Ultra-high voltage transmission line construction carbon emission accounting method considering carbon emission uncertainty

Abstract

The invention discloses an ultra-high voltage transmission line construction carbon emission accounting method considering carbon emission uncertainty, and the method comprises the following steps: dividing the carbon emission into an easily traceable carbon emission source and a difficultly traceable carbon emission source based on a mixed life cycle method, and accounting the total carbon emission of ultra-high voltage transmission line construction; carrying out uncertainty quantification on the easily traceable carbon emission source and the difficultly traceable carbon emission source; and uncertainty transfer analysis is carried out in the whole process. The method has the beneficial effects that the influence of currency value change is eliminated through the exchange rate and the GDP flat subtraction index, a prediction model considering the total factor productivity and the unit energy carbon emission intensity is established, and the department carbon emission intensity can be reasonably predicted based on historical data under the condition that a department carbon emission input-output table corresponding to the accounting year is lacked; the model is simple in structure, clear in physical meaning, easy in parameter acquisition and good in fitting effect, and the space-time adaptability and the accounting precision of carbon emission accounting are improved.

Description

A carbon emission accounting method for the construction of ultra-high voltage transmission lines considering carbon emission uncertainties Technical Field

[0001] This invention relates to the field of carbon emission accounting technology for the construction of ultra-high voltage transmission lines, and specifically to a carbon emission accounting method for the construction of ultra-high voltage transmission lines that takes into account the uncertainty of carbon emissions. Background Technology

[0002] Ultra-high voltage (UHV) transmission lines, as key infrastructure of China's energy internet, play a vital role in promoting the large-scale transmission and optimized inter-regional allocation of clean energy. However, the intensive construction of UHV transmission lines consumes a large amount of equipment, building materials, and energy, generating significant carbon emissions. Currently, the power construction industry has not established carbon emission accounting standards for UHV transmission line construction, making it difficult to accurately assess the environmental impact of the UHV transmission line construction process.

[0003] Existing carbon emission accounting methods for power transmission line construction mainly include the process life cycle method, the input-output life cycle method, and the hybrid life cycle method. However, these methods have the following shortcomings when applied to carbon emission accounting for ultra-high voltage (UHV) power transmission line construction:

[0004] Problem 1: The system boundary is ambiguous. Existing methods mostly focus only on the carbon emissions of building materials, equipment and energy consumed during the construction phase, while ignoring the carbon emissions of the early stages of ultra-high voltage power transmission projects, such as land acquisition and resettlement, exploration and design.

[0005] Question 2: A severe lack of carbon emission factor data. While carbon emission factors for conventional building materials (such as steel, concrete, and aluminum) are relatively easy to obtain, reliable carbon emission factors for specialized equipment and building materials used in UHV transmission lines (such as OPGW lines, insulators, and hardware) are scarce due to their relatively niche nature and complex manufacturing processes. Furthermore, the human, material, and energy consumption in UHV transmission line construction activities, including surveying and design, land acquisition and clearing, feasibility studies, and engineering supervision, are difficult to calculate in detail, making carbon footprint tracking challenging and resulting in a lack of usable data.

[0006] Problem 3: Weak Characterization of Parameter Uncertainty. The carbon emission accounting for UHV transmission line construction involves parameters such as carbon emission factors, building material input, building material loss rate, number of machine shifts, mechanical energy consumption, investment amount, and greenhouse gas input-output intensity, all of which are subject to uncertainty due to multiple factors. However, existing carbon emission accounting for transmission line construction often treats these parameters as constants, lacking a systematic framework for uncertainty quantification and transmission analysis. Existing accounting results are typically presented as single numerical values ​​without providing confidence intervals, failing to reflect the reliability and error range of the accounting results, and thus failing to meet the requirements of carbon verification, carbon trading, and other application scenarios.

[0007] Question 4: Poor temporal and spatial adaptability of the input-output method. Existing studies often directly multiply the nominal investment amount in the accounting year by the carbon emission input-output intensity in the base year when applying the input-output method, without considering the changes in purchasing power, exchange rates, and carbon emission intensity in different years and regions; or they use China's overall carbon emission intensity to roughly correct the carbon emission intensity of various sectors in different years, ignoring industry heterogeneity, resulting in insufficient accounting accuracy.

[0008] In summary, existing calculation methods for transmission lines have shortcomings in terms of system boundary integrity, availability of key data, characterization of parameter uncertainties, and spatiotemporal adaptation of the input-output method. There is an urgent need to construct a scientific, complete, accurate, quantifiable, and clearly defined carbon emission accounting method for the construction of ultra-high voltage transmission lines.

[0009] Therefore, further solutions to the above problems are needed. The applicant has proposed a carbon emission accounting method for the construction of ultra-high voltage transmission lines that takes into account the uncertainty of carbon emissions. Summary of the Invention

[0010] To address the shortcomings of existing technologies, this invention provides a carbon emission accounting method for the construction of ultra-high voltage transmission lines that considers the uncertainty of carbon emissions. This method solves the problems of ambiguous system boundary delineation, severe lack of carbon emission factor data, weak parameter uncertainty characterization, and poor spatiotemporal adaptability of input-output methods in existing carbon emission accounting methods for transmission line construction mentioned in the background.

[0011] To achieve the above objectives, the present invention provides the following technical solution:

[0012] This invention provides a carbon emission accounting method for the construction of ultra-high voltage transmission lines that considers the uncertainty of carbon emissions. The method includes the following steps:

[0013] S1: Using a hybrid life cycle approach, carbon emissions are divided into easily traceable carbon emission sources and difficult-to-trace carbon emission sources to calculate the total carbon emissions from the construction of ultra-high voltage transmission lines;

[0014] S2: Quantify the uncertainty of easily traceable carbon emission sources;

[0015] S3: Quantify the uncertainty of carbon emission sources that are difficult to trace;

[0016] S4: Perform uncertainty propagation analysis throughout the entire process;

[0017] Step S3 specifically involves categorizing difficult-to-trace carbon emission sources into difficult-to-trace building materials and difficult-to-trace project activities, estimating carbon emission intensity using the input-output life cycle method, and performing calculations based on the China sectoral greenhouse gas input-output table in the EXIOBASE database. The calculated greenhouse gases include carbon dioxide, methane, and nitrous oxide. The specific steps are as follows:

[0018] S31: Information available from the annual input-output table

[0019] If an input-output table exists for the same year as the project's commencement, it can be converted using currency exchange rates, as follows:

[0020]

[0021] In the formula, Carbon emission equivalents that are difficult to trace, in tons of carbon dioxide equivalent. ; For the department In the accounting year CO2 equivalent input-output intensity, expressed in tons of CO2 equivalent per million euros. And that was the price at the time; For China in the accounting year Department The total amount of investment in the project, in millions of RMB. , In the accounting year The exchange rate of RMB to Euro, in units of RMB. ;

[0022] S32: Cases where the annual input-output table is unavailable

[0023] If the available greenhouse gas intensity input-output table is earlier than the accounting year, predict the sectoral greenhouse gas input-output intensity for the accounting year using the following steps:

[0024] Step 1: Obtain historical data. Based on the EXIOBASE database, calculate the sectoral greenhouse gas input-output intensity for historical years as model fitting data.

[0025] Step 2: Matching departments, matching the input items in the budget with the industry departments in the input-output table;

[0026] Step 3: Data standardization, collecting greenhouse gas input-output intensity data for relevant departments for historical years, in tons per million euros. It is converted into greenhouse gas input-output intensity based on constant prices in the accounting year using exchange rates and the GDP deflator, in tons per million RMB. (Calculated at constant prices) to eliminate the impact of exchange rate and currency value fluctuations:

[0027]

[0028] In the formula, For the department In the year greenhouse gases Emission intensity, in tons per million RMB (Calculated at constant prices in the accounting year) For the department In the year greenhouse gases Input-output intensity, in tons per million euros (Price at the time) The average annual exchange rate of RMB to Euro , , Years and accounting year GDP deflator;

[0029] Step 4: Construct a prediction model, considering the impact of changes in energy structure and technology level on sectoral carbon emission intensity, and construct a sectoral carbon emission intensity prediction equation:

[0030]

[0031] In the formula, Represents computational inertia, The total factor productivity index represents my country's total factor productivity in a given year. Overall technical level Carbon intensity per unit of energy production represents my country's carbon emission intensity in a given year. energy structure, For the disturbance term, ;

[0032] Take both sides of the prediction equation formula Taking the natural logarithm with base 1, we obtain the linear regression equation:

[0033]

[0034] In the formula, For the department In the year greenhouse gases Emission intensity, all in tons per million RMB (Calculated at constant prices) For the department greenhouse gases The fixed effects coefficient, For the department greenhouse gases The coefficient of technological inertia, For the department greenhouse gases The technical elasticity coefficient, For the department greenhouse gases The energy structure elasticity coefficient, For random disturbance terms,

[0035] After obtaining the model parameters through regression of historical data, the accounting year can be obtained through recursive calculation. Department greenhouse gases Input-output intensity;

[0036] Step 5: Convert to CO2 equivalent. Convert the emission intensity of each greenhouse gas into CO2 equivalent:

[0037]

[0038] In the formula, For the year Department CO2 equivalent input-output intensity, expressed in tons of CO2 equivalent per million RMB. (Calculated at constant prices) , and Years Department The input-output intensity of carbon dioxide, methane, and nitrous oxide, in tons per million RMB. (Calculated at constant prices) and The global warming potentials of methane and nitrous oxide, respectively.

[0039]

[0040] In the formula, Carbon emission equivalents for difficult-to-trace carbon emission sources, expressed in tons of carbon dioxide equivalent. , For China in the accounting year department Carbon emission equivalent input-output intensity, expressed in tons of CO2 equivalent per million RMB. , To account for the project in the accounting year Classified as a department The total amount of investment in the project, in millions of RMB. .

[0041] As a further aspect of the present invention: Step S1 specifically includes the following steps:

[0042] S11: Easily Traceable Carbon Emission Source Accounting

[0043] For the majority of building materials and energy consumed in the construction of power transmission lines, their carbon footprints are relatively clear, and there are relatively many carbon emission factor reference values. The carbon emission factor method is more accurate and specific than the input-output method, and can effectively distinguish the carbon emission intensity of different technologies and products. Therefore, building materials and energy with clear carbon footprints are classified as easily traceable carbon emission sources, and the carbon emission factor method is used for accounting. The basic formula is:

[0044]

[0045] In the formula, For the first Carbon emissions from easily traceable carbon emission sources. For the first Carbon emission factors of various carbon emission sources For the first Activity data of various carbon emission sources;

[0046] As a further aspect of the present invention, step S1 specifically includes:

[0047] S12: Accounting for Difficult-to-Trace Carbon Emission Sources

[0048] For certain building materials lacking reliable carbon emission factors (such as insulators, fittings, OPGW wires, etc.), and for activities whose carbon footprints are difficult to trace due to vague preliminary surveys, planning, and design processes, these are classified as difficult-to-trace carbon emission sources, and their carbon footprints are estimated based on the input-output method.

[0049]

[0050] In the formula, For the first Carbon emissions from difficult-to-trace carbon emission sources. For the first Departments to which carbon emission sources belong carbon emission intensity, For the first Investment in carbon emission sources;

[0051] S13: Total Carbon Emissions

[0052] Total carbon emissions from the construction of ultra-high voltage transmission line projects Carbon emissions consist of two parts: easily traceable and difficult-to-trace carbon emission sources.

[0053] .

[0054] As a further aspect of the present invention: Step S2 specifically involves dividing the traceable carbon emission sources into traceable building materials and energy consumed by construction machinery, calculating their carbon emissions using carbon emission factors, and applying different uncertainty treatments based on the characteristics of building materials and energy. Specific steps include:

[0055] S21: Uncertain Fit of Carbon Emission Factors for Building Materials

[0056] Data from academic literature, databases, standards, and corporate environmental product declarations (EDPs) were collected. The uncertainty distribution of carbon emission factors for building materials was fitted using normal, log-normal, Weibull, and Gamma distributions. The fitted distributions were then subjected to the Kolmogorov-Smirnov test to determine their significance level. =0.05, and by combining the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) for fitting and optimization, the optimal distribution function of the uncertainty of carbon emission factor of building materials is obtained.

[0057] As a further aspect of the present invention, step S2 further includes the following steps:

[0058] S22: Uncertainty Simulation of Energy Carbon Emission Factors

[0059] The uncertainty of energy consumption by machinery and the carbon emission factor only consider carbon emissions from diesel and gasoline combustion, and the greenhouse calculation includes carbon dioxide. methane nitrous oxide Its carbon emission calculation formula is:

[0060]

[0061] In the formula, Carbon emission factor of fuel combustion, per unit , Carbon content per unit of calorific value of fuel, per unit , It is a carbon oxidation factor. Net calorific value of fuel, in units , and They are respectively and The 100-year global warming potential value, and They are respectively and Emission factor, unit ;

[0062] As a further aspect of the present invention, step S2 further includes the following steps:

[0063] S23: Uncertainty Simulation of Carbon Emission Factors for Some Building Materials

[0064] For building materials with well-defined production processes and readily available raw material inputs and process parameters, the uncertainty of carbon emission factors can be simulated using Monte Carlo simulation methods, provided that the probability distribution of each uncertain parameter is obtained.

[0065] As a further aspect of the present invention: Step S4 specifically involves using uncertainty to represent energy, building material carbon emission factors, global warming potential, building material input, building material loss rate, mechanical energy consumption, mechanical shift input, and other cost input during the calculation. The specific uncertain parameter values ​​are set according to the actual engineering situation and references. Latin hypercube sampling combined with Monte Carlo simulation is used to calculate the carbon emission uncertainty distribution of the entire power transmission project and to provide the confidence interval and error range.

[0066] Compared with the prior art, the beneficial effects of the present invention are:

[0067] 1. This method adopts a hybrid life cycle approach, using different methods to characterize the uncertainty of carbon emission sources of different natures, thereby improving the scientificity, robustness and credibility of the accounting results.

[0068] 2. This method eliminates the impact of currency value fluctuations by using exchange rates and the GDP deflator, and establishes a prediction model that considers total factor productivity and carbon emission intensity per unit of energy. It can reasonably predict sectoral carbon emission intensity based on historical data even when there is a lack of sectoral carbon emission input-output tables for the corresponding accounting year. The model has a simple structure, clear physical meaning, and easy-to-obtain parameters, and has a good fitting effect. It overcomes the spatiotemporal mismatch problem caused by the use of outdated data or coarse corrections in existing methods, and improves the spatiotemporal adaptability and accuracy of carbon emission accounting. Attached Figure Description

[0069] Figure 1 is a flowchart of the carbon emission accounting framework of the carbon emission accounting method for the construction of ultra-high voltage transmission lines that considers the uncertainty of carbon emissions according to the present invention.

[0070] Figure 2 is a total factor productivity diagram of an embodiment of the carbon emission accounting method for the construction of ultra-high voltage transmission lines considering the uncertainty of carbon emissions according to the present invention;

[0071] Figure 3 is a carbon emission intensity diagram of an embodiment of the carbon emission accounting method for the construction of ultra-high voltage transmission lines that considers the uncertainty of carbon emissions according to the present invention;

[0072] Figure 4 is a GDP deflator diagram of an embodiment of the carbon emission accounting method for the construction of ultra-high voltage transmission lines that considers the uncertainty of carbon emissions according to the present invention.

[0073] Figure 5 is an exchange rate chart of an embodiment of the carbon emission accounting method for the construction of ultra-high voltage transmission lines that considers the uncertainty of carbon emissions according to the present invention;

[0074] Figure 6 is a CO2 emission intensity diagram of an embodiment of the carbon emission accounting method for the construction of ultra-high voltage transmission lines considering the uncertainty of carbon emissions according to the present invention;

[0075] Figure 7 is a CH4 emission intensity diagram of an embodiment of the carbon emission accounting method for the construction of ultra-high voltage transmission lines considering the uncertainty of carbon emissions according to the present invention;

[0076] Figure 8 is an N2O emission intensity diagram of an embodiment of the carbon emission accounting method for the construction of ultra-high voltage transmission lines considering the uncertainty of carbon emissions according to the present invention;

[0077] Figure 9 shows the goodness-of-fit model of the greenhouse gas emission input-output intensity prediction model for each sector in an embodiment of the carbon emission accounting method for the construction of ultra-high voltage transmission lines considering the uncertainty of carbon emissions according to the present invention.

[0078] Figure 10 is a carbon emission distribution per unit length of UHVDC transmission line construction, based on an embodiment of the present invention's method for calculating carbon emissions in UHVDC transmission line construction considering carbon emission uncertainties. Detailed Implementation

[0079] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0080] Example

[0081] Taking a ±800kV UHV overhead transmission line project section as an example, this method is used to conduct carbon emission accounting during the construction phase of the UHV transmission line. The project started in 2023, with the line running generally east-west. The terrain is mountainous and mountainous, and it is a single-circuit, double-pole transmission line. The section with a wind speed of 27 m / s and 10 mm ice weather zone is 18.7 km long, with a total of 31 towers, including 17 straight-line self-supporting towers and 14 tension (angle) towers. The conductors use 6×JL1 / G2A-1250 / 100 steel-cored aluminum stranded wire, weighing 4252.3 kg / km. One ground wire is a common ground wire, model JLB20A-150 aluminum-clad steel stranded wire, weighing 989.4 kg / km, and the other is an OPGW wire, model OPGW-150, weighing 1055 kg / km. The specific accounting steps are as follows:

[0082] 1. Uncertainty fitting of carbon emission factors for easily traceable building materials

[0083] Data on carbon emission factors of domestic building materials were collected and compiled from papers, databases, and corporate environmental product statements over the past 15 years. Log-normal, normal, Weibull, and Gamma distributions were used to fit the carbon emission factors. The Kolmogorov-Smirnov (KS) test was conducted (significance level...). The optimal fit was performed using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The results show that the log-normal distribution fits the carbon emission factors of various building materials well. The log-normal distribution parameters of some building material carbon emission factors are shown in Table 1 below:

[0084] ;

[0085] 2. Monte Carlo simulation of energy and conductor / ground wire carbon emission factors

[0086] Referring to the uncertainty parameters in the IPCC 2006 National Greenhouse Gas Inventory Guidelines, the China Energy Statistics Yearbook-2023, and the GB / T51366-2019 Standard for Calculating Carbon Emissions from Buildings, the uncertainty of carbon emission factors for gasoline, diesel, and natural gas is quantified by combining Latin hypercube distribution with Monte Carlo method.

[0087] Based on the uncertainty distribution of carbon emission factors of energy and building materials, and combined with the production process parameters in the "Guidelines for Carbon Footprint Evaluation of Electrical Products - Overhead Conductors" and its compilation instructions, 50,000 Monte Carlo simulations were conducted on the carbon emission factors of steel-cored aluminum stranded wire 6×JL1 / G2A-1250 / 100 (including jumpers) and aluminum-clad steel stranded wire JLB20A-150. The Monte Carlo simulation results of energy and conductor / ground wire carbon emission factors are shown in Table 2 below:

[0088] ;

[0089] 3. Forecast of sectoral carbon emission intensity

[0090] Based on the EXIOBASE Version 3.9.6 database, the sectoral greenhouse gas input-output intensity of China from 2001 to 2022 was calculated and used as the training dataset for the prediction data, and the prediction model parameters were determined. Values ​​are referenced from Our World inData. The values ​​are referenced from the RTF-PNA indicator in Penn World Table version 11.0 (PWT 11.0), which is the GDP deflator. According to World Bank data, exchange rates Referring to data from the European Central Bank (ECB), the time values ​​of each parameter are shown in Figures 2-5. Predictive models were established using the prediction equation formula and the linear regression equation formula. The emission intensities of CO2, CH4, and N2O for the eight sectors involved were fitted respectively. The fitting results and predicted values ​​are shown in Figures 6, 7, and 8, respectively. The goodness of fit R0 of the prediction models for the three greenhouse gases for the eight sectors is shown. 2 As shown in Figure 9, the fitting results indicate that the model fits well for most departments, with R0... 2 The values ​​are all above 0.85, proving the effectiveness of the prediction model. Based on the prediction model, the greenhouse gas input-output intensity of China in 2023 is consistent with the calculation year. Table 3 below shows some of the predicted greenhouse gas input-output intensity (t / M.CNY) for certain sectors in China in 2023.

[0091] ;

[0092] 4. Compilation of carbon emission accounting inventory

[0093] (1) Carbon emission accounting during the building materials production stage

[0094] Based on the project budget quota, the consumption of equipment materials per unit length is calculated. Depending on whether the materials are traceable, the carbon emission factor method or the input-output method is used for accounting. The specific calculation methods for equipment material kilometer index and carbon emission factor are shown in Table 4 below:

[0095] ;

[0096] (2) Carbon emission accounting during the construction phase

[0097] The types of construction machinery, the number of machine shifts, and the corresponding energy consumption per machine shift (fuel / electricity consumption) involved in the construction phase of a sub-project can be determined comprehensively based on the specific project budget quota and industry-standard quotas. The types of machinery and machine shift consumption can be referenced from the recommended machinery configurations and machine shift quantities for each quota item in quota materials such as the "Budget Quota for Power Construction Projects (2018 Edition)". The energy consumption per machine shift can be referenced from the typical consumption standard values ​​given in materials such as the "Quota for Construction Machinery Shift Costs in Power Construction Projects (2018 Edition)". Specifically:

[0098] Every power transmission project will have a preliminary budget table prepared, which will give the quota quantities of the projects required to complete the project. Then, based on the quota quantities and in conjunction with the publicly available "Electric Power Construction Project Budget Quotas (2018 Edition) Volume 4 Overhead Transmission Line Engineering" and "Electric Power Construction Project Construction Machinery Shift Cost Quotas (2018 Edition)", the fuel and power consumption can be estimated.

[0099] Taking a certain section as an example, as shown in Table 5 of the unit project budget for the power transmission line, this project has a sub-item project 1.1.1 manual transportation, which includes a project with quota number "YX1-17" "manual transportation of hardware, insulators, and miscellaneous steel materials". Referring to the "Electric Power Construction Engineering Budget Quota (2018 Edition) Volume 4 Overhead Transmission Line Engineering", the quota will give the recommended number of machine shifts for the corresponding quota number; as shown in Table 6 below, YX1-17 uses 0.0263 shifts of dedicated transmission trucks (4t) per t⋅km; referring to the "Electric Power Construction Engineering Construction Machinery Shift Cost Quota (2018 Edition)", the standard value of fuel and power consumption per shift of the corresponding machinery can be found, as shown in Table 7 below. The dedicated transmission truck (4t), code JT13-44, has a gasoline consumption of 18.54kg per shift;

[0100] In summary, the quantity of work for this section, quota number YX1-17, is 356.73 t⋅km, and it is necessary to use... The daily power transmission truck (4t) consumes kg of gasoline.

[0101]

[0102]

[0103] .

[0104] (3) Carbon emission accounting in other stages

[0105] Based on the project budget, the costs of other projects such as surveying and design, land acquisition and clearing, and project supervision are matched with the industry sectors in the input-output table. The carbon emissions are then calculated using the input-output table, as shown in Table 8 below, which details the budget for other projects related to transmission lines.

[0106] ;

[0107] 5. Uncertainty Parameter Setting and Monte Carlo Simulation

[0108] (1) Uncertain parameter settings

[0109] In addition to the uncertainties of global warming potential, building material carbon emission factors, and energy carbon emission factors, which have already been quantified using the aforementioned methods, based on relevant literature and experience, a 15% uncertainty is set for building material input and loss rate, and a 10% uncertainty is set for machinery shift input, machinery energy consumption, and other cost input. All are assumed to follow a normal distribution.

[0110] (2) Monte Carlo simulation

[0111] The Latin hypercube sampling method combined with Monte Carlo simulation was used to conduct 50,000 random sampling simulations to realize the transmission of uncertainty in the entire accounting process. The probability distribution of carbon emission intensity per unit length is shown in Figure 10.

[0112] 6. Calculation Results

[0113] The carbon emission intensity per unit length during the construction phase of this UHVDC transmission project is 1801.10 tCO2e / km, with a 95% confidence interval of [1324.18, 2425.84] tCO2e / km. The breakdown of carbon emissions for the UHVDC transmission line is shown in Table 9 below.

[0114] .

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A carbon emission accounting method for the construction of ultra-high voltage transmission lines considering the uncertainty of carbon emissions, characterized in that: The method includes the following steps: S1: Using a hybrid life cycle approach, carbon emissions are divided into easily traceable carbon emission sources and difficult-to-trace carbon emission sources to calculate the total carbon emissions from the construction of ultra-high voltage transmission lines; S2: Uncertainty quantification is performed on easily traceable carbon emission sources; S3: Uncertainty quantification is performed on difficult-to-trace carbon emission sources; S4: Uncertainty propagation analysis is performed throughout the entire process; Step S3 specifically involves dividing difficult-to-trace carbon emission sources into difficult-to-trace building materials and difficult-to-trace project activities, estimating carbon emission intensity using the input-output life cycle approach, and calculating based on the Chinese sectoral greenhouse gas input-output table in the EXIOBASE database. The greenhouse gases calculated include carbon dioxide, methane, and nitrous oxide. The specific steps are as follows: S31: Calculate the annual input-output table. If an input-output table exists for the same year as the project commencement year, it can be converted using currency exchange rates, as follows: In the formula, Carbon emission equivalents that are difficult to trace, in tons of carbon dioxide equivalent. ； For the department In the accounting year CO2 equivalent input-output intensity, expressed in tons of CO2 equivalent per million euros. And that was the price at the time; For China in the accounting year Department The total amount of investment in the project, in millions of RMB. ， In the accounting year The exchange rate of RMB to Euro, in units of RMB. S32: Case where the input-output table for the accounting year is unavailable: If the available greenhouse gas intensity input-output table for a year earlier than the accounting year, the sectoral greenhouse gas input-output intensity for the accounting year is predicted using the following steps: Step 1: Obtain historical data. Based on the EXIOBASE database, calculate the sectoral greenhouse gas input-output intensity for historical years as model fitting data; Step 2: Sectoral matching. Match the input items in the preliminary budget with the industry sectors in the input-output table; Step 3: Data standardization. Collect greenhouse gas input-output intensity data for relevant sectors for historical years, in tons per million euros. It is converted into greenhouse gas input-output intensity based on constant prices in the accounting year using exchange rates and the GDP deflator, in tons per million RMB. (Calculated at constant prices) to eliminate the impact of exchange rate and currency value fluctuations: In the formula, For the department In the year greenhouse gases Emission intensity, in tons per million RMB (Calculated at constant prices in the accounting year) For the department In the year greenhouse gases Input-output intensity, in tons per million euros (Price at the time) The average annual exchange rate of RMB to Euro ， 、 Years and accounting year The GDP deflator; Step 4: Construct a forecasting model, considering the impact of changes in energy structure and technology level on sectoral carbon emission intensity, and construct a sectoral carbon emission intensity forecasting equation: In the formula, Represents computational inertia, The total factor productivity index represents my country's total factor productivity in a given year. Overall technical level Carbon intensity per unit of energy production represents my country's carbon emission intensity in a given year. energy structure, For the disturbance term, Take both sides of the prediction equation formula with Taking the natural logarithm with base 1, we obtain the linear regression equation: In the formula, For the department In the year greenhouse gases Emission intensity, all in tons per million RMB (Calculated at constant prices) For the department greenhouse gases The fixed effects coefficient, For the department greenhouse gases The coefficient of technological inertia, For the department greenhouse gases The technical elasticity coefficient, For the department greenhouse gases The energy structure elasticity coefficient, As the random disturbance term, the calculation year can be obtained by recursively calculating the model parameters obtained through regression of historical data. Department greenhouse gases Input-output intensity; Step 5: Convert to CO2 equivalent, converting the emission intensity of each greenhouse gas into CO2 equivalent: In the formula, For the year Department CO2 equivalent input-output intensity, expressed in tons of CO2 equivalent per million RMB. (Calculated at constant prices) 、 and Years Department The input-output intensity of carbon dioxide, methane, and nitrous oxide, in tons per million RMB. (Calculated at constant prices) and The global warming potentials of methane and nitrous oxide, respectively. In the formula, Carbon emission equivalents for difficult-to-trace carbon emission sources, expressed in tons of carbon dioxide equivalent. ， For China in the accounting year department Carbon emission equivalent input-output intensity, expressed in tons of CO2 equivalent per million RMB. ， To account for the project in the accounting year Classified as a department The total amount of investment in the project, in millions of RMB. 。 2. The carbon emission accounting method for the construction of ultra-high voltage transmission lines considering the uncertainty of carbon emissions as described in claim 1, characterized in that: Step S1 specifically includes: S11: Traceable carbon emission source accounting. For the vast majority of building materials and energy consumed in the construction of transmission lines, their carbon footprints are relatively clear, and there are relatively many carbon emission factor reference values. The carbon emission factor method is more accurate and specific than the input-output method, and can effectively distinguish the carbon emission intensity of different technologies and products. Therefore, building materials and energy with clear carbon footprints are classified as traceable carbon emission sources, and the carbon emission factor method is used for accounting. The basic formula is: In the formula, For the first Carbon emissions from easily traceable carbon emission sources. For the first Carbon emission factors of various carbon emission sources For the first Activity data of various carbon emission sources.

3. The carbon emission accounting method for the construction of ultra-high voltage transmission lines considering the uncertainty of carbon emissions as described in claim 2, characterized in that: Step S1 further includes: S12: Accounting for difficult-to-trace carbon emission sources. For some building materials lacking reliable carbon emission factors, and for activities whose carbon footprints are unclear and difficult to trace during the preliminary survey, planning, and design processes, these are classified as difficult-to-trace carbon emission sources and estimated based on the input-output method. In the formula, For the first Carbon emissions from difficult-to-trace carbon emission sources. For the first Departments to which carbon emission sources belong carbon emission intensity, For the first Investment in carbon emission sources; S13: Total carbon emissions from ultra-high voltage transmission line construction projects. Carbon emissions consist of two parts: easily traceable and difficult-to-trace carbon emission sources. 。 4. The carbon emission accounting method for the construction of ultra-high voltage transmission lines considering the uncertainty of carbon emissions as described in claim 1, characterized in that: Step S2 specifically involves categorizing traceable carbon emission sources into traceable building materials and energy consumed by construction machinery. Carbon emission factors are used to calculate their carbon emissions. Different uncertainty handling methods are applied based on the characteristics of building materials and energy. Specific steps include: S21: Uncertainty fitting of building material carbon emission factors. Data from academic literature, databases, standards, and corporate environmental product declarations (EDPs) are collected. Normal, log-normal, Weibull, and Gamma distributions are used to fit the uncertainty distribution of building material carbon emission factors. The fitted distribution is then subjected to the Kolmogorov-Smirnov test to determine the significance level. =0.05, and by combining the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) for fitting and optimization, the optimal distribution function of the uncertainty of carbon emission factor of building materials is obtained.

5. The carbon emission accounting method for the construction of ultra-high voltage transmission lines considering the uncertainty of carbon emissions, as described in claim 4, is characterized in that: Step S2 further includes: S22: Uncertainty of energy carbon emission factor simulation. The uncertainty of energy consumption of machinery is considered. The carbon emission factor only considers the carbon emissions from diesel and gasoline combustion, and calculates the greenhouse carbon dioxide emissions. methane nitrous oxide Its carbon emission calculation formula is: In the formula, Carbon emission factor of fuel combustion, per unit ， Carbon content per unit of calorific value of fuel, per unit ， As a carbon oxidation factor, Net calorific value of fuel, in units ， and They are respectively and The 100-year global warming potential value, and They are respectively and Emission factor, unit 。 6. The carbon emission accounting method for the construction of ultra-high voltage transmission lines considering the uncertainty of carbon emissions, as described in claim 5, is characterized in that: The specific steps of step S2 also include: S23: Uncertainty simulation of carbon emission factors of some building materials. For building materials with clear production processes, raw material input and process parameters, the uncertainty of carbon emission factors of building materials can be simulated by Monte Carlo simulation method, provided that the probability distribution of each uncertain parameter is obtained.

7. The carbon emission accounting method for the construction of ultra-high voltage transmission lines considering the uncertainty of carbon emissions according to claim 1, characterized in that: Step S4 specifically involves representing the carbon emission factors of energy and building materials, global warming potential, building material input, building material loss rate, mechanical energy consumption, mechanical shift input, and other cost input in the calculation with uncertainty. The specific uncertainty parameter values ​​are set according to the actual project and references. Latin hypercube sampling combined with Monte Carlo simulation is used to calculate the carbon emission uncertainty distribution of the entire power transmission project and give the confidence interval and error range.

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