Building material carbon emission factor uncertainty influence analysis method

Abstract

The invention provides a building material carbon emission factor uncertainty influence analysis method. The method comprises the following steps: selecting a carbon emission factor of any building material in a railway construction project; constructing a multi-dimensional evaluation index of data quality evaluation; setting a weight coefficient of each evaluation index; making a scoring rule, and forming a carbon emission factor data quality pedigree matrix; scoring the selected material according to the pedigree matrix; taking the score as input, and determining a probability density function; sampling for multiple times, calculating carbon emission distribution, and determining Monte Carlo simulation output standard deviation and expected value; calculating a variable coefficient and a carbon emission range of a 95% confidence interval, and determining an influence degree of a carbon emission factor; according to the method, a method of combining data quality evaluation and Monte Carlo simulation is adopted, and the influence of the uncertainty of the railway building material carbon emission factors on the carbon emission calculation result can be effectively calculated.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission analysis of building materials, and in particular to a method for analyzing the uncertainty impact of carbon emission factors of building materials. Background Art

[0002] The construction industry is one of the main sources of global energy consumption and greenhouse gas emissions. Buildings generate a large amount of carbon emissions throughout their life cycle, from construction to operation to final demolition. Therefore, correctly estimating the carbon emission factor of building materials is of great significance for achieving emission reduction targets for construction projects, conducting environmental impact assessments, and formulating environmental policies.

[0003] At present, the commonly used carbon emission accounting method at home and abroad is the emission factor method, which has a relatively simple process and mainly involves activity level data and emission factor library; however, in actual work, due to the timeliness of carbon emission factor standards, regions, material types, material production processes and other reasons, the measurement of carbon emission factors of various building materials is prone to large deviations, which becomes the main source of error in carbon emission accounting results; how to calculate the impact of the uncertainty of carbon emission factors on carbon emission accounting results is the main problem solved by the present invention;

[0004] Therefore, the present invention provides a method for analyzing the uncertainty impact of carbon emission factors of building materials to solve the above problems. Summary of the invention

[0005] The present invention provides a method for analyzing the impact of uncertainty in carbon emission factors of building materials, the purpose of which is to effectively calculate the impact of the uncertainty of the carbon emission factors of railway building materials on the calculation results of carbon emissions by using a method combining data quality assessment and Monte Carlo simulation.

[0006] The present invention provides a method for analyzing the uncertainty impact of carbon emission factors of building materials, comprising:

[0007] Step 1: Select the carbon emission factor of any building material used in the life cycle of the railway construction project;

[0008] Step 2: Establish multi-dimensional evaluation indicators for data quality assessment methods;

[0009] Step 3: Determine the weight coefficients of various multi-dimensional evaluation indicators;

[0010] Step 4: Set the scoring rules for various multi-dimensional evaluation indicators to form a data quality spectrum matrix of railway building materials carbon emission factors;

[0011] Step 5: Score the data quality of carbon emission factors of the selected materials based on the pedigree matrix;

[0012] Step 6, using the obtained score as input, and determining the probability density function of the input;

[0013] Step 7: Perform multiple sampling based on the probability density function and use the carbon emission factor method to calculate the distribution of the material's life cycle carbon emissions, thereby determining the standard deviation and expected value of the normal distribution curve output by the Monte Carlo simulation;

[0014] Step eight, calculate the coefficient of variation of the output normal distribution curve and the range of carbon emissions under the 95% confidence interval; determine the degree of uncertainty impact of the carbon emission factor based on the coefficient of variation.

[0015] According to a method for analyzing the uncertainty impact of carbon emission factors of building materials provided by the present invention, the carbon emission factors of building materials are selected from relevant standards.

[0016] According to a method for analyzing the uncertainty impact of carbon emission factors of building materials provided by the present invention, the multi-dimensional evaluation indicators include:

[0017] Reliability Ir, which is measured by the authority of the data publishing agency;

[0018] Data source Is, which is measured by sample size and representativeness;

[0019] The time factor It, which is measured by the difference between the data release time and the project construction time;

[0020] The geographical factor Ig, which is measured by the area represented by the data;

[0021] The technical factor Ite is measured by the technical gap between the data and the manufacturer.

[0022] According to a method for analyzing the uncertainty impact of carbon emission factors of building materials provided by the present invention, the weight coefficients of various multi-dimensional evaluation indicators are determined including:

[0023] By analyzing the impact of each multi-dimensional evaluation indicator on the value of the carbon emission factor, the weight is set;

[0024] Among them, the weight coefficient of reliability Ir is 0.167, the weight coefficient of data source Is is 0.167, the weight coefficient of time factor It is 0.167, the weight coefficient of geographical factor Ig is 0.25, and the weight coefficient of technical factor Ite is 0.25.

[0025] According to a method for analyzing the uncertainty impact of carbon emission factors of building materials provided by the present invention, the scoring rules for setting various multi-dimensional evaluation indicators include:

[0026] The score range of each multi-dimensional evaluation indicator is an integer ranging from 1 to 5 points;

[0027] Among them, the scoring rule of reliability Ir is determined based on the accuracy of verification or testing. The higher the accuracy of verification or testing, the lower the score;

[0028] The scoring rule of data source Is is determined based on the suitability of the case and time range. The higher the suitability of the case and time range, the lower the score;

[0029] The scoring rule for the time factor It is determined based on the difference between the data release time and the project construction time. The smaller the difference, the lower the score.

[0030] The scoring rule for the geographical factor Ig is determined based on the accuracy of the regional data source. The higher the accuracy, the lower the score;

[0031] The scoring rule for the technical factor Ite is based on the consistency between the manufacturer's name and the material production process. The higher the consistency between the manufacturer's name and the material production process, the lower the score.

[0032] According to a method for analyzing the uncertainty impact of carbon emission factors of building materials provided by the present invention, the forming of a data quality spectrum matrix of carbon emission factors of railway building materials comprises:

[0033] The scoring ranges and scoring rules of various multi-dimensional evaluation indicators are associated to generate the data quality spectrum matrix of the railway construction material carbon emission factor.

[0034] According to a method for analyzing the uncertainty impact of carbon emission factors of building materials provided by the present invention, the quality scoring of carbon emission factor data of selected materials based on the pedigree matrix includes:

[0035] The material's multi-dimensional evaluation indicators are scored as DQI n Substitute it into the quality calculation formula and output the carbon emission factor data quality score of the material;

[0036] The mass calculation formula is:

[0037] in, is the carbon emission factor data quality score of the material, x i is the weight of the i-th multi-dimensional evaluation index, f i is the score DQI of the i-th multi-dimensional evaluation index i , n is the number of multi-dimensional evaluation indicators.

[0038] According to a method for analyzing the uncertainty impact of carbon emission factors of building materials provided by the present invention, the method of using the obtained score as an input quantity and determining the probability density function of the input quantity comprises:

[0039] The input is the carbon emission factor data quality score of the material Its probability distribution adopts triangular distribution;

[0040] Set the carbon emission factor value of the material to the mode c, based on the mode c and the data quality score Calculate the values ​​of the lower limit a and the upper limit b;

[0041] The lower limit

[0042] The upper limit

[0043] Among them, T1 is the minimum score of each multi-dimensional evaluation indicator, that is, T1 = minDQI n =1; T2 is the highest score of each multi-dimensional evaluation index, that is, T2 = maxDQI n =5;

[0044] The probability density function of the triangular distribution is obtained according to the values ​​of the lower limit a, the upper limit b and the mode c. The probability density function is:

[0045]

[0046] According to a method for analyzing the uncertainty impact of carbon emission factors of building materials provided by the present invention, the process of step seven includes:

[0047] Determine the probability density function of the output and establish a Monte Carlo model;

[0048] Conduct multiple random samplings to extract a large number of data points; for each sampling, use the carbon emission factor method to calculate the carbon emissions of the material during its life cycle; repeat the sampling process to collect a sufficient amount of simulated carbon emission data;

[0049] Substituting the extracted sample values ​​for the parameters in the Monte Carlo model to perform model calculations;

[0050] Repeat random sampling and model calculations many times to establish the statistical characteristics of the output results;

[0051] After a large number of iterations are completed, the results of all simulations are analyzed; the statistics of the output variables are calculated to determine the standard deviation and expected value of the normal distribution curve output by the Monte Carlo model.

[0052] According to a method for analyzing the uncertainty impact of a building material carbon emission factor provided by the present invention, the coefficient of variation of the normal distribution curve output by the calculation and the range of carbon emissions under a 95% confidence interval, the degree of uncertainty impact of the carbon emission factor determined based on the coefficient of variation includes:

[0053] The coefficient of variation

[0054] Among them, C v (x) is the coefficient of variation of the output normal distribution curve, σ(x) is the standard deviation of the output normal distribution curve, and E(x) is the expected value of the output normal distribution curve;

[0055] Calculate the carbon emission range CI under the 95% confidence interval;

[0056] The carbon emission range

[0057] Where Z is the marginal error based on the confidence level. For a 95% confidence interval, the Z value is 1.96; m is the sample size, that is, the number of samples drawn;

[0058] The carbon emission range CI reflects the peak-to-bottom width of the carbon emission probability distribution curve. The narrower the peak-to-bottom width, the higher the coefficient of variation C. v (x) is small, indicating that the discrete degree of the material data is low; on the contrary, the wider the peak base width is, the higher the coefficient of variation C v The larger the (x), the higher the degree of dispersion and uncertainty of the material data.

[0059] The beneficial effects of the uncertainty impact analysis method of building material carbon emission factors provided by the present invention are:

[0060] 1. Comprehensive evaluation: The quality of data sources is comprehensively evaluated through a multi-dimensional evaluation index system, including reliability, timeliness, geographic applicability and technical consistency, which is conducive to comprehensively identifying and quantifying the degree of uncertainty and improving the accuracy and reliability of analysis;

[0061] 2. Quantify uncertainty: By quantifying uncertainties from different sources and using Monte Carlo simulation to determine their potential impact on carbon emission estimates, the analysis results will be more scientifically based and transparent;

[0062] 3. Improve decision-making quality: Provide carbon emission range and coefficient of variation under 95% confidence interval to help project developers and decision makers understand the possible maximum and minimum carbon emissions, so as to better plan and manage emission reduction measures.

[0063] 4. Risk Management: Understanding the uncertainty range of carbon emission factors enables project managers to foresee and control the risks associated with carbon emissions, such as avoiding potential costs or planning risks caused by underestimating or overestimating carbon emissions.

[0064] 5. Optimize resource allocation: By identifying the extent of the impact of uncertainty, the project team can focus resources and attention on areas where uncertainty is greatest and may have the greatest impact on the project's net carbon footprint.

[0065] 6. Policy and specification formulation support: The analysis results can provide scientific data support for policy formulation, standard setting and green certification, which is conducive to promoting the construction industry to develop towards higher standards.

[0066] 7. Enhance transparency: This method can enable project participants, regulators and the public to have a deeper understanding of the environmental impact of the project, thereby improving the transparency and credibility of the project.

[0067] 8. Promote continuous improvement: The approach provides a basis for identifying key parameters and improving data quality, facilitating continuous improvement of the project assessment process and carbon emissions estimation.

[0068] 9. Easy adaptability: Although this method is proposed for the carbon emission factors of building materials, its analysis principles and technical framework can be applied to other types of environmental impact analysis and life cycle assessment.

[0069] Through these beneficial effects, the environmental impact assessment of construction projects can be more comprehensive and accurate, which can make positive contributions to the development of a sustainable construction industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0071] Figure 1 It is a flow chart of a method for analyzing the uncertainty impact of carbon emission factors of building materials provided by the present invention;

[0072] Figure 2 Schematic diagram of carbon emission distribution of recycled steel produced by EAF process in an embodiment of the present invention;

[0073] Figure 3 Schematic diagram of carbon emission distribution of primary steel produced by BF-BOF process in an embodiment of the present invention;

[0074] Figure 4 Schematic diagram of carbon emission distribution of international average production level of steel in the embodiment of the present invention. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] In one embodiment, see Figure 1 , a method for analyzing the uncertainty impact of carbon emission factors of building materials, including:

[0077] Step 1: Select the carbon emission factor of any building material used in the life cycle of the railway construction project;

[0078] Step 2: Establish multi-dimensional evaluation indicators for data quality assessment methods;

[0079] Step 3: Determine the weight coefficients of various multi-dimensional evaluation indicators;

[0080] Step 4: Set the scoring rules for various multi-dimensional evaluation indicators to form a data quality spectrum matrix of railway building materials carbon emission factors;

[0081] Step 5: Score the data quality of carbon emission factors of the selected materials based on the pedigree matrix;

[0082] Step 6, using the obtained score as input, and determining the probability density function of the input;

[0083] Step 7: Perform multiple sampling based on the probability density function and use the carbon emission factor method to calculate the distribution of the material's life cycle carbon emissions, thereby determining the standard deviation and expected value of the normal distribution curve output by the Monte Carlo simulation;

[0084] Step eight, calculate the coefficient of variation of the output normal distribution curve and the range of carbon emissions under the 95% confidence interval; determine the degree of uncertainty impact of the carbon emission factor based on the coefficient of variation.

[0085] The principle of the above embodiment is: in the above method, the uncertainty analysis of the carbon emission factor is based on a quantitative data quality assessment process; this process starts with selecting relevant building material carbon emission factors and conducts a multi-dimensional evaluation of various aspects of the data; this method helps to scientifically understand and quantify the uncertainty of material carbon emission factors; step one ensures that the basic data for the analysis is determined based on actual projects, making the results closer to the actual situation; steps two to four increase the structure and objectivity of the evaluation method by establishing detailed evaluation criteria and scoring rules; steps five to seven deal with data uncertainty by assigning data quality scores, using probability statistics methods and Monte Carlo simulation, and solving the statistical characteristics of carbon emissions; finally, step eight describes the impact of uncertainty by calculating the coefficient of variation and confidence interval.

[0086] The beneficial effects of the above embodiments are:

[0087] 1. Improve the accuracy of evaluation. Through multi-dimensional evaluation and weight coefficient determination, it can comprehensively evaluate data quality, reduce the impact of inaccurate data on analysis results, and improve the accuracy of results;

[0088] 2. Enhance the reliability of the results. Use the Monte Carlo simulation method to repeatedly sample and calculate the results. The data characteristics described by the normal distribution curve obtained make the results more reliable.

[0089] 3. Clarify the quantitative range of uncertainty, and clearly indicate the statistical fluctuation range of carbon emission data by calculating the coefficient of variation and confidence interval, so as to facilitate engineering decision makers to manage and control risks;

[0090] 4. Promote the optimization of environmental management decisions. When the uncertainty of carbon emission factors of building materials can be accurately measured and described, building materials can be selected more reasonably, design solutions can be optimized, green building design and construction can be achieved, and effective environmental management can be achieved;

[0091] 5. Standardized methodology: This method provides a set of standardized processes that can be used as a reference and basis for environmental assessors to use when conducting life cycle assessments, thereby improving the comparability of assessment results between different projects.

[0092] In order to further optimize the above embodiment, the carbon emission factors of building materials are selected from relevant standards.

[0093] It should be noted that relevant standards include but are not limited to IPCC, ISO series, Greenhouse Gas Protocol, etc.

[0094] In order to further optimize the above embodiment, the multi-dimensional evaluation indicators include:

[0095] Reliability Ir, which is measured by the authority of the data publishing agency;

[0096] Data source Is, which is measured by sample size and representativeness;

[0097] The time factor It, which is measured by the difference between the data release time and the project construction time;

[0098] The geographical factor Ig, which is measured by the area represented by the data;

[0099] The technical factor Ite is measured by the technical gap between the data and the manufacturer.

[0100] It should be noted that in the process of analyzing the impact of uncertainty in carbon emission factors of building materials, each multi-dimensional evaluation indicator is intended to provide a more comprehensive and detailed data quality assessment; these evaluation indicators each focus on different aspects of data quality and promote a deeper understanding of data quality in their own unique way;

[0101] The reliability index evaluates the reliability of data by measuring the authority of the data publishing agency; the reputation, professionalism and historical record of the agency are usually factors in weighing the reliability of its data;

[0102] The Data Source Is indicator measures the accuracy of the data source by evaluating the sample size and its representativeness; the size and breadth of the sample directly affect the accuracy of the data analysis and the generalizability of the inferences;

[0103] The time factor IT indicator is evaluated by considering the gap between the data release time and the project construction time; the smaller the time difference, the more the data can reflect the current situation, thereby improving the relevance and validity of the data;

[0104] Geographical factors Ig indicators determine the applicability and transferability of data by analyzing the area covered by the data; the closer the geographical scope represented by the data is to the actual location of the project, the higher its relevance and accuracy;

[0105] The technical factor Ite indicator is measured by evaluating the difference between the data used and the actual technical level of the manufacturer; if the data reflects the latest technical level, its representativeness and forward-looking nature will be stronger.

[0106] In order to further optimize the above embodiment, the weight coefficients of various multi-dimensional evaluation indicators are determined including:

[0107] By analyzing the impact of each multi-dimensional evaluation indicator on the value of the carbon emission factor, the weight is set;

[0108] Among them, the weight coefficient of reliability Ir is 0.167, the weight coefficient of data source Is is 0.167, the weight coefficient of time factor It is 0.167, the weight coefficient of geographical factor Ig is 0.25, and the weight coefficient of technical factor Ite is 0.25.

[0109] It should be noted that the setting of the above weight coefficients is determined based on the importance of each evaluation indicator on the value of the carbon emission factor; this weight distribution means that geographical factors (Ig) and technical factors (Ite) are regarded as the most critical factors in evaluating the quality of building material carbon emission factor data, and the weight of each factor is 0.25; relatively speaking, although reliability (Ir), data source (Is) and time factor (It) are also important, their impact in this analysis framework is considered to be equal and slightly lower than the first two, and the weight of each factor is set to 0.167;

[0110] Once the weight coefficients are determined, they can be applied to the corresponding indicator scores in the pedigree matrix to perform a weighted synthesis of each multidimensional evaluation indicator; this ensures that the impact of different indicators is properly reflected and their effects are balanced in the multidimensional evaluation;

[0111] In actual application, the setting of weights can be adjusted according to specific circumstances, and can be determined by factors such as expert knowledge, historical data analysis, or the difficulty of data acquisition; they need to be evaluated based on the specific characteristics of the project and the environmental conditions to ensure that the weight distribution most truly reflects the actual impact of each evaluation indicator; this process can be assisted by expert consultation, data analysis, or the use of decision support tools such as fuzzy logic and hierarchical analysis method (AHP).

[0112] In order to further optimize the above embodiment, the scoring rules for setting various multi-dimensional evaluation indicators include:

[0113] The score range of each multi-dimensional evaluation indicator is an integer ranging from 1 to 5 points;

[0114] Among them, the scoring rule of reliability Ir is determined based on the accuracy of verification or testing. The higher the accuracy of verification or testing, the lower the score;

[0115] The scoring rule of data source Is is determined based on the suitability of the case and time range. The higher the suitability of the case and time range, the lower the score;

[0116] The scoring rule for the time factor It is determined based on the difference between the data release time and the project construction time. The smaller the difference, the lower the score.

[0117] The scoring rule for the geographical factor Ig is determined based on the accuracy of the regional data source. The higher the accuracy, the lower the score;

[0118] The scoring rule for the technical factor Ite is based on the consistency between the manufacturer's name and the material production process. The higher the consistency between the manufacturer's name and the material production process, the lower the score.

[0119] It should be noted that the data quality spectrum matrix of carbon emission factors of railway construction materials includes:

[0120] Associate the scoring ranges and scoring rules of various multi-dimensional evaluation indicators to generate a data quality spectrum matrix of carbon emission factors of railway construction materials;

[0121] The data quality spectrum matrix is ​​shown in the following table:

[0122]

[0123]

[0124] In order to further optimize the above embodiment, the quality scoring of the carbon emission factor data of the selected material based on the pedigree matrix includes:

[0125] The material's multi-dimensional evaluation indicators are scored as DQI n Substitute it into the quality calculation formula and output the carbon emission factor data quality score of the material;

[0126] The quality calculation formula is:

[0127] in, is the carbon emission factor data quality score of the material, x i is the weight of the i-th multi-dimensional evaluation index, f i is the score DQI of the i-th multi-dimensional evaluation index i , n is the number of multi-dimensional evaluation indicators.

[0128] It should be noted that when using the above quality calculation formula to calculate the carbon emission factor data quality score, 1. Calculate the weighted score of each indicator: First, multiply the score of each indicator by the corresponding weight, that is, x i *f i ; 2. Sum: Then, add all the weighted scores together, that is 3. Calculate the average: Finally, divide the sum of the weighted scores by the number of indicators n to get the average. It is the material’s carbon emission factor data quality score;

[0129] In actual operation, in order to ensure the accuracy of the calculation results:

[0130] All evaluation indicator scores need to use consistent metrics and scoring systems so that they can be directly compared and calculated;

[0131] The weighting coefficients must be accurately set in advance, usually based on professional knowledge or research results in related fields;

[0132] If there are incomplete indicator data, it is necessary to use appropriate estimation methods to complete the data or make appropriate adjustments to avoid bias;

[0133] At the same time, this calculation result can provide decision makers with a quantitative indicator to help compare and select building materials with higher data quality scores, making construction projects more reliable and accurate in environmental evaluation and carbon emission management.

[0134] In order to further optimize the above embodiment, the obtained score is used as an input quantity, and the probability density function of the input quantity is determined including:

[0135] The input is the carbon emission factor data quality score of the material Its probability distribution adopts triangular distribution;

[0136] Set the carbon emission factor value of the material to the mode c, based on the mode c and the data quality score Calculate the values ​​of the lower limit a and the upper limit b;

[0137] Low limit

[0138] Upper limit

[0139] Among them, T1 is the minimum score of each multi-dimensional evaluation indicator, that is, T1 = minDQI n =1; T2 is the highest score of each multi-dimensional evaluation index, that is, T2 = maxDQI n =5;

[0140] According to the values ​​of lower limit a, upper limit b and mode c, the probability density function of triangular distribution is obtained. The probability density function is:

[0141]

[0142] It should be noted that when using triangular distribution to model the probability density function of the material's carbon emission factor data quality score, the uncertainty and possibility range of the data quality score are reflected by setting the lower limit a, upper limit b and mode c. Triangular distribution is a continuous probability distribution, which can be used as an approximation when there is insufficient information to determine the exact probability distribution.

[0143] In this embodiment, the parameters a, b, and c of the triangular distribution are related to the carbon emission factor data quality score, where the mode c represents the most likely score, and the lower limit a and upper limit b are determined based on the score fluctuation range and the lowest and highest evaluation standards. The score, the lowest score T1, and the highest score T2 are used when calculating these parameters.

[0144] The probability density function (PDF) of the triangular distribution of this example is divided into two linear parts:

[0145] -As x increases from the lower limit a to the mode c, the PDF increases linearly.

[0146] -As x decreases from the mode c to the upper limit b, the PDF decreases linearly.

[0147] According to the above parameter settings, a triangular distribution graph can be obtained, which is shaped like a triangle, with the base located between a and b and the peak located at c.

[0148] The probability distributions calculated using this approach can help decision makers understand uncertainty and account for variability in data quality when making carbon emission-related decisions.

[0149] In order to further optimize the above embodiment, the process of step seven includes:

[0150] Determine the probability density function of the output and establish a Monte Carlo model;

[0151] Conduct multiple random samplings to extract a large number of data points; for each sampling, use the carbon emission factor method to calculate the carbon emissions of the material during its life cycle; repeat the sampling process to collect a sufficient amount of simulated carbon emission data;

[0152] Use the extracted sample values ​​to replace the parameters in the Monte Carlo model and perform model operations;

[0153] Repeat random sampling and model calculations many times to establish the statistical characteristics of the output results;

[0154] After a large number of iterations are completed, the results of all simulations are analyzed; statistics of the output variables are calculated and the standard deviation and expected value of the normal distribution curve output by the Monte Carlo model are determined.

[0155] It should be noted that the Monte Carlo model is a statistical calculation method that estimates the possible distribution of results by establishing a probability model and performing random sampling, and is particularly used to evaluate systems with uncertainty. In this embodiment, the Monte Carlo simulation is used to analyze the uncertainty and variability of life cycle carbon emission data. The following is a more detailed description of step seven:

[0156] 1. Determine the probability density function. It is necessary to first determine the probability distribution of the output parameter under study (carbon emissions during the life cycle of a material). In this embodiment, a triangular probability distribution has been used to simulate the uncertainty of these parameters.

[0157] 2. Establishing the model and random sampling. The Monte Carlo model simulates possible outputs by randomly sampling its input parameters. These random samples are generated based on the probability density function of the input parameters. In this embodiment, a triangular distribution is used, and each sampling will randomly select a possible carbon emission factor value from this distribution.

[0158] 3. Calculate carbon emissions. For each sampling, use the carbon emission factor and other relevant information (such as material usage, energy consumption, etc.) to estimate carbon emissions according to the carbon emission calculation method.

[0159] 4. Repeated sampling and calculation. In order to obtain statistically reliable results, this sampling and calculation process needs to be repeated many times (for example, thousands or tens of thousands of times) to ensure that a wide space of probability distribution is covered.

[0160] 5. Analyze the results. Once enough data points have been collected, the simulated carbon emissions data can be analyzed to determine the statistical characteristics of the output variable (the average carbon emissions of all simulation results).

[0161] 6. Standard deviation and expected value. Finally, these simulation results are used to estimate the expected value (mean or median) of the output parameter of interest and its variability (such as standard deviation or confidence interval);

[0162] It is important to note that the input parameters of the model must accurately express the randomness of the actual problem to ensure the validity of the model results; the obtained statistical data (mean, standard deviation, etc.) allow decision makers to understand the uncertainty range of the results, which is an important basis for risk assessment or decision making.

[0163] In order to further optimize the above embodiment, the coefficient of variation of the output normal distribution curve and the range of carbon emissions under the 95% confidence interval are calculated, and the degree of uncertainty influence of the carbon emission factor is determined based on the coefficient of variation, including:

[0164] Coefficient of variation

[0165] Among them, C v (x) is the coefficient of variation of the output normal distribution curve, σ(x) is the standard deviation of the output normal distribution curve, and E(x) is the expected value of the output normal distribution curve;

[0166] Calculate the carbon emission range CI under the 95% confidence interval;

[0167] Carbon emissions range

[0168] Where Z is the marginal error based on the confidence level. For a 95% confidence interval, the Z value is 1.96; m is the sample size, that is, the number of samples drawn;

[0169] The carbon emission range CI reflects the peak-to-bottom width of the carbon emission probability distribution curve. The narrower the peak-to-bottom width, the higher the coefficient of variation C. v (x) is small, indicating that the discrete degree of the material data is low; on the contrary, the wider the peak base width is, the higher the coefficient of variation C v The larger the (x), the higher the degree of dispersion and uncertainty of the material data.

[0170] It should be noted that the coefficient of variation (CV) is a dimensionless indicator for measuring relative variability, which is used to describe the degree of dispersion of data distribution relative to its mean value; the larger the CV, the higher the relative dispersion of the data;

[0171] CV is a useful indicator for determining the degree of uncertainty in carbon emission factors because it shows the proportional relationship between the standard deviation and the mean, thus providing decision makers with a relative description of uncertainty rather than relying solely on the absolute value of the standard deviation;

[0172] The 95% confidence interval (CI) of the normal distribution curve is an important indicator of statistical results. It provides an interval estimate to determine the possible range of the population mean. The calculation of CI depends not only on the standard deviation and mean of the sample data, but also on the statistical characteristics of the population. When we say 95% confidence interval, it means that if we perform an infinite number of random sampling and calculations, then approximately 95% of the intervals will contain the true population mean.

[0173] In the formula, the Z value is the Z score, which indicates how far away from the mean the data is, with standard deviation as the unit; in the standard normal distribution, a Z value of 1.96 corresponds to a 95% probability that the data is on both sides of the mean; It is the standard error, which is the standard deviation divided by the square root of the sample size. It is usually used to judge the accuracy of the sample mean obtained in multiple sampling;

[0174] According to the above analysis, the method combining data quality assessment and Monte Carlo simulation can effectively calculate the impact of the uncertainty of the carbon emission factor of building materials on the calculation results of carbon emissions.

[0175] In one embodiment, see Figure 2-4Taking steel as an example, the impact of the uncertainty of carbon emission factors under different production processes on its life cycle carbon emissions is analyzed, and three scenarios are set, namely:

[0176] (1) Primary steel produced by blast furnace-converter (BF-BOF) process;

[0177] (2) Recycled steel produced by the electric arc furnace (EAF) process;

[0178] (3) International average steel production level.

[0179] The above method is used to calculate the probability distribution of carbon emissions of steel under three scenarios (see Figure 2-4 ) analysis, we can get the following conclusions:

[0180] (1) The peak-base widths under the three scenarios are 9.63, 3.14, and 8.47 ktCO2, respectively, and the coefficients of variation are 13.86%, 13.33%, and 16.13%, respectively;

[0181] (2) EAF Figure 2 ) and BF-BOF( Figure 3 ) is relatively simple in process, its carbon emission factor has strong timeliness and low coefficient of variation;

[0182] (3) International average production level of steel Figure 4 ) does not match any specific actual production situation, the data has the largest degree of discreteness and the greatest uncertainty;

[0183] Therefore, the peak-to-bottom width of the carbon emission probability distribution curve expresses the value range of the carbon emission calculation results (i.e., carbon emission range CI), and the coefficient of variation C v (x) expresses the degree of dispersion of the carbon emission calculation results; the wider the peak base, the higher the C v The larger the (x) value is, the more dispersed the calculation results are and the greater the uncertainty impact of the carbon emission factor is.

[0184] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing the impact of uncertainty on carbon emission factors of building materials. It is characterized in that include: Step 1: Select the carbon emission factor of any building material used in the life cycle of the railway construction project; Step 2: Establish multi-dimensional evaluation indicators for data quality assessment methods; Step 3: Determine the weight coefficients of various multi-dimensional evaluation indicators; Step 4: Set the scoring rules for various multi-dimensional evaluation indicators to form a data quality spectrum matrix of railway building materials carbon emission factors; Step 5: Score the data quality of carbon emission factors of the selected materials based on the pedigree matrix; Step 6, using the obtained score as input, and determining the probability density function of the input; Step 7: Perform multiple sampling based on the probability density function and use the carbon emission factor method to calculate the distribution of the material's life cycle carbon emissions, thereby determining the standard deviation and expected value of the normal distribution curve output by the Monte Carlo simulation; Step 8, calculating the coefficient of variation of the output normal distribution curve and the range of carbon emissions under the 95% confidence interval; The degree of uncertainty impact of carbon emission factors is determined based on the coefficient of variation.

2. According to claim 1, a method for analyzing the uncertainty impact of carbon emission factors of building materials, It is characterized in that The carbon emission factors of the building materials are selected from relevant standards.

3. According to claim 2, a method for analyzing the uncertainty impact of carbon emission factors of building materials, It is characterized in that The multi-dimensional evaluation indicators include: Reliability Ir, which is measured by the authority of the data publishing agency; Data source Is, which is measured by sample size and representativeness; The time factor It, which is measured by the difference between the data release time and the project construction time; The geographical factor Ig, which is measured by the area represented by the data; The technical factor Ite is measured by the technical gap between the data and the manufacturer.

4. According to claim 3, a method for analyzing the uncertainty impact of carbon emission factors of building materials, It is characterized in that The weight coefficients for determining the various multi-dimensional evaluation indicators include: By analyzing the impact of each multi-dimensional evaluation indicator on the value of the carbon emission factor, the weight is set; Among them, the weight coefficient of reliability Ir is 0.167, the weight coefficient of data source Is is 0.167, the weight coefficient of time factor It is 0.167, the weight coefficient of geographical factor Ig is 0.25, and the weight coefficient of technical factor Ite is 0.

25.

5. A method for analyzing the uncertainty impact of carbon emission factors of building materials according to claim 4, It is characterized in that The scoring rules for setting various multi-dimensional evaluation indicators include: The score range of each multi-dimensional evaluation indicator is an integer ranging from 1 to 5 points; Among them, the scoring rule of reliability Ir is determined based on the accuracy of verification or testing. The higher the accuracy of verification or testing, the lower the score; The scoring rule of data source Is is determined based on the suitability of the case and time range. The higher the suitability of the case and time range, the lower the score; The scoring rule for the time factor It is determined based on the difference between the data release time and the project construction time. The smaller the difference, the lower the score. The scoring rule for the geographical factor Ig is determined based on the accuracy of the regional data source. The higher the accuracy, the lower the score; The scoring rule for the technical factor Ite is based on the consistency between the manufacturer's name and the material production process. The higher the consistency between the manufacturer's name and the material production process, the lower the score.

6. A method for analyzing the uncertainty impact of carbon emission factors of building materials according to claim 5, It is characterized in that The formation of the railway construction material carbon emission factor data quality spectrum matrix includes: The scoring ranges and scoring rules of various multi-dimensional evaluation indicators are associated to generate the data quality spectrum matrix of the railway construction material carbon emission factor.

7. A method for analyzing the uncertainty impact of carbon emission factors of building materials according to claim 6, It is characterized in that The carbon emission factor data quality scoring for the selected material based on the pedigree matrix includes: The material's multi-dimensional evaluation indicators are scored as DQI n Substitute it into the quality calculation formula and output the carbon emission factor data quality score of the material; The mass calculation formula is: in, is the carbon emission factor data quality score of the material, x i is the weight of the i-th multi-dimensional evaluation index, f i is the score DQI of the i-th multi-dimensional evaluation index i , n is the number of multi-dimensional evaluation indicators.

8. A method for analyzing the uncertainty impact of carbon emission factors of building materials according to claim 7, It is characterized in that The method of using the obtained score as an input quantity and determining the probability density function of the input quantity comprises: The input is the carbon emission factor data quality score of the material Its probability distribution adopts triangular distribution; Set the carbon emission factor value of the material to the mode c, based on the mode c and the data quality score Calculate the values ​​of the lower limit a and the upper limit b; The lower limit The upper limit Among them, T 1 is the minimum score of each multi-dimensional evaluation index, that is, T 1 =minDQI n =1; T 2 is the highest score of each multi-dimensional evaluation index, that is, T 2 =maxDQI n =5; The probability density function of the triangular distribution is obtained according to the values ​​of the lower limit a, the upper limit b and the mode c. The probability density function is:

9. A method for analyzing the uncertainty impact of carbon emission factors of building materials according to claim 8, It is characterized in that The process of step seven includes: Determine the probability density function of the output and establish a Monte Carlo model; Conduct multiple random samplings to extract a large number of data points; for each sampling, use the carbon emission factor method to calculate the carbon emissions of the material during its life cycle; repeat the sampling process to collect a sufficient amount of simulated carbon emission data; Substituting the extracted sample values ​​for the parameters in the Monte Carlo model to perform model calculations; Repeat random sampling and model calculations many times to establish the statistical characteristics of the output results; After a large number of iterations are completed, the results of all simulations are analyzed; the statistics of the output variables are calculated to determine the standard deviation and expected value of the normal distribution curve output by the Monte Carlo model.

10. A method for analyzing the uncertainty impact of carbon emission factors of building materials according to claim 9, It is characterized in that The coefficient of variation of the normal distribution curve of the calculation output and the range of carbon emissions under the 95% confidence interval, and the degree of uncertainty of the carbon emission factor determined based on the coefficient of variation include: The coefficient of variation Among them, C v (x) is the coefficient of variation of the output normal distribution curve, σ(x) is the standard deviation of the output normal distribution curve, and E(x) is the expected value of the output normal distribution curve; Calculate the carbon emission range CI under the 95% confidence interval; The carbon emission range Where Z is the marginal error based on the confidence level. For a 95% confidence interval, the Z value is 1.96; m is the sample size, that is, the number of samples drawn; The carbon emission range CI reflects the peak-to-bottom width of the carbon emission probability distribution curve. The narrower the peak-to-bottom width, the higher the coefficient of variation C. v (x) is small, indicating that the discrete degree of the material data is low; on the contrary, the wider the peak base width is, the higher the coefficient of variation C v The larger the (x), the higher the degree of dispersion and uncertainty of the material data.

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