Evaluation and analysis method and system for regional agricultural carbon peak robustness

Abstract

The invention discloses an evaluation and analysis method and system for regional agricultural carbon peak robustness. Relates to the technical field of agricultural carbon peak robustness. The method comprises the following steps: step 1, acquiring various types of agricultural non-CO2 gas emission data; 2, judging whether the agricultural non-CO2 gas emission data reaches a peak or not; 3, when the agricultural non-CO2 gas emission data reaches the peak, constructing a gas robustness model after the agricultural non-CO2 gas emission data reaches the peak, and if the agricultural non-CO2 gas emission data does not reach the peak, checking the emission data again and repeating peak reaching judgment until a peak reaching condition is met; and 4, quantitatively evaluating the peak reaching robustness degree of the agricultural non-CO2 gas emission data according to the gas robustness model. The invention provides the method for quantitatively evaluating the fluctuation threat after the agricultural carbon reaches the peak, and the applicability of the CPCI index method in the aspect of evaluating the robustness after the carbon reaches the peak is verified.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural carbon peak robustness, and more specifically to an evaluation and analysis method and system for regional agricultural carbon peak robustness. Background Art

[0002] Currently, determining the path and timing of carbon peaks across regions and industries is a hot topic. Consequently, current research focuses on the path and timing of carbon peaks, but ignores the challenges that post-peak fluctuations pose to peak stability. In particular, the implicit assumption that achieving emission reduction targets is equivalent to peaking emissions limits objective understanding of post-peak fluctuations and overlooks the potentially disruptive impact that these fluctuations could have on emission reduction stability.

[0003] The global agricultural sector contributes approximately 53.0% of non-CO2 greenhouse gas emissions, making its emission reduction progress crucial for achieving temperature control targets. However, agricultural non-CO2 greenhouse gas emissions are influenced by multiple factors, including natural endowments, land use, agricultural production, policy measures, and unexpected events. The peaking process is complex, and post-peak fluctuations are significant.

[0004] At present, there is still a lack of methods to quantitatively assess the threat of fluctuations after agricultural carbon peak.

[0005] Therefore, it is an urgent problem that technicians in this field need to solve to propose an evaluation and analysis method and system for the robustness of regional agricultural carbon peaking to quantitatively assess the threat of fluctuations after agricultural carbon peaking. Summary of the Invention

[0006] In view of this, the present invention provides an evaluation and analysis method and system for the robustness of regional agricultural carbon peak, and verifies the applicability of the CPCI index method in the robustness assessment after carbon peak.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for evaluating and analyzing the robustness of regional agricultural carbon peaking, including:

[0009] Step 1: Obtain various types of agricultural non-CO2 gas emission data;

[0010] Step 2: Determine whether agricultural non-CO2 gases have peaked;

[0011] Step 3: When the agricultural non-CO2 gas emission data reaches a peak, a gas robustness model for the agricultural non-CO2 gas emission data after the carbon peak is constructed. If the peak is not reached, the emission data is recalculated and the peak judgment is repeated until the peak conditions are met;

[0012] Step 4: Quantitatively assess the robustness of the peak of agricultural non-CO2 gas emissions data based on the gas robustness model.

[0013] Preferably, the agricultural non-CO2 gas emission data include: CH4 gas emissions from rice fields and N2O gas emissions from farmland; CH4 gas emissions from animal intestinal fermentation in animal husbandry and CH4 and N2O gas emissions from manure management.

[0014] Preferably, the calculation formula for agricultural non-CO2 gas emissions data is as follows:

[0015] I=∑I i =∑AD i ×EF i ;

[0016] I is the total amount of non-CO2 greenhouse gas emissions from agriculture; I i AD is the non-CO2 greenhouse gas emissions from the i-th greenhouse gas source; i EF is the non-CO2 activity data of the i-th category greenhouse gas emission source; i is the emission factor of the i-th category non-CO2 greenhouse gas emission source.

[0017] Preferably, the calculation formula for CH4 gas emissions from the rice field is as follows:

[0018]

[0019] in: is the total methane emission from rice fields; is the methane emission factor of rice fields by type; is the rice planting area corresponding to the emission factor; the subscript i indicates the type of rice field;

[0020] The calculation formula for N2O gas emissions from the farmland is as follows:

[0021]

[0022] in: is the total N2O emission from agricultural land; N 农田输入 is the nitrogen input in the agricultural production process; EF is the corresponding N2O emission factor.

[0023] Preferably, the calculation formula for CH4 gas emissions from animal intestinal fermentation is as follows:

[0024]

[0025] in: is the methane emission of the i-th animal; is the methane emission factor of the i-th animal; AP i is the number of the i-th animal;

[0026] The calculation formula for the total CH4 gas emissions from animal intestinal fermentation is as follows:

[0027]

[0028] in: is the total CH4 gas emissions from animal intestinal fermentation;

[0029] The formula for calculating CH4 gas emissions from animal manure management is as follows:

[0030]

[0031] in: CH4 emissions from manure management for the i-th animal; CH4 emission factor for manure management of animal species i;

[0032] The formula for calculating total methane emissions from manure management is as follows:

[0033]

[0034] in: Managing total methane emissions for animal manure.

[0035] Preferably, the step 2 comprises:

[0036] Step 21: Construct the time series T corresponding to regional agricultural non-CO2 gas emissions I;

[0037] Step 22: Based on the C40 and WRI standards, determine whether the emissions I have reached a historical peak in the time series T. If the emissions I have reached a historical peak and meet the first constraint, proceed to step 25; otherwise, proceed to step 23.

[0038] Step 23: Trend turning point identification: Use piecewise linear regression to determine the turning points of the upward and downward trends of emissions I. When the turning point meets the second constraint, return to step 22 for judgment. When the turning point does not meet the second constraint, proceed to step 24.

[0039] Step 24: Mann-Kendall test to identify the time when the time series T mutation begins and the mutation period. If the test result shows that there is a mutation point and there is a descending interval after the mutation point, then proceed to step 25. If the test result shows that there is no mutation point, then agricultural non-CO2 gases have not reached their peak.

[0040] Step 25: Use the Tapio decoupling index test method to determine whether agricultural non-CO2 gases have peaked. When the Tapio decoupling index meets the third constraint, agricultural non-CO2 gases have peaked; otherwise, agricultural non-CO2 gases have not peaked.

[0041] Preferably, the post-carbon peak gas robustness model quantitatively evaluates the post-carbon peak peak robustness by setting the carbon peak challenge index CPCI.

[0042] Preferably, an evaluation and analysis system for the robustness of regional agricultural carbon peaking includes:

[0043] Data acquisition module: used to obtain various types of agricultural non-CO2 gas emission data;

[0044] Judgment module: used to determine whether agricultural non-CO2 gases have reached their peak;

[0045] Model construction module: used to construct a gas robustness model for agricultural non-CO2 gas emissions after the carbon peak is reached when the agricultural non-CO2 gas emissions data reaches the peak. If the peak is not reached, the emission data is recalculated and the peak judgment is repeated until the peak conditions are met;

[0046] Evaluation module: used to quantitatively evaluate the robustness of agricultural non-CO2 gas emission data peaking based on the gas robustness model.

[0047] Through the above technical solutions, it can be seen that compared with the existing technology, the present invention discloses a method and system for evaluating and analyzing the robustness of regional agricultural carbon peaking, focusing on the effect assessment of fluctuations after regional carbon peaking, and proposing the concept of peak challenge point and CPCI index method to quantitatively evaluate the robustness after carbon peaking, and verifies the applicability of CPCI index method in the assessment of robustness after carbon peaking. The present invention is an exploration of the assessment of robustness after peaking. Since the academic value of robustness after peaking in different regions will become increasingly important, it is necessary to develop more quantitative methods to provide theoretical and technical support for post-peak carbon emission management. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0049] Figure 1 A schematic diagram of the process provided by the present invention;

[0050] Figure 2 A schematic diagram of gas peak determination provided by the present invention;

[0051] Figure 3 Flowchart of the robustness assessment method for agricultural carbon peak provided by the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0053] like Figure 1 As shown, the embodiment of the present invention discloses a method for evaluating and analyzing the robustness of regional agricultural carbon peak, including:

[0054] Step 1: Obtain various types of agricultural non-CO2 gas emission data;

[0055] Step 2: Determine whether agricultural non-CO2 gas emissions have peaked;

[0056] Step 3: When the agricultural non-CO2 gas emission data reaches a peak, a gas robustness model for the agricultural non-CO2 gas emission data after the carbon peak is constructed. If the peak is not reached, the emission data is recalculated and the peak judgment is repeated until the peak conditions are met;

[0057] Step 4: Quantitatively assess the robustness of the peak of agricultural non-CO2 gas emissions data based on the gas robustness model.

[0058] Specifically, the agricultural non-CO2 gases include: CH4 gas emissions from rice fields and N2O gas emissions from farmland; CH4 gas emissions from animal intestinal fermentation in animal husbandry and CH4 and N2O gas emissions from manure management.

[0059] Specifically, the calculation formula for agricultural non-CO2 gas emissions data is as follows:

[0060] I=∑I i ×∑AD i ×EF i ;

[0061] I is the total amount of non-CO2 greenhouse gas emissions from agriculture; I i AD is the non-CO2 greenhouse gas emissions from the i-th greenhouse gas source; i EF is the non-CO2 activity data of the i-th category greenhouse gas emission source; i is the emission factor of the i-th category non-CO2 greenhouse gas emission source.

[0062] Specifically, the calculation formula for CH4 gas emissions from the rice field is as follows:

[0063]

[0064] in: is the total methane emission from rice fields; is the methane emission factor of rice fields by type; is the rice planting area corresponding to the emission factor; the subscript i indicates the type of rice field;

[0065] The calculation formula for N2O gas emissions from the farmland is as follows:

[0066]

[0067] in: is the total N2O emission from agricultural land; N 农田输入 is the nitrogen input in the agricultural production process; EF is the corresponding N2O emission factor.

[0068] Specifically, the calculation formula for CH4 gas emissions from animal intestinal fermentation is as follows:

[0069]

[0070] in: is the methane emission of the i-th animal; is the methane emission factor of the i-th animal; AP i is the number of the i-th animal;

[0071] The calculation formula for the total CH4 gas emissions from animal intestinal fermentation is as follows:

[0072]

[0073] in: is the total CH4 gas emissions from animal intestinal fermentation;

[0074] The formula for calculating CH4 gas emissions from animal manure management is as follows:

[0075]

[0076] in: CH4 emissions from manure management for the i-th animal; CH4 emission factor for manure management of animal species i;

[0077] The formula for calculating total methane emissions from manure management is as follows:

[0078]

[0079] in: Managing total methane emissions for animal manure.

[0080] like Figure 2-3 As shown, specifically, step 2 includes:

[0081] Step 21: Construct the time series T of regional agricultural non-CO2 gas emissions I;

[0082] Step 22: Based on the C40 and WRI standards, determine whether emissions I have reached a historical peak in time series T. If emissions I have reached a historical peak and meet the conditions of a 10% reduction in emissions within five years and an annual reduction rate R of 2.0%, then proceed to Step 25; otherwise, proceed to Step 23.

[0083] Step 23: Identify the trend turning point. Use piecewise linear regression to determine the turning point, which is the year when the upward and downward trends of carbon emissions change. If the turning point meets the annual decline rate of 0≤R<2.0% within the T+5 period, proceed to step 22 for judgment. If the turning point does not meet the conditions, proceed to step 24.

[0084] Step 24: Mann-Kendall test to identify the time when the carbon emission time series mutation begins and the mutation period. If the test result shows that there is a mutation point and there is a decline interval after the mutation point, then proceed to step 25. If the test result shows that there is no mutation point, agricultural non-CO2 gases have not reached their peak.

[0085] Step 25: Use the Tapio decoupling index test method to determine the relationship between economic growth and carbon emissions. If the Tapio decoupling index is less than 0.8 within three years and is less than or equal to 0 after three years, agricultural non-CO2 gases have peaked; otherwise, agricultural non-CO2 gases have not peaked.

[0086] In another specific embodiment of the present invention, a time series of total agricultural non-CO2 gas emissions (I) over a 20-year period is constructed for a particular administrative region. The following substeps are used to determine whether agricultural non-CO2 greenhouse gas emissions have peaked. This determination system includes empirical analysis (step 22), trend analysis (steps 23 and 24), and a decoupling trend test (step 25).

[0087] Furthermore, if step 22 is qualified, step 25 can be directly used to test the decoupling index, and the peak time can be determined if it is satisfied; if the emissions are on a downward trend but do not meet the conditions of step 22, it can be determined as a plateau period, and step 23 needs to be used to find the turning point of the trend. If there is a turning point and the decoupling index test of step 25 is passed, it is the peak time; when the data sample has a turning point after step 23, but the emission reduction rate has not met the standard of step 22, the Mann-Kendall test of step 24 is performed. If the test result shows that there is a mutation point and there is a significant decline interval after the mutation point, a decoupling index test can be performed. If the test is passed, it is the peak time, and if the test is not passed, it has not reached the peak.

[0088] In another specific embodiment of the present invention, the first constraint is: the C40 and WRI standards are that carbon emissions have reached a historical high, and there must be a period of more than 5.0 years after reaching the historical high; emissions are reduced by 10% within five years, and the annual decline rate is 2.0%; the piecewise linear regression divides the value range of the independent variable into multiple intervals, and an independent linear model is established in each interval to fit the overall "broken line" relationship, and the year of transition between the upward trend and the downward trend is determined as the turning point (T); the plateau period is because there is no obvious upward or downward trend in emissions, and the second constraint is: when the annual decline rate (R) in the "T+5" period satisfies "0≤R<2.0%", step 22 is used to verify the peak condition five years (T+5) after the turning point (T).

[0089] The Mann-Kendall test (MK test) can identify the time when the carbon emission time series mutation begins and the interval of the mutation period; according to the carbon emission time series data {X i |i=1,2,…,N} constructs a sorted sequence; a ij Used to compare the size relationship between the i-th and j-th data points in the time series. The formula is as follows:

[0090]

[0091] This formula is used to calculate the time series, each subsequent data point x i Is it strictly greater than all previous data points x? j .

[0092] S K is the cumulative ranking statistic, defined as the sum of all a from the 1st to the Kth data point ij The sum of , the formula is as follows:

[0093]

[0094] Quantify the strength of the increasing trend of the first K data points in the time series. K The larger the value is, the more significant the increasing trend of the first K data points is; K is the time series position of the current analysis, and its value range is 1≤K≤N.

[0095] The statistical variables are defined as:

[0096]

[0097] C(S K ) and are the mathematical expectation and variance respectively;

[0098] UF curve (forward statistic curve): calculate each UF in chronological order (K=1→N) K , draw its curve of change with k. UB curve (reverse statistic curve): calculate UB in reverse time order (K=N→1) K , draw its curve of variation with K. UF K is a standard normal distribution; if |UF K ∣>Uα, then within the confidence interval of α=0.05, the continuous growth trend is obvious and significant. According to the reverse order of the carbon emission time series X, repeating the above steps can determine UB K Assuming that the UF curve and the UB curve intersect within the confidence interval, the parameter of that year shows an increasing mutation, and the time corresponding to the intersection is the time when the mutation begins, and vice versa.

[0099] Significance threshold: When the confidence level is set to α = 0.05, the critical value is Uα = ±1.96.

[0100] Mutation point criterion: First, the UF and UB curves intersect within the confidence interval (±1.96); after the intersection, the UF curve continues to exceed the threshold (such as turning from negative to positive and exceeding 1.96); the time point corresponding to the intersection is the year when the mutation begins.

[0101] In another specific embodiment of the present invention, the calculation formulas for regional agricultural non-CO2 greenhouse gas emissions and the Tapio decoupling index are as follows:

[0102]

[0103] DI Tapio stands for the Talber Decoupling Index, ΔCO2% is the percentage change in agricultural non-CO2 greenhouse gas emissions, and ΔGDP% is the percentage change in economic output (usually measured as GDP). According to the definition of the short-term (annual) Talber Decoupling Index, the third constraint is: DI TapioThe Talbot Decoupling Index (DI) should be less than 0.8 to achieve the decoupling of carbon emissions from economic growth, that is, while achieving economic growth, carbon emissions are on a downward trend. Tapio ) should remain less than 0 continuously.

[0104] Specifically, the post-carbon peak gas robustness model quantitatively evaluates the post-carbon peak robustness by setting the Carbon Peak Challenge Index (CPCI). The judgment criteria are as follows:

[0105]

[0106] Among them, n is the number of indicators that do not meet the carbon peak stability test conditions; E is the peak point.

[0107] Specifically, the indicators that do not meet the carbon peak stability test conditions include: Mann-Kendall test (MK test), C40 standard, decoupling index, piecewise linear regression, and failure to break through the peak line.

[0108] In another specific embodiment of the present invention, taking into account the diversity of carbon peaks and their complex relationship with the socio-economic background, a Carbon Peaking Challenging Index (CPCI) is designed to quantitatively evaluate the robustness of carbon peaks after peaking. Let A represent compliance with the MK test (A=1 when compliant, A=0 when not compliant); let B represent compliance with the C40 standard (B=1 when compliant, B=0 when not compliant); let C represent compliance with the decoupling index (C=1 when compliant, C=0 when not compliant); let D represent compliance with the piecewise linear regression (D=1 when compliant, D=0 when not compliant); let E represent failure to break through the peak line (E=1 when not broken through, E=0 when broken through); when E does not break through the peak line (E=1), let n=4-(A+B+C+D), if ABCD can pass the test in step 2 (that is, when it meets the test criteria in step 2), the corresponding letters are assigned a value of 1, and if all pass, it is A+B+C+D=4. Finally, the robustness of the CPCI carbon peak challenge index is judged based on the size of the n value. By calculating the n value, it is possible to determine the number of CPCI indexes that have not passed the stability test after reaching the peak, and quantitatively assess the robustness of the peak. If the challenge point breaks through the peak line (E = 0), the status of the carbon peak is overturned. That is, if the challenge point breaks through the peak line, the n value will no longer be calculated, and the E value will be used directly, that is, E = 0, indicating that the agricultural non-CO2 greenhouse gas emission curve has broken through the carbon peak peak, and the original peak status has been overturned.

[0109] Furthermore, the present invention takes Chengdu as an implementation case:

[0110] Using the accounting method for agricultural non-CO2 greenhouse gas emissions in Step 1, a time series of non-CO2 greenhouse gas emissions from Chengdu's agriculture was obtained. Non-CO2 greenhouse gas emissions decreased by 37.4% from 3,486,696 tons in 1980 to 2,181,298 tons CO2-eq in 2020. In Chengdu, CH4 from rice paddies accounts for approximately 60% of non-CO2 greenhouse gas emissions, CH4 from dryland crops accounts for approximately 20%, CH4 from livestock enteric waste accounts for 15%, and CH4 from manure management accounts for approximately 5%. The reduction in non-CO2 greenhouse gas emissions is primarily due to the significant reduction in rice cultivation area, which accounts for 93.6% of total emissions. The conversion of 147,600 hectares of cultivated land to construction land, representing 16.6% of Chengdu's total area from 1980 to 2020, was significant. Furthermore, the proportion of grain crops, primarily rice and wheat, decreased by 184,700 hectares and 156,100 hectares, respectively, and their proportion of total crop area fell from 83.8% to 51.4% between 1980 and 2020. Meanwhile, cash crops, vegetables, and rapeseed increased by 136,100 hectares and 48,800 hectares, respectively, accounting for 48.6% of total crop area. Urbanization and crop structure adjustments are the primary factors contributing to the peak in agricultural non-CO2 greenhouse gas emissions.

[0111] Based on the determination of the peak of agricultural non-CO2 greenhouse gases in Step 2, Step 21 determined that Chengdu's agricultural non-CO2 GHG emissions peaked in 1985. However, the annual emission reductions since 1985 have not met the standards set by C40 and WRI (i.e., a 10.0% reduction within five years after 1985, or a 2.0% reduction per year). The results of the piecewise linear regression in Step 22 indicate that 1995 was an inflection point. A Tapio decoupling index analysis for 1995 indicates that agricultural non-CO2 GHG emissions were decoupled from GDP, meeting the carbon peak standard. Therefore, Chengdu's agricultural greenhouse gas emissions peaked in 1995.

[0112] Chengdu’s rapid development has led to administrative adjustments, with neighboring agricultural counties incorporated into Chengdu as urban areas. This has resulted in a surge in agricultural non-CO2 GHG emissions, disrupting the downward trend in carbon emissions.

[0113] Major emergencies (MEs) are disruptive factors in agricultural non-CO2 GHG emissions. The African swine fever epidemic in 2019 led to a significant reduction in pig numbers (57.0%) and a significant decrease in GHG emissions (8.2%). However, government intervention measures in 2020 led to a rapid rebound in pig inventories by the end of the year. Agricultural GDP grew by 7.0%, showing a weak decoupling trend. Policies regulating the production, safety, and disposal of manure required the closure or relocation of substandard breeding areas, leading to a decline in livestock production in 2009. Policy interventions slowed non-CO2 GHG emissions from the livestock sector, leading to a shift from weak decoupling to implicit decoupling.

[0114] The Agricultural Development Plan (ADP) also played a key role. Regional planning led to a decrease in year-end pig stocks and a 2.5% reduction in non-CO2GHG emissions. Rice planting area decreased (20.4%), and year-end sheep stocks decreased (20.5%), resulting in a 12.4% reduction in non-CO2GHG emissions. Planning measures related to the utilization of livestock and poultry waste resources led to a 37.3% reduction in non-CO2GHG emissions from agricultural activities by 2020, shifting the decoupling index to a stable decoupling state. In summary, three key factors (administrative divisions, emergencies, and regional planning) significantly interfered with the decoupling of non-CO2GHG peaks in Chengdu's agriculture.

[0115] According to step three, the Carbon Peak Challenge Index (CPCI) is calculated, and the MK test of the Chengdu agricultural non-CO2GHG time series from 1995 to 2020 is calculated. It does not meet the MK test, so A=0; the C40 standard of the Chengdu agricultural non-CO2GHG time series from 1995 to 2020 is calculated. It does not meet the C40 standard, so B=0; the decoupling index of the Chengdu agricultural non-CO2GHG time series from 1995 to 2020 is calculated. It does not meet the decoupling index, so C=0; the piecewise linear regression of the Chengdu agricultural non-CO2GHG time series from 1995 to 2020 is calculated. It meets the piecewise linear regression, so D=1; it is observed that the Chengdu agricultural non-CO2GHG time series from 1995 to 2020 did not break through the peak line, so E=1, and according to n=4-(A+B+C+D), n=3 is calculated.

[0116] Therefore, according to the results of the carbon peak robustness assessment, Chengdu's agricultural non-CO2GHG emissions were third-order unstable after reaching their peak in 1995, but did not exceed the peak.

[0117] Specifically, an evaluation and analysis system for the robustness of regional agricultural carbon peaking includes:

[0118] Data acquisition module: used to obtain various types of agricultural non-CO2 gas emission data;

[0119] Judgment module: used to determine whether agricultural non-CO2 gases have reached their peak;

[0120] Model construction module: used to construct a gas robustness model for agricultural non-CO2 gas emissions after the carbon peak is reached when the agricultural non-CO2 gas emissions data reaches the peak. If the peak is not reached, the emission data is recalculated and the peak judgment is repeated until the peak conditions are met;

[0121] Evaluation module: used to quantitatively evaluate the robustness of agricultural non-CO2 gas emission data peaking based on the gas robustness model.

[0122] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0123] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating and analyzing the robustness of regional agricultural carbon peak, characterized by: include: Step 1: Obtain various types of agricultural non-CO2 gas emission data; Step 2: Determine whether agricultural non-CO2 gas emissions have peaked; Step 3: When the agricultural non-CO2 gas emission data reaches a peak, a gas robustness model for the agricultural non-CO2 gas emission data after the carbon peak is constructed. If the peak is not reached, the emission data is recalculated and the peak judgment is repeated until the peak conditions are met; Step 4: Quantitatively assess the robustness of the peak of agricultural non-CO2 gas emissions data based on the gas robustness model.

2. The evaluation and analysis method for the robustness of regional agricultural carbon peak according to claim 1 is characterized in that: The agricultural non-CO2 gas emission data include: CH4 gas emissions from rice fields and N2O gas emissions from farmland; CH4 gas emissions from animal intestinal fermentation in animal husbandry, and CH4 and N2O gas emissions from manure management.

3. The evaluation and analysis method for the robustness of regional agricultural carbon peak according to claim 1 is characterized in that: The calculation formula for agricultural non-CO2 gas emissions data is as follows: I=I i =AD i ×EF i ; I is the total amount of non-CO2 greenhouse gas emissions from agriculture; I i AD is the non-CO2 greenhouse gas emissions from the i-th greenhouse gas source; i EF is the non-CO2 activity data of the i-th category greenhouse gas emission source; i is the emission factor of the i-th category non-CO2 greenhouse gas emission source.

4. The evaluation and analysis method for the robustness of regional agricultural carbon peak according to claim 2 is characterized in that: The calculation formula for CH4 gas emissions from the rice field is as follows: in: is the total methane emission from rice fields; is the methane emission factor of rice fields by type; is the rice planting area corresponding to the emission factor; the subscript i indicates the type of rice field; The calculation formula for N2O gas emissions from the farmland is as follows: in: is the total N2O emission from agricultural land; N 农田输入 is the nitrogen input in the agricultural production process; EF is the corresponding N2O emission factor.

5. The evaluation and analysis method for the robustness of regional agricultural carbon peak according to claim 2 is characterized in that: The calculation formula for CH4 gas emissions from animal intestinal fermentation is as follows: in: is the methane emission of the i-th animal; is the methane emission factor of the i-th animal; AP i is the number of the i-th animal; The calculation formula for the total CH4 gas emissions from animal intestinal fermentation is as follows: in: is the total CH4 gas emissions from animal intestinal fermentation; The formula for calculating CH4 gas emissions from animal manure management is as follows: in: CH4 emissions from manure management for the i-th animal; CH4 emission factor for manure management of animal species i; The formula for calculating total methane emissions from manure management is as follows: in: Managing total methane emissions for animal manure.

6. The evaluation and analysis method for the robustness of regional agricultural carbon peak according to claim 1 is characterized in that: The step 2 includes: Step 21: Construct the time series T corresponding to regional agricultural non-CO2 gas emissions I; Step 22: Based on the C40 and WRI standards, determine whether the emissions I have reached a historical peak in the time series T. If the emissions I have reached a historical peak and meet the first constraint, proceed to step 25; otherwise, proceed to step 23. Step 23: Trend turning point identification: Use piecewise linear regression to determine the turning points of the upward and downward trends of emissions I. When the turning point meets the second constraint, return to step 22 for judgment. When the turning point does not meet the second constraint, proceed to step 24. Step 24: Mann-Kendall test to identify the time when the time series T mutation begins and the mutation period. If the test result shows that there is a mutation point and there is a descending interval after the mutation point, then proceed to step 25. If the test result shows that there is no mutation point, then agricultural non-CO2 gases have not reached their peak. Step 25: Use the Tapio decoupling index test method to determine whether agricultural non-CO2 gases have peaked. When the Tapio decoupling index meets the third constraint, agricultural non-CO2 gases have peaked; otherwise, agricultural non-CO2 gases have not peaked.

7. The evaluation and analysis method for the robustness of regional agricultural carbon peak according to claim 1 is characterized in that: The post-carbon peak gas robustness model quantitatively evaluates the post-carbon peak peak robustness by setting the carbon peak challenge index CPCI.

8. An evaluation and analysis system for the robustness of regional agricultural carbon peak, characterized by: include: Data acquisition module: used to obtain various types of agricultural non-CO2 gas emission data; Judgment module: used to determine whether agricultural non-CO2 gases have reached their peak; Model construction module: used to construct a gas robustness model for agricultural non-CO2 gas emissions after the carbon peak is reached when the agricultural non-CO2 gas emissions data reaches the peak. If the peak is not reached, the emission data is recalculated and the peak judgment is repeated until the peak conditions are met; Evaluation module: used to quantitatively evaluate the robustness of agricultural non-CO2 gas emission data peaking based on the gas robustness model.

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