A method for predicting a whole life cycle highway future carbon effect quantity

Abstract

The application discloses a kind of whole life cycle road future carbon effect quantity prediction method.Based on literature research, data research, scientific analysis, research, supplement the carbon source and sink factor database of highway engineering and its surrounding built environment, establish the carbon source calculation model of whole life cycle of highway engineering, build the dynamic measurement method of carbon source and sink of surrounding environment of highway, use grey weighted Markov model to predict the carbon effect trend of highway engineering, enrich the method system of traffic carbon emission, provide technical support for long-life low-carbon highway construction, objectively quantitatively evaluate the carbon emission level and emission reduction potential of highway industry.The application has important significance for improving domestic highway carbon source factor database and surrounding environment carbon source and sink factor database, standardizing highway engineering carbon emission calculation standard, enriching the method system of traffic carbon emission, evaluating the emission reduction potential of highway engineering and formulating measures.

Description

Technical Field

[0001] This invention belongs to the field of carbon source and sink calculation and prediction technology, and in particular relates to a method for predicting the future carbon effects of highways throughout their entire life cycle. Background Technology

[0002] Highway construction is a crucial component of national infrastructure, greatly promoting socio-economic development and bringing significant benefits to various industries. However, highway engineering consumes substantial amounts of energy and resources during construction, maintenance, and demolition / recycling, and also generates significant amounts of waste and greenhouse gases. Simultaneously, changes in land use and cover types within a certain buffer zone along highways also generate and absorb a certain level of CO2. Statistics show that land use change has become the world's second-largest source of greenhouse gas emissions, second only to CO2 emissions from fossil fuel combustion. The ecological and environmental effects (including carbon emission effects) caused by land use and cover change have become an important area of ​​academic research.

[0003] Currently, highway construction projects are mostly divided into sub-projects such as roadbed, pavement, and tunnels, without defining the entire life cycle of the highway and carbon emission boundaries, or studying the carbon emission composition and measurement at each stage. Furthermore, most highway construction projects do not consider carbon emissions and absorption caused by land use changes, cover changes, and human activities in the surrounding built environment, lacking investigation and research into the spatiotemporal changes of carbon sources and sinks in the highway's environment. Moreover, the carbon measurement and prediction models used domestically and internationally have not had their applicability verified, the accuracy of the prediction results calculated and explained, or the applicability and accuracy of the prediction models demonstrated. Summary of the Invention

[0004] To overcome the shortcomings and deficiencies of existing technologies, the purpose of this invention is to provide a method for predicting the future carbon effects of highways throughout their entire life cycle.

[0005] This invention is implemented as follows: a method for predicting the future carbon effects of highways throughout their entire life cycle, the method comprising the following steps:

[0006] S1. The highway project is divided into stages according to the whole life cycle. According to the set construction sequence and level, the entire highway is divided into independent unit processes with unified carbon emission quantification. Combining the carbon emission factor database, actual engineering data and quotas, the carbon emission factor method is used to obtain the carbon source of the highway throughout its whole life cycle.

[0007] S2. Determine the buffer zone along the highway, use carbon source and sink carrier technology to obtain the area of ​​each land use type in the buffer zone, and combine it with the surrounding environmental carbon source and sink factor database to calculate the carbon source and carbon sink around the highway to obtain the net carbon source or net carbon sink.

[0008] S3. Using the annual carbon effect after the road opens to traffic as the original data sequence, after satisfying the grade ratio test, the gray mean GM(1,1) model is used to make a preliminary prediction of the future carbon effect of the highway and to conduct an accuracy test.

[0009] S4. After satisfying the Markov property test, the weighted Markov model is used to calculate the autocorrelation coefficient, establish the transition probability matrix, and calculate the simulated values ​​to test the model and further predict the future carbon effect of highways.

[0010] Preferably, in step S1, the carbon emission factor method specifically includes:

[0011] A. According to formula E kp =C kp ·F p Calculate the anthropogenic carbon emissions in year k, where E kp C represents the anthropogenic carbon emissions in year k, expressed in kg CO2. kp F represents the comprehensive man-days corresponding to the quota in year k, in man-days; p The comprehensive man-day factor is set to 19.76, with units of kgCO2 / man-day.

[0012] B. According to formula E kci =C kci ·F ci ·M ci Calculate the carbon emissions of the i-th material or energy source in year k, where E kci C represents the carbon emissions generated by the i-th type of building material or energy in year k, expressed in kgCO2. kci F represents the consumption of the i-th type of building material or energy in year k; ci M is the carbon emission factor of the i-th material or energy source; ci Let be the correlation coefficient of the i-th material or energy source. The correlation coefficient is an "intermediate amount" generated because the carbon emission factor and the quota cannot be multiplied separately, so that the final carbon emission quantity is in the dimension of "kgCO2".

[0013] C. According to formula Ek jl =Ck jl ·F jl ·M jl Calculate the carbon emissions of the l-th type of construction machinery in year k, where E kjl Ck represents the carbon emissions generated by the l-th type of construction machinery in year k, expressed in kgCO2. jl F represents the consumption of type l construction machinery in year k; jl The carbon emission factor of the first type of construction machinery; M jl The correlation coefficient for the l-th type of construction machinery;

[0014] D. According to the formula Calculate the carbon emissions from highway construction in year k, where, is the carbon source of the highway in year k; i is the type of building materials or energy, i = 1, 2, 3...n; l is the type of construction machinery, l = 1, 2, 3...n.

[0015] Preferably, in step S2, the calculation of the carbon source and carbon sink around the highway specifically includes:

[0016] A. According to the formula Calculate the carbon absorption, i.e., carbon sink, along the buffer zone in year k, where, The amount of carbon absorbed along the buffer zone in year k is expressed in kgCO2. F represents the carbon absorption area of ​​the t-th land use type along the buffer zone in year k; ht The carbon absorption factor for the t-th land use type;

[0017] B. According to the formula Calculate the carbon emissions along the buffer zone in year k, i.e., the carbon source amount, where, The carbon emissions along the buffer zone in year k are expressed in kgCO2. F represents the carbon emission area of ​​the t-th land use type along the buffer zone in year k; rt For the t-th land use type, the carbon absorption factor is...

[0018] C. According to the formula Calculate the net carbon source or net carbon sink along the buffer zone in year k, where, This represents the net carbon source or net carbon sink along the buffer zone in year k; if This is the net carbon source; if This is the net carbon sink.

[0019] Preferably, step S3 includes the following specific steps:

[0020] The annual carbon effect of the highway after its opening to traffic is used as the original data sequence X of the grey mean GM(1,1) model. (0) , which corresponds to the time series k;

[0021] X (0) Perform accumulation processing to generate sequence X by accumulating the original data sequence once. (1) ; as X (1) The nearest neighbor mean generating sequence Z (1) ;

[0022] Perform a grade ratio test on the original data sequence. If the grade ratio test is satisfied, the grey mean GM(1,1) model can be established.

[0023] After satisfying the grade ratio test, the differential equation of the grey mean GM(1,1) model is established, and the development coefficient and grey action quantity are solved by the least squares method to obtain the response function.

[0024] Simulation and prediction of the original data sequence using time response function;

[0025] The accuracy of the simulation results is verified by using residual tests, correlation tests, and posterior error tests.

[0026] Preferably, step S4 includes the following specific steps:

[0027] The preliminary predicted values ​​are divided into states; a Markov property test is performed on them. If the Markov property test is satisfied, a weighted Markov model can be established.

[0028] After satisfying the Markov property test, a multi-step state transition probability matrix is ​​established;

[0029] Calculate the autocorrelation coefficient and weights;

[0030] The weighted Markov model was tested by calculating simulated values.

[0031] Calculate the predicted values ​​and analyze the errors.

[0032] This invention overcomes the shortcomings of existing technologies and provides a method for predicting the future carbon effects of highways throughout their entire life cycle. Based on literature review, data research, and scientific analysis, it studies and supplements the carbon source and sink factor database of highway engineering and its surrounding built environment, establishes a carbon source calculation model for the entire life cycle of highway engineering, constructs a dynamic measurement method for carbon source and sink in the surrounding environment of highways, and uses a grey weighted Markov model to predict the carbon effect trend of highway engineering. This enriches the method system for transportation carbon emissions, provides technical support for the construction of long-life, low-carbon highways, and objectively and quantitatively assesses the carbon emission level and emission reduction potential of the highway industry.

[0033] The innovation of the method of this invention lies in:

[0034] (1) According to the full life cycle assessment, the highway stage is divided into four stages: production and transportation stage, construction stage, use and maintenance stage, and demolition and recycling stage. According to the construction level and sequence of "unit-sub-item", the carbon emission boundary composition of "human-material-machinery" in each stage is determined from bottom to top.

[0035] (2) By combining the built environment carbon source and sink factor database and remote sensing technology, we can grasp the changes in land use types in a timely and accurate manner and construct a spatiotemporal carbon measurement method for land use and cover changes along highways.

[0036] (3) Based on the grey weighted Markov model, a domestic highway carbon effect prediction model is established. This model has good effect on prediction problems with "small data", "poor information" and long-term randomness. The level ratio test and Markov property test are used to judge whether the model is applicable. The residual test, correlation test and posterior error test are used to judge whether the model is accurate.

[0037] This invention is of great significance for improving the domestic highway carbon source factor database and the surrounding environment carbon source sink factor database, standardizing the calculation standards for carbon emissions from highway engineering, enriching the transportation carbon emission method system, assessing the emission reduction potential of highway engineering and formulating measures, and has a positive impact on reducing CO2 emissions, mitigating the greenhouse effect, and alleviating carbon emissions from my country's transportation industry.

[0038] Compared with the shortcomings and deficiencies of existing technologies, the present invention has the following beneficial effects:

[0039] (1) This invention establishes a data list of carbon source and sink factors for the life cycle of domestic highways and the dynamic carbon source and sink factors along the routes. Through literature review, database retrieval, and field surveys, existing analytical databases, software, literature, and reports were compiled and compared to identify carbon source and sink factors that align with the analysis of the life cycle and carbon source and sink areas of domestic highways, resulting in a data list. The main contents include two aspects: a carbon source database for the entire life cycle of highways and a carbon source and sink database along highways. The carbon source and sink database along highways includes a land use change carbon source and sink database and a crop carbon sink database.

[0040] (2) This invention establishes a theoretical calculation method for carbon emissions throughout the life cycle of domestic highways. Based on the characteristics of the highway life cycle and construction quotas, carbon emission factor data that conforms to the entire life cycle of highways are collected. Combined with the carbon emission factor database, construction quotas and engineering stage definitions, a carbon emission calculation model for domestic highway projects is studied to simulate the total carbon emission trend and the evolution of cumulative carbon emission values ​​of highway projects, revealing the changes and research patterns of carbon emissions of different highways.

[0041] (3) This invention establishes a dynamic carbon source and sink calculation model for areas along highways in Gansu Province. The research highway was identified, and through literature review, database retrieval, and field surveys, the effect depth along both sides of the highway was determined to be 5 kilometers. Remote sensing was used to obtain timely and accurate data on current land use, and real-time monitoring of land use type conversions at different times was conducted. Combined with the collected and organized built environment carbon source and sink database, a spatiotemporal carbon effect measurement model for land use and cover changes along the highway was constructed.

[0042] (4) This invention establishes a domestic highway carbon effect prediction model based on the grey weighted Markov model. The grey mean GM(1,1) model was created by Professor Deng Julong in 1982 as a new method for studying uncertainties related to "small data" and "poor information." The weighted Markov model was developed by the mathematician Markov in the early 20th century through extensive data research to solve the long-term prediction problem of large fluctuations in random data sequences. Through level ratio tests and Markov property tests, differential equations are established, states are divided, a probability matrix is ​​built, and a highway system carbon effect prediction model is constructed. Attached Figure Description

[0043] Figure 1 This is a simplified flowchart of the method of the present invention;

[0044] Figure 2 This is a detailed flowchart of the method of the present invention;

[0045] Figure 3 This is a simplified version of the carbon effect prediction model flowchart in the method of this invention;

[0046] Figure 4 This is a detailed flowchart of the carbon effect prediction model in the method of this invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0048] like Figure 1 and Figure 2 As shown, this invention provides a method for predicting the future carbon effects of highways throughout their entire life cycle. This method includes the following steps:

[0049] S1. The highway project is divided into stages according to its entire life cycle. Based on the set construction sequence and level, the entire highway is divided into independent unit processes with a unified quantitative expression of carbon emissions. Combining the carbon emission factor database, actual engineering data and quotas, the carbon emission factor method is used to obtain the carbon source of the highway throughout its entire life cycle.

[0050] S2. Determine the buffer zone along the highway, use carbon source and sink carrier technology (i.e., remote sensing technology) to obtain the area of ​​each land use type in the buffer zone, and combine it with the surrounding environmental carbon source and sink factor database to calculate the carbon source and carbon sink around the highway to obtain the net carbon source or net carbon sink.

[0051] S3. Using the annual carbon effect after the road opens to traffic as the original data sequence, after satisfying the grade ratio test, the gray mean GM(1,1) model is used to make a preliminary prediction of the future carbon effect of the highway and to conduct an accuracy test.

[0052] S4. After satisfying the Markov property test, the weighted Markov model is used to calculate the autocorrelation coefficient, establish the transition probability matrix, and calculate the simulated values ​​to test the model and further predict the future carbon effect of highways.

[0053] In practical applications, step S1 specifically includes: dividing the entire life cycle of a highway into the production and transportation stage, the construction stage, the use and maintenance stage, and the demolition and recycling stage; combining the carbon emission factor database, actual engineering data, and quotas, using the "carbon emission factor method," calculating the carbon emissions of human labor, materials or energy, and construction machinery for each life cycle sub-item project; summing these to obtain the carbon emissions of the sub-project and unit project, i.e., the carbon source of the highway project; specifically including:

[0054] A. According to formula E kp =C kp ·F p Calculate the anthropogenic carbon emissions in year k, where E kp C represents the anthropogenic carbon emissions in year k, expressed in kg CO2. kp F represents the comprehensive man-days corresponding to the quota in year k, in man-days; p The comprehensive man-day factor is set to 19.76, with units of kgCO2 / man-day.

[0055] B. According to formula E kci =C kci ·F ci ·M ci Calculate the carbon emissions of the i-th material or energy source in year k, where E kci C represents the carbon emissions generated by the i-th type of building material or energy in year k, expressed in kgCO2. kci F represents the consumption of the i-th type of building material or energy in year k; ci M is the carbon emission factor of the i-th material or energy source; ci Let be the correlation coefficient of the i-th material or energy source. The correlation coefficient is an "intermediate amount" generated because the carbon emission factor and the quota cannot be multiplied separately, so that the final carbon emission quantity is in the dimension of "kgCO2".

[0056] C. According to formula E kjl =C kjl ·F jl ·M jl Calculate the carbon emissions of the l-th type of construction machinery in year k, where E kilC represents the carbon emissions generated by the l-th type of construction machinery in year k, expressed in kgCO2. kjl This represents the consumption of type l construction machinery in year j; F jl The carbon emission factor of the first type of construction machinery; M jl The correlation coefficient for the l-th type of construction machinery;

[0057] D. According to the formula Calculate the carbon emissions from highway construction in year k, where, is the carbon source of the highway in year k; i is the type of building materials or energy, i = 1, 2, 3…n; l is the type of construction machinery, l = 1, 2, 3…n;

[0058] Table 1 shows an example of a database of carbon source factors for highways.

[0059] Table 1. Highway Carbon Source Factor Database (Example)

[0060]

[0061] In practical applications, step S2 specifically includes:

[0062] A. Identify the research highway: Through literature review, database retrieval, and field surveys, determine the effect depth along both sides of the highway to be 5 kilometers. Using carbon source and sink carrier technologies (i.e., remote sensing or geographic information systems), obtain the area of ​​each land use type in the buffer zone. Combined with a database of surrounding environmental carbon source and sink factors, calculate the carbon source and carbon sink quantities around the highway to obtain the net carbon source or net carbon sink quantity, specifically including:

[0063] B. According to the formula Calculate the carbon absorption, i.e., carbon sink, along the buffer zone in year k, where, The amount of carbon absorbed along the buffer zone in year k is expressed in kgCO2. F represents the carbon absorption area of ​​the t-th land use type along the buffer zone in year k; ht The carbon absorption factor for the t-th land use type;

[0064] C. According to the formula Calculate the carbon emissions along the buffer zone in year k, i.e., the carbon source amount, where, The carbon emissions along the buffer zone in year k are expressed in kgCO2. F represents the carbon emission area of ​​the t-th land use type along the buffer zone in year k; rt The carbon absorption factor for the t-th land use type;

[0065] D. According to the formula Calculate the net carbon source or net carbon sink along the buffer zone in year k, where, This represents the net carbon source or net carbon sink along the buffer zone in year k; if This is the net carbon source; if This is the net carbon sink.

[0066] E. According to the formula The calculation of the net carbon effect of the highway in year k takes into account both the carbon source of the highway itself throughout its life cycle and the carbon source and sink of the surrounding environment.

[0067] Examples of carbon source and sink factors in the environment surrounding highways are shown in Tables 2 and 3:

[0068] Table 2. Land Use Type Carbon Source and Sink Factor Database (Example) Unit: tC ha -1 yr -1

[0069]

[0070] Table 3 Carbon absorption rates of major crops (examples)

[0071]

[0072] In step S3, the above gray GM(1,1) model, as follows: Figure 3 and Figure 4 As shown, step S3 specifically includes:

[0073] A. Using the annual carbon effect after the road opens as the original data sequence, let X be... (0) The original data sequence corresponds to the time series k, i.e., X. (0) ={x (0) (1), x (0) (2), ..., x (0) (n)}, k=1,2,…n.

[0074] B. Accumulate X(0) to obtain the 1-AGO sequence X of the original data. (1) To reduce the randomness of the original data, i.e., X (1) ={x (1) (1), x (1) (2), ..., x (1) (n)}, where,

[0075] C. (X) (1) The nearest neighbor mean generating sequence Z (1) Z (1) ={z (1) (2), z (1) (3), ..., z (1) (n)}, where,

[0076] D. Perform a premise test for the GM(1,1) model, namely the grade ratio test, using the formula: For sequence X (1) The series ratio; if the series ratio satisfies When the mean GM(1,1) model can be established.

[0077] E. Establish a grey mean GM(1,1) prediction model, specifically including the following:

[0078] (1) Establish the differential equation: -a is the development coefficient, and u is the grey effect quantity;

[0079] (2) Solve for a and u using the least squares method, i.e., calculate Where Y and B are respectively

[0080] (3) Solving the differential equation, we obtain the time response expression of the grey mean GM(1,1) model as follows:

[0081] Prediction is based on time response.

[0082] F. The applicability of the development coefficient -a and the GM(1,1) model is shown in Table 4:

[0083] Table 4. Applicability of the Development Coefficient -a and the GM(1,1) Model

[0084]

[0085] G. Establish an error model.

[0086] Original sequence X (0) ={x (0) (1), x (0) (2), ..., x (0) (n)}, k=1,2,…n;

[0087] Grey model predicts sequence

[0088] (1) Residual inspection

[0089] Grey model predicts residual sequence ε (0) ={ε (0) (1), ε (0) (2), …, ε (0) (n)},

[0090] ε (0)It is a predicted sequence The absolute residuals, k = 1, 2, ..., n;

[0091] The relative residual sequence φ = {φ1, φ2, φ3, ... φ n},

[0092] Mean relative residual

[0093] accuracy

[0094] (2) Correlation test

[0095] The formula for the correlation coefficient:

[0096] P is typically taken as 0.5, k = 1, 2, ... n; the average value of the correlation coefficient, i.e., the Dun correlation degree, is:

[0097] If γ > 0.6, the accuracy can be considered acceptable.

[0098] (3) Posterior error test

[0099] Variance of the original data sequence:

[0100] Variance of the residual sequence:

[0101] Posterior difference ratio (mean squared error ratio):

[0102] Small residual probability:

[0103] The accuracy inspection level reference table is shown in Table 5:

[0104] Table 5. Reference Table for Accuracy Inspection Levels

[0105]

[0106] In practical applications, the aforementioned weighted Markov model, such as Figure 3 and Figure 4 As shown, step S4 specifically includes:

[0107] A. Regarding the original data sequence X (0) and predicted data sequence X (1) Make reasonable divisions. Different division methods will result in very different prediction results. Therefore, the state division should be reasonably allocated according to the actual situation. Common division methods include the sample mean-mean-variance classification method, ordered cluster analysis method, and constant partitioning method.

[0108] B. Using χ 2 A statistical test is used to check whether the model exhibits Mahalanobis property. Given M' = {M'1, M'2, ..., M'} n Let} be the set of states of the system, and n ij Representing state M' i After one step, the state transitions to state M'. j frequency, p j Representative (n) ij ) n×n The ratio of the sum of the j-th column to the sum of all rows and columns is denoted as:

[0109] and

[0110] The statistic follows a sequence with (n-1) degrees of freedom. 2 χ 2 Step by step. When the confidence level is α, we can obtain... make like The available data exhibits Markovian properties, allowing for the establishment of a weighted Markov model.

[0111] C. Establish a state transition probability matrix for one or more steps.

[0112] Let the system's state set be M' = {M'1, M'2, ..., M'}. n}, p ij Represents system state M' i After one step, the state transitions to state M'. j The probability of . Let:

[0113]

[0114] P is called the one-step transition probability matrix of the system. Where:

[0115]

[0116] transition probability p ij The following statistical calculations were performed:

[0117] Let state M' i There is m i There are data points inside, and at the start of the next moment, there will be m. ij The data is transferred to state M' j Above, then from state M' i To state M' j The one-step transition frequency is Then p ij =F ij For state M' i To state M' jThe one-step transition frequency. Similarly, the m-step state transition probability is... According to p ij To predict the future state of the system.

[0118] D. Calculate the autocorrelation coefficient and weights.

[0119] The formula for the autocorrelation coefficient is:

[0120]

[0121] Where, r i Let represent the autocorrelation coefficient of the i-th order (with a lag period of i years), and n be the sequence length.

[0122] Normalization formula: The normalized result is used as the weight of the weighted Markov chain for each step size, where m is the maximum order that needs to be calculated for prediction.

[0123] E. Weighted Markov Prediction. The weighted sum of the predicted probabilities for the same state is used as the predicted probability of the data sequence being in that state. max{P i The i corresponding to i∈M'} is the predicted state of the indicator value for that period. Combining the state division method and object, simulation or prediction is performed, and model verification is conducted.

[0124] F. Error inspection.

[0125] Original sequence X (0) ={x (0) (1), x (0) (2), ..., x (0) (n)}, k=1,2,…n;

[0126] Grey Weighted Markov Prediction Sequence

[0127] Grey weighted Markov residual sequence

[0128] It is a predicted sequence The absolute residuals, k = 1, 2, ..., n;

[0129] The relative residual sequence φ' = {φ'1, φ'2, φ'3, ... φ'} n},

[0130] Mean relative residual

[0131] accuracy If the accuracy ω' ≥ 10%, the model can be considered qualified.

[0132] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting the future carbon effects of highways throughout their entire life cycle, characterized in that, The method includes the following steps: S1. The highway project is divided into stages according to the whole life cycle. According to the set construction sequence and level, the entire highway is divided into independent unit processes with unified carbon emission quantification. Combining the carbon emission factor database, actual engineering data and quotas, the carbon emission factor method is used to obtain the carbon source of the highway throughout its whole life cycle. S2. Determine the buffer zone along the highway, use carbon source and sink carrier technology to obtain the area of ​​each land use type in the buffer zone, and combine it with the surrounding environmental carbon source and sink factor database to calculate the carbon source and carbon sink around the highway to obtain the net carbon source or net carbon sink. S3. Using the annual carbon effect after the road opens to traffic as the original data sequence, after satisfying the grade ratio test, the gray mean GM(1,1) model is used to make a preliminary prediction of the future carbon effect of the highway and to conduct an accuracy test. S4. After satisfying the Markov property test, the weighted Markov model is used to calculate the autocorrelation coefficient, establish the transition probability matrix, and calculate the simulated values ​​to test the model and further predict the future carbon effect of highways.

2. The method as described in claim 1, characterized in that, In step S1, the carbon emission factor method specifically includes: A. According to formula E kp =C kp ·F p Calculate the anthropogenic carbon emissions in year k, where E kp C represents the anthropogenic carbon emissions in year k, expressed in kg CO2. kp F represents the comprehensive man-days corresponding to the quota in year k, in man-days; p To take into account the labor man-day factor; B. According to formula E kci =C kci ·F ci ·M ci Calculate the carbon emissions of the i-th material or energy source in year k, where E kci C represents the carbon emissions generated by the i-th type of building material or energy in year k, expressed in kgCO2. kci F represents the consumption of the i-th type of building material or energy in year k; ci M is the carbon emission factor of the i-th material or energy source; ci The correlation coefficient for the i-th material or energy source; C. According to formula E kjl =C kjl ·F jl ·M jl Calculate the carbon emissions of the l-th type of construction machinery in year k, where E kjl C represents the carbon emissions generated by the l-th type of construction machinery in year k, expressed in kgCO2. kjl F represents the consumption of type l construction machinery in year k; jl The carbon emission factor of the first type of construction machinery; M jl The correlation coefficient for the l-th type of construction machinery; D. According to the formula Calculate the carbon emissions from highway construction in year k, where, is the carbon source of the highway in year k; i is the type of building materials or energy, i = 1, 2, 3...n; l is the type of construction machinery, l = 1, 2, 3...n.

3. The method as described in claim 1, characterized in that, In step S2, the calculation of the carbon source and carbon sink quantities around the highway specifically includes: A. According to the formula Calculate the carbon absorption, i.e., carbon sink, along the buffer zone in year k, where, The amount of carbon absorbed along the buffer zone in year k is expressed in kgCO2. F represents the carbon absorption area of ​​the t-th land use type along the buffer zone in year k; ht For the t-th land use type, the carbon absorption factor is... B. According to the formula Calculate the carbon emissions along the buffer zone in year k, i.e., the carbon source amount, where, The carbon emissions along the buffer zone in year k are expressed in kgCO2. F represents the carbon emission area of ​​the t-th land use type along the buffer zone in year k; rt For the t-th land use type, the carbon absorption factor is... C. According to the formula Calculate the net carbon source or net carbon sink along the buffer zone in year k, where, This represents the net carbon source or net carbon sink along the buffer zone in year k; if This is the net carbon source; if This is the net carbon sink.

4. The method as described in claim 1, characterized in that, Step S3 includes the following specific steps: The annual carbon effect of the highway after its opening to traffic is used as the original data sequence X of the grey mean GM(1,1) model. (0) , which corresponds to the time series k; X (0) Perform accumulation processing to generate sequence X by accumulating the original data sequence once. (1) ; as X (1) The nearest neighbor mean generating sequence Z (1) ; Perform a grade ratio test on the original data sequence. If the grade ratio test is satisfied, the grey mean GM(1,1) model can be established. After satisfying the grade ratio test, the differential equation of the grey mean GM(1,1) model is established, and the development coefficient and grey action quantity are solved by the least squares method to obtain the response function. Simulation and prediction of the original data sequence using time response function; The accuracy of the simulation results is verified by using residual tests, correlation tests, and posterior error tests.

5. The method as described in claim 1, characterized in that, Step S4 includes the following specific steps: The preliminary predicted values ​​are divided into states; a Markov property test is performed on them. If the Markov property test is satisfied, a weighted Markov model can be established. After satisfying the Markov property test, a multi-step state transition probability matrix is ​​established; Calculate the autocorrelation coefficient and weights; The weighted Markov model was tested by calculating simulated values. Calculate the predicted values ​​and analyze the errors.

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