Road life cycle carbon emission uncertainty analysis method, system, device and medium

Abstract

The present application provides a road full cycle carbon emission uncertainty analysis method, system, device and medium, belongs to the field of road construction evaluation, including obtaining the material and energy consumption in the road full life cycle, and the economic cost of service activity, matching the corresponding complete emission coefficient and expectation value; through the pedigree matrix evaluation data quality, the DQI score of the carbon emission factor and complete emission coefficient is obtained; further determine the triangular distribution describing the uncertainty of the carbon emission factor and complete emission coefficient; the process carbon emission and input-output carbon emission of road full life cycle are determined by each carbon emission factor, complete emission coefficient and corresponding consumption, cost, the total carbon emission of current simulation is determined by process carbon emission and input-output carbon emission; the uncertainty analysis result in the road full life cycle is determined based on the total carbon emission of multiple simulations. The method improves the accuracy and reliability of the final evaluation result.

Description

Technical Field

[0001] This invention belongs to the field of road construction assessment, specifically involving methods, systems, equipment, and media for uncertainty analysis of carbon emissions throughout the entire road lifecycle. Background Technology

[0002] As the core of infrastructure networks, roads consume enormous resources in their construction and long-term maintenance. From raw material extraction and construction to periodic maintenance and repair during long-term use and eventual demolition, roads generate significant carbon emissions throughout their entire lifecycle. Against this backdrop, uncertainty analysis of carbon emissions throughout the road lifecycle refers to a process that, based on a quantitative assessment of carbon emissions throughout the entire process, further identifies and quantifies the inherent quality defects and volatility of various key input parameters due to differences in data sources and technologies. Probabilistic statistical methods are then used to transfer this uncertainty into the final calculation results, thereby characterizing the reliability range and risk level of the carbon emission assessment results using statistical forms such as probability distributions and confidence intervals. This provides crucial quantitative evidence for low-carbon planning and design of road engineering, comparison of the emission reduction potential of different maintenance schemes, and full lifecycle carbon risk management.

[0003] To address these needs, existing technologies often employ process-based inventory analysis to trace and accumulate detailed material and energy consumption throughout the entire road lifecycle, matching it with corresponding carbon emission factors to quantify the impact of data uncertainty on the final result. However, due to the numerous service activities hidden within complex economic supply chains throughout the road lifecycle, these activities cannot be directly calculated based solely on material consumption. This results in uncertainty analysis being built on an incomplete and flawed carbon emission inventory, leading to poor accuracy and reliability of the analysis results. Summary of the Invention

[0004] To address the issue of poor accuracy and reliability of existing uncertainty analysis results, this invention provides a method, system, equipment, and medium for uncertainty analysis of carbon emissions throughout the entire road lifecycle.

[0005] To achieve the above objectives, the present invention provides the following technical solution: Methods for analyzing the uncertainty of carbon emissions throughout the entire lifecycle of roads include: Obtain the material and energy consumption throughout the entire life cycle of a road, as well as the economic costs of service activities where material or energy consumption cannot be tracked; match the corresponding carbon emission factors and expected values ​​based on the types of materials and energy, and match the corresponding total emission coefficients and expected values ​​based on the types of service activities. The data quality of each carbon emission factor and total emission coefficient throughout the road life cycle is evaluated by using a spectral matrix to obtain the DQI score of each carbon emission factor and total emission coefficient. The coefficient of variation of each carbon emission factor and total emission coefficient is determined based on the DQI score of each emission factor and total emission coefficient. The triangular distribution describing the uncertainty of the carbon emission factor and total emission coefficient is determined by the expected value and the coefficient of variation. In the triangular distribution of each carbon emission factor and total emission coefficient, the values ​​of the carbon emission factor and total emission coefficient in the current simulation are randomly selected. The process carbon emissions of the road throughout its entire life cycle are determined by each carbon emission factor and its corresponding consumption. The input-output carbon emissions of the road throughout its entire life cycle are determined by each total emission coefficient and its corresponding economic cost. The total carbon emissions of the current simulation are determined by the process carbon emissions and the input-output carbon emissions. The uncertainty analysis results in the road throughout its entire life cycle are determined based on the total carbon emissions of multiple simulations.

[0006] Optionally, the materials include road construction materials, and the energy includes fossil fuels and electricity. The uncertainty analysis method for the full life cycle carbon emissions of roads provided by this invention also includes: Determine the expected value of the carbon emission factor for each material in the carbon emission factor of road construction materials based on the types of road construction materials; The expected carbon emission factor values ​​for each fossil energy source and each type of electrical energy source are determined based on the types of fossil energy and electrical energy, respectively.

[0007] Optionally, the service activities include those during the material transportation, road construction, road maintenance, and pavement removal phases. The road full-cycle carbon emission uncertainty analysis method provided by this invention also includes: The expected value of the total emission coefficient for each service activity in the total emission coefficient is determined based on the type of service activity in the material transportation, road construction, road maintenance and pavement demolition stages.

[0008] Optionally, road maintenance includes pavement micro-surfacing maintenance. The road full-cycle carbon emission uncertainty analysis method provided by this invention also includes: Based on the consumption of road surface micro-surfacing maintenance materials in the materials, and combined with the carbon emission factor value extracted from the triangular distribution of road surface micro-surfacing maintenance materials, the total carbon emissions of road surface micro-surfacing maintenance materials in the process carbon emissions are determined. Based on the energy consumption of road surface micro-surfacing maintenance, and combined with the carbon emission factor value extracted from the triangular distribution of maintenance energy, the total carbon emissions of road surface micro-surfacing maintenance energy in the process carbon emissions are determined. Based on the economic costs of road micro-surfacing maintenance material logistics support services, road micro-surfacing maintenance operation services, and road micro-surfacing maintenance management services in the service activities, and combined with the complete emission coefficient values ​​extracted from the triangular distribution for road micro-surfacing maintenance material logistics support services, road micro-surfacing maintenance operation services, and road micro-surfacing maintenance management services, the input-output carbon emissions of road micro-surfacing maintenance are determined.

[0009] Optionally, road maintenance also includes surface milling and repaving. The road full-cycle carbon emission uncertainty analysis method provided by this invention also includes: Based on the consumption of road resurfacing material in the materials, and combined with the carbon emission factor value extracted from the triangular distribution of road resurfacing material, the total carbon emission of surface milling and resurfacing material in the process carbon emission is determined. Based on the energy consumption of road milling and road repaving, and combined with the carbon emission factor values ​​extracted from the triangular distribution of energy used for road milling and road repaving, the total carbon emissions of surface layer milling and repaving energy in the process carbon emissions are determined. Based on the economic costs of road resurfacing material logistics support services, surface milling and resurfacing operation services, and surface milling and resurfacing management services in the service activities, and combined with the complete emission coefficient values ​​extracted from the triangular distribution for road resurfacing material logistics support services, surface milling and resurfacing operation services, and surface milling and resurfacing management services, the input-output carbon emissions of surface milling and resurfacing are determined.

[0010] Optionally, the road full-cycle carbon emission uncertainty analysis method provided by the present invention further includes: The lower limit of the carbon emission factor is determined based on its expected value and coefficient of variation. The upper limit of the carbon emission factor is determined based on its lower limit and expected value; A triangular distribution is constructed from the lower limit, upper limit, and expected value of the carbon emission factor.

[0011] Optionally, the road full-cycle carbon emission uncertainty analysis method provided by the present invention further includes: The probability distribution of total carbon emissions throughout the road's life cycle is obtained by using maximum likelihood estimation on the total carbon emissions from multiple simulations. Based on the probability distribution of total carbon emissions, determine the mean, standard deviation, coefficient of variation, and confidence interval in the uncertainty analysis results.

[0012] This invention also provides a system for analyzing the uncertainty of carbon emissions throughout the entire lifecycle of roads, including: The carbon emission expectation matching module is used to obtain the material and energy consumption throughout the entire life cycle of a road, as well as the economic cost of service activities where material or energy consumption cannot be tracked; it matches the corresponding carbon emission factors and expected values ​​according to the types of materials and energy, and matches the corresponding total emission coefficients and expected values ​​according to the types of service activities. The triangular distribution construction module is used to evaluate the data quality of each carbon emission factor and total emission coefficient throughout the road's life cycle through a spectral matrix, and obtain the DQI score of each carbon emission factor and total emission coefficient. Based on the DQI score of each emission factor and total emission coefficient, the coefficient of variation of each carbon emission factor and total emission coefficient is determined. The expected value and the coefficient of variation are used to determine the triangular distribution describing the uncertainty of the carbon emission factor and total emission coefficient. The uncertainty analysis module is used to randomly extract the values ​​of each carbon emission factor and the total emission coefficient in the current simulation from the triangular distribution of each carbon emission factor and the total emission coefficient. It determines the process carbon emissions of the road throughout its entire life cycle from each carbon emission factor and its corresponding consumption, determines the input-output carbon emissions of the road throughout its entire life cycle from each total emission coefficient and its corresponding economic cost, and determines the total carbon emissions of the current simulation from the process carbon emissions and the input-output carbon emissions. Based on the total carbon emissions from multiple simulations, it determines the uncertainty analysis results in the road's entire life cycle.

[0013] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement any of the steps in the method for uncertainty analysis of carbon emissions throughout the road cycle.

[0014] The present invention also provides a computer-readable storage medium storing a computer program that, when loaded by a processor, can execute any step of the method for uncertainty analysis of carbon emissions throughout the road lifecycle.

[0015] The method for uncertainty analysis of road carbon emissions throughout its entire lifecycle provided by this invention has the following beneficial effects: Because this invention simultaneously acquires material and energy consumption and the economic costs of service activities, and separately matches carbon emission factors and total emission coefficients, it incorporates the carbon emissions of various service activities implicit in the economic supply chain, such as construction management and equipment leasing, into the calculation, avoiding omissions that can occur when carbon accounting is based solely on material or energy consumption. Furthermore, during the Monte Carlo simulation, process-based analysis is used to assess the emissions of major processes, and input-output analysis is employed for the implicit emissions generated by service activities such as construction, maintenance, and repair. This ensures that the uncertainty analysis is built upon a more complete and realistic carbon emission system, thereby improving the accuracy and reliability of the final assessment results. Attached Figure Description

[0016] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the uncertainty analysis method for road full-cycle carbon emissions provided in an embodiment of the present invention; Figure 2 This is an example of the boundary of a road surface lifecycle carbon emission system provided in an embodiment of the present invention; Figure 3 Examples of life cycle carbon emission probability density function and cumulative distribution function of composite pavement based on process method provided in embodiments of the present invention; Figure 4 Examples of the life cycle carbon emission probability density function and cumulative distribution function of composite pavement based on the layered mixing method provided in embodiments of the present invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0019] Example 1 This invention provides a method for uncertainty analysis of carbon emissions throughout the entire lifecycle of roads, specifically as follows: Figure 1 As shown, it includes the following steps: Step 11: Obtain the material and energy consumption throughout the road's entire life cycle, as well as the economic costs of service activities where material or energy consumption cannot be tracked; match the corresponding carbon emission factors and expected values ​​based on the types of materials and energy, and match the corresponding total emission coefficients and expected values ​​based on the types of service activities.

[0020] The materials include road construction materials, and the energy sources include fossil fuels and electricity. The carbon emission factor can be determined through the following steps: Step 111: Determine the expected value of the carbon emission factor for each type of road construction material based on the type of road construction material carbon emission factor.

[0021] Step 112: Determine the expected carbon emission factor for each fossil energy source and the expected carbon emission factor for each type of electrical energy source, respectively, based on the types of fossil energy and electrical energy.

[0022] Service activities include those related to material transportation, road construction, road maintenance, and pavement removal. The complete emission factor can be determined through the following steps: Step 113: Determine the expected value of the total emission coefficient for each service activity in the total emission coefficient based on the type of service activity in the material transportation, road construction, road maintenance and pavement demolition stages.

[0023] Specifically, for roads requiring carbon emission uncertainty analysis, this invention, based on life cycle theory, divides the road's entire life cycle carbon emission stages into, as follows: Figure 2 The diagram illustrates multiple stages of road construction, including raw material production, material transportation, construction, maintenance, and demolition. The raw material production stage encompasses the mining and processing of basic raw materials such as asphalt and cement, as well as the production processes of upstream energy industries such as fuel oil and electricity. The construction stage includes key processes such as mixing, paving, and compaction in the laying of the pavement structure layers. The material transportation stage mainly involves transporting raw materials from the production site to the construction site via road transport.

[0024] Regarding the maintenance and repair phase, considering the differences in road surface materials and road service life, road maintenance is generally divided into three types: routine maintenance, preventative maintenance, and periodic maintenance. Routine maintenance mainly includes road sweeping, debris removal, and road condition inspections. This part requires relatively little manpower and resources, and obtaining actual data for routine maintenance is difficult. Preventative maintenance and periodic maintenance require a certain amount of road construction materials and construction machinery, which are the main sources of carbon emissions during the maintenance phase. Therefore, this invention focuses on preventative maintenance and periodic maintenance for carbon emissions during the maintenance phase. The carbon emission sources for these two maintenance methods include carbon emissions from the production and transportation of maintenance materials and construction activities.

[0025] For the demolition phase, considering that road demolition involves the recycling of milled material, the core issue is how to allocate the environmental load and environmental credit of the recycling process to avoid the production of raw materials and the disposal of waste. In the life cycle analysis adopted in this invention, considering that, according to the cutoff criteria, the demolition phase includes road milling and transportation from the construction site to the milled material processing plant, and that the processing and transportation of the milled material to the new construction site falls under the category of using recycled asphalt pavement systems, not currently producing recycled asphalt, the carbon emission factors of materials and energy involved in the demolition phase are clearly defined, as well as the complete emission factors of service activities where the consumption of materials or energy cannot be traced.

[0026] The carbon emission factors involved in the road surface life cycle mainly include carbon emission factors from fossil fuels, carbon emission factors from electricity, and carbon emission factors from road construction materials. Specific carbon emission factors related to raw materials, fuels, and electricity are shown in Table 1. Table 1 Road Carbon Emission Factors Specifically, for the expected or average value of fossil fuel carbon emission factors, considering the main fossil fuels consumed throughout the road's life cycle, including crude oil, heavy oil, gasoline, diesel, and coal, the carbon emission sources mainly consist of two parts: one part is the carbon emissions generated during the production of fossil fuels, and the other part is the carbon emissions generated during the use of fossil fuels. This invention calculates the greenhouse gas emissions per unit mass of fossil fuels based on various greenhouse gas emission factors provided by the IPCC database, combined with the main energy carbon emission factors provided in the "Building Carbon Emission Calculation Standard" (GBT 51366-2019), and converts them into carbon dioxide equivalents using global warming potential.

[0027] For the expected or average value of the carbon emission factor of electricity, considering that electricity is a secondary energy source converted from the consumption of other energy sources, the carbon emissions during its use can be ignored, or the specific value can be determined by referring to the data in the greenhouse gas emission factor database.

[0028] Regarding the expected or average carbon emission factor of road construction materials, this invention, based on the Boustead, Ecoinvent, ELCD, and CLCD databases and the research results of relevant scholars on carbon emission factors, determines the carbon emission factor of various major road construction materials used in the road life cycle.

[0029] Step 12: Evaluate the data quality of each carbon emission factor and total emission coefficient throughout the road's life cycle using the spectral matrix to obtain the DQI score for each carbon emission factor and total emission coefficient; determine the coefficient of variation for each carbon emission factor and total emission coefficient based on the DQI score for each carbon emission factor and total emission coefficient; and determine the triangular distribution describing the uncertainty of the carbon emission factor and total emission coefficient using the expected value and the coefficient of variation.

[0030] The parameters of the triangular distribution can be determined through the following steps: Step 121: Determine the lower limit of the carbon emission factor based on its expected value and coefficient of variation.

[0031] Step 122: Determine the upper limit of the carbon emission factor based on the lower limit and the expected value of the carbon emission factor.

[0032] Step 123: Construct a triangular distribution based on the lower limit, upper limit, and expected value of the carbon emission factor.

[0033] Specifically, considering that a genealogical matrix can transform qualitative descriptions of relevant aspects of a research object into quantitative numerical evaluations, this invention adopts a genealogical matrix that includes reliability, completeness, time range, geographical range, and technical range, as shown in Table 2: Table 2. Lineage Matrix The genealogical matrix assigns a score of 1-5 to each DQI, with DQI=1 representing the highest data quality rating and DQI=5 representing the lowest, thus completing a semi-quantitative assessment of the input data. Considering that the entire lifecycle of road surfaces involves the production, transportation, and processing of various materials, and that the sources of inventory data are complex, with emissions from building materials and energy significantly correlated with factors such as region, time, and technology, and that the sources of road construction materials and energy consumption are often singular (e.g., clearly defined in JTG / T 3832-2018 Highway Engineering Budget Quota and JTG / T 3833-2018 Highway Engineering Machinery Shift Cost Quota), this invention combines the aforementioned data to calculate material consumption and energy consumption at each stage of the road surface lifecycle. However, carbon emission factors are mostly derived from comprehensive values ​​obtained from related studies, lacking local specific data support, making it difficult to accurately match local building material production processes, energy supply structures, and other specific scenarios. Ignoring these uncertainties can easily lead to distorted carbon emission calculation results, thereby affecting the scientific validity of subsequent low-carbon design strategies. Therefore, this invention considers the uncertainty of carbon emission factors, constructs a distribution model by combining the coefficient of variation, and then completes the analysis of the total carbon emission uncertainty involved in the carbon emission factors with the specific road construction materials and energy consumption.

[0034] Similar to carbon emission factors, the service costs at each stage of the pavement lifecycle involved in the input-output method are directly derived from engineering cost documents. However, the total emission factor, on the one hand, references relevant literature from different periods and research scopes, and these literatures have different calculation boundaries and regional samples, which can easily lead to bias if directly cited. On the other hand, the total emission factor relies on subjective judgment, such as the selection of data screening criteria and emission responsibility allocation methods, and the basic data statistics of the input-output table itself may also have lags or errors, ultimately leading to uncertainty in the data quality of the total emission factor. Therefore, this invention also considers the uncertainty of the total emission factor, constructs a corresponding distribution model in conjunction with the coefficient of variation, and then, together with a defined service cost, completes the analysis of the uncertainty of total carbon emissions involved in the total emission factor.

[0035] After completing data quality analysis through the genealogy matrix and obtaining the DQI scores for each carbon emission factor and total emission coefficient, this invention can also link data quality indicators with probability distributions. For example, it can determine the shape parameters of the extended beta function based on "rules of thumb" by using the average scores of the five indicators. and range endpoints The data quality indicators are correlated with the probability distribution, and the specific conversion relationship is shown in formulas (1) and (2): (1) (2) in, The average score of the DQI score for each carbon emission factor or total emission factor. This represents the data for the carbon emission factor and the total emission coefficient, such as the average or expected value, likelihood value, etc. While the above method integrates the combined effects of various indicators and is simple and clear, it's important to consider that the quality of the five indicators might be masked during the aggregation process. For example, (3,3,3,3,3) and (2,3,3,3,4) will both result in an ADQI of 3, leading to the same distribution parameters. Therefore, parameter uncertainty can be divided into basic uncertainty and additional uncertainty. Basic uncertainty corresponds to inherent variability and random error, which can be obtained through empirical and statistical methods and expressed as variance. For road surfaces, basic uncertainty is typically 0.0006. Additional uncertainty corresponds to the incompleteness of the data used and can be estimated using the DQI method. The log-normal distribution between DQI and other uncertainties is obtained through a transformation matrix expressed as variance, as shown in Table 3.

[0036] Table 3. Conversion of data quality indices of the genealogy matrix into uncertainty factors with added uncertainty. Here, it is assumed that the uncertainty of each indicator is independent, and the total uncertainty is as shown in formula (3): (3) in, , and represents the standard deviation of the total uncertainty, basic uncertainty, and additional uncertainty of the indicator h, respectively.

[0037] Based on this, the uncertainty of each carbon emission factor or total emission factor can be characterized by the coefficient of variation (CV), thus facilitating the extension of the conversion relationship to other probability functions such as normal distribution, uniform distribution, triangular distribution, and Beta-PERT distribution. The relationship between the standard deviation of the total uncertainty and the CV is shown in formula (4):

[0038] (4) Based on formula (4), the parameters of other distributions can be estimated based on the most probable value of the collected data, the estimated CV, and other additional assumptions such as symmetry. Considering that the triangular distribution is a continuous probability distribution, its core characteristics are defined by three key parameters: the lower limit, the upper limit, and the most probable value. Its probability density function presents a piecewise linear form that "peaks at the most probable value and decreases linearly towards the upper and lower limits." Compared with the uniform distribution, the triangular distribution, by introducing the most probable value, is more in line with the actual characteristics of "data fluctuating around the typical value and the probability of taking values ​​gradually decreasing towards the boundary." Compared with the normal distribution, which relies on the assumptions of mean and variance and is sensitive to outliers, the triangular distribution does not require strict assumptions about the distribution shape. It can be constructed only through clear boundaries and core value points, and has lower requirements for the amount of data. It is especially suitable for uncertainty analysis scenarios where the data source is limited and only the range and typical value can be determined. In addition, the triangular distribution has the advantages of intuitive parameters and simple calculation. It can achieve symmetrical or skewed distribution by adjusting the position of the most likely value, and flexibly adapt to the value rules of different research objects. Therefore, this invention uses a symmetrical triangular distribution to simulate the uncertainty of each index. The model parameters in the triangular distribution are shown in formula (5) and formula (6) respectively:

[0039] (5) (6) in, , and These represent the upper bound, lower bound, and most likely value of the triangular distribution function, respectively. When 'a' is the low value of the triangular distribution, 'b' is the high value of the triangular distribution, and 'c' is the mode of the triangular distribution, the probability density function of the triangular distribution is... and cumulative distribution function As shown in formulas (7) and (8) respectively:

[0040] (7) (8) Step 13: In the triangular distribution of each carbon emission factor and the total emission coefficient, randomly select the value of the carbon emission factor and the total emission coefficient in the current simulation. Determine the process carbon emissions of the road throughout its entire life cycle from each carbon emission factor and its corresponding consumption. Determine the input-output carbon emissions of the road throughout its entire life cycle from each total emission coefficient and its corresponding economic cost. Determine the total carbon emissions of the current simulation from the process carbon emissions and the input-output carbon emissions. Determine the uncertainty analysis results in the road's entire life cycle based on the total carbon emissions from multiple simulations.

[0041] Specifically, after constructing the triangular distributions of each carbon emission factor and total emission coefficient, considering that Monte Carlo simulation treats unknown parameters as random variables and uses probability to understand the changes in a certain unknown variable, the frequency of an event is obtained by simulating the occurrence of real events, given a specific probability of the event occurring. When the sample size is large enough, the frequency of the event becomes the probability. Therefore, the essence of the Monte Carlo method is to use a probabilistic model to describe the outcome of an event. This invention analyzes the changes in total carbon emissions based on Monte Carlo simulation.

[0042] For example, Monte Carlo simulation, based on probability and statistical theory, is often used to generate random or pseudo-random numbers for a probabilistic model to obtain approximate solutions to real-world problems. It utilizes random sampling of a probability distribution for distribution propagation. Suppose a function contains n random variables, determined by the input... The probability density function (i=1,2,3,…,N) is obtained by considering the input quantity. Discrete sampling of the probability density function of the output quantity Y is performed by propagating the distribution of the input quantity from the model. This allows for the calculation of the discrete sampled values ​​of the probability density function of the output quantity Y, thereby obtaining the best estimate, standard uncertainty, and coverage interval of the output quantity. As mentioned earlier, the Monte Carlo method is a sampling-dependent method; increasing the sample size enhances the reliability of the results. Subsequently, the discrete values ​​G of the distribution function of the output quantity Y are obtained through Monte Carlo simulation. From G, the estimate y of Y, the standard uncertainty u(y) of y, and the coverage interval of Y given the coverage probability p can be calculated. .

[0043] Specifically, for the uncertainty analysis of carbon emissions throughout the entire lifecycle of roads, this invention treats each carbon emission factor and the total emission coefficient as random variables. Discrete sampling is performed from the triangular distribution of each random variable to obtain its value in the current simulation round. The sum of carbon emissions corresponding to all carbon emission factors and the total emission coefficient, i.e., the total carbon emissions, is used as the output Y of the model in the current simulation round. After obtaining the total carbon emissions from multiple simulations, the probability distribution of the total carbon emissions is determined from these calculation results. This leads to the determination of data such as the mean, standard deviation, and confidence interval, which serve as the results of the road uncertainty analysis.

[0044] Total carbon emissions can be based on, for example, Figure 2 The road life cycle is divided as shown, and carbon emissions are calculated for each stage. The emissions of the main processes are evaluated based on process analysis, and input-output analysis is used for the implicit emissions generated by construction, maintenance and repair services. The total emissions are shown in formula (9). (9) in, Total carbon emissions, Carbon emissions calculated based on input and output. This represents carbon emissions calculated based on process analysis.

[0045] For example, for the production stage of road construction raw materials, this invention calculates carbon emissions based on the process method. The main raw materials used in this stage include crushed stone, asphalt, cement, lime fly ash and other binders, sand, water and other materials. The carbon emission calculation model for the road construction raw material production stage is shown in formula (10):

[0046] (10) in, Carbon emissions during the production of road construction raw materials For the consumption of the i-th main road construction material, Let be the carbon emission factor of the i-th main road construction material, for example, the value of the carbon emission factor of the i-th main road construction material in each simulation.

[0047] For the material transportation and construction phases, considering the road construction material transportation phase, the main types of vehicles used for transporting road construction materials include trucks and dump trucks, while the types of machinery and equipment required for the construction phase include mixing equipment, paving equipment, compaction equipment, and related auxiliary construction equipment. The carbon emissions from these equipment operations mainly come from energy consumption. This invention establishes a carbon emission calculation model for the transportation and construction phases by summarizing the total energy consumption and combining it with the carbon emission factor method, as shown in formula (11):

[0048] (11) in, Carbon emissions are generated during material transportation and construction. Let i be the consumption of the i-th type of energy. Let be the carbon emission factor of the i-th energy source, for example, the value of the carbon emission factor of the i-th energy source in each simulation.

[0049] Road maintenance includes pavement micro-surfacing maintenance. The total carbon emissions in the Monte Carlo simulation can be calculated and determined through the following steps: Step 131: Based on the consumption of road surface micro-surfacing maintenance materials in the materials, and combined with the carbon emission factor value extracted from the triangular distribution of road surface micro-surfacing maintenance materials, determine the total carbon emission of road surface micro-surfacing maintenance materials in the process carbon emission amount.

[0050] Step 132: Based on the energy consumption of road surface micro-surfacing maintenance, and combined with the carbon emission factor value extracted from the triangular distribution of maintenance energy, determine the total carbon emissions of road surface micro-surfacing maintenance energy in the process carbon emissions.

[0051] Step 133: Based on the economic costs of road micro-surfacing maintenance material logistics support services, road micro-surfacing maintenance operation services, and road micro-surfacing maintenance management services in the service activities, and combined with the complete emission coefficient values ​​extracted from the triangular distribution for road micro-surfacing maintenance material logistics support services, road micro-surfacing maintenance operation services, and road micro-surfacing maintenance management services, determine the input-output carbon emissions of road micro-surfacing maintenance.

[0052] Road maintenance also includes surface milling and repaving. The total carbon emissions in the Monte Carlo simulation can be calculated and determined through the following steps: Step 134: Based on the consumption of road resurfacing material in the materials, and combined with the carbon emission factor value extracted from the triangular distribution of road resurfacing material, determine the total carbon emission of surface milling and resurfacing material in the process carbon emission amount.

[0053] Step 135: Based on the energy consumption of road milling and road repaving, and combined with the carbon emission factor values ​​extracted from the triangular distribution of energy used for road milling and road repaving, determine the total carbon emissions of surface layer milling and repaving energy in the process carbon emissions.

[0054] Step 136: Based on the economic costs of road resurfacing material logistics support services, surface milling and resurfacing operation services, and surface milling and resurfacing management services in the service activities, and in conjunction with the complete emission coefficient values ​​extracted from the triangular distribution for road resurfacing material logistics support services, surface milling and resurfacing operation services, and surface milling and resurfacing management services, determine the input-output carbon emissions of surface milling and resurfacing.

[0055] Specifically, considering that road surfaces generally do not suffer from severe damage, with the main types being cracks and ruts, and ruts being the primary type of road surface damage, milling technology is required when ruts are deep, while preventative maintenance techniques such as micro-surfacing can be used to ensure road surface performance when the ruts are shallow. For cracks and other damage, appropriate preventative maintenance methods can be selected to treat the damage. This invention uses micro-surfacing as the primary preventative maintenance technique and surface milling as the primary method for regular maintenance.

[0056] Among them, micro-surfacing maintenance technology involves mixing polymer-modified emulsified asphalt, graded aggregate, filler, and water in proportion at room temperature and then paving it to form a functional layer on the original pavement surface to seal micro-cracks. Meanwhile, surface milling technology requires using a road milling machine to remove the pavement surface with severe defects such as deep ruts and large-area cracks at a preset depth of 2-5cm. After cleaning up the waste, a new surface layer is paved to completely restore the pavement elevation and smoothness and prevent the defects from spreading to the base layer. Based on the above maintenance technologies, the carbon emission calculation model for the pavement maintenance stage is shown in formula (12):

[0057] (12) in, Carbon emissions during the maintenance and repair phase. The carbon emissions of performing a single micro-surfacing maintenance operation, The carbon emissions from a single milling and resurfacing operation of the road surface are as follows: This refers to the number of microsurfacing maintenance procedures performed during the service life of the road surface. The number of times the pavement surface layer is milled and rebuilt during the service life of the pavement. The material production, transportation and construction stages involved in each micro-surfacing maintenance operation and milling and rebuilding of the pavement can be calculated using the carbon emission calculation model for the pavement construction raw material production stage shown in formula (10) and the carbon emission calculation model for the transportation and construction stages shown in formula (11).

[0058] Furthermore, the road demolition phase often involves road milling and the transportation of milled material from the construction site to a processing plant. Carbon emissions can be calculated using the carbon emission calculation model for the transportation and construction phases shown in formula (11). Then, by superimposing the carbon emissions from each of the above stages, the total carbon emissions for the entire life cycle process can be determined, as shown in formula (13);

[0059] (13) Regarding the implicit carbon emissions over the road surface life cycle, this invention calculates the relevant carbon emissions based on input and output. For example, based on the general equilibrium assumption that total economic input equals total output, the Leontief matrix is ​​used to consider the intermediate relationships between economic sectors, and emission analysis is performed by combining sectoral direct carbon emission coefficients, as shown in formulas (14), (15), and (16):

[0060] (14) (15) (16) in, It is the Leontief inverse matrix. and Let the row vectors represent the direct and total carbon emission coefficients of the department's products. The direct carbon emissions of sector j can be determined based on a carbon accounting database. The total output of department j can be determined using an input-output table. It is the identity matrix. The direct consumption coefficient matrix can be determined through input-output calculations. Based on formulas (14), (15), and (16), carbon emissions can be determined by the product of monetary costs and emission coefficients of relevant sectors, as shown in formula (17):

[0061] (17) in, The total carbon emissions are calculated based on input and output, where q represents the total number of components involved in the entire life cycle of the road. For department j, the carbon emissions are calculated based on input and output. The vector represents the monetary costs of each department. The specific costs can be obtained from the engineering cost documents corresponding to the road. It should be emphasized that the carbon emissions calculated based on input and output in this invention are mainly carbon emissions generated by service activities in the construction, maintenance and repair stages. However, the implicit carbon emissions involved in other stages, such as material transportation, road construction, road maintenance and pavement demolition, can also be determined by the above input and output calculation model. This invention does not impose any limitations.

[0062] Furthermore, the specific uncertainty analysis results can be determined through the following steps: Step 137: Obtain the probability distribution of total carbon emissions throughout the road's life cycle by using maximum likelihood estimation on the total carbon emissions from multiple simulations.

[0063] Step 138: Based on the probability distribution of total carbon emissions, determine the mean, standard deviation, coefficient of variation, and confidence interval in the uncertainty analysis results.

[0064] Specifically, maximum likelihood estimation refers to the parametric statistical method used to obtain the relevant probability density function of a sample set. For a certain random variable, the probability density function of the random variable can be obtained by Monte Carlo simulation. However, the probability density function that the random variable follows is still not definite. Therefore, this invention determines the probability density function of total carbon emissions by repeated sampling in the triangular distribution of each parameter, and obtains uncertainty analysis results such as mean, standard deviation, coefficient of variation and confidence interval.

[0065] Example 2 Based on Example 1, this invention also takes a highway project as an example to conduct a study on the calculation of carbon emissions throughout the life cycle of highway pavement. The entire highway construction project adopts highway technical standards, with four lanes in both directions and a design speed of 120 km / h; transition sections are set at the beginning and end of the project to achieve smooth connection with existing highways, and the design speed of the transition sections is 100 km / h.

[0066] The core objective of this LCA (Limited Course Assessment) is to determine the carbon emission level of the pavement throughout its entire life cycle. Based on this, the mainstream pavement type used in this project, namely composite pavement, is selected as the research object. Considering the actual conditions of this project, the functional unit is determined to be a "1km two-way four-lane" pavement with a total lane width of 26m. The pavement of this project adopts a semi-rigid structure, consisting of a surface layer, a base layer, and a subbase layer from top to bottom. The specific structure is shown in Table 4.

[0067] Table 4. Pavement Structure of a Certain Expressway In this embodiment, the data used in the process analysis is based on the bill of quantities for the highway construction project. It also references the relevant standards in the "Highway Engineering Budget Quota" (JTG / T3832-2018) to systematically review and summarize the material usage and main construction machinery shifts involved in the construction of the two types of pavements. Furthermore, it supplements the "Highway Engineering Budget Quota" (JTG / T3832-2018) with reference to "Highway Maintenance Budget Compilation Method and Quota Part 1: Highway Maintenance Engineering Budget Compilation Method and Quota" (DB 45 / T 2228.1--2020) for maintenance operations not covered in the "Highway Engineering Budget Quota" (JTG / T3832-2018). The energy consumption of equipment in each process is calculated according to the "Highway Engineering Machinery Shift Cost Quota" (JTG / T 3833-2018).

[0068] During the site survey, the raw material procurement plant was 50km away from the mixing plant, the mixing plant was 30km away from the construction site, and the nearest milling material processing plant was 50km away from the construction site. The pavement was designed for a 30-year lifespan. Based on local pavement maintenance experience, micro-surfacing maintenance was carried out every 5 years, and surface repaving was performed every 10 years. The material, fuel, and electricity consumption at each stage of the composite pavement's life cycle is shown in Table 5.

[0069] Table 5. Statistics on Material, Fuel, and Electricity Consumption at Each Stage of the Composite Pavement Life Cycle The service costs during the road construction phase can be directly obtained from the engineering cost documents, and the data source is clear and reliable. However, the service costs during the road maintenance, repair, and demolition phases are difficult to obtain because there are no direct corresponding cost records in this case. It is necessary to refer to the cost documents of other road projects in Guangxi, which makes the actual process more difficult. Moreover, the maintenance and repair of different road surfaces vary in terms of technology, materials, and frequency, making it difficult to standardize the cost. This embodiment only includes the service costs during the road construction phase, as shown in Table 6:

[0070] Table 6. Statistics on Service Costs During Road Construction Period The data quality of carbon emission factors determined by the conversion method based on the Ecoinvent database is shown in Table 7: Table 7. Data Quality Scoring Results for Carbon Emission Factors Using the Lineage Matrix Furthermore, considering that Python is an object-oriented high-level programming language with dynamic data types, and that it was invented by Guido van Rossum in 1989, Python itself is developed based on related languages, including ABC, C, C++, and Unix shell, etc. Currently, Python has become one of the mainstream programming languages, widely used in scientific computing and statistics, artificial intelligence, web scraping, software development, and other fields. This example uses Python 3.7.1 64-bit to conduct Monte Carlo simulations. The runtime platform is Visual Studio Code. Visual Studio Code is an excellent integrated development environment with features such as open source, cross-platform compatibility, modularity, and rich plugins.

[0071] The Python standard library covers regular expressions, networking, web browsers, GUIs, databases, and text processing. In addition to the standard library, this example also uses the SciPy, NumPy, and Matplotlib extension libraries. The combined performance of SciPy, NumPy, and Matplotlib rivals that of MATLAB. NumPy is a scientific computing package that includes powerful N-dimensional array objects, robust linear algebra, Fourier transform, and random number generation capabilities, and can store and process large matrices. Its random number generation function is crucial for Monte Carlo simulations, typically generated by the `random` module. SciPy is a commonly used scientific computing package capable of handling interpolation, integration, optimization, image processing, and ordinary differential equation solving. SciPy can compute NumPy matrices and work in conjunction with them, significantly improving computational efficiency. SciPy provides various probability density functions needed for Monte Carlo simulations; Matplotlib is a Python data visualization tool widely used in Python 2D plotting. Matplotlib requires only a small amount of code to generate graphs such as histograms, bar charts, error plots, and scatter plots. Matplotlib does not directly participate in the stochastic simulation process, but is used for visualizing simulation results.

[0072] Specifically, once the triangular distribution of each parameter is determined, an uncertainty analysis is conducted using the Monte Carlo simulation method for the life-cycle carbon emissions of the composite pavement. In this embodiment, 106 independent simulation tests are performed, and the system collects the corresponding output data to construct the probability density function and cumulative distribution function. For example... Figure 3 As shown, the mean life-cycle carbon emissions of the composite pavement calculated based on the process method are 5295 tons, with a corresponding 95% confidence interval of [4588, 6001] tons. From the cumulative probability distribution, the carbon emissions are 6555 tons when the cumulative probability reaches 100%, and 4070 tons when the cumulative probability is 0. That is, under extreme conditions, the upper limit of carbon emissions for this pavement is 6555 tons, and the lower limit is 4070 tons. Furthermore, the coefficient of variation for this carbon emission result is 6.91%.

[0073] Based on this, considering that the service cost types involved in this embodiment are only three categories, and that calculating the full emission coefficients of each department through the input-output table is a large workload and difficult to implement, the full emission coefficients are determined with reference to the full emission coefficients in the construction field. The full emission coefficient data quality scoring results using the genealogy matrix are shown in Table 8: Table 7. Data Quality Scoring Results for Complete Emission Coefficients Using the Lineage Matrix Considering the probability density function and cumulative distribution function after full emission factor, as follows: Figure 4 As shown, the mean life-cycle carbon emissions of the composite pavement calculated using the stratified hybrid method is 5951 tons, which is about 12.4% higher than the result obtained using only the process method, with a corresponding 95% confidence interval of [5223, 6680] tons. From the cumulative probability distribution, the extreme boundaries of the pavement's carbon emissions can be further clarified: the upper limit is 7311 tons, and the lower limit is 4080 tons. Furthermore, the CV of the stratified hybrid method is 6.34%. Comparing the carbon emission fluctuations with the process method, it can be seen that the volatility of the composite pavement's life-cycle carbon emissions is slightly lower when using the stratified hybrid method than when considering only the process method.

[0074] In summary, the implicit carbon emissions from all life-cycle services of pavement must be included in the calculation of pavement carbon emissions. This measure can broaden the system boundaries, ensure the completeness of the calculation, make the carbon emission data more consistent with actual engineering scenarios, and ultimately improve the accuracy and reference value of the results.

[0075] Example 3 This invention also provides a system for analyzing the uncertainty of carbon emissions throughout the entire lifecycle of roads, including: The carbon emission expectation matching module is used to obtain the material and energy consumption throughout the entire life cycle of a road, as well as the economic cost of service activities for which material or energy consumption cannot be tracked; it matches the corresponding carbon emission factors and expected values ​​according to the types of materials and energy, and matches the corresponding total emission coefficients and expected values ​​according to the types of service activities.

[0076] The triangular distribution construction module is used to evaluate the data quality of each carbon emission factor and total emission coefficient throughout the road's life cycle through a spectral matrix, and obtain the DQI score of the carbon emission factor and total emission coefficient. Based on the DQI score of each emission factor and total emission coefficient, the coefficient of variation of the carbon emission factor and total emission coefficient is determined. The expected value and the coefficient of variation are used to determine the triangular distribution describing the uncertainty of the carbon emission factor and total emission coefficient.

[0077] The uncertainty analysis module is used to randomly extract the values ​​of each carbon emission factor and the total emission coefficient in the current simulation from the triangular distribution of each carbon emission factor and the total emission coefficient. It determines the process carbon emissions of the road throughout its entire life cycle from each carbon emission factor and its corresponding consumption, determines the input-output carbon emissions of the road throughout its entire life cycle from each total emission coefficient and its corresponding economic cost, and determines the total carbon emissions of the current simulation from the process carbon emissions and the input-output carbon emissions. Based on the total carbon emissions from multiple simulations, it determines the uncertainty analysis results in the road's entire life cycle.

[0078] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps in the embodiments of the method for uncertainty analysis of carbon emissions throughout the road cycle. Specific implementation methods can be found in the method embodiments, and will not be repeated here.

[0079] Furthermore, the present invention also provides a non-transitory computer-readable storage medium containing instructions, on which a computer program is stored. For example, a memory containing instructions that can be executed by a processor of a computer device to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc. When the computer program is executed by the processor, it can implement the steps in the embodiments of the method for uncertainty analysis of carbon emissions throughout the road cycle. Specific implementation methods can be found in the method embodiments, which will not be repeated here.

[0080] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0082] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0083] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0084] It should be noted that the specific embodiments described above enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail in this specification and embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the present invention patent. No reference numerals in the claims should be construed as limiting the scope of the claims. Any simple variations or equivalent substitutions of technical solutions that can be readily obtained by those skilled in the art within the scope of the technology disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A method for analyzing uncertainty of life-cycle carbon emissions of a road, characterized by, include: Obtain the material and energy consumption throughout the entire life cycle of a road, as well as the economic costs of service activities where material or energy consumption cannot be tracked; match the corresponding carbon emission factors and expected values ​​based on the types of materials and energy, and match the corresponding total emission coefficients and expected values ​​based on the types of service activities. The data quality of each carbon emission factor and total emission coefficient throughout the road life cycle is evaluated by using a spectral matrix to obtain the DQI score of the carbon emission factor and total emission coefficient. The coefficient of variation of each carbon emission factor and total emission coefficient is determined based on the DQI score of each emission factor and total emission coefficient. The expected value and the coefficient of variation are used to determine the triangular distribution describing the uncertainty of the carbon emission factor and total emission coefficient. In the triangular distribution of each carbon emission factor and total emission coefficient, the values ​​of the carbon emission factor and total emission coefficient in the current simulation are randomly selected. The process carbon emissions of the road throughout its entire life cycle are determined by each carbon emission factor and its corresponding consumption. The input-output carbon emissions of the road throughout its entire life cycle are determined by each total emission coefficient and its corresponding economic cost. The total carbon emissions of the current simulation are determined by the process carbon emissions and the input-output carbon emissions. The uncertainty analysis results in the road's entire life cycle are determined based on the total carbon emissions from multiple simulations.

2. The method of claim 1, wherein, The materials include road construction materials, and the energy sources include fossil fuels and electricity. The corresponding carbon emission factors and expected values ​​are matched according to the types of materials and energy sources, including: Determine the expected value of the carbon emission factor for each material in the road construction material carbon emission factor according to the types of road construction materials; The expected carbon emission factor values ​​for each fossil energy source and each electrical energy source are determined according to the types of fossil energy and electrical energy, respectively.

3. The road life cycle carbon emission uncertainty analysis method of claim 2, wherein, The service activities include those related to material transportation, road construction, road maintenance, and pavement removal. The corresponding total emission coefficients and expected values ​​are matched according to the type of service activity, including: The expected value of the total emission coefficient for each service activity in the total emission coefficient is determined according to the type of service activity in the material transportation, road construction, road maintenance and pavement demolition stages.

4. The method of claim 3, wherein, The road maintenance includes pavement micro-surfacing maintenance. The process carbon emissions throughout the road's entire life cycle are determined by each carbon emission factor and its corresponding consumption. The input-output carbon emissions throughout the road's entire life cycle are determined by each total emission factor and its corresponding economic cost. The total carbon emissions in the current simulation are determined by the process carbon emissions and the input-output carbon emissions, including: Based on the consumption of road surface micro-surfacing maintenance materials in the aforementioned materials, and combined with the carbon emission factor value extracted from the triangular distribution of road surface micro-surfacing maintenance materials, the total carbon emissions of road surface micro-surfacing maintenance materials in the process carbon emissions are determined. Based on the energy consumption of road surface micro-surfacing maintenance, and combined with the carbon emission factor value extracted from the triangular distribution of maintenance energy, the total carbon emissions of road surface micro-surfacing maintenance energy in the process carbon emissions are determined. Based on the economic costs of the road micro-surfacing maintenance material logistics support services, road micro-surfacing maintenance operation services, and road micro-surfacing maintenance management services in the aforementioned service activities, and combined with the complete emission coefficient values ​​extracted from the triangular distribution for the road micro-surfacing maintenance material logistics support services, road micro-surfacing maintenance operation services, and road micro-surfacing maintenance management services, the input-output carbon emissions of road micro-surfacing maintenance are determined.

5. The method of claim 3, wherein, The road maintenance also includes surface milling and repaving. The process carbon emissions throughout the road's life cycle are determined by each carbon emission factor and its corresponding consumption. The input-output carbon emissions throughout the road's life cycle are determined by each total emission factor and its corresponding economic cost. The total carbon emissions in the current simulation are determined by the process carbon emissions and the input-output carbon emissions, including: Based on the consumption of road resurfacing material in the aforementioned materials, and combined with the carbon emission factor value extracted from the triangular distribution of the road resurfacing material, the total carbon emission of the surface milling and resurfacing material in the process carbon emission is determined. Based on the energy consumption of road milling and road repaving, and combined with the carbon emission factor values ​​extracted from the triangular distribution of energy used for road milling and road repaving, the total carbon emissions of surface layer milling and repaving energy in the process carbon emissions are determined. Based on the economic costs of the road resurfacing material logistics support services, surface milling and resurfacing operation services, and surface milling and resurfacing management services in the aforementioned service activities, and combined with the complete emission coefficient values ​​extracted from the triangular distribution for the road resurfacing material logistics support services, surface milling and resurfacing operation services, and surface milling and resurfacing management services, the input-output carbon emissions of surface milling and resurfacing are determined.

6. The method for road life cycle carbon emission uncertainty analysis according to claim 1, characterized in that, The triangular distribution describing the uncertainty of this carbon emission factor, determined by the expected value and the coefficient of variation, includes: The lower limit of the carbon emission factor is determined based on its expected value and coefficient of variation. The upper limit of the carbon emission factor is determined based on its lower limit and expected value; A triangular distribution is constructed from the lower limit, upper limit, and expected value of the carbon emission factor.

7. The method of claim 1, wherein, The results of uncertainty analysis in determining the total carbon emissions throughout the road life cycle based on multiple simulations include: The probability distribution of total carbon emissions throughout the road's life cycle is obtained by using maximum likelihood estimation to calculate the total carbon emissions from the multiple simulations. Based on the probability distribution of the total carbon emissions, the mean, standard deviation, coefficient of variation, and confidence interval in the uncertainty analysis results are determined.

8. A road full-cycle carbon emission uncertainty analysis system, characterized in that, include: The carbon emission expectation matching module is used to obtain the material and energy consumption throughout the entire life cycle of a road, as well as the economic cost of service activities for which material or energy consumption cannot be tracked; it matches the corresponding carbon emission factors and expected values ​​according to the types of materials and energy, and matches the corresponding total emission coefficients and expected values ​​according to the types of service activities. A triangular distribution construction module is used to evaluate the data quality of each carbon emission factor and total emission coefficient throughout the road's life cycle through a spectral matrix, and obtain the DQI score of the carbon emission factor and total emission coefficient; the coefficient of variation of each carbon emission factor and total emission coefficient is determined based on the DQI score of each emission factor and total emission coefficient; and the triangular distribution describing the uncertainty of the carbon emission factor and total emission coefficient is determined by the expected value and the coefficient of variation. The uncertainty analysis module is used to randomly extract the values ​​of the carbon emission factor and the total emission coefficient in the current simulation from the triangular distribution of each carbon emission factor and the total emission coefficient. It determines the process carbon emissions of the road throughout its entire life cycle from each carbon emission factor and its corresponding consumption, determines the input-output carbon emissions of the road throughout its entire life cycle from each total emission coefficient and its corresponding economic cost, and determines the total carbon emissions of the current simulation from the process carbon emissions and the input-output carbon emissions. The uncertainty analysis results in the road's entire life cycle are determined based on the total carbon emissions from multiple simulations.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the road full-cycle carbon emission uncertainty analysis method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is loaded by the processor, it is able to execute the steps of the road full-cycle carbon emission uncertainty analysis method according to any one of claims 1 to 7.

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