Method and system for carbon emission accounting and emission reduction optimization in plateau mountain area highway construction period

Abstract

The invention relates to the technical field of carbon emission accounting and emission reduction optimization, and discloses a carbon emission accounting and emission reduction optimization method and system in a plateau mountain area highway construction period. The problems of low accounting precision, inaccurate emission reduction target spot identification and insufficient measure pertinence caused by fuzzy boundary, neglect of network topology characteristics and dependence on static factors in carbon emission accounting in the plateau mountain area highway construction period in the prior art are solved. The method comprises the steps of firstly obtaining a plateau mountain area highway construction period multi-source data set and performing preprocessing; constructing a supply chain network-graph theory fusion model, and abstracting a supply chain network system into a directed weighted multi-attribute graph; then, a multi-dimensional accounting method is adopted to calculate the carbon emission of the whole chain in the construction period; then, in combination with a network analysis algorithm and node carbon emission intensity, identifying a carbon emission key node and a key inferior path; and finally, generating a node-path-network three-level differential emission reduction optimization strategy based on an identification result of the carbon emission key node and the key inferior path.

Description

Technical Field

[0001] This invention relates to the field of carbon emission accounting and emission reduction optimization technology, specifically to a method and system for carbon emission accounting and emission reduction optimization during the construction period of highways in plateau and mountainous areas. Background Technology

[0002] The low-carbon transformation of transportation infrastructure has become a key issue for sustainable development. Due to the complex terrain, high bridge-to-tunnel ratio, and long supply chain network of highways in plateau and mountainous areas, carbon emissions during the construction period not only involve direct construction machinery operations, but are also widely distributed in upstream links such as building material production and long-distance transportation, forming a complex emission system with multiple nodes and multiple paths.

[0003] Currently, Life Cycle Assessment (LCA) is the mainstream method for quantifying carbon emissions from infrastructure. However, traditional LCA models have certain limitations: on the one hand, the accounting boundaries are vague, making it difficult to comprehensively cover indirect emissions and losses across the entire supply chain; on the other hand, simplifying the supply chain network into a linear process ignores its topological characteristics, failing to reveal the deep-seated driving mechanisms of spatial heterogeneity in carbon emissions. Furthermore, existing research largely relies on static emission factors, failing to fully consider dynamic energy consumption changes in high-altitude mountainous areas, additional losses due to terrain, and regional differences. Some models are limited by specific geological conditions, lacking universality and failing to accurately match the carbon emission accounting needs under complex terrain.

[0004] The shortcomings of traditional methods directly lead to bottlenecks in carbon emission control: First, there is a lack of quantitative analysis of the coupling relationship between "network structure and carbon emission distribution," making it impossible to clarify the transmission path through which natural geographical conditions indirectly affect carbon emissions by shaping network structure. Second, it is difficult to identify key emission reduction targets at the system structure level; existing emission reduction measures are mostly "one-size-fits-all," failing to develop differentiated strategies for high-emission nodes and inefficient pathways, resulting in insufficient targeting and effectiveness. Furthermore, the unique natural conditions of plateau mountainous areas amplify these problems. Steep terrain leads to longer transportation routes, significantly increased empty-load losses and additional emissions from steep slopes, and complex geological conditions result in vastly different carbon emission intensity differences at engineering nodes. Traditional methods fail to fully incorporate these nonlinear influencing factors, leading to poor accuracy in carbon emission accounting, obscuring key emission pathways and core nodes, and severely hindering the precise control of carbon emissions and the low-carbon transformation process during the construction period of highways in plateau mountainous areas. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a method and system for carbon emission accounting and emission reduction optimization during the construction period of highways in plateau and mountainous areas, which solves the problems of low accounting accuracy, inaccurate identification of emission reduction targets, and insufficient targeting of measures caused by the fuzzy boundaries, neglect of network topology characteristics, and reliance on static factors in the carbon emission accounting of highways in plateau and mountainous areas during the construction period of highways.

[0006] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0007] On the one hand, this invention provides a method for carbon emission accounting and emission reduction optimization during the construction period of highways in plateau and mountainous areas, including the following steps:

[0008] S1. Obtain and preprocess multi-source datasets from the construction period of highways in plateau and mountainous areas to obtain standardized datasets;

[0009] S2. Based on the standardized dataset, construct a supply chain network-graph theory fusion model, abstract the supply chain network system into a directed weighted multi-attribute graph, and define the node set, edge set, and attribute vector;

[0010] S3. Based on the aforementioned supply chain network-graph theory fusion model, a multi-dimensional accounting method is used to calculate the total carbon emissions during the construction period, including direct emissions, indirect emissions, and virtual node carbon emissions;

[0011] S4. Combining network analysis algorithms with node carbon emission intensity, identify key carbon emission nodes and key poor-quality paths;

[0012] S5. Based on the identification results of key carbon emission nodes and key poor-quality pathways, and combined with network structure characteristics and node attributes, a three-level differentiated emission reduction optimization strategy of nodes-pathways-network is generated.

[0013] Furthermore, in step S1, the multi-source dataset includes engineering design data, supply chain network data, geospatial data, mechanical fuel efficiency data, emission factor data, and vehicle model and road condition data;

[0014] The engineering design data includes the layout and scale of engineering nodes, as well as the physical quantity information of five core engineering stages: roadbed, bridges and culverts, tunnels, pavement, and roadside facilities.

[0015] The supply chain network data includes node connection relationships, material transportation routes and effective transportation distances, load factor, and terrain correction coefficient.

[0016] The geospatial data includes terrain slope, elevation, and geological conditions.

[0017] Furthermore, in step S1, the preprocessing includes missing value imputation, outlier removal, and construction of a dynamic loss coefficient library; the dynamic loss coefficient library covers the unloaded coefficient and terrain coefficient corresponding to different vehicle types, road conditions, and average slope ranges.

[0018] Furthermore, step S2 specifically includes:

[0019] S21. Define the set of nodes and the set of edges: The construction site, material yard, and transfer station are defined as the set of nodes, and the material transportation path is defined as the set of edges. The attribute vector of the edge includes the effective transportation distance, the carbon emission intensity per unit of transportation, the terrain correction coefficient, and the empty return coefficient.

[0020] S22. Divide the network into layers: Divide the supply chain network into supply layer, transit layer and consumption layer, classify the nodes of each layer based on topology sorting, and clarify the connection relationship between nodes and the flow of materials;

[0021] S23. Construct a node-edge association matrix: Integrate node geographic coordinates, carbon emission potential, node type, and edge attribute information to form a complete supply chain network-graph theory fusion model.

[0022] Furthermore, in step S3, the calculation of the total carbon emissions during the construction period using a multi-dimensional accounting method includes:

[0023] S31. Calculate carbon emissions from mechanical operations ,in, For the actual quantity of the project, Fuel consumption coefficient per unit volume Carbon emission factors for diesel fuel;

[0024] S32. Calculate carbon emissions from building materials production. ,in, For the quality of all kinds of building materials, This corresponds to the carbon emission factors in building materials production;

[0025] S33. Calculate carbon emissions from building material transportation. ;in, For building material transportation volume, For effective transportation distance, This is the load factor. This is the terrain correction factor. This is the no-load return coefficient. Carbon emission factors from fuel;

[0026] S34. Calculate carbon emissions from electricity consumption. ;in, Rated power of the equipment This refers to the actual running time. For load rate, For regional power grid emission factors;

[0027] S35. Calculate the carbon emissions of virtual nodes. carbon emissions of virtual nodes The sum of carbon emissions from empty return transport and additional carbon emissions from steep slopes;

[0028] S36. Calculate the total carbon emissions of the network. ;in, , , , These are the mechanical operations, building material production, electricity consumption, and virtual node carbon emissions for each node.

[0029] Furthermore, step S4 specifically includes:

[0030] S41. A hierarchical BFS algorithm is adopted to allocate carbon emissions layer by layer according to the topological sorting of the supply layer, transit layer and consumption layer, and to obtain the carbon emissions received by the consumption layer nodes.

[0031] S42. The upstream path of high-emission work sites is tracked by the multi-objective constrained DFS algorithm, and the carbon emission contribution of each path to the shared road segment is decomposed and quantified by combining the Shapley value.

[0032] S43. Constructing Node Carbon Emission Intensities: Supply-Driven Node Carbon Intensities Carbon intensity at the consumption node Carbon intensity at transition nodes ,in, The total carbon emissions of supply-side nodes. For total supply, This refers to the total carbon emissions from consumption-based nodes. For the scale of the consumption node project, The total carbon emissions at the transition node, Total transit volume;

[0033] S44. The Brandes algorithm is used to calculate node betweenness centrality, carbon emission communities are divided through spectral clustering, and path evaluation indices are combined. Identify key defective paths, among which, Carbon emissions from the pathway This represents the total carbon emissions from the network. The mean of the betweenness centrality of path-related nodes. For path length, The average carbon emission intensity along the route. The average carbon intensity of the network. , , These are the weighting coefficients;

[0034] S45. By using variance decomposition analysis, we can quantify the explanatory power of natural factors, engineering factors, and network structure characteristics on the spatial heterogeneity of carbon emissions, and screen key emission reduction nodes with high betweenness centrality and high carbon intensity.

[0035] Furthermore, step S5 specifically includes:

[0036] S51. Target Information Integration and Constraint Analysis: Summarize the core emission issues of key emission reduction nodes and key poor emission pathways, and combine geospatial data, engineering design data and supply chain network data to clarify the boundary constraints of terrain, production capacity and transportation capacity for strategy implementation;

[0037] S52. Node-level strategy generation: Based on the differences in carbon intensity characteristics and attributes of supply-oriented, consumption-oriented, and medium-term transformation key nodes, differentiated measures such as low-emission substitution, equipment energy-saving retrofit, and transportation loss optimization are adopted respectively.

[0038] S53. Path-level strategy generation: Based on the structural defects of key poor-quality paths, improve path emission efficiency through transportation scheduling optimization, route adjustment, and tool upgrades;

[0039] S54. Network-level strategy generation: Combining the characteristics of the supply chain network topology, a multi-center, distributed network structure is constructed by adding backup nodes, path diversion, and hierarchical collaborative optimization to reduce single node / path dependence.

[0040] Secondly, the present invention also provides a carbon emission accounting and emission reduction optimization system for the construction period of highways in plateau mountainous areas, including: a multi-source data acquisition and preprocessing module, a supply chain network-graph theory fusion modeling module, a multi-dimensional carbon emission accounting module, a network analysis and target identification module, and a differentiated emission reduction strategy generation module connected in sequence.

[0041] The multi-source data acquisition and preprocessing module is used to acquire multi-source datasets, perform preprocessing, and output standardized datasets.

[0042] The supply chain network-graph theory fusion modeling module is used to construct a supply chain network-graph theory fusion model based on a standardized dataset, abstracting the supply chain network system into a directed weighted multi-attribute graph, and specifying the node set, edge set, and attribute vector;

[0043] The multi-dimensional carbon emission accounting module is used to calculate the total carbon emissions during the construction period based on the supply chain network-graph theory fusion model and using a multi-dimensional accounting method. The carbon emissions include direct emissions, indirect emissions, and virtual node carbon emissions.

[0044] The network analysis and target identification module is used to combine network analysis algorithms with node carbon emission intensity to identify key carbon emission nodes and key poor-quality paths.

[0045] The differentiated emission reduction strategy generation module is used to generate a three-level differentiated emission reduction optimization strategy based on the identification results of key carbon emission nodes and key poor-quality paths, combined with network structure characteristics and node attributes.

[0046] Thirdly, the present invention also provides a computer device, including a processor and a memory, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the carbon emission accounting and emission reduction optimization method for the construction period of highways in plateau mountainous areas as described above.

[0047] Fourthly, the present invention also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the carbon emission accounting and emission reduction optimization method for the construction period of highways in plateau mountainous areas as described above.

[0048] The beneficial effects of this invention are:

[0049] This invention addresses the shortcomings of traditional methods in terms of data completeness and accuracy through multi-source data preprocessing and the construction of a dynamic loss coefficient library, providing high-quality basic data support for carbon emission accounting. By leveraging a supply chain network-graph theory fusion model, the accounting dimension is expanded from geographic space to network topology space, overcoming the limitations of traditional linear accounting and achieving precise quantification of direct emissions, indirect emissions, and virtual node losses across the entire chain. Through hierarchical BFS, multi-objective constrained DFS, Shapley value decomposition, and other algorithms combined with a node carbon profiling system, it accurately identifies key nodes with high betweenness centrality and critical poor paths, clearly analyzing the transmission mechanism of "natural conditions → network structure → carbon emissions," solving the problem of ambiguous emission reduction targets in traditional methods. The resulting three-tiered differentiated emission reduction strategy of "node-path-network" achieves efficient allocation of emission reduction resources, addressing the lack of specificity in traditional emission reduction measures. It provides a scientific tool for precise carbon emission control during the construction of highways in plateau and mountainous areas, effectively supporting the low-carbon transformation of transportation infrastructure, and has significant engineering practice value and policy reference significance. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating the carbon emission accounting and emission reduction optimization method during the construction period of a highway in a plateau mountainous area, as described in this embodiment of the invention.

[0051] Figure 2 This is a structural block diagram of the carbon emission accounting and emission reduction optimization system during the construction period of a highway in a plateau mountainous area, as described in this embodiment of the invention. Detailed Implementation

[0052] This invention aims to provide a method and system for carbon emission accounting and emission reduction optimization during the construction period of highways in high-altitude mountainous areas. It addresses the problems of low accuracy, inaccurate target identification, and insufficient targeted measures in existing technologies for carbon emission accounting during highway construction in high-altitude mountainous areas due to ambiguous boundaries, neglect of network topology characteristics, reliance on static factors, and other issues. The core idea is to deeply integrate supply chain networks with graph theory, abstracting the supply chain network into a directed weighted multi-attribute graph, incorporating dynamic losses and terrain features of high-altitude mountainous areas to achieve precise quantification of carbon emissions across the entire chain. Network analysis algorithms are used to uncover the coupling relationship between "network structure and carbon emissions," accurately locating key emission reduction nodes and poorly performing paths. Finally, differentiated strategies are formulated from three dimensions: nodes, paths, and network, achieving precise control and efficient emission reduction of carbon emissions during the construction period of highways in high-altitude mountainous areas, providing a systematic solution for the low-carbon transformation of transportation infrastructure.

[0053] More specifically, the technical means employed by this invention to achieve the above-mentioned core ideas include:

[0054] (1) Systematically collect six types of multi-source data, including engineering design, supply chain network, geospatial data, mechanical energy consumption, emission factors, and vehicle and road conditions, covering multiple dimensions such as engineering, supply chain, geography, and energy consumption, to solve the problem of traditional data fragmentation.

[0055] (2) The method of multiple imputation of missing values ​​and Grubbs test to remove outliers is adopted to ensure data integrity and reliability; a dynamic loss coefficient library is innovatively constructed, covering the empty load coefficient and terrain coefficient of different vehicle types, road conditions and slope ranges, to adapt to the dynamic energy consumption change characteristics of plateau and mountainous areas.

[0056] (3) The supply chain network is abstracted into a directed weighted multi-attribute graph. Construction sites, material yards, and transfer stations are defined as node sets, and material transportation paths are defined as edge sets. Edge attributes include key parameters such as effective distance and terrain correction coefficient, so as to realize the topological expression of the supply chain network.

[0057] (4) The network is divided into supply layer, transit layer and consumption layer by topological sorting, the connection relationship between nodes and the flow of materials are clarified, the "node-edge" correlation matrix is ​​constructed, and the connection bridge between network topology and carbon emissions is established, breaking through the limitations of traditional linear modeling.

[0058] (5) Innovatively adopts a dual-dimensional accounting approach of “node emissions + path emissions”, which covers emissions from nodes such as mechanical operations, building material production, and electricity consumption, and separately accounts for emissions from transportation paths. At the same time, a virtual node carbon emission item is added to accurately quantify empty return and additional losses on steep slopes.

[0059] (6) Design exclusive accounting formulas for different emission sources, and introduce parameters with characteristics of plateau and mountainous areas such as terrain correction coefficient, empty return coefficient, and load rate to achieve accurate quantification of the entire chain of direct emission, indirect emission, and virtual node emission, and solve the problem of fuzzy boundary in traditional accounting.

[0060] (7) Integrate hierarchical BFS algorithm (network emission distribution calculation), multi-objective constrained DFS algorithm (path tracing), Shapley value decomposition (path contribution quantification), Brandes algorithm (node ​​centrality calculation), and spectral clustering (carbon emission community division) to accurately identify key nodes and key inferior paths with high betweenness centrality and high carbon intensity.

[0061] (8) Based on the target identification results, combined with network structure characteristics and node attributes, we formulate targeted differentiated strategies for precise carbon reduction at nodes, path efficiency improvement and network structure reconstruction, namely, the three levels of “node-path-network”, to achieve optimal allocation of emission reduction resources and break through the traditional “one-size-fits-all” emission reduction mode.

[0062] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0063] Example 1

[0064] This embodiment uses the Jiweishan Tunnel Cluster of a highway in the southwestern plateau mountainous region as an application example to illustrate the application of the present invention's solution in carbon emission accounting and optimization of key emission reduction targets for extra-long tunnel clusters. (See also...) Figure 1 The specific implementation process is as follows:

[0065] S1. Multi-source data acquisition and preprocessing

[0066] This step addresses the issues of scattered, missing, and abnormal data, as well as insufficient dynamic loss parameters in plateau and mountainous areas, by constructing a comprehensive data acquisition system and performing standardized preprocessing. This provides a high-quality, standardized data foundation for subsequent modeling and calculation.

[0067] The multi-source data acquisition and preprocessing process in this embodiment is as follows:

[0068] S11. Data Acquisition System Setup

[0069] Construct a multi-dimensional data collection framework that includes engineering design data, supply chain network data, geospatial data, mechanical fuel efficiency data, emission factor data, and vehicle and road condition data.

[0070] Engineering design data: Extract physical quantity information such as tunnel excavation volume, concrete usage, and steel usage from project design drawings, covering five core engineering stages: roadbed, bridges and culverts, tunnels, pavement, and roadside facilities.

[0071] Supply chain network data: Through material yard transportation ledgers and transfer station scheduling records, the connection relationship between 12 quarries, 6 transfer stations and the Jiweishan Tunnel construction site was collected, as well as attributes such as core route transportation frequency, effective transportation distance and load factor.

[0072] Geospatial data: Based on GIS technology, parameters such as topographic slope, elevation, and geological conditions of the project area are extracted and processed using ArcGIS software to generate a slope raster map.

[0073] Mechanical fuel efficiency data: On-site measurements were conducted for 7 consecutive days on 12 main construction equipment, including excavators and loaders, and the fuel consumption coefficient per unit of physical quantity was recorded.

[0074] Emission factor data: Referencing the "Guidelines for the Compilation of Provincial Greenhouse Gas Inventories," the carbon emission factors for diesel fuel were determined. Regional power grid emission factors Emissions from concrete production .

[0075] Vehicle and road condition data: Collect data on the load capacity, fuel consumption characteristics, and road condition monitoring of transport vehicles (15 heavy trucks).

[0076] S12. Data Preprocessing

[0077] Preprocessing includes missing value imputation, outlier removal, and construction of a dynamic loss coefficient library; the dynamic loss coefficient library covers the unload coefficient and terrain coefficient corresponding to different vehicle types, road conditions, and average slope ranges.

[0078] Missing value imputation: Multiple imputation methods were used to supplement 3.2% of missing fuel consumption data and 1.5% of missing transportation distance data to ensure data integrity.

[0079] Outlier removal: 2.1% of the abnormal excavation data and 1.8% of the abnormal transportation data were identified and removed by Grubbs test. The test statistic G=2.89 (P<0.05).

[0080] A dynamic loss coefficient library was constructed to determine the no-load coefficient for this scenario based on different vehicle types, road conditions, and average gradient ranges. Topographic coefficient This provides parameter support for subsequent accounting.

[0081] S2. Supply Chain Network - Graph Theory Fusion Modeling

[0082] This step abstracts the complex supply chain network into a quantifiable and analyzable directed weighted multi-attribute graph, clarifying the relationships and attribute characteristics between nodes and paths, thus laying the model foundation for revealing the impact mechanism of network topology on carbon emissions.

[0083] In this embodiment, the process of supply chain network-graph theory fusion modeling is as follows:

[0084] S21. Node and Edge Attribute Definitions

[0085] Node set: The core node set is defined as the Jiweishan Tunnel construction site (consumption-type node), 12 quarries (supply-type nodes), and 6 transfer stations (transfer-type nodes), totaling 19 nodes. The geographical coordinates, node type, carbon emission potential, and other attributes of each node are recorded.

[0086] Edge set: The material transportation path is defined as an edge set, consisting of 28 core edges. The attribute vector of each edge contains the effective transportation distance. Carbon emission intensity per unit of transportation (initial value) Terrain correction factor No-load return coefficient .

[0087] S22. Network Hierarchy Division

[0088] The supply chain network is divided into three levels using a topological sorting algorithm: the supply layer contains 12 quarries, which are the source of material supply; the transit layer contains 6 transit stations, which are responsible for the temporary storage and transfer of materials; and the consumption layer is the Jiweishan Tunnel construction site, which is the final consumption node of materials. The material flow direction is clearly defined as "supply layer - transit layer - consumption layer", forming a unidirectional flow network structure.

[0089] S23. Construction of the "Node-Edge" Association Matrix

[0090] By integrating the attribute information of nodes and edges, a 19×19 dimension association matrix is ​​constructed. The matrix elements represent the connection relationship and path weight between nodes (weight = material transportation volume × effective transportation distance × terrain correction coefficient). Among them, the connection weight between the Jiweishan Tunnel node and the three core transfer stations accounts for 68%, highlighting its core consumption status.

[0091] S3. Multidimensional carbon emission accounting

[0092] This step achieves precise quantification of carbon emissions across the entire chain from five dimensions: mechanical operations, building material production, transportation, electricity consumption, and virtual nodes, clarifying the composition and proportion of each emission item.

[0093] In this embodiment, the multi-dimensional carbon emission accounting process is as follows:

[0094] S31. Carbon emissions from mechanical operations ( )calculate

[0095] Carbon emissions from mechanical operations mainly come from fuel consumption of equipment used in tunnel excavation, concrete pouring, and other similar activities. The calculation formula is as follows:

[0096] ;

[0097] In this embodiment, (Total tunnel excavation volume) (Fuel consumption coefficient per unit excavation volume of excavator). (Diesel carbon emission factor), after substituting, we get:

[0098] .

[0099] S32. Carbon emissions from building materials production ( )calculate

[0100] Carbon emissions from building materials production encompass the production processes of major building materials such as concrete and steel. The calculation formula is as follows:

[0101] ;

[0102] In this embodiment, the amount of concrete used , Steel consumption , Substituting the values, we get: .

[0103] S33. Carbon emissions from building materials transportation ( )calculate

[0104] Carbon emissions from transportation, taking into account factors such as terrain and load factor, are calculated using the following formula:

[0105] ;

[0106] In this embodiment, the total building material transportation volume , (Average effective transport distance) (Average load factor) (Terrain correction factor) (No-load return coefficient) Substituting the values, we get: .

[0107] S34. Carbon emissions from electricity consumption ( )calculate

[0108] Electricity consumption mainly comes from ventilation, lighting, and mixing equipment. The calculation formula is as follows:

[0109] ;

[0110] In this embodiment, the ventilation equipment Running time , Mixing equipment , , , Substituting the values, we get: .

[0111] S35. Carbon emissions from virtual nodes ( )calculate

[0112] The calculation formula for unloaded returns and additional emissions on steep slopes is as follows:

[0113] ;

[0114] In this embodiment, the no-load distance , Steep slope distance , Substituting the values, we get:

[0115] S36. Total carbon emissions from the network ( )calculate

[0116] Total emissions are the sum of emissions from each node and along the path, calculated using the following formula:

[0117] ;

[0118] Substituting the data into each item, we get: .

[0119] S4. Network Analysis and Target Identification

[0120] This step uses network analysis algorithms to uncover the driving mechanisms of spatial heterogeneity in carbon emissions, accurately identify key emission reduction nodes and key poor pathways, and solve the problems of vague target identification and lack of quantitative support in traditional methods.

[0121] In this embodiment, the network analysis and target identification process is as follows:

[0122] S41. Hierarchical BFS network distributed computation

[0123] Algorithm parameter settings: Starting from the supply layer node, the topology sorting priority is sorted by the material supply quantity, and the weight coefficient = material quantity × effective transportation distance × terrain-road condition comprehensive correction coefficient (β=0.27).

[0124] Calculation process: Nodes were visited layer by layer from the supply layer to the transit layer to the consumption layer to allocate carbon emission shares. The results showed that the carbon emissions received by the Jiweishan Tunnel construction site accounted for 62.3% of the total emissions of the cluster, making it a core emission node.

[0125] S42. Multi-objective constrained DFS path tracing

[0126] Constraint settings: capacity constraint (daily transport volume per single route ≤ 5000 tons), time window constraint (transport period 6:00-20:00).

[0127] Path identification results: Three core upstream paths were traced, among which "Yongping County Taian Quarry → Transfer Station C → Jiweishan Tunnel" is the most critical path, accounting for 38% of the transportation volume.

[0128] Shapley value decomposition: The core route contributes 41.2% to the carbon emissions of the shared transit section, which is higher than the simple proportional allocation result (28.5%).

[0129] S43. Construction of Nodal Carbon Emission Intensity System

[0130] Supply-side node carbon intensity: Taian Quarry, Yongping County .

[0131] Consumption-based node carbon intensity: Jiweishan Tunnel .

[0132] Carbon intensity at transit nodes: Transit station C .

[0133] S44. Key Target Identification

[0134] Centrality calculation: Using Brandes' algorithm, the betweenness centrality of Jiweishan Tunnel is 0.87 (first in the entire network), and the compactness centrality is 0.65.

[0135] Spectral cluster analysis: One core carbon emission community was identified (including the Jiweishan Tunnel and three transit stations).

[0136] Path Evaluation Index: Core Path .

[0137] This path ranks first, making it a critical, low-quality path.

[0138] S5. Generate differentiated emission reduction strategies

[0139] In this step, based on the key target identification results, differentiated strategies are formulated from three dimensions: nodes, paths, and networks, to achieve precise allocation of emission reduction resources and to verify the effectiveness and engineering applicability of the method of this invention.

[0140] In this embodiment, the process of generating differentiated emission reduction strategies and verifying their implementation effects is as follows:

[0141] S51. Formulation of a Three-Tier Emission Reduction Strategy

[0142] At the node level: Energy-saving retrofitting of construction machinery was implemented at the Jiweishan Tunnel (a high-carbon intensity, high-centrality node) (replacing equipment with electric ventilation systems, reducing power consumption by 30%), directly reducing direct emissions at the consumption node; a nearby low-emission material yard was introduced. This strategy replaces 30% of the supply from the Taian Quarry in Yongping County, reducing the emission contribution of the supply node. It is based on the carbon intensity and centrality characteristics of the node and is highly targeted.

[0143] At the route level: Optimize core poor-quality routes, increase the load factor Fl to 0.85 (to solve the problem of low route efficiency), adjust transportation routes to shorten the effective distance to 58km, and reduce terrain loss. This strategy is based on the structural defects reflected by the route evaluation index and directly addresses the core problems of the route.

[0144] At the network level: Two backup supply nodes (quarry D and E) are added to build a "multi-center, distributed" network structure to reduce single path dependence. This strategy is based on the network structure-driven mechanism and improves the network's anti-interference capability and emission reduction potential at the system level.

[0145] S52. Implementation Effect Verification

[0146] Six key indicators were selected for comparison before and after optimization, and the results are shown in Table 1 below:

[0147] Table 1. Comparison of the effects before and after emission reduction optimization.

[0148]

[0149] Verification results show that the method of the present invention can accurately identify key emission reduction targets, achieve efficient emission reduction through differentiated strategies, significantly improve the calculation accuracy, and fully meet the carbon emission control requirements during the construction period of highways in plateau and mountainous areas.

[0150] Example 2

[0151] This embodiment uses the supply chain transportation network of the Panshi-Laohongpo tunnel group on a highway in the mountainous area of ​​southwestern plateau as an example to illustrate the application of the present invention's solution in optimizing critical inferior routes in complex terrain supply chain networks. This tunnel group includes 5 tunnels (total length 9870 meters), with a bridge-to-tunnel ratio of 82.7%. The core transportation route suffers from the problems of "long distance, high gradient, and low efficiency." See also... Figure 1 The specific implementation process is as follows:

[0152] S1. Multi-source data acquisition and preprocessing

[0153] S11. Data Acquisition

[0154] Engineering design data: Extracted concrete usage of 350,000 m³ and steel usage of 28,000 tons for the tunnel group, clarifying the material demand distribution at each tunnel construction site.

[0155] Supply chain network data: Records three quarries (A, B, C) and three core transportation routes (P1, P2, P3) of the tunnel group, and collects data such as transportation volume, effective distance, and load factor for each route.

[0156] Geospatial data: Path slope data (P1=28°, P2=22°, P3=19°) and elevation difference (P1 maximum elevation difference 420m) were obtained through GIS technology.

[0157] Vehicle and road condition data: Technical parameters of 18 transport vehicles (load capacity 30 tons) were collected, a dynamic loss coefficient database was constructed, and the empty load coefficient (P1=0.58, P2=0.55, P3=0.52) and terrain coefficient (P1=0.30, P2=0.25, P3=0.22) for each route were determined.

[0158] Emission factor data: diesel Regional power grid .

[0159] S12. Data Preprocessing

[0160] Outlier removal: 1.8% of outlier transport volume data were removed using the Grubbs test (G=2.76, P<0.05).

[0161] Missing value supplementation: Multiple interpolation was used to supplement 2.5% of the missing slope data and 1.3% of the missing fuel consumption data.

[0162] Data standardization: Z-score standardization is performed on data such as path length, slope, and transportation volume to provide a basis for modeling.

[0163] S2. Supply Chain Network - Graph Theory Fusion Modeling

[0164] S21. Node and Edge Attribute Definitions

[0165] Node set: includes 3 material yards, 5 tunnel construction sites, and 2 transfer stations, totaling 10 core nodes, recording the geographical coordinates of the nodes and their material demand / supply capacity.

[0166] Edge set: 3 transportation paths (P1, P2, P3), edge attribute vectors are shown in Table 2 below:

[0167] Table 2. List of edge attribute vectors

[0168]

[0169] S22. Network Hierarchy

[0170] Supply layer: material yards A, B, and C.

[0171] Transit Level: Transit stations M1 and M2.

[0172] Consumer layer: 5 tunnel construction sites (the core being Panshi Tunnel and Laohongpo Tunnel).

[0173] S23. Construction of the association matrix

[0174] Construct a 10×10 node-edge association matrix and identify path P1 as connecting material yard A and Panshi Tunnel (core consumption node), accounting for 37% of the transportation volume, making it the main transportation path.

[0175] S3. Multidimensional carbon emission accounting

[0176] S31. Carbon emissions from transportation along various routes ( )calculate

[0177] Path P1: .

[0178] Path P2: .

[0179] Path P3: .

[0180] S32. Carbon emissions from virtual nodes ( )calculate

[0181] Path P1: (Steep slope losses account for 58.2%, and no-load losses account for 41.8%).

[0182] Path P2: .

[0183] Path P3: .

[0184] S303. Calculation of Total Emissions from Transport .

[0185] It accounts for 45.7% of the total indirect emissions from the tunnel complex.

[0186] S4. Network Analysis and Target Identification

[0187] S41. Hierarchical BFS Calculation

[0188] Carbon emissions are allocated from the supply layer to the transit layer to the consumption layer. Pathway P1 contributes 37.2% of total transportation emissions and is the core emission path.

[0189] S42. Shapley value decomposition

[0190] Route P1 contributes 39.6% to the carbon emissions of the shared transit section (transit station M1), which is higher than the 32.1% contribution of a simple proportional allocation.

[0191] S43. Path Evaluation Index ( )calculate .

[0192] Similarly, calculate , Therefore, path P1 is the core inferior path.

[0193] S44. Constraints and Problem Diagnosis

[0194] Terrain constraints: P1 has an average slope of 28°, and three consecutive steep slope sections (total length 12km) result in additional energy consumption.

[0195] Efficiency constraints: The load factor is only 62%, lower than the industry average (75%).

[0196] Structural constraints: high single-path dependency and lack of alternative paths.

[0197] S5. Generate differentiated emission reduction strategies

[0198] S51. Three-level optimization strategy

[0199] Route-level improvements: ① Optimized transport scheduling, increasing load factor to 0.85; ② Route adjustments to avoid three steep slopes, shortening the effective distance to 66km; ③ Use of new energy vehicles, reducing carbon emission intensity per unit of transport to [missing information]. .

[0200] At the node level: A transfer station M3 is added in the middle of P1 to split the long-distance transportation into "material yard A → M3 → tunnel group" to reduce empty load losses.

[0201] At the network level: 30% of the traffic volume of P1 will be diverted to P2 and P3 to build a "primary and secondary" network structure.

[0202] S52. Implementation Effectiveness Verification

[0203] The comparison of key indicators before and after optimization is shown in Table 3 below:

[0204] Table 3 Comparison of Emission Reduction Optimization Effects Before and After

[0205]

[0206] The above verification results show that the method of the present invention can accurately locate critical poor-quality routes in complex terrain. Through route optimization, node addition and network structure adjustment, it can significantly reduce transportation emissions, improve transportation efficiency and reduce costs, and has strong engineering applicability.

[0207] Example 3

[0208] This embodiment provides a carbon emission accounting and emission reduction optimization system for the construction period of highways in plateau mountainous areas. (See also...) Figure 2 The system includes a multi-source data acquisition and preprocessing module, a supply chain network-graph theory fusion modeling module, a multi-dimensional carbon emission accounting module, a network analysis and target identification module, and a differentiated emission reduction strategy generation module, which are connected in sequence.

[0209] The specific functions of each module are explained below:

[0210] Multi-source data acquisition and preprocessing module: used to acquire multi-source datasets and preprocess them to output standardized datasets.

[0211] Supply Chain Network-Graph Theory Fusion Modeling Module: Used to construct a supply chain network-graph theory fusion model based on a standardized dataset, abstracting the supply chain network system into a directed weighted multi-attribute graph, and specifying the set of nodes, the set of edges, and the attribute vectors;

[0212] Multi-dimensional carbon emission accounting module: Based on the supply chain network-graph theory fusion model, it uses a multi-dimensional accounting method to calculate the total carbon emissions during the construction period. The carbon emissions include direct emissions, indirect emissions, and virtual node carbon emissions.

[0213] Network analysis and target identification module: used to combine network analysis algorithms with node carbon emission intensity to identify key carbon emission nodes and key poor-quality paths;

[0214] Differentiated emission reduction strategy generation module: Based on the identification results of key carbon emission nodes and key poor-quality paths, combined with network structure characteristics and node attributes, it generates a three-level differentiated emission reduction optimization strategy of nodes-path-network.

[0215] In addition, the system may also include a visualization module, which is signal-connected to the differentiated emission reduction strategy generation module, for real-time display of supply chain network topology, carbon emission spatial distribution characteristics, key target locations, and the implementation effect of emission reduction strategies.

[0216] It should be noted that the corresponding functions of each module in the aforementioned carbon emission accounting and reduction optimization system for the construction period of expressways in plateau and mountainous areas correspond to the steps in the aforementioned carbon emission accounting and reduction optimization method for the construction period of expressways in plateau and mountainous areas. Based on the detailed description of the specific implementation process of each step, it also applies to the corresponding functions of each module in the system. Therefore, the specific implementation of the corresponding functions of each module in the system will not be elaborated here.

[0217] Although embodiments of the present invention have been described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present invention, and all such changes and alterations shall not depart from the protection scope of the present invention.

Claims

1. A method for carbon emission accounting and emission reduction optimization during the construction period of expressways in plateau and mountainous areas, characterized in that... Includes the following steps: S1. Obtain and preprocess multi-source datasets from the construction period of highways in plateau and mountainous areas to obtain standardized datasets; S2. Based on the standardized dataset, construct a supply chain network-graph theory fusion model, abstract the supply chain network system into a directed weighted multi-attribute graph, and define the node set, edge set, and attribute vector; S3. Based on the supply chain network-graph theory fusion model, a multi-dimensional accounting method is used to calculate the total carbon emissions during the construction period, including direct emissions, indirect emissions, and virtual node carbon emissions; S4. Combining network analysis algorithms with node carbon emission intensity, identify key carbon emission nodes and key poor-quality paths; S5. Based on the identification results of key carbon emission nodes and key poor-quality pathways, and combined with network structure characteristics and node attributes, a three-level differentiated emission reduction optimization strategy of nodes-pathways-network is generated.

2. The method for carbon emission accounting and emission reduction optimization during the construction period of expressways in plateau mountainous areas as described in claim 1, characterized in that, In step S1, the multi-source dataset includes engineering design data, supply chain network data, geospatial data, mechanical fuel efficiency data, emission factor data, and vehicle model and road condition data. The engineering design data includes the layout and scale of engineering nodes, as well as the physical quantity information of five core engineering stages: roadbed, bridges and culverts, tunnels, pavement, and roadside facilities. The supply chain network data includes node connection relationships, material transportation routes and effective transportation distances, load factor, and terrain correction coefficient. The geospatial data includes terrain slope, elevation, and geological conditions.

3. The method for carbon emission accounting and emission reduction optimization during the construction period of expressways in plateau mountainous areas as described in claim 1, characterized in that, In step S1, the preprocessing includes missing value imputation, outlier removal, and construction of a dynamic loss coefficient library; the dynamic loss coefficient library covers the unload coefficient and terrain coefficient corresponding to different vehicle types, road conditions, and average slope ranges.

4. The method for carbon emission accounting and emission reduction optimization during the construction period of expressways in plateau mountainous areas as described in claim 1, characterized in that, Step S2 specifically includes: S21. Define the set of nodes and the set of edges: The construction site, material yard, and transfer station are defined as the set of nodes, and the material transportation path is defined as the set of edges. The attribute vector of the edge includes the effective transportation distance, the carbon emission intensity per unit of transportation, the terrain correction coefficient, and the empty return coefficient. S22. Divide the network into layers: Divide the supply chain network into supply layer, transit layer and consumption layer, classify the nodes of each layer based on topology sorting, and clarify the connection relationship between nodes and the flow of materials; S23. Construct a node-edge association matrix: Integrate node geographic coordinates, carbon emission potential, node type and edge attribute information to form a complete supply chain network-graph theory fusion model.

5. The carbon emission accounting and emission reduction optimization method for the construction period of expressways in plateau mountainous areas as described in claim 4, characterized in that, In step S3, the calculation of the total carbon emissions during the construction period using a multi-dimensional accounting method includes: S31. Calculate carbon emissions from mechanical operations ,in, For the actual quantity of the project, Fuel consumption coefficient per unit volume Carbon emission factor for diesel fuel; S32. Calculate carbon emissions from building materials production. ,in, For the quality of all kinds of building materials, This corresponds to the carbon emission factors in building materials production; S33. Calculate carbon emissions from building material transportation ;in, For building material transportation volume, For effective transportation distance, This is the load factor. This is the terrain correction factor. This is the no-load return coefficient. Carbon emission factors from fuel; S34. Calculate carbon emissions from electricity consumption ;in, Rated power of the equipment This refers to the actual running time. For load rate, For regional power grid emission factors; S35. Calculate the carbon emissions of virtual nodes. carbon emissions of virtual nodes The sum of carbon emissions from empty return transport and additional carbon emissions from steep slopes; S36. Calculate the total carbon emissions of the network. ;in, , , , These are the mechanical operations, building material production, electricity consumption, and virtual node carbon emissions for each node.

6. The method for carbon emission accounting and emission reduction optimization during the construction period of expressways in plateau mountainous areas as described in claim 5, characterized in that, Step S4 specifically includes: S41. A hierarchical BFS algorithm is adopted to allocate carbon emissions layer by layer according to the topological sorting of the supply layer, transit layer and consumption layer, and to obtain the carbon emissions received by the consumption layer nodes. S42. The upstream path of high-emission work sites is tracked by the multi-objective constrained DFS algorithm, and the carbon emission contribution of each path to the shared road segment is decomposed and quantified by combining the Shapley value. S43. Constructing Node Carbon Emission Intensities: Supply-Driven Node Carbon Intensities Carbon intensity at consumption nodes Carbon intensity at transition nodes ,in, The total carbon emissions of supply-side nodes. For total supply, This refers to the total carbon emissions from consumption-based nodes. For the scale of the consumption node project, The total carbon emissions at the transition node, Total transit volume; S44. The Brandes algorithm is used to calculate node betweenness centrality, carbon emission communities are divided through spectral clustering, and path evaluation indices are combined. Identify key defective paths, among which, Carbon emissions from the pathway This represents the total carbon emissions from the network. The mean of the betweenness centrality of path-related nodes. For path length, The average carbon emission intensity along the route. The average carbon intensity of the network. , , These are the weighting coefficients; S45. By using variance decomposition analysis, we can quantify the explanatory power of natural factors, engineering factors, and network structure characteristics on the spatial heterogeneity of carbon emissions, and screen key emission reduction nodes with high betweenness centrality and high carbon intensity.

7. The method for carbon emission accounting and emission reduction optimization during the construction period of expressways in plateau mountainous areas as described in any one of claims 4 to 6, characterized in that, Step S5 specifically includes: S51. Target Information Integration and Constraint Analysis: Summarize the core emission issues of key emission reduction nodes and key poor emission pathways, and combine geospatial data, engineering design data and supply chain network data to clarify the terrain, production capacity and transportation capacity boundary constraints for strategy implementation; S52. Node-level strategy generation: Based on the differences in carbon intensity characteristics and attributes of supply-oriented, consumption-oriented, and medium-term transformation key nodes, differentiated measures such as low-emission substitution, equipment energy-saving retrofit, and transportation loss optimization are adopted respectively. S53. Path-level strategy generation: Based on the structural defects of key poor-quality paths, improve path emission efficiency through transportation scheduling optimization, route adjustment, and tool upgrades; S54. Network-level strategy generation: Combining the characteristics of the supply chain network topology, a multi-center, distributed network structure is constructed by adding backup nodes, path diversion, and hierarchical collaborative optimization to reduce single node / path dependence.

8. A carbon emission accounting and emission reduction optimization system for highways in plateau and mountainous areas during construction, characterized in that... include: The system consists of a multi-source data acquisition and preprocessing module, a supply chain network-graph theory fusion modeling module, a multi-dimensional carbon emission accounting module, a network analysis and target identification module, and a differentiated emission reduction strategy generation module, which are connected sequentially by signal. The multi-source data acquisition and preprocessing module is used to acquire multi-source datasets, perform preprocessing, and output standardized datasets. The supply chain network-graph theory fusion modeling module is used to construct a supply chain network-graph theory fusion model based on a standardized dataset, abstracting the supply chain network system into a directed weighted multi-attribute graph, and specifying the node set, edge set, and attribute vector; The multi-dimensional carbon emission accounting module is used to calculate the total carbon emissions during the construction period based on the supply chain network-graph theory fusion model and using a multi-dimensional accounting method. The carbon emissions include direct emissions, indirect emissions, and virtual node carbon emissions. The network analysis and target identification module is used to combine network analysis algorithms with node carbon emission intensity to identify key carbon emission nodes and key poor-quality paths. The differentiated emission reduction strategy generation module is used to generate a three-level differentiated emission reduction optimization strategy based on the identification results of key carbon emission nodes and key poor-quality paths, combined with network structure characteristics and node attributes.

9. A computer device comprising a processor and a memory, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the carbon emission accounting and emission reduction optimization method for the construction period of highways in plateau mountainous areas as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the carbon emission accounting and emission reduction optimization method for the construction period of highways in plateau mountainous areas as described in any one of claims 1 to 7.

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