transit and transportation phases of freight activities within intermodal transport networks of varying dimensions. The transit phase encompasses carbon emissions from transshipment operations under different intermodal transport modes, including water-to-water, water-to-road, sea-to-rail, and road-to-rail. Transport phase carbon emissions encompass road, rail, and water transport. Different intermodal transport modes constitute different carbon emission accounting combinations.

[0053] The carbon emission accounting method for container intermodal transport network includes the following steps:

[0054] Step S1: Determine the container intermodal transport network modeling boundary and data set, wherein the modeling boundary includes the spatial scope, time scope, and modeling objects, and the data set includes the node attributes, edge attributes, and flow data required to construct the intermodal transport network model;

[0055] Step S2: establishing a data-driven multimodal transport network model;

[0056] Step S3: Calculate carbon emissions based on the multimodal transport network model in step S2 and in combination with the energy consumption characteristics of different transport modes and different intermodal transit modes.

[0057] Specific embodiments, such as Figures 1 to 6 As shown,

[0058] In order to illustrate the effectiveness of the method proposed in the present invention, the technical solution of the present invention is described in detail below through a specific embodiment. Figure 1 As shown, a method for calculating carbon emissions in a container multimodal transport network is disclosed, and the specific implementation steps are as follows:

[0059] Step S1: Determine the container multimodal transport network modeling boundary and data set

[0060] Step S1-1: Determine the spatial scope of the multimodal transport network modeling, covering the carbon emissions generated by key facilities such as ports, railway hubs, highway nodes, highways, railways, and inland waterways used in the process of container freight volume movement between hubs and radial nodes.

[0061] like Figure 2 As shown in the figure, from the perspective of a single chain of container intermodal transport, the scope of carbon emission accounting for the entire container intermodal transport includes the entire process of connecting different types of hubs with radiation nodes through different modes of transportation, covering the operational process of container transshipment at different nodes, the in-transit transportation process, and the distribution process at the terminal node.

[0062] From the perspective of carbon emissions from the multimodal transport network of a single hub, the multimodal transport carbon emissions based on a single hub include the carbon emissions generated by container freight activities between the hub and multiple interconnected radiating nodes within a regional multimodal transport network over a certain period of time.

[0063] From the perspective of regional intermodal transport network carbon emissions, regional intermodal transport network carbon emissions also include the total carbon emissions generated by key hub networks within the region.

[0064] Step S1-2: Define the time range for network modeling and estimate network traffic based on annual, quarterly, or monthly data.

[0065] Step S1-3: Determine the modeling object of the multimodal transport network, including:

[0066] ① Hub Node: This node is a carrier within the intermodal transport transit service network and serves as the primary collector and sender of intermodal containers within the network. These nodes primarily include road-rail-water transport hubs, rail-water transport hubs, water-water transport hubs, and road-rail and road-water transport hubs. A regional intermodal transport network can include multiple hub nodes. In this embodiment, each hub node has a multimodal transport sub-network centered on the hub itself and connected by container flows.

[0067] ② Hinterland node: As a hinterland node within the hub network, it is a feeding node for the hub node. It only serves the delivery and arrival of local goods and does not have a transit nature.

[0068] ③ Link: Container transport routes connecting intermodal network hubs and hinterland nodes, as well as between hub nodes. Transport routes are composed of routes connecting different transport modes. Connections between hinterland nodes are not covered in this embodiment.

[0069] ④ Transportation mode: Establishing a transportation carrier that connects the paths. The transportation modes of this embodiment include rail transportation, inland waterway transportation centered around inland waterway vessels, and road transportation centered around container trucks.

[0070] 5. Container Traffic: This refers to the volume of container freight between hubs and hinterland nodes, and between hubs and nodes within an intermodal transport network over a given period. This volume is divided into dispatched and arrived volumes, reflecting the demand for intermodal container freight transport between nodes within the intermodal transport network. Container traffic within the intermodal transport network only occurs between hubs and hinterland nodes, and between hubs; traffic between hinterlands is not considered.

[0071] ⑥ Single hub container intermodal transport network: It is composed of the freight traffic relationship between the hub and its hinterland. Containers move on the intermodal transport chain between the intermodal transport hinterland nodes and the hub based on different modes of transport, thus forming a hub-and-spoke container intermodal transport network centered on this hub.

[0072] ⑦ Regional container intermodal transport network: This network is composed of multiple single-hub container intermodal transport networks that fit into the regional intermodal transport network. Each single-hub container intermodal transport network has its own intermodal transport sub-network based on the freight radiation relationship between the hub and hinterland nodes. However, the hub networks are connected through the inter-hub trunk transport network, thus forming a multi-level regional container intermodal transport network that reflects the container freight relations in a specific region.

[0073] Step S1-4: Collect data required for multimodal transport network modeling, including:

[0074] Node attributes: regional economic environment, infrastructure level, freight throughput, etc.

[0075] Edge attributes: transportation mode, transportation cost, transportation time, traffic capacity, etc.

[0076] Traffic data: Estimate freight traffic between nodes based on historical data and machine learning predictions.

[0077] Step S2: Establish a data-driven multimodal transport network model. Figure 4 As shown in the figure, a machine learning model is introduced to construct a multidimensional container intermodal transport network node identification model based on random forests, and a multimodal transport physical network consisting of nodes and edges is constructed. Based on the freight and economic dependencies between network nodes, a container intermodal transport network flow estimation model based on an improved gravity model is constructed, and a container intermodal transport logistics network model is constructed.

[0078] Step S2-1, hub node identification model based on random forest: use machine learning technology to identify key hub nodes, such as Figure 5As shown in the figure, node connectivity, economic environment, infrastructure, and freight volume are used as indicators to evaluate regional logistics levels. Feature data for machine learning is constructed to fully explore the characteristics of the intermodal transport network. With identified key hub nodes as the core, the cargo collection and distribution network at key nodes is tracked to form a physical network framework for container intermodal transport.

[0079] Step S2-2, traffic estimation based on the improved gravity model: select indicators that can reflect the freight attraction between network nodes, use the gravity model to obtain the freight attraction value that can reflect the distribution of container volume, and then obtain the traffic quota of each route through the attraction distribution ratio value.

[0080]

[0081] in, Indicates time period node To Node The container freight volume estimation model represents a collection of multimodal transport nodes, , mainly composed of nodes in the regional multimodal transport network Total freight volume and nodes To Node The logistics attractiveness index is composed of Indicates time period node To Node Logistics attractiveness model, which is composed of logistics influence coefficient , relative logistics quality coefficient Comprehensive distance function to different modes of transportation This reflects that the attractiveness of logistics increases with the improvement of logistics quality and decreases with the extension of the comprehensive distance between cities. represents the gravitational weight correction factor; It is the node Logistics quality value Relative coefficient after forward processing constitute, The nodes are obtained by constructing a logistics development evaluation index system for multimodal transport hub nodes within the network and using factor analysis methods. The preliminary comprehensive score value of the node is: the greater the logistics influence and logistics quality of the node, the greater the attractiveness; and the logistics comprehensive distance coefficient comprehensively reflects the transportation cost of different transportation modes between nodes. ,time and distance The three factors work together to negatively impact the attraction index. The larger the comprehensive logistics index is, the smaller the attraction between nodes is. Represents a collection of transport modes ,and . Indicates time period node To Node Use transportation method of freight volume. Expression period node Use transportation method proportion. Including time period node To Node Container transport volume using container trucks , railway container transport volume and container volume of ships . Represents a time period collection

[0082] Step S3, carbon emission estimation of container intermodal transport network: This step is based on the intermodal transport network structure and combines the energy consumption characteristics of different transport modes and different intermodal transit modes to calculate carbon emissions.

[0083] Step S3-1: Clarify carbon emission accounting boundaries and carbon emissions:

[0084] The Well-to-Wheel (WTW) and Tank-to-Wheel (TTW) models are used to calculate carbon emissions during the transportation and transit processes. The WTW model considers carbon emissions from the entire process of energy production, fuel transportation and final consumption; the TTW model only considers carbon emissions during the fuel consumption stage.

[0085] Step S3-2: Calculate carbon emissions from different modes of transportation: Carbon emissions from transportation are mainly calculated from the energy consumption of road, rail, and water transportation:

[0086]

[0087] in, Indicates a transport node and Fuel used between Means of transportation Energy consumption, Indicates means of transportation Fuel usage The carbon emission coefficient here The specific expression method depends on the energy consumption unit. Units include: kg-CO2 / kg, kg-CO2 / MJ, or kg-CO2 / kWh. If the energy unit is kg or kWh, the carbon emission factor needs to be converted using the calorific value.

[0088] (1) Energy consumption model for container road transport: The calculation of carbon emissions from road transport is based on the fuel consumption model of trucks, taking into account factors such as load, road conditions, and transport distance. The calculation formula is as follows:

[0089]

[0090] in, , ( ) represent the given empty and full load energy consumption of the truck respectively; Indicates highway mileage; Indicates the truck load factor, which is the ratio of the actual load to the effective load ( ), The maximum weight of cargo that can be loaded by the transport vehicle; is the empty weight mileage coefficient, ,in represents the empty mileage factor, i.e. The ratio of truck mileage to loaded mileage. is the road condition coefficient, ,in It represents the road condition factor, which is composed of the ratio of ordinary road mileage to highway mileage and the resistance coefficient of different roads; Indicates that the same batch of containers are at the transport node and The fuel required The number of trucks.

[0091] (2) Container railway transport energy consumption model: Train section transport energy consumption formula based on the method of historical statistical unit consumption of locomotives

[0092]

[0093] in, The historical statistics of energy consumption per net ton-kilometer of diesel or electric locomotives in the railway sector, in units of ; The historical statistics of energy consumption per gross ton-kilometer of diesel or electric locomotives in the railway sector, in units of ; and is the empty mileage coefficient, , is the empty vehicle mileage factor, is the railway operating coefficient, , is the road condition factor, is the net relationship factor, is the empty weight of the truck, is the train's payload capacity, is the capacity utilization of the train.

[0094] (3) Energy consumption model of container inland waterway transport

[0095]

[0096] in, is the energy consumption model of container inland waterway transport, and They represent the average power of the main engine and energy consumption per kilowatt-hour during the ship's cruising period, It represents the function of ship specific energy consumption and engine load ratio. Generally speaking, Indicates the specific fuel consumption of different engines and fuels at a load rate of 80% MCR (maximum rated power); Indicates the load rate, with a value of 0-1; Indicates the energy consumption factor of auxiliary engines and boilers during navigation, is the drag coefficient, , is the energy consumption resistance coefficient considering load, flow and wind; is the average speed of the ship, The number of containers in a given batch Energy consumption of shipping, Refers to the total number of containers loaded on board within the permitted deadweight tonnage.

[0097] Step S3-3: Calculate carbon emissions during transshipment in intermodal transport:

[0098]

[0099] This formula indicates that the carbon emissions during the transport process are determined by the node Unit energy consumption of transshipment operation ( ) and unit transport energy consumption of transport vehicles ( )constitute; Representation node Number of containers transshipped.

[0100]

[0101] in, Indicates the unit TEU energy consumption of the transfer operation at the transfer node, Indicates that during the operation phase Using the device , otherwise it is 0; For operating equipment The carbon emission coefficient, is the unit operating energy consumption of the equipment, is the energy consumption index of the operation, is the empty weight ratio load factor, , It represents the product of empty weight energy consumption ratio and empty weight time ratio; Unit container operation efficiency is calculated by historical statistical segment Batch job time and bulk container handling volume ( ) is obtained by taking the average value of .

[0102]

[0103] in, For transportation Auxiliary Port Energy consumption of loading and unloading activities is determined by hourly energy consumption , working time , transport vehicles Number , carbon emission coefficient composition.

[0104] S3-4. Calculating the total carbon emissions of the intermodal transport network: Based on hub nodes Carbon emission model of multimodal transport network

[0105]

[0106] in, Indicates the time period Container traffic per transport route Equal to the hub node Total shipments ; Indicates from the hub To the spoke node The transport route is ,otherwise .

[0107] Based on hub nodes A single intermodal process chain Carbon emissions The specific formula is:

[0108]

[0109] Representation based on hub A single intermodal process chain carbon emissions; Indicates a transport node and Transportation between of transport carbon emissions; Indicates the mode of transport Transfer to transportation mode Carbon emissions of the process; Indicates a transport node and Whether to use transportation mode ,otherwise, ; Indicates whether the node The mode of transport Transfer to transportation mode , Represents a 0-1 variable, when hour, , indicating that the goods are at the node On transfer, when hour, Indicates that the goods are in transit.

[0110]

[0111] This formula means that only one mode of transportation can be selected between two nodes. .

[0112]

[0113] This formula represents the transport volume Cannot exceed the actual load of the transport vehicle .

[0114] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, call all or part of the steps of the method.

[0115] If the above functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0116] It should be understood that this solution is not limited to the specific implementation methods described above. Devices and structures not described in detail should be understood to be implemented in a common manner in the art. Any person skilled in the art can, without departing from the scope of this solution, use the methods and technical content disclosed above to make many possible changes and modifications to this solution, or modify it into equivalent embodiments with equivalent changes, without affecting the essence of this solution. Therefore, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of this solution without departing from the content of this solution are still within the scope of protection of this solution.

Claims

1. A method for calculating carbon emissions from container intermodal transport networks, characterized by: The steps include: Step S1: Determine the container intermodal transport network modeling boundary and data set, wherein the modeling boundary includes the spatial scope, time scope, and modeling objects, and the data set includes the node attributes, edge attributes, and flow data required to construct the intermodal transport network model; Step S2: establishing a data-driven multimodal transport network model; Step S3: Calculate carbon emissions based on the multimodal transport network model in step S2 and in combination with the energy consumption characteristics of different transport modes and different intermodal transit modes.

2. The method for calculating carbon emissions from container multimodal transport networks according to claim 1, characterized in that: Step S1 specifically includes the following steps: Step S1-1: Determine the spatial scope of the multimodal transport network modeling, covering key facilities such as ports, railway hubs, highway nodes, and inland waterways involved in container multimodal transport; Step S1-2: Define the time range for network modeling and estimate network traffic based on annual, quarterly, or monthly data; Step S1-3: determining a modeling object of a multimodal transport network including nodes, edges, and flows; Step S1-4: Collect data required for multimodal transport network modeling, including node attributes, edge attributes, and flow data.

3. The method for calculating carbon emissions from container multimodal transport networks according to claim 1, characterized in that: Step S2 specifically includes the following steps: Step S2-1: Construct a hub node identification model based on random forests, use machine learning technology to identify key hub nodes, use node connectivity, economic environment, infrastructure, and freight volume levels as regional logistics level evaluation indicators, and construct feature data for machine learning; With the identified key hub nodes as the core, the cargo collection and distribution network of key nodes is tracked to form a physical network framework for container multimodal transport; Step S2-2: Construct a traffic estimation model based on the improved gravity model, select indicators that can reflect the freight attraction between network nodes, use the gravity model to obtain the freight attraction value that can reflect the container volume distribution, and then obtain the traffic quota of each route through the attraction distribution ratio value.

4. The method for calculating carbon emissions from container multimodal transport networks according to claim 1, characterized in that: Step S3 specifically includes the following steps: Step S3-1: Clarify the carbon emission accounting boundary and carbon emissions, and calculate the carbon emissions during the transportation and transit processes; Step S3-2: Calculate the carbon emissions of three different modes of transportation: road, rail, and water transport; Step S3-3, calculating the carbon emissions of the transshipment process in intermodal transport; Step S3-4: Calculate the total carbon emissions of the multimodal transport network.

5. The method for calculating carbon emissions from container multimodal transport networks according to claim 4, characterized in that: Carbon emissions from container road transport are calculated using the following formula: in, , ( ) represent the given empty and full load energy consumption of the truck respectively; represents highway mileage; Indicates the truck load factor, which is the ratio of actual load to payload The ratio of is the empty weight mileage coefficient, ,in represents the empty mileage factor, i.e. The ratio of the truck's empty mileage to the loaded mileage. is the road condition coefficient, ,in It represents the road condition factor, which is composed of the ratio of ordinary road mileage to highway mileage and the resistance coefficient of different roads. Indicates that the same batch of containers are at the transport node and The fuel required The number of trucks.

6. The method for calculating carbon emissions from container multimodal transport networks according to claim 4, characterized in that: Carbon emissions from container rail transport are calculated using the following formula: in, The historical statistics of energy consumption per net ton-kilometer of diesel or electric locomotives in the railway sector, in units of , The historical statistics of energy consumption per gross ton-kilometer of diesel or electric locomotives in the railway sector, in units of , and is the empty mileage coefficient, , is the empty vehicle mileage factor, is the railway operating coefficient, , is the road condition factor, is the net relationship factor, is the empty weight of the truck, is the train's payload capacity, is the capacity utilization of the train.

7. The method for calculating carbon emissions from container multimodal transport networks according to claim 4, characterized in that: Carbon emissions from container inland waterway transport are calculated using the following formula: in, is the energy consumption model of container inland waterway transport, and They represent the average power of the main engine and energy consumption per kilowatt-hour during the ship's cruising period, It represents the function of ship specific energy consumption and engine load ratio. Generally speaking, Indicates the specific fuel consumption of different engines and fuels at 80% MCR load rate. Indicates the load rate, the value is 0-1, Indicates the energy consumption factor of auxiliary engines and boilers during navigation, is the drag coefficient, , is the energy consumption resistance coefficient considering load, flow and wind, is the average speed of the ship, The number of containers in a given batch Energy consumption of shipping, Refers to the total number of containers loaded on board within the permitted deadweight tonnage.

8. The method for calculating carbon emissions from container multimodal transport networks according to claim 4, characterized in that: The carbon emissions from the transshipment process in the intermodal transport are calculated based on the unit energy consumption of the node transshipment operation, the unit transport energy consumption of the transport vehicle, and the number of containers transshipped at the node.

9. The method for calculating carbon emissions from a container multimodal transport network according to claim 4, characterized in that: The total carbon emissions of the intermodal transport network are calculated based on the carbon emissions of a single intermodal transport hub network. The specific calculation method is as follows: Based on the determined network nodes and by assigning values ​​to the container freight volume on the nodes and the path flow of different transport modes between nodes, the container volume of the transportation process on each multimodal transport chain path and the transshipment process on the node are obtained; for each chain, the carbon emissions of different transport modes on this chain are calculated in combination with the operating box volume of different transportation processes, and the carbon emissions of the transportation modes are added to form the total carbon emissions of the transportation process of this chain; the carbon emissions of node transshipment are calculated in combination with the container volume of the transshipment process, and the carbon emissions of the nodes on the chain are added; the carbon emissions of each chain are obtained by adding the carbon emissions of the transportation process and the transshipment process; finally, the carbon emissions of all chains in the network are added to estimate the carbon emissions of the entire network.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, call all or part of the steps of the method according to any one of claims 1 to 9.

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