Regional carbon emission accounting method

Abstract

The invention discloses a regional carbon emission accounting method, and relates to the technical field of environmental protection. According to the method, through fusion of multi-source data and a dynamic emission factor model, the actual carbon emission condition of each region can be reflected more accurately, errors of a traditional estimation method are avoided, and the accuracy is greatly improved; the automation degree is high, data of a large-scale area can be rapidly processed, manual intervention is reduced, and the working efficiency is improved; moreover, the method can be adjusted according to the characteristics of different regions, is suitable for the carbon emission accounting of various regions, is higher in flexibility and universality, and is high in adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental protection, and specifically relates to a method for regional carbon emission accounting. Background Technique

[0002] With the increasingly serious problem of global climate change, reducing carbon emissions has become the consensus of countries around the world. In order to effectively control carbon emissions, it is necessary to accurately account for the carbon emissions of a specific region. However, the existing regional carbon emission accounting methods generally have the following problems:

[0003] Simple accounting method: Traditional methods usually rely on static emission factors or data models based on fixed assumptions, lacking the ability to consider regional specific factors (such as geographical differences, economic activity differences).

[0004] Difficult data acquisition: Traditional methods often rely on manual statistics and estimation, resulting in poor accounting accuracy. Especially in areas lacking efficient data acquisition means, it is often impossible to accurately reflect the actual carbon emissions of the region.

[0005] Poor adaptability: Different regions have differences in economic development, emission source types, technical levels, etc. Therefore, it is necessary to customize accounting methods according to the characteristics of specific regions.

[0006] Therefore, a new solution needs to be proposed for the above problems. Summary of the Invention

[0007] The purpose of the present invention is to provide a method for regional carbon emission accounting, aiming to achieve accurate and rapid accounting of regional carbon emissions by comprehensively using multiple data sources and combining dynamic calculation models. The present invention has high accuracy, operability, and can adapt to the characteristics of different regions to solve the technical problems proposed in the background technique.

[0008] To achieve the above purpose, the present invention provides the following technical solution: A method for regional carbon emission accounting, at least including the following steps:

[0009] A method for regional carbon emission accounting, at least including the following steps:

[0010] S1: Collect carbon emission source data within the region, including the collection of main data and auxiliary data. In the process of collecting the main data, the data sources at least include satellite remote sensing, sensor networks, and energy consumption statistics. The auxiliary data at least includes meteorological data and economic activity data of the collection region. The auxiliary data is used to assist in the calibration and adjustment of the carbon emission model.

[0011] In the above S1, for different carbon emission sources in the collection of main data, the carbon emission of each source is expressed as:

[0012] Esource = A source × F source

[0013] Wherein:

[0014] E source is the amount of carbon emitted by a certain carbon emission source; A source is the activity level related to the emission source; F source is the emission factor of the emission source, with the unit being the ratio of carbon emission to activity volume;

[0015] In the collection of the auxiliary data, the meteorological data and economic activity data can adjust or affect the emission factor;

[0016] Influence of meteorological data: The meteorological data may affect the seasonal adjustment of the carbon emission factor;

[0017] F seasonal = f(Temperature, Precipitation, Humidity, …)

[0018] Influence of economic activities: Changes in economic activities may affect the calculation of the emission factor. By adjusting the indicators, the emission factor is dynamically adjusted:

[0019] F economic = f(GDP, Energy Consumption, …)

[0020] Where GDP is the regional GDP, and the Energy Consumption is the energy consumption, both of which are categories in the indicators.

[0021] S2: Data processing and cleaning. Clean the original data, remove invalid data and abnormal data, fill in missing data, standardize the data through statistical methods to ensure data consistency, and standardize the data from different sources to ensure that all data can be compatible for subsequent application of the model.

[0022] In the process of data cleaning in S2, what is involved is removing invalid data, handling missing values, and standardizing data;

[0023] In order to make the units and dimensions of different data sources consistent, a standardization method is adopted, and the standardization formula is:

[0024]

[0025] Where: X norm is the standardized data, X is the original data, μ is the mean of the data, and σ is the standard deviation of the data;

[0026] Further outlier detection is carried out. Outliers are detected by statistical methods, as shown in the following formula:

[0027]

[0028] If |Z| > 3, then this data point is considered an outlier.

[0029] S3: Emission factor calculation. According to the emission characteristics of different fields, a dynamic emission factor model is used to calculate the emission factor. The calculation of the emission factor takes into account the changes in regional economic activities and the carbon emission fluctuations in different seasons. For special emission sources, a customized emission factor model is adopted.

[0030] The calculation of the emission factor in S3 is expressed by the following formula:

[0031] F sector = λ 0 × A sector × (1 + ΔF seasonal ) × (1 + ΔF economic )

[0032] Where: F sector is the emission factor of a specific department; λ 0 is the baseline emission factor, usually provided according to historical data or standard literature; A sector is the activity level of this department; ΔF seasonal is the adjustment factor related to seasonal changes, based on meteorological data; ΔF economic is the adjustment factor related to economic activities.

[0033] In the dynamic emission factor model in S3, the dynamic emission factor will change following multiple factors. Therefore, time correlation is introduced:

[0034] F sector,t = F sector,t-1 × (1 + ΔF seasonal,t ) × (1 + ΔF economic,t )

[0035] Where:

[0036] F sector,t is the emission factor at the current time t; F sector,t-1 is the emission factor at the previous time t - 1; ΔF seasonal,t is the seasonal adjustment factor; ΔF economic,t is the adjustment factor related to economic activities.

[0037] S4: Regional carbon emission accounting. Based on the processed data and emission factors, use the regional carbon emission accounting model to calculate the carbon emissions of each region. The regional carbon emission accounting model conducts sub-regional calculations based on the characteristics of different activities and emission sources within the region, and uses geographic information system technology to analyze the spatial distribution of different regions.

[0038] The regional carbon emissions calculated by the regional carbon emission accounting model in S4 are the sum of the emissions of all departments;

[0039] Suppose there are multiple emission sources in the region, and the regional carbon emissions are calculated by the following formula:

[0040]

[0041] Where: E region is the total carbon emissions of the region; E sector,i is the carbon emissions of the i-th department; n is the total number of emission sources in the region;

[0042] Specifically for the calculation of the carbon emissions of each department, the formula is:

[0043] E sector,i = A sector,i × F sector,i

[0044] Where:

[0045] A sector,i is the activity level of the i-th department; F sector,i is the emission factor of the i-th department.

[0046] S5: Result verification and optimization. Use historical data for verification to check the accuracy of the accounting results. If there is a large deviation from the actual emissions, that is, 10% to 15%, adjust the model parameters and optimize the data source means for optimization, and optimize through multiple iterations.

[0047] The results after accounting in S5 need to be compared with historical data and optimized according to the error;

[0048] Suppose the historical carbon emissions are E historical , and the carbon emissions obtained by accounting are E calculated , then the error is calculated by the following formula:

[0049]

[0050] If the error exceeds the set threshold ∈, then it is necessary to adjust the emission factor model or optimize the data source;

[0051] If the error is large, optimize the calculation model by adjusting the baseline value of the emission factor or the economic adjustment factor. See the following formula:

[0052]

[0053] Wherein: ΔF adjustment is an adjustment factor based on the error.

[0054] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] 1. Through the fusion of multi-source data and the dynamic emission factor model, the present invention can more accurately reflect the actual carbon emissions of each region, avoid the errors of traditional estimation methods, and greatly improve the accuracy.

[0056] 2. The present invention has a high degree of automation, can quickly process data of large-scale regions, reduce manual intervention, and improve work efficiency.

[0057] 3. The present invention can be adjusted according to the characteristics of different regions, is applicable to carbon emission accounting of various regions, has strong flexibility and generality, and thus has strong adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for describing the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0059] Figure 1 It is a schematic flow chart of the whole of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, rather than all, of the embodiments of the present invention.

[0061] A regional carbon emission accounting method includes at least the following steps:

[0062] S1: Collection of main data and collection of auxiliary data. Collect carbon emission source data within the region. During the collection process, the data sources include at least satellite remote sensing, sensor networks, and energy consumption statistics. The auxiliary data includes at least meteorological data and economic activity data of the collection region. The auxiliary data is used to assist in the calibration and adjustment of the carbon emission model.

[0063] In the collection of main data, for different carbon emission sources (such as industry, transportation, agriculture), the carbon emissions of each source can be expressed as:

[0064] E source = Asource ×F source

[0065] Wherein:

[0066] E source is the carbon amount emitted by a certain carbon emission source (such as industry, transportation, agriculture, etc.); A source is the activity level related to the emission source (such as energy consumption, traffic flow, agricultural machinery usage, etc.); F source is the emission factor of the emission source, and the unit is usually the ratio of carbon emission to activity;

[0067] In the auxiliary data collection, meteorological data and economic activity data can adjust or affect the emission factor;

[0068] Influence of meteorological data: Meteorological data (such as temperature, humidity, precipitation, etc.) may affect the seasonal adjustment of the carbon emission factor;

[0069] F seasonal = f(Temperature, Precipitation, Humidity, …)

[0070] Influence of economic activities: Changes in economic activities may affect the calculation of the emission factor. Through indicators such as regional GDP and energy consumption, the emission factor is dynamically adjusted:

[0071] F economic = f(GDP, Energy Consumption, …).

[0072] S2: Data processing and cleaning. Clean the original data, remove invalid data and abnormal data, fill in missing data, standardize the data through statistical methods to ensure data consistency, and standardize the data from different sources to ensure that all data can be compatible for subsequent model applications.

[0073] In S2 during the data cleaning process, what is involved is removing invalid data, dealing with missing values, and standardizing data; in order to make the units and dimensions of different data sources consistent, a standardization method is usually adopted, and the standardization formula is:

[0074]

[0075] Wherein: X norm is the standardized data, X is the original data, μ is the mean of the data, and σ is the standard deviation of the data;

[0076] Further conduct outlier detection. Outliers can be detected through statistical methods. Refer to the following formula:

[0077]

[0078] If |Z| > 3, then this data point is considered an outlier.

[0079] S3: The emission factor calculation adopts a dynamic emission factor model according to the emission characteristics of different fields. The calculation of the emission factor takes into account the changes in regional economic activities and the carbon emission fluctuations in different seasons. For different sectors, a customized emission factor model is used.

[0080] The calculation of the emission factor in S3 is expressed by the following formula:

[0081] F sector = λ 0 × A sector × (1 + ΔF seasonal ) × (1 + ΔF economic )

[0082] Where: F sector is the emission factor for a specific sector (such as industry, transportation, agriculture, etc.); λ 0 is the baseline emission factor, usually provided according to historical data or standard literature; A sector is the activity level of this sector (such as energy consumption, traffic flow, etc.); ΔF seasonal is the adjustment factor related to seasonal changes, based on meteorological data (such as temperature, precipitation, etc.); ΔF economic is the adjustment factor related to economic activities (such as GDP, energy consumption, etc.).

[0083] In the dynamic emission factor model in S3, the dynamic emission factor will change with multiple factors, so time correlation is introduced:

[0084] F sector,t = F sector,t-1 × (1 + ΔF seasonal,t ) × (1 + ΔF economic,t )

[0085] Where:

[0086] F sector,t is the emission factor at the current time t; F sector,t-1 is the emission factor at the previous time t - 1; ΔF seasonal,t is the seasonal adjustment factor; ΔF economic,t is the adjustment factor related to economic activities.

[0087] S4: Regional carbon emission accounting. Based on the processed data and emission factors, a regional carbon emission accounting model is used to calculate the carbon emissions of each region. The accounting model can accurately calculate by region based on the characteristics of different activities and emission sources within the region. Spatial distribution analysis of different regions is carried out using geographic information system technology. Spatial distribution analysis is an existing technology, such as hotspot analysis, buffer analysis, etc. Thus, the spatial accuracy of the accounting results is improved.

[0088] The regional carbon emissions in S4 are the sum of the emissions of all departments;

[0089] Assume there are multiple emission sources within the region (such as industry, transportation, agriculture, etc.). The regional carbon emissions are calculated by the following formula:

[0090]

[0091] Where: E region is the total carbon emissions of the region; E sector,i is the carbon emissions of the i-th department (such as industry, transportation, agriculture, etc.); n is the total number of emission sources within the region;

[0092] Specifically, for the calculation of the carbon emissions of each department, the formula is:

[0093] E sector,i = A sector,i × F sector,i

[0094] Where:

[0095] A sector,i is the activity level of the i-th department; F sector,i is the emission factor of the i-th department.

[0096] S5: Result verification and optimization. Use historical data for verification to check the accuracy of the accounting results. If there is a large deviation from the actual emissions, it can be optimized by adjusting the model parameters and optimizing the data source means. Through multiple iterative optimization methods, the adaptability and accuracy of the accounting model are gradually improved.

[0097] In S5, the accounted results need to be compared with historical data and optimized according to the error;

[0098] Assume the historical carbon emissions are E historical , and the accounted carbon emissions are E calculated . Then the error is calculated by the following formula:

[0099]

[0100] If the error exceeds the set threshold ∈, then the emission factor model needs to be adjusted or the data source needs to be optimized;

[0101] For example, if the error is large, optimize the calculation model by adjusting the baseline value of the emission factor or the economic adjustment factor. Refer to the following formula:

[0102]

[0103] where: ΔF adjustment is the adjustment factor based on the error.

[0104] According to the above regional carbon emission accounting method, the following is a case of specific application. Suppose we need to conduct carbon emission accounting in a certain city (e.g., "City X") to support the city's carbon emission reduction policies and planning. The following is the specific application process:

[0105] 1. Project background and objectives

[0106] Objective: By calculating the total carbon emissions of "City X", analyze its carbon emission hotspots, and provide policy support and emission reduction suggestions.

[0107] Background: City X is a medium-sized city with relatively high industrial and transportation carbon emissions. The city plans to optimize its energy structure and promote green development through accurate carbon emission accounting and data support.

[0108] 2. Data collection and integration

[0109] Remote sensing data:

[0110] Obtain data from satellite remote sensing images, analyze the land use situation of City X, and identify major areas such as industrial areas, commercial areas, and residential areas.

[0111] Use satellite images to analyze the city's green spaces and vegetation coverage to help estimate carbon absorption.

[0112] Meteorological data:

[0113] Obtain data on temperature, humidity, precipitation, etc. for the past year from the meteorological bureau to provide environmental information affecting carbon emission factors.

[0114] Energy consumption data:

[0115] Obtain the consumption data of various types of energy in City X from energy companies and the government, including the usage amounts of electricity, natural gas, coal, etc.

[0116] Calculate the carbon emissions of various types of energy based on the combustion efficiency and carbon emission factors of different energies.

[0117] Traffic flow data:

[0118] Obtain traffic flow data provided by the traffic management department, including the usage of public transportation, private cars, and freight vehicles, and estimate the carbon emissions of the transportation department.

[0119] Industrial emission data:

[0120] Collect emission data of major industrial enterprises from enterprises and environmental protection departments, especially high-emission industries such as steel, chemical industry, and building materials.

[0121] 3. Establishment of emission factor model

[0122] Determine industry emission factors:

[0123] Set applicable emission factors for various industries in City X (such as transportation, industry, construction, agriculture, etc.). For example:

[0124] The emission factors of the transportation department are based on different types of vehicles, transportation modes, and fuel types (such as gasoline vehicles, diesel vehicles, public transportation, etc.).

[0125] The emission factors of the industrial department are based on the energy consumption of various production activities, such as metallurgy, chemical industry, manufacturing, etc.

[0126] The emission factors of the construction department are based on factors such as building materials, building energy consumption, and building design.

[0127] Dynamic adjustment mechanism:

[0128] Combine meteorological data to adjust emission factors. For example, in the cold season, when the heating demand increases, the emission factors may increase.

[0129] In years with rapid economic growth, based on the GDP growth forecast, dynamically adjust the growth trends of energy consumption and carbon emissions.

[0130] 4. Construction of carbon emission accounting model

[0131] Integrate data and model:

[0132] Integrate the collected remote sensing data, traffic flow, energy consumption, meteorological data, etc. through a database, and set up a carbon emission calculation model according to various activities (such as transportation, energy consumption, industrial emissions, etc.).

[0133] GIS spatial analysis:

[0134] Use GIS technology to conduct spatial distribution analysis of carbon emissions in each region and draw a carbon emission hotspot area map. This helps to identify key emission reduction areas such as traffic congestion areas and industrial areas.

[0135] 5. Verification and optimization

[0136] Model verification:

[0137] Compare the accounting results with past data (such as national carbon emission statistics, regional environmental monitoring data, etc.) to ensure the accuracy of the accounting model.

[0138] Compare the actual carbon emission measurement data with the model prediction data, and adjust and optimize the accounting method.

[0139] Error analysis and adjustment:

[0140] Compare the errors generated by different models, identify the sources of errors, and adjust the emission factors and accounting models through machine learning methods to optimize the results.

[0141] 6. Result analysis and reporting

[0142] Total carbon emissions and regional distribution:

[0143] According to the accounting results, calculate the total carbon emissions of the whole city of "City X" and decompose them by industry and region. For example:

[0144] Emissions from the transportation sector account for 40% of the total emissions.

[0145] Emissions from the industrial sector account for 30% of the total emissions.

[0146] Emissions from residential energy consumption account for 20% of the total emissions.

[0147] Identification of hotspots:

[0148] Through GIS analysis, determine the areas with intensive carbon emissions, such as the city center with heavy traffic or industrial parks, and propose targeted emission reduction measures.

[0149] Analysis of carbon absorption and emission differences:

[0150] Analyze the carbon absorption of carbon sinks such as green spaces and forests in City X, evaluate the difference from carbon emissions, and propose suggestions for improving the carbon sink capacity.

[0151] To sum up, further analyze the technology of this case:

[0152] 1. Innovative integration of multiple data sources

[0153] The present invention combines multiple data sources such as satellite remote sensing, sensor networks, energy consumption statistics, meteorological data, and economic activity data, greatly improving the accuracy and comprehensiveness of carbon emission accounting. Traditional carbon emission accounting methods often rely on a single data source (such as energy consumption data or emission factors), and the innovation of this method lies in the integration of multi-dimensional data, ensuring the monitoring and calculation of carbon emissions from different perspectives.

[0154] 2. Dynamic Emission Factor Model. The design of dynamic emission factors is one of the core innovations of the present invention. Traditional carbon emission accounting usually adopts static emission factors. However, by introducing a dynamic emission factor model and adjusting the emission factors in real time according to factors such as different seasons, meteorological conditions, and economic activities, the adaptability and accuracy of the model can be significantly improved. This dynamic adjustment mechanism takes into account the fluctuations of time, season, and economic activities, providing a more flexible and real-time responsive calculation method for emission factors, enhancing the timeliness and accuracy of the accounting results.

[0155] 3. Geographical Information System (GIS) Spatial Analysis

[0156] In regional carbon emission accounting, spatial distribution analysis is carried out through GIS technology to provide a more accurate spatial division for the carbon emissions of each region. This improvement in spatial accuracy is of great significance for actual decision-making and the implementation of carbon emission reduction measures, especially in regions with large geographical differences. Combining GIS spatial analysis technology with carbon emission accounting not only improves the accuracy of carbon emissions but also enables differential management and optimization in different regions.

[0157] 4. Error Feedback and Optimization Mechanism

[0158] The present invention verifies and optimizes the accounting results by comparing with historical data and using an error threshold feedback mechanism. This iterative optimization method enables the accounting model to continuously improve over time and gradually increase accuracy. This self-adjusting ability is the key to ensuring long-term applicability and accuracy. Through error detection and model optimization, the present invention has a high degree of adaptability. This process is not limited to the initial accounting but can also respond to future data changes and new monitoring requirements, having strong long-term practicality.

[0159] 5. Outlier Detection and Data Standardization

[0160] The combination of outlier detection and data standardization is the key to ensuring data quality. When cleaning data, statistical methods are used to process the data to ensure that data from different sources can be used compatibly. The innovation of this method lies in that it incorporates data quality control throughout the entire accounting process to avoid inaccurate or biased results caused by low data quality. Through strict data cleaning and standardization processes, the accuracy of the data can be effectively improved, ensuring that data from different sources and forms can be correctly applied to subsequent model calculations.

[0161] 6. The combined impact of economic activities and meteorological data, which jointly considers economic activities (such as GDP, energy consumption, etc.) and meteorological data (such as temperature, precipitation, etc.) as adjustment factors for emission factors, represents a breakthrough in traditional carbon emission models. The influence of economic activities on the adjustment of emission factors can effectively address the impact of regional economic changes on carbon emissions, while the introduction of meteorological data can better reflect the fluctuations of seasonal and environmental factors on carbon emissions. The dual impact of meteorological and economic activity data endows the present invention with stronger adaptability and flexibility, especially in providing more accurate emission factors when dealing with seasonal changes and economic fluctuations.

[0162] In summary, these elements make the present invention not only have a breakthrough in theory but also have high flexibility and adaptability in practical applications. It can meet the carbon emission accounting requirements in different regions, at different times, and under different economic environments, and has great potential and value.

[0163] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed within the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. A regional carbon emissions accounting method, characterized by: At least the following steps are included: S1: Collect carbon emission source data in the region, including the collection of main data and auxiliary data. The data sources in the main data collection process include at least satellite remote sensing, sensor networks and energy consumption statistics. The auxiliary data include at least meteorological data and economic activity data in the collection area. The auxiliary data is used to assist in the calibration and adjustment of the carbon emission model. S2: Data processing and cleaning: clean the original data, remove invalid data and abnormal data, fill in missing data, standardize the data through statistical methods to ensure data consistency, standardize data from different sources to ensure that all data are compatible, and facilitate the application of subsequent models; S3: Emission factor calculation is based on the emission characteristics of different fields, and a dynamic emission factor model is used to calculate the emission factor. The calculation of the emission factor takes into account the changes in regional economic activities and the fluctuations in carbon emissions in different seasons. For special emission sources, a customized emission factor model is used; S4: Regional carbon emission accounting, based on the processed data and emission factors, the regional carbon emission accounting model is used to calculate the carbon emissions of each region. The regional carbon emission accounting model is based on the characteristics of different activities and emission sources in the region, and the spatial distribution analysis of different regions is performed using geographic information system technology; S5: Result verification and optimization, use historical data for verification, check the accuracy of the accounting results, if there is a large deviation from the actual emissions, i.e. 10% to 15%, adjust the model parameters and optimize the data source means, and optimize through multiple iterations.

2. A regional carbon emission accounting method according to claim 1, characterized in that: In S1, for different carbon emission sources in the main data collection, the carbon emission of each source is expressed as: AND source =A source ×F source in: E source A is the amount of carbon emitted by a carbon emission source; source is the activity level associated with the emission source; F source is the emission factor of the emission source, expressed as the ratio of carbon emissions to activity; The meteorological data and economic activity data in the auxiliary data collection can adjust or affect the emission factors; Impact of meteorological data: meteorological data may affect the seasonal adjustment of carbon emission factors; F seasonal =f(Temperature,Precipitation,Humidity,…) Impact of economic activities: Changes in economic activities may affect the calculation of emission factors. Emission factors can be adjusted dynamically through indicator adjustments: F economic =f(GDP,Energy Consumption,…) Among them, GDP refers to regional GDP, and Energy Consumption refers to energy consumption, both of which are a type of indicator.

3. A regional carbon emission accounting method according to claim 1, characterized in that: In the data cleaning process of S2, what is involved is the removal of invalid data, processing of missing values ​​and standardization of data; In order to make the units and dimensions of different data sources consistent, a standardization method is adopted. The standardization formula is: Where: X norm is the standardized data, X is the original data, v is the mean of the data, and σ is the standard deviation of the data; Further outlier detection is performed. Outliers are detected by statistical methods, see the following formula: If |Z|>3, the data point is considered an outlier.

4. A regional carbon emission accounting method according to claim 1, characterized in that: The calculation of the emission factor in S3 is expressed by the following formula: F sector =λ0×A sector ×(1+ΔF seasonal )×(1+ΔF economic ) Among them: F sector is the emission factor for a specific sector; λ0 is the benchmark emission factor, which is usually provided based on historical data or standard literature; A sector is the activity level of the sector; ΔF seasonal is the adjustment factor related to seasonal changes, based on meteorological data; ΔF economic is an adjustment factor related to economic activities.

5. A regional carbon emission accounting method according to claim 4, characterized in that: The dynamic emission factor in the dynamic emission factor model in S3 will change with multiple factors, so time correlation is introduced: F sector,t =F sector,t-1 ×(1+ΔF seasonal, t)×(1+ΔF economic, t) in: F sector,t is the emission factor at the current time t; F sector,t-1 is the emission factor at the previous time t-1; ΔF seasonal,t is the seasonal adjustment factor; ΔF economic,t Adjustment factors related to economic activities.

6. A regional carbon emission accounting method according to claim 5, characterized in that: The regional carbon emissions calculated by the regional carbon emissions accounting model in S4 are the sum of emissions from all sectors; Assuming there are multiple emission sources in the region, the regional carbon emissions are calculated using the following formula: Where: E region is the total carbon emissions of the region; E sector,i is the carbon emissions of the ith sector; n is the total number of emission sources in the region; Specifically, the carbon emissions calculation formula for each department is: AND sector,i =A sector,i ×F sector,i in: A sector,i is the activity level of the ith sector; F sector,i is the emission factor of the ith sector.

7. A regional carbon emission accounting method according to claim 6, characterized in that: The result calculated in S5 needs to be compared with historical data and optimized according to the error; Assume that the historical carbon emissions are E historical The calculated carbon emissions are E calculated , the error is calculated by the following formula: If the error exceeds the set threshold ∈, it is necessary to adjust the emission factor model or optimize the data source; If the error is large, the calculation model can be optimized by adjusting the baseline value of the emission factor or the economic adjustment factor, see the following formula: Where: ΔF adjustment is an adjustment factor based on the error.

Images