Carbon sink dynamic estimation method and system for summarizing multi-village carbon emission data

Abstract

The invention belongs to the field of carbon sequestration, and particularly relates to a carbon sequestration dynamic estimation method for summarizing multi-village carbon emission data, which comprises the following steps: step 1, collecting multi-village carbon emission source data and carbon confluence data with annual time as a period, and numbering the carbon emission source data and the carbon confluence data after the accounting range and boundary are determined according to village levels; the method comprises the following steps: counting carbon emission and carbon sink data of multiple villages in a historical period, acquiring a carbon balance correlation coefficient related to the carbon emission and carbon sink data by virtue of a carbon emission estimation model and a carbon sink dynamic estimation model, and confirming a carbon sink dynamic variable by virtue of the carbon balance correlation coefficient when carbon emission is increased in rural construction. According to the method, the area and space related to carbon sequestration are dynamically adjusted in the country, positive correlation of a carbon sequestration dynamic variable and a carbon emission dynamic variable is ensured, and a corresponding strategy and scheme are proposed based on the carbon sequestration dynamic variable, so that the requirements of carbon peak reaching and carbon neutralization are met when the country is constructed.

Description

Technical Field

[0001] The present invention relates to the field of carbon sinks, and in particular to a method for dynamically estimating carbon sinks by aggregating carbon emission data from multiple villages. Background Art

[0002] Global climate issues have become a huge challenge facing mankind, and China's work to achieve the goals of carbon peak and carbon neutrality is also proceeding in an orderly manner. With the construction and development of rural work in my country, the construction of some villages has blindly imitated cities, resulting in a situation where rural construction is increasingly high-carbon. Rural areas have large areas of carbon sink environments such as forests and gardens, which are important tools for implementing carbon reduction and emission reduction. Increasing research on the implementation path of rural low-carbonization will help reduce carbon emissions during its construction, enhance carbon sink capacity, and reduce greenhouse gas emissions, thereby contributing to global climate change response.

[0003] During rural construction, carbon emissions increase year by year, while carbon sinks grow slowly accordingly. In addition, there is a lack of correlation between conventionally statistical carbon emission data and carbon sink data. When formulating rural construction plans, there is a lack of dynamic adjustment plans for carbon sinks that change in sync with the growth of carbon emissions. The goals of carbon peak and carbon neutrality are ignored, which is insufficient. Summary of the invention

[0004] (I) Purpose of the invention

[0005] In order to solve the technical problems existing in the background technology, the present invention proposes a carbon sink dynamic estimation method that aggregates the carbon emission data of multiple villages. Through advanced data collection and processing technology, it provides a low-cost, easy-to-operate, and high-precision carbon emission and carbon sink dynamic calculation and management solution to support the sustainable development of rural areas.

[0006] (II) Technical solution

[0007] In order to solve the above technical problems, the present invention provides a carbon sink dynamic estimation method for aggregating carbon emission data of multiple villages, comprising the following steps:

[0008] Step 1: Collect carbon emission source data and carbon sink source data from multiple villages on an annual basis. The carbon emission source data and carbon sink source data are numbered after the accounting scope and boundaries are clearly defined at the village level;

[0009] Step 2: Classify the carbon emission source data and carbon sink source data according to the carbon emission category and carbon sink category, obtain several sub-carbon emission data sets and sub-carbon sink data sets in the time series, and clean, organize and pre-process the several sub-carbon emission data sets and sub-carbon sink data sets;

[0010] Step 3: Call the carbon emission estimation model and carbon sink dynamic estimation model trained by big data and artificial intelligence, conduct a comprehensive analysis of the cleaned, sorted and pre-processed data, obtain the carbon balance correlation coefficient between carbon emissions and carbon sinks in the historical period, and select the best carbon balance correlation coefficient;

[0011] Step 4: Confirm the corresponding carbon sink dynamic variables through the carbon balance correlation coefficient and the carbon emission dynamic variables, customize the village-level directional carbon sink adjustment plan through the carbon sink dynamic variables, and estimate the carbon emissions and carbon sink dynamics for future cycles.

[0012] Preferably, in step three, the carbon emission estimation model is an emission coefficient model, i.e., "carbon emissions = activity level * emission factor", which obtains the energy consumption type and consumption in rural life and refers to relevant energy conversion parameters to estimate the activity level, which is the carbon source amount.

[0013] Preferably, the source of emission factors is the emission factor basic database, which includes: ecological environment carbon sink factors, commodity energy factors, building unit carbon emission factors, infrastructure carbon emission factors, and economic industry carbon emission factors. Each sub-carbon emission data set is used to estimate the carbon source amount in the corresponding historical period. The increase or decrease of carbon source amount changes dynamically with the historical period.

[0014] Preferably, the carbon balance correlation coefficient corresponds to the carbon neutrality coefficient, and the carbon neutrality coefficient is: the ratio of the absorption of CO2-eq to the emission of CO2-eq in the year. When the carbon neutrality coefficient = 1, the carbon emission and carbon sink are balanced with each other;

[0015] The carbon balance correlation coefficient is a fixed value. When the carbon balance correlation coefficient is less than the carbon neutrality coefficient, it means that the increase in carbon emissions is greater than the fixed amount of carbon sinks, and the carbon sink needs to be increased;

[0016] The value of the carbon balance correlation coefficient close to the carbon neutrality coefficient is the optimal carbon balance correlation coefficient.

[0017] Preferably, the carbon emission dynamic variable is the absolute value of the difference between the planned carbon emissions and the carbon emissions in the adjacent period. Under the premise that the carbon balance correlation coefficient remains constant, the increasing carbon sink dynamic variable is positively correlated with the absolute value of the difference between the planned carbon emissions and the carbon emissions in the adjacent period.

[0018] Customize the village-level directional carbon sink adjustment plan based on the dynamic variables of carbon sinks. The carbon sink adjustment plan increases the wetland carbon sink, forest carbon sink and farmland carbon sink in the village, and dynamically customizes the growth amount through the sub-carbon sink data set of the adjacent cycle;

[0019] When the carbon sinks in wetlands, forests and farmlands in rural areas reach their maximum values;

[0020] The superior authorities of the villages will carry out a multi-village collaborative carbon sink adjustment plan, and distribute the carbon sink growth to the remaining villages where the carbon sink has not reached the maximum value for carbon sink compensation.

[0021] The present invention also proposes a carbon sink dynamic estimation method that aggregates carbon emission data from multiple villages, including:

[0022] The data acquisition module collects carbon emission source data and carbon sink source data of multiple villages on an annual basis. The carbon emission source data and carbon sink source data are numbered after the accounting scope and boundaries are clearly defined at the village level;

[0023] The data sorting module classifies the carbon emission source data and the carbon sink source data according to the carbon emission category and the carbon sink category, obtains several sub-carbon emission data sets and sub-carbon sink data sets in the time series, and cleans, sorts and pre-processes the several sub-carbon emission data sets and sub-carbon sink data sets;

[0024] The model processing module calls the carbon emission estimation model and carbon sink dynamic estimation model trained based on big data and artificial intelligence, conducts a comprehensive analysis of the cleaned, sorted and pre-processed data, obtains the carbon balance correlation coefficient between carbon emissions and carbon sinks in the historical period, and selects the best carbon balance correlation coefficient;

[0025] The carbon sink dynamic scheme module confirms the corresponding carbon sink dynamic variables through the carbon balance correlation coefficient and the carbon emission dynamic variables, customizes the village-level directional carbon sink adjustment plan through the carbon sink dynamic variables, and estimates the carbon emissions and carbon sink dynamics for future cycles.

[0026] The above-mentioned technical scheme of the present invention has the following beneficial technical effects: statistically analyzing the carbon emission and carbon sink data of multiple villages in the historical period, obtaining the carbon balance correlation coefficient related to the carbon emission and carbon sink data based on the carbon emission estimation model and the carbon sink dynamic estimation model, and when the carbon emissions are increased in rural construction, confirming the carbon sink dynamic variables through the carbon balance correlation coefficient, and dynamically adjusting the area and space related to the carbon sink in the village to ensure the positive correlation between the carbon sink dynamic variables and the carbon emission dynamic variables, and proposing corresponding strategies and plans based on the carbon sink dynamic variables to meet the needs of carbon peak and carbon neutrality during rural construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the method of the present invention;

[0028] Figure 2 This is a schematic diagram of carbon emission boundary definition of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0030] like Figure 1-2 As shown, the present invention proposes a carbon sink dynamic estimation method for aggregating carbon emission data of multiple villages, comprising the following steps:

[0031] Step 1: Collect carbon emission source data and carbon sink source data from multiple villages on an annual basis. The carbon emission source data and carbon sink source data are numbered after the accounting scope and boundaries are clearly defined at the village level;

[0032] It is understandable that the accounting object of carbon emission source data is: the greenhouse effect is due to the large amount of greenhouse gases such as carbon dioxide (CO2), chlorofluorocarbons (CFCS), methane (CH4), nitrogen dioxide (NO2) in the atmosphere, among which the most important greenhouse gas is CO2, accounting for about 77%. Therefore, this study takes the emission and absorption of CO2 as the main research object.

[0033] The increase in rural CO2 is mainly due to emissions from agricultural production, transportation, daily activities, some industrial activities, etc., and people's living habits also cause the release of CO2, such as driving, using electricity and using fossil fuels. In addition, changes in land use will also release or absorb CO2. In recent years, with the advancement of rural construction, the production and living standards in rural areas have improved, and rural carbon emissions have also increased significantly.

[0034] Among them, an annual cycle should cover a continuous period of 12 months, consistent with the calendar year or fiscal year, and consistent with the time period most commonly used in the village;

[0035] Among them, the main population of the village is the agricultural population, and the main land use is agricultural land and construction land. The geographical boundaries of the administrative village are used as the accounting scope;

[0036] The accounting boundary includes the following areas:

[0037] Scope 1 is the relevant carbon emission processes where both emission sources and demand activities occur within the geographical boundary;

[0038] Scope 2 refers to the relevant carbon emission processes where the demand activities occur within the geographical boundary and the emission sources occur outside the geographical boundary;

[0039] Scope 3 is the relevant carbon emission processes where both emission sources and demand activities occur outside the geographical boundaries.

[0040] Step 2: Classify the carbon emission source data and carbon sink source data according to the carbon emission category and carbon sink category, obtain several sub-carbon emission data sets and sub-carbon sink data sets in the time series, and clean, organize and pre-process the several sub-carbon emission data sets and sub-carbon sink data sets;

[0041] The collected carbon emission source data are classified into several sub-carbon emission data sets, including:

[0042] Building single carbon emission dataset: carbon emissions from daily energy consumption, carbon emissions from renewable energy, and carbon emissions from water consumption;

[0043] Infrastructure carbon emission dataset: road traffic carbon emission, waste carbon emission;

[0044] Economic industry carbon emissions dataset: agricultural production carbon emissions, industrial production carbon emissions, and tourism production carbon emissions;

[0045] The collected carbon sink source data are classified into several sub-carbon sink data sets, including:

[0046] Wetland carbon sink dataset, forest carbon sink dataset, farmland carbon sink dataset.

[0047] Step 3: Call the carbon emission estimation model and carbon sink dynamic estimation model trained by big data and artificial intelligence, conduct a comprehensive analysis of the cleaned, sorted and pre-processed data, obtain the carbon balance correlation coefficient between carbon emissions and carbon sinks in the historical period, and select the best carbon balance correlation coefficient;

[0048] Step 4: Confirm the corresponding carbon sink dynamic variables through the carbon balance correlation coefficient and the carbon emission dynamic variables, customize the village-level directional carbon sink adjustment plan through the carbon sink dynamic variables, and estimate the carbon emissions and carbon sink dynamics for future cycles.

[0049] In this embodiment, the carbon emission and carbon sink data of multiple villages in the historical period are statistically collected, and the carbon balance correlation coefficient related to the carbon emission and carbon sink data is obtained based on the carbon emission estimation model and the carbon sink dynamic estimation model. When the carbon emissions increase during rural construction, the carbon sink dynamic variables are confirmed through the carbon balance correlation coefficient, and the area and space related to the carbon sink are dynamically adjusted in the village to ensure the positive correlation between the carbon sink dynamic variables and the carbon emission dynamic variables. Based on the carbon sink dynamic variables, corresponding strategies and plans are proposed to meet the needs of carbon peak and carbon neutrality during rural construction.

[0050] It should be noted that the carbon emission estimation model is an emission coefficient model, that is, "carbon emissions = activity level * emission factor", which obtains the energy consumption type and consumption in rural life and refers to relevant energy conversion parameters to estimate the activity level, which is the carbon source amount.

[0051] It can be understood that the source of emission factors is the basic database of emission factors. The basic database of emission factors includes: ecological environment carbon sink factors, commodity energy factors, building unit carbon emission factors, infrastructure carbon emission factors, and economic industry carbon emission factors. The carbon source amount in the corresponding historical period is estimated in conjunction with each sub-carbon emission data set. The increase or decrease of carbon source amount changes dynamically with the historical period.

[0052] Among them, the emission coefficient method is a method used to estimate the statistical average of the amount of gas emitted per unit of product, also known as the emission factor. This is the first carbon emission estimation method proposed by the IPCC and is also a widely used method. Calculation formula: E = ∑Q × EF;

[0053] In the formula: E is CO2 emissions; Q is the activity level, that is, the amount of activities that cause CO2 gas emissions, such as electricity consumption, water consumption, oil consumption, etc.; EF is the emission factor, that is, the CO2 emissions corresponding to each unit of activity level, such as how much CO2 emissions correspond to each kilowatt-hour of electricity or each ton of gasoline.

[0054] In one embodiment, the carbon balance correlation coefficient corresponds to the carbon neutrality coefficient, and the carbon neutrality coefficient is: the ratio of the absorption of CO2-eq to the emission of CO2-eq in the current year. When the carbon neutrality coefficient = 1, the carbon emission and the carbon sink are balanced with each other;

[0055] The carbon balance correlation coefficient is a fixed value. When the carbon balance correlation coefficient is less than the carbon neutrality coefficient, it means that the increase in carbon emissions is greater than the fixed amount of carbon sinks, and the carbon sink needs to be increased;

[0056] Since there are multiple carbon balance correlation coefficients in the historical period and the carbon balance correlation coefficients are different every year, the carbon balance correlation coefficient close to the carbon neutrality coefficient is selected as the optimal carbon balance correlation coefficient. Using the optimal carbon balance correlation coefficient as a reference, the carbon sink adjustment plan closest to carbon neutrality can be obtained.

[0057] It can be understood that the dynamic variable of carbon emissions is the absolute value of the difference between the planned carbon emissions and the carbon emissions in the adjacent period. Under the premise that the carbon balance correlation coefficient remains constant, the increasing dynamic variable of carbon sink is positively correlated with the absolute value of the difference between the planned carbon emissions and the carbon emissions in the adjacent period.

[0058] In one embodiment, a village-level directional carbon sink adjustment plan is customized according to the dynamic variables of carbon sinks. The carbon sink adjustment plan increases the wetland carbon sink, forest carbon sink and farmland carbon sink in the village, and the growth amount is dynamically customized through the sub-carbon sink data set of the adjacent period, so that the best wetland carbon sink, forest carbon sink and farmland carbon sink are selected as the adjustment objects;

[0059] When the carbon sinks in wetlands, forests and farmlands in rural areas reach their maximum values, the carbon sinks in existing villages cannot be supplemented by increasing the area or space.

[0060] The village superiors will implement a multi-village collaborative carbon sink adjustment plan, distribute the carbon sink growth to the remaining villages where the carbon sink has not reached the maximum value for carbon sink compensation, coordinate multiple villages for collaborative development, and increase the area and space of villages that have not yet reached the maximum value of wetland carbon sink, forest carbon sink and farmland carbon sink to supplement carbon sinks. Make full use of idle carbon sink resources, facilitate village superiors to make overall arrangements, and carry out carbon neutrality in a macro direction.

[0061] A carbon sink dynamic estimation system that aggregates carbon emission data from multiple villages, including:

[0062] The data acquisition module collects carbon emission source data and carbon sink source data of multiple villages on an annual basis. The carbon emission source data and carbon sink source data are numbered after the accounting scope and boundaries are clearly defined at the village level;

[0063] The data sorting module classifies the carbon emission source data and the carbon sink source data according to the carbon emission category and the carbon sink category, obtains several sub-carbon emission data sets and sub-carbon sink data sets in the time series, and cleans, sorts and pre-processes the several sub-carbon emission data sets and sub-carbon sink data sets;

[0064] The model processing module calls the carbon emission estimation model and carbon sink dynamic estimation model trained based on big data and artificial intelligence, conducts a comprehensive analysis of the cleaned, sorted and pre-processed data, obtains the carbon balance correlation coefficient between carbon emissions and carbon sinks in the historical period, and selects the best carbon balance correlation coefficient;

[0065] The carbon sink dynamic scheme module confirms the corresponding carbon sink dynamic variables through the carbon balance correlation coefficient and the carbon emission dynamic variables, customizes the village-level directional carbon sink adjustment plan through the carbon sink dynamic variables, and estimates the carbon emissions and carbon sink dynamics for future cycles.

[0066] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

Claims

1. A carbon sink dynamic estimation method that aggregates carbon emission data from multiple villages, characterized in that: The following steps are involved: Step 1: Collect carbon emission source data and carbon sink source data from multiple villages on an annual basis. The carbon emission source data and carbon sink source data are numbered after the accounting scope and boundaries are clearly defined at the village level; Step 2: Classify the carbon emission source data and carbon sink source data according to the carbon emission category and carbon sink category, obtain several sub-carbon emission data sets and sub-carbon sink data sets in the time series, and clean, organize and pre-process the several sub-carbon emission data sets and sub-carbon sink data sets; Step 3: Call the carbon emission estimation model and carbon sink dynamic estimation model trained by big data and artificial intelligence, conduct a comprehensive analysis of the cleaned, sorted and pre-processed data, obtain several carbon balance correlation coefficients between carbon emissions and carbon sinks in the historical period, and select the best carbon balance correlation coefficient; Step 4: Confirm the corresponding carbon sink dynamic variables through the carbon balance correlation coefficient and the carbon emission dynamic variables, customize the village-level directional carbon sink adjustment plan through the carbon sink dynamic variables, and estimate the carbon emissions and carbon sink dynamics for future cycles.

2. According to claim 1, a carbon sink dynamic estimation method for aggregating carbon emission data of multiple villages is characterized in that: In step 1, an annual cycle should cover a continuous period of 12 months, consistent with the calendar year or fiscal year, and consistent with the time period most commonly used by the village; Among them, the main population of the village is the agricultural population, and the main land use is agricultural land and construction land. The geographical boundaries of the administrative village are used as the accounting scope; The accounting boundary includes the following areas: Scope 1 is the relevant carbon emission processes where both emission sources and demand activities occur within the geographical boundary; Scope 2 refers to the relevant carbon emission processes where the demand activities occur within the geographical boundary and the emission sources occur outside the geographical boundary; Scope 3 is the relevant carbon emission processes where both emission sources and demand activities occur outside the geographical boundaries.

3. The carbon sink dynamic estimation method for aggregating carbon emission data of multiple villages according to claim 1 is characterized in that: In step 2, the collected carbon emission source data are classified into several sub-carbon emission data sets, including: Building single carbon emission dataset: carbon emissions from daily energy consumption, carbon emissions from renewable energy, and carbon emissions from water consumption; Infrastructure carbon emission dataset: road traffic carbon emission, waste carbon emission; Economic industry carbon emissions dataset: agricultural production carbon emissions, industrial production carbon emissions, and tourism production carbon emissions; The collected carbon sink source data are classified into several sub-carbon sink data sets, including: Wetland carbon sink dataset, forest carbon sink dataset, farmland carbon sink dataset.

4. The carbon sink dynamic estimation method for aggregating carbon emission data of multiple villages according to claim 1 is characterized in that: In step three, the carbon emission estimation model is an emission coefficient model, that is, "carbon emissions = activity level * emission factor", which obtains the energy consumption type and consumption in rural life and refers to relevant energy conversion parameters to estimate the activity level, which is the carbon source amount.

5. The carbon sink dynamic estimation method for aggregating carbon emission data of multiple villages according to claim 4 is characterized in that: The source of emission factors is the emission factor basic database, which includes: ecological environment carbon sink factors, commodity energy factors, building unit carbon emission factors, infrastructure carbon emission factors, and economic industry carbon emission factors. The carbon source amount in the corresponding historical period is estimated in conjunction with each sub-carbon emission data set. The increase or decrease of carbon source amount changes dynamically with the historical period.

6. The carbon sink dynamic estimation method for aggregating carbon emission data of multiple villages according to claim 1 is characterized in that: The carbon balance correlation coefficient corresponds to the carbon neutrality coefficient. The carbon neutrality coefficient is the ratio of the absorption of CO2-eq to the emission of CO2-eq in the current year. When the carbon neutrality coefficient = 1, carbon emissions and carbon sinks are balanced with each other. The carbon balance correlation coefficient is a fixed value. When the carbon balance correlation coefficient is less than the carbon neutrality coefficient, it means that the increase in carbon emissions is greater than the fixed amount of carbon sinks, and the carbon sink needs to be increased; The value of the carbon balance correlation coefficient close to the carbon neutrality coefficient is the optimal carbon balance correlation coefficient.

7. The carbon sink dynamic estimation method for aggregating carbon emission data of multiple villages according to claim 1 is characterized in that: The dynamic variable of carbon emissions is the absolute value of the difference between the planned carbon emissions and the carbon emissions in the adjacent period. Under the premise that the carbon balance correlation coefficient remains constant, the increasing dynamic variable of carbon sink is positively correlated with the absolute value of the difference between the planned carbon emissions and the carbon emissions in the adjacent period.

8. The carbon sink dynamic estimation method for aggregating carbon emission data of multiple villages according to claim 1 is characterized in that: Customize the village-level directional carbon sink adjustment plan based on the dynamic variables of carbon sinks. The carbon sink adjustment plan increases the wetland carbon sink, forest carbon sink and farmland carbon sink in the village, and dynamically customizes the growth amount through the sub-carbon sink data set of the adjacent cycle; When the carbon sinks in wetlands, forests and farmlands in rural areas reach their maximum values; The superior authorities of the villages will carry out a multi-village collaborative carbon sink adjustment plan, and distribute the carbon sink growth to the remaining villages where the carbon sink has not reached the maximum value for carbon sink compensation.

9. A carbon sink dynamic estimation system for aggregating carbon emission data of multiple villages according to any one of claims 1 to 8, characterized in that: include: The data acquisition module collects carbon emission source data and carbon sink source data of multiple villages on an annual basis. The carbon emission source data and carbon sink source data are numbered after the accounting scope and boundaries are clearly defined at the village level; The data sorting module classifies the carbon emission source data and the carbon sink source data according to the carbon emission category and the carbon sink category, obtains several sub-carbon emission data sets and sub-carbon sink data sets in the time series, and cleans, sorts and pre-processes the several sub-carbon emission data sets and sub-carbon sink data sets; The model processing module calls the carbon emission estimation model and carbon sink dynamic estimation model trained based on big data and artificial intelligence, conducts a comprehensive analysis of the cleaned, sorted and pre-processed data, obtains the carbon balance correlation coefficient between carbon emissions and carbon sinks in the historical period, and selects the best carbon balance correlation coefficient; The carbon sink dynamic scheme module confirms the corresponding carbon sink dynamic variables through the carbon balance correlation coefficient and the carbon emission dynamic variables, customizes the village-level directional carbon sink adjustment plan through the carbon sink dynamic variables, and estimates the carbon emissions and carbon sink dynamics for future cycles.

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