A method and system for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources

Abstract

The present invention discloses a method and system for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources. The method comprises: obtaining agricultural activity data of a target area in historical years, and obtaining the annual average non-CO2 greenhouse gas emissions of the target area according to the agricultural activity data; obtaining an emission reduction strategy database, and selecting a prediction strategy from the emission reduction strategy database based on the agricultural characteristics of the target area; constructing a multi-flow fusion material flow analysis model, and respectively calculating the non-CO2 greenhouse gas emission reduction of the target area after executing each prediction strategy; calculating the cost curve corresponding to the prediction strategy; and obtaining the assessment result of the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources in the target area according to the non-CO2 greenhouse gas emission reduction and the cost curve corresponding to the prediction strategy. The present invention can comprehensively predict and assess the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources in a region.

Description

Technical Field

[0001] The present invention relates to the technical field of greenhouse gas emission reduction potential assessment, and in particular to a method and system for assessing the emission reduction potential of non-carbon dioxide greenhouse gases from agricultural sources. Background Art

[0002] The greenhouse effect caused by non-CO2 greenhouse gases cannot be ignored, and agriculture is the main source of non-CO2 greenhouse gas emissions. Agriculture accounts for as much as 48% of non-CO2 greenhouse gas emissions. Therefore, the accounting, emission reduction and potential research of non-CO2 greenhouse gases from agricultural sources has attracted widespread attention.

[0003] Agriculture is both a major source of non-CO2 greenhouse gas emissions and has huge carbon sink potential. It is of great scientific value to simulate the impact of future non-CO2 greenhouse gas emission reductions from agricultural sources under multiple scenarios and evaluate its emission reduction potential.

[0004] However, the current scientific methods for estimating emission reduction potential are relatively simple and cannot comprehensively predict and evaluate the potential for non-CO2 greenhouse gas emission reductions in a region. Summary of the invention

[0005] The embodiment of the present invention provides a method and system for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources, which can comprehensively predict and assess the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources in a region.

[0006] An embodiment of the present invention provides a method for assessing the potential for reducing non-carbon dioxide greenhouse gas emissions from agricultural sources, comprising:

[0007] Obtain agricultural activity data of the target area in historical years, and obtain the annual average non-carbon dioxide greenhouse gas emissions of the target area based on the agricultural activity data;

[0008] Acquire an emission reduction strategy database, and select a prediction strategy from the emission reduction strategy database based on the agricultural characteristics of the target area;

[0009] Constructing a multi-flow fusion material flow analysis model to respectively calculate the non-carbon dioxide greenhouse gas emission reduction in the target area after executing each of the prediction strategies;

[0010] Calculating a cost curve corresponding to the prediction strategy;

[0011] The agricultural source non-CO2 greenhouse gas emission reduction potential assessment result of the target area is obtained based on the non-CO2 greenhouse gas emission reduction amount corresponding to the prediction strategy and the cost curve.

[0012] As an improvement of the above scheme, the multi-flow fusion material flow analysis model is constructed to calculate the non-carbon dioxide greenhouse gas emission reduction of the target area after executing each of the prediction strategies, including:

[0013] A multi-flow fusion material flow analysis model based on planting volume, breeding volume, storage volume, processing volume, transportation volume and circulation volume is constructed. The multi-flow fusion material flow analysis model includes:

[0014] Abs=i (1,1) PL1AL (1,1) EF (1,1) +i (1,2) PL2AL (1,2) EF (1,2) +...+i (1,a) PL a AL (1,a) EF (1,a) +...+

[0015] i (2,1) F1AL (2,1) EF (2,1) +...+i (2,b) F b AL (2,b) EF (2,b) +...+i (3,c) S c AL (3,c) EF (3,c) +...+

[0016] i (4,d) PR d AL (4,d) EF (4,d) +...+i (5,e) TR e AL (5,e) EF (5,e) +...+i (6,f) CI f AL (6,f) EF (6,f)

[0017] Emi=j1PL1CA1+...+j a PL a CA a

[0018] Re=R'-Abs-Emi

[0019] Where R' is the annual average non-CO2 greenhouse gas emissions of the target area, Abs is the non-CO2 greenhouse gas emissions, Emi is the non-CO2 greenhouse gas absorption, i is the emission adjustment coefficient of the prediction strategy, a, b, c, d, e, f are the number of planting types, breeding types, storage types, processing types, transportation types and recycling types, respectively, PL a is the planting area of ​​the ath crop, F b is the number of animals of type b raised, S c is the scale of the cth type of storage, PR d is the scale of the dth processing plant, TR e is the capacity of the e-th means of transport, CI f is the circulation amount of the fth cycle, AL is the activity level, EF is the emission factor; j is the absorption adjustment coefficient of the prediction strategy, CA a is the average carbon absorption rate of vegetation of the ath crop, and Re is the non-carbon dioxide greenhouse gas emission reduction in the target area after executing each of the prediction strategies.

[0020] As an improvement of the above solution, the step of calculating the cost curve corresponding to the prediction strategy includes:

[0021] According to the prediction strategy, the corresponding input cost, planting economic change and breeding economic change are calculated;

[0022] Calculate the total cost of the prediction strategy based on the input cost, the planting economic change and the breeding economic change;

[0023] The time for executing the prediction strategy is used as the horizontal axis data, and the total cost of the prediction strategy is used as the vertical axis data to generate a cost curve corresponding to the prediction strategy.

[0024] As an improvement of the above scheme, the agricultural source non-CO2 greenhouse gas emission reduction potential assessment result of the target area is obtained according to the non-CO2 greenhouse gas emission reduction amount corresponding to the prediction strategy and the cost curve, including:

[0025] The assessment results of the non-CO2 greenhouse gas emission reduction potential from agricultural sources in the target area are calculated by the following formula:

[0026]

[0027] Wherein, Pot is the potential assessment parameter for reducing non-CO2 greenhouse gas emissions from agricultural sources in the target area, y is the year predicted by the multi-stream fusion material flow analysis model, Y is the total number of years predicted by the multi-stream fusion material flow analysis model, Re(y) is the amount of non-CO2 greenhouse gas emissions reduced in the target area in the yth year after the implementation of the prediction strategy, f(y) is the cost curve corresponding to the prediction strategy, and k1 and k2 are preset weight coefficients respectively.

[0028] As an improvement of the above solution, the step of obtaining agricultural activity data of the target area in historical years and obtaining the annual average non-carbon dioxide greenhouse gas emissions of the target area according to the agricultural activity data includes:

[0029] Collecting agricultural activity data of the target area in historical years, wherein the agricultural activity data includes crop planting area, fertilizer application amount, irrigation amount, and livestock and poultry breeding scale;

[0030] Identifying non-CO2 greenhouse gas emission sources in the target area, wherein the non-CO2 greenhouse gas emission sources include methane and nitrous oxide;

[0031] The non-CO2 greenhouse gas emissions in historical years are calculated by the emission factor method, and the non-CO2 greenhouse gas emissions in the historical years are averaged to obtain the annual average non-CO2 greenhouse gas emissions in the target area.

[0032] As an improvement of the above solution, a prediction strategy is selected from the emission reduction strategy database based on the agricultural characteristics of the target area, including:

[0033] According to the crop planting area and livestock and poultry breeding scale, identifying the planting area of ​​different types of crops and the breeding scale of different types of animals in the target area;

[0034] Based on the planting areas of different types of crops and the breeding scales of different types of animals in the target area, the crops with a planting area greater than a preset area threshold are classified as characteristic crops, and the animals with a breeding scale greater than a preset threshold are classified as characteristic breeding animals;

[0035] In the emission reduction strategy database, strategies related to the characteristic crops and the characteristic farmed animals are selected as the prediction strategies.

[0036] As an improvement of the above solution, the step of obtaining the emission reduction strategy database and selecting a prediction strategy from the emission reduction strategy database based on the agricultural characteristics of the target area includes:

[0037] Obtain the area of ​​agricultural vacant land in the target area;

[0038] In the emission reduction strategy database, a waste treatment plant strategy that is consistent with the agricultural vacant land area is selected as the prediction strategy.

[0039] Another embodiment of the present invention provides a system for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources, including:

[0040] A data acquisition module, used to acquire agricultural activity data of a target area in historical years, and obtain the annual average non-carbon dioxide greenhouse gas emissions of the target area based on the agricultural activity data;

[0041] A strategy selection module, used to obtain an emission reduction strategy database, and select a prediction strategy from the emission reduction strategy database based on the agricultural characteristics of the target area;

[0042] An emission reduction simulation module is used to construct a multi-flow fusion material flow analysis model to calculate the non-carbon dioxide greenhouse gas emission reduction in the target area after executing each of the prediction strategies;

[0043] A cost calculation module, used to calculate the cost curve corresponding to the prediction strategy;

[0044] The emission reduction assessment module is used to obtain the agricultural source non-CO2 greenhouse gas emission reduction potential assessment result of the target area according to the non-CO2 greenhouse gas emission reduction amount corresponding to the prediction strategy and the cost curve.

[0045] Another embodiment of the present invention provides a system for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the method for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources as described in the above-mentioned embodiment of the invention when executing the computer program.

[0046] Another embodiment of the present invention provides a system for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources, comprising a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the method for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources as described in the above-mentioned embodiment of the invention.

[0047] Compared with the prior art, in the embodiments of the present invention, by selecting prediction strategies in the emission reduction strategy database in a targeted manner in combination with regional characteristics, a variety of emission reduction approaches can be evaluated in combination with each other to improve the comprehensive emission reduction potential evaluation results; a multi-flow fusion material flow analysis model is adopted to comprehensively predict and evaluate the non-CO2 greenhouse gas emission reduction potential in the region; the emission reduction cost is considered on the basis of the emission reduction strategy to further improve the comprehensiveness and economy of the emission reduction potential evaluation; in summary, evaluating the emission reduction potential of non-CO2 greenhouse gases from agricultural sources can not only effectively respond to the challenges of climate change, but also promote the sustainable development of agriculture and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of a method for assessing the potential for reducing greenhouse gas emissions from agricultural sources other than carbon dioxide provided by one embodiment of the present invention;

[0049] Figure 2 It is a structural schematic diagram of a system for assessing the potential for reducing greenhouse gas emissions from agricultural sources other than carbon dioxide provided by one embodiment of the present invention;

[0050] Figure 3 It is a structural schematic diagram of a system for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0052] See also Figure 1 , is a flow chart of a method for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources provided by an embodiment of the present invention, comprising steps S101 to S105:

[0053] S101. Obtain agricultural activity data of the target area in historical years, and obtain the annual average non-CO2 greenhouse gas emissions of the target area based on the agricultural activity data;

[0054] Specifically, the agricultural activity data of the target area in different historical years may include crop planting area, fertilizer and pesticide usage, livestock and poultry breeding scale, and agricultural waste treatment methods, etc. The above data can be obtained through official statistics or relevant literature.

[0055] S102, obtaining an emission reduction strategy database, and selecting a prediction strategy from the emission reduction strategy database based on agricultural characteristics of the target area;

[0056] Specifically, the prediction strategy can be composed of a single emission reduction adjustment method or a combination of multiple emission reduction adjustment methods, such as reducing the area of ​​crop A + increasing the breeding scale of animal B + building a waste treatment plant. The composition of the specific prediction strategy can be selected according to the actual needs of the target area, and this embodiment will not be elaborated here.

[0057] Specifically, the emission reduction strategy database may be obtained by statistics of existing emission reduction strategies, or may be obtained by screening by researchers themselves, which will not be elaborated in this embodiment.

[0058] S103, constructing a multi-flow fusion material flow analysis model, and calculating the non-CO2 greenhouse gas emission reduction in the target area after executing each prediction strategy;

[0059] Specifically, Material Flow Analysis (MFA) is a method for studying the metabolic process of material resources in economic activities. While promoting the development of a circular economic structure, it also plays a positive role in resource and environmental protection. The multi-flow fusion material flow analysis model in this embodiment includes material flow, trade flow, environmental flow, etc.

[0060] S104, calculating the cost curve corresponding to the prediction strategy;

[0061] Specifically, the costs required to implement different emission reduction strategies are not the same. For example, some strategies require initial equipment costs, labor costs, etc.; and the economic benefits brought by different emission reduction strategies are also different. For example, some strategies may cause a reduction in the yield of high-value crops. Therefore, when evaluating the emission reduction potential, the cost factor needs to be considered.

[0062] S105. Obtain the evaluation results of the agricultural source non-CO2 greenhouse gas emission reduction potential in the target area based on the non-CO2 greenhouse gas emission reduction amount and cost curve corresponding to the prediction strategy.

[0063] Specifically, after obtaining the non-CO2 greenhouse gas emission reduction and cost curve corresponding to the prediction strategy, the agricultural source non-CO2 greenhouse gas emission reduction potential assessment results of the target area can be obtained. When there are multiple prediction strategies, the final emission reduction potential assessment results can be calculated by weighted averaging.

[0064] In this embodiment, preferably, a multi-flow fusion material flow analysis model is constructed to calculate the non-carbon dioxide greenhouse gas emission reduction in the target area after executing each prediction strategy, including:

[0065] Construct a multi-flow fusion material flow analysis model based on planting volume, breeding volume, storage volume, processing volume, transportation volume and circulation volume. The multi-flow fusion material flow analysis model includes:

[0066] Abs=i (1,1) PL1AL (1,1) EF (1,1) +i (1,2) PL2AL (1,2) EF (1,2) +...+i (1,a) PL a AL (1,a) EF (1,a) +...+

[0067] i (2,1) F1AL (2,1) EF (2,1) +...+i (2,b) F b AL (2,b) EF (2,b) +...+i (3,c) S c AL (3,c) EF (3,c) +...+

[0068] i (4,d) PR d AL (4,d) EF (4,d) +...+i (5,e) TR e AL (5,e) EF (5,e) +...+i (6,f) CI f AL (6,f) EF (6,f)

[0069] Emi=j1PL1CA1+...+j a PL a CA a

[0070] Re=R'-Abs-Emi

[0071] Where R' is the annual average non-CO2 greenhouse gas emissions in the target area, Abs is the non-CO2 greenhouse gas emissions, Emi is the non-CO2 greenhouse gas absorption, i is the emission adjustment coefficient of the prediction strategy, a, b, c, d, e, f are the number of planting types, breeding types, storage types, processing types, transportation types and recycling types, respectively, PL a is the planting area of ​​the ath crop, F b is the number of animals of type b raised, S c is the scale of the cth type of storage, PR d is the scale of the dth processing plant, TRe is the capacity of the e-th means of transport, CI f is the circulation amount of the fth cycle, AL is the activity level, EF is the emission factor; j is the absorption adjustment coefficient of the prediction strategy, CA a is the average carbon absorption rate of vegetation of the ath crop, and Re is the non-CO2 greenhouse gas emission reduction in the target area after implementing each prediction strategy.

[0072] Specifically, through the planting volume, breeding volume, storage volume, processing volume, transportation volume and circulation volume, the multi-flow integration of material flow, trade flow and environmental flow can be considered in the material flow analysis model, so as to improve the comprehensiveness of the assessment and characterize the balance of material and product supply and demand under the joint action of various factors.

[0073] Specifically, after the multi-stream fusion material flow analysis model calculates and executes the prediction strategy, the non-CO2 greenhouse gas emission reduction in the first year can be calculated according to the above formula Re=R'-Abs-Emi. When it is executed in the second year, the non-CO2 greenhouse gas emission reduction Re2 in the second year can be calculated by Re2=Re-Abs2-Emi2. Subsequently, the non-CO2 greenhouse gas emission reduction Rex in the xth year can be calculated in sequence by Re(x)=Re(x-1)-Abs(x)-Emi(x).

[0074] Specifically, when the prediction strategy is to reduce the area of ​​a certain type of crop by 30%, the emission adjustment coefficient i corresponding to this type of crop is set to 0.7; when the prediction strategy is to increase the breeding scale of a certain type of poultry by 20%, the emission adjustment coefficient i corresponding to the poultry is set to 1.2; when the prediction strategy is to eliminate a certain factor, the emission adjustment coefficient i corresponding to the factor is set to 0; in the specific implementation method, it can be adjusted according to the prediction strategy, and this embodiment will not be repeated.

[0075] In a specific implementation, the multi-stream fusion material flow analysis model has good scalability. For example, consumption data related to the value stream can be added. At this time, the non-CO2 greenhouse gas emissions Abs are as follows:

[0076] Abs=i (1,1) PL1AL (1,1) EF (1,1) +i (1,2) PL2AL (1,2) EF (1,2) +...+i (1,a) PL a AL (1,a) EF (1,a) +...+

[0077] i (2,1) F1AL (2,1) EF (2,1)+...+i (2,b) F b AL (2,b) EF (2,b) +...+i (3,c) S c AL (3,c) EF (3,c) +...+

[0078] i (4,d) PR d AL (4,d) EF (4,d) +...+i (5,e) TR e AL (5,e) EF (5,e) +...+i (6,f) CI f AL (6,f) EF (6,f) +...+

[0079] i (7,g) CO g AL (7,g) EF (7,g)

[0080] In the formula, g is the number of consumption behavior types, CO g The consumption amount of the g-th consumption behavior.

[0081] In this embodiment, preferably, calculating the cost curve corresponding to the prediction strategy includes:

[0082] According to the forecasting strategy, the corresponding input costs, planting economic changes and breeding economic changes are calculated;

[0083] Calculate the total cost of the forecast strategy based on input costs, planting economic changes, and breeding economic changes;

[0084] The time to execute the prediction strategy is used as the horizontal axis data, and the total cost of the prediction strategy is used as the vertical axis data to generate a cost curve corresponding to the prediction strategy.

[0085] Specifically, input costs include direct investment, operating expenses, maintenance costs, etc., and also include equipment purchase, installation, human resources and management costs.

[0086] For example, when the forecasting strategy is only to build a new waste treatment plant, the cost curve should theoretically rise in the early stage and fall in the later stage; when the forecasting strategy involves a reduction in the planting area of ​​a certain high-value crop and an increase in the planting area of ​​a low-value crop, the cost curve may continue to decline.

[0087] In this embodiment, preferably, the agricultural source non-CO2 greenhouse gas emission reduction potential assessment result of the target area is obtained according to the non-CO2 greenhouse gas emission reduction amount and cost curve corresponding to the prediction strategy, including:

[0088] The assessment results of the potential reduction of non-CO2 greenhouse gas emissions from agricultural sources in the target area are calculated by the following formula:

[0089]

[0090] Where Pot is the potential assessment parameter for reducing non-CO2 greenhouse gas emissions from agricultural sources in the target area, y is the year predicted by the multi-stream fusion material flow analysis model, Y is the total number of years predicted by the multi-stream fusion material flow analysis model, Re(y) is the non-CO2 greenhouse gas emission reduction in the target area in the yth year after the implementation of the prediction strategy, f(y) is the cost curve corresponding to the prediction strategy, and k1 and k2 are the preset weight coefficients respectively.

[0091] Specifically,

[0092] In this embodiment, preferably, agricultural activity data of the target area in historical years are obtained, and the annual average non-carbon dioxide greenhouse gas emissions of the target area are obtained according to the agricultural activity data, including:

[0093] Collect agricultural activity data in the target area in historical years, including crop planting area, fertilizer application amount, irrigation amount, and livestock and poultry breeding scale;

[0094] Identify non-CO2 greenhouse gas emission sources in the target area, where non-CO2 greenhouse gas emission sources include methane and nitrous oxide;

[0095] The non-CO2 greenhouse gas emissions in historical years are calculated using the emission factor method, and the average of the non-CO2 greenhouse gas emissions in historical years is calculated to obtain the average annual non-CO2 greenhouse gas emissions in the target area.

[0096] Specifically, the non-CO2 greenhouse gas emissions in historical years are calculated by the emission factor method, that is, the non-CO2 greenhouse gas emissions are obtained by multiplying agricultural activity data, activity level, emission factor and global warming potential.

[0097] Specifically, the emission factor is the unit emission generated by the unit activity level, which can be obtained from government departments, international organizations (such as IPCC), industry associations or scientific literature; the global warming potential (GWP) of methane (CH4) and nitrous oxide (N2O) can be based on the IPCC values.

[0098] In this embodiment, preferably, obtaining an emission reduction strategy database, and selecting a prediction strategy from the emission reduction strategy database based on the agricultural characteristics of the target area include:

[0099] According to the crop planting area and livestock and poultry breeding scale, identify the planting area of ​​different types of crops and the breeding scale of different types of animals in the target area;

[0100] Based on the planting areas of different types of crops and the breeding scales of different types of animals in the target area, the crops with a planting area greater than a preset area threshold are classified as characteristic crops, and the animals with a breeding scale greater than a preset threshold are classified as characteristic breeding animals;

[0101] In the emission reduction strategy database, strategies related to characteristic crops and characteristic livestock are selected as prediction strategies.

[0102] Specifically, selecting prediction strategies based on characteristic crops and livestock in the target area can improve the feasibility and pertinence of the strategies and avoid conflicts between emission reduction strategies and objective factors such as the climate and topography of the target area.

[0103] In this embodiment, preferably, obtaining an emission reduction strategy database, and selecting a prediction strategy from the emission reduction strategy database based on the agricultural characteristics of the target area include:

[0104] Obtain the area of ​​agricultural vacant land in the target area;

[0105] In the emission reduction strategy database, the waste treatment plant strategy that matches the agricultural vacant land area is selected as the prediction strategy.

[0106] Specifically, selecting waste treatment plant strategies based on the vacant agricultural land area in the target area can also improve the feasibility and pertinence of the strategy, and avoid the problem of emission reduction strategies failing to be implemented or affecting the surrounding environment.

[0107] In summary, in the embodiments of the present invention, by selecting prediction strategies in the emission reduction strategy database in a targeted manner in combination with regional characteristics, a variety of emission reduction approaches can be evaluated in combination with each other to improve the comprehensive emission reduction potential evaluation results; the multi-flow fusion material flow analysis model is adopted to comprehensively predict and evaluate the non-CO2 greenhouse gas emission reduction potential in the region; the emission reduction cost is considered on the basis of the emission reduction strategy to further improve the comprehensiveness and economy of the emission reduction potential evaluation; in summary, evaluating the emission reduction potential of non-CO2 greenhouse gases from agricultural sources can not only effectively respond to the challenges of climate change, but also promote the sustainable development of agriculture and economic benefits.

[0108] See also Figure 2 , is a schematic diagram of a system for assessing the potential for reducing greenhouse gas emissions from agricultural sources other than carbon dioxide provided by an embodiment of the present invention, comprising:

[0109] The data acquisition module 201 is used to acquire agricultural activity data of the target area in historical years, and obtain the annual average non-carbon dioxide greenhouse gas emissions of the target area based on the agricultural activity data;

[0110] A strategy selection module 202 is used to obtain an emission reduction strategy database and select a prediction strategy from the emission reduction strategy database based on agricultural characteristics of the target area;

[0111] The emission reduction simulation module 203 is used to construct a multi-flow fusion material flow analysis model to calculate the non-CO2 greenhouse gas emission reduction in the target area after executing each prediction strategy;

[0112] A cost calculation module 204, used to calculate a cost curve corresponding to the prediction strategy;

[0113] The emission reduction assessment module 205 is used to obtain the agricultural source non-CO2 greenhouse gas emission reduction potential assessment result of the target area according to the non-CO2 greenhouse gas emission reduction amount and cost curve corresponding to the prediction strategy.

[0114] Furthermore, a multi-flow fusion material flow analysis model is constructed to calculate the non-CO2 greenhouse gas emission reduction in the target area after implementing each prediction strategy, including:

[0115] Construct a multi-flow fusion material flow analysis model based on planting volume, breeding volume, storage volume, processing volume, transportation volume and circulation volume. The multi-flow fusion material flow analysis model includes:

[0116] Abs=i (1,1) PL1AL (1,1) EF (1,1) +i (1,2) PL2AL (1,2) EF (1,2) +...+i (1,a) PL a AL (1,a) EF (1,a) +...+

[0117] i (2,1) F1AL (2,1) EF (2,1) +...+i (2,b) F b AL (2,b) EF (2,b) +...+i (3,c) S c AL (3,c) EF (3,c) +...+

[0118] i (4,d) PR d AL (4,d)EF (4,d) +...+i (5,e) TR e AL (5,e) EF (5,e) +...+i (6,f) CI f AL (6,f) EF (6,f)

[0119] Emi=j1PL1CA1+...+j a PL a CA a

[0120] Re=R'-Abs-Emi

[0121] Where R' is the annual average non-CO2 greenhouse gas emissions in the target area, Abs is the non-CO2 greenhouse gas emissions, Emi is the non-CO2 greenhouse gas absorption, i is the emission adjustment coefficient of the prediction strategy, a, b, c, d, e, f are the number of planting types, breeding types, storage types, processing types, transportation types and recycling types, respectively, PL a is the planting area of ​​the ath crop, F b is the number of animals of type b raised, S c is the scale of the cth type of storage, PR d is the scale of the dth processing plant, TR e is the capacity of the e-th means of transport, CI f is the circulation amount of the fth cycle, AL is the activity level, EF is the emission factor; j is the absorption adjustment coefficient of the prediction strategy, CA a is the average carbon absorption rate of vegetation of the ath crop, and Re is the non-CO2 greenhouse gas emission reduction in the target area after implementing each prediction strategy.

[0122] Furthermore, the cost curve corresponding to the prediction strategy is calculated, including:

[0123] According to the forecasting strategy, the corresponding input costs, planting economic changes and breeding economic changes are calculated;

[0124] Calculate the total cost of the forecast strategy based on input costs, planting economic changes, and breeding economic changes;

[0125] The time to execute the prediction strategy is used as the horizontal axis data, and the total cost of the prediction strategy is used as the vertical axis data to generate a cost curve corresponding to the prediction strategy.

[0126] Furthermore, the non-CO2 greenhouse gas emission reduction and cost curve corresponding to the prediction strategy are used to obtain the potential assessment results of non-CO2 greenhouse gas emission reduction from agricultural sources in the target area, including:

[0127] The assessment results of the potential reduction of non-CO2 greenhouse gas emissions from agricultural sources in the target area are calculated by the following formula:

[0128]

[0129] Where Pot is the potential assessment parameter for reducing non-CO2 greenhouse gas emissions from agricultural sources in the target area, y is the year predicted by the multi-stream fusion material flow analysis model, Y is the total number of years predicted by the multi-stream fusion material flow analysis model, Re(y) is the non-CO2 greenhouse gas emission reduction in the target area in the yth year after the implementation of the prediction strategy, f(y) is the cost curve corresponding to the prediction strategy, and k1 and k2 are the preset weight coefficients respectively.

[0130] Furthermore, the agricultural activity data of the target area in historical years are obtained, and the annual average non-CO2 greenhouse gas emissions of the target area are obtained based on the agricultural activity data, including:

[0131] Collect agricultural activity data in the target area in historical years, including crop planting area, fertilizer application amount, irrigation amount, and livestock and poultry breeding scale;

[0132] Identify non-CO2 greenhouse gas emission sources in the target area, where non-CO2 greenhouse gas emission sources include methane and nitrous oxide;

[0133] The non-CO2 greenhouse gas emissions in historical years are calculated using the emission factor method, and the average of the non-CO2 greenhouse gas emissions in historical years is calculated to obtain the average annual non-CO2 greenhouse gas emissions in the target area.

[0134] Furthermore, an emission reduction strategy database is obtained, and a prediction strategy is selected from the emission reduction strategy database based on the agricultural characteristics of the target area, including:

[0135] According to the crop planting area and livestock and poultry breeding scale, identify the planting area of ​​different types of crops and the breeding scale of different types of animals in the target area;

[0136] Based on the planting areas of different types of crops and the breeding scales of different types of animals in the target area, the crops with a planting area greater than a preset area threshold are classified as characteristic crops, and the animals with a breeding scale greater than a preset threshold are classified as characteristic breeding animals;

[0137] In the emission reduction strategy database, strategies related to characteristic crops and characteristic livestock are selected as prediction strategies.

[0138] Furthermore, an emission reduction strategy database is obtained, and a prediction strategy is selected from the emission reduction strategy database based on the agricultural characteristics of the target area, including:

[0139] Obtain the area of ​​agricultural vacant land in the target area;

[0140] In the emission reduction strategy database, the waste treatment plant strategy that matches the agricultural vacant land area is selected as the prediction strategy.

[0141] In summary, in the embodiments of the present invention, by selecting prediction strategies in the emission reduction strategy database in a targeted manner in combination with regional characteristics, a variety of emission reduction approaches can be evaluated in combination with each other to improve the comprehensive emission reduction potential evaluation results; the multi-flow fusion material flow analysis model is adopted to comprehensively predict and evaluate the non-CO2 greenhouse gas emission reduction potential in the region; the emission reduction cost is considered on the basis of the emission reduction strategy to further improve the comprehensiveness and economy of the emission reduction potential evaluation; in summary, evaluating the emission reduction potential of non-CO2 greenhouse gases from agricultural sources can not only effectively respond to the challenges of climate change, but also promote the sustainable development of agriculture and economic benefits.

[0142] See also Figure 3 , is a schematic diagram of a system for assessing the potential for reducing emissions of non-CO2 greenhouse gases from agricultural sources provided by an embodiment of the present invention. The system for assessing the potential for reducing emissions of non-CO2 greenhouse gases from agricultural sources of this embodiment includes: a processor 1, a memory 2, and a computer program stored in the memory 2 and executable on the processor, such as a program for assessing the potential for reducing emissions of non-CO2 greenhouse gases from agricultural sources. When the processor 1 executes the computer program, the steps in each of the above-mentioned methods for assessing the potential for reducing emissions of non-CO2 greenhouse gases from agricultural sources are implemented. Alternatively, when the processor 1 executes the computer program, the functions of each module / unit in each of the above-mentioned device embodiments are implemented.

[0143] Exemplarily, the computer program can be divided into one or more modules / units, one or more modules / units are stored in a memory and executed by a processor to complete the present invention. One or more modules / units can be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the agricultural source non-CO2 greenhouse gas emission reduction potential assessment system.

[0144] The agricultural source non-CO2 greenhouse gas emission reduction potential assessment system may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of the agricultural source non-CO2 greenhouse gas emission reduction potential assessment system and does not constitute a limitation on the agricultural source non-CO2 greenhouse gas emission reduction potential assessment system. The agricultural source non-CO2 greenhouse gas emission reduction potential assessment system may include more or fewer components than shown in the diagram, or a combination of certain components, or different components. For example, the agricultural source non-CO2 greenhouse gas emission reduction potential assessment system may also include input and output devices, network access devices, CAN buses, etc.

[0145] An embodiment of the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the method for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources as described in Embodiment 1 of the present invention.

[0146] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the agricultural source non-CO2 greenhouse gas emission reduction potential assessment system, and uses various interfaces and lines to connect various parts of the entire agricultural source non-CO2 greenhouse gas emission reduction potential assessment system.

[0147] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the agricultural source non-CO2 greenhouse gas emission reduction potential assessment system by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0148] Among them, if the module / unit integrated in the agricultural source non-CO2 greenhouse gas emission reduction potential assessment system is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electrical carrier signals and telecommunications signals.

[0149] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0150] The above are preferred embodiments of the present invention. It should be noted that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources, characterized in that: include: Obtain agricultural activity data of the target area in historical years, and obtain the annual average non-carbon dioxide greenhouse gas emissions of the target area based on the agricultural activity data; Acquire an emission reduction strategy database, and select a prediction strategy from the emission reduction strategy database based on the agricultural characteristics of the target area; Constructing a multi-flow fusion material flow analysis model to respectively calculate the non-carbon dioxide greenhouse gas emission reduction in the target area after executing each of the prediction strategies; Calculating a cost curve corresponding to the prediction strategy; Obtaining an assessment result of the agricultural source non-CO2 greenhouse gas emission reduction potential in the target area according to the non-CO2 greenhouse gas emission reduction amount corresponding to the prediction strategy and the cost curve; The step of obtaining agricultural activity data of the target area in historical years and obtaining the annual average non-carbon dioxide greenhouse gas emissions of the target area based on the agricultural activity data includes: Collecting agricultural activity data of the target area in historical years, wherein the agricultural activity data includes crop planting area, fertilizer application amount, irrigation amount, and livestock and poultry breeding scale; Identifying non-CO2 greenhouse gas emission sources in the target area, wherein the non-CO2 greenhouse gas emission sources include methane and nitrous oxide; Calculate the non-CO2 greenhouse gas emissions in historical years by using the emission factor method, and average the non-CO2 greenhouse gas emissions in the historical years to obtain the annual average non-CO2 greenhouse gas emissions in the target area; The step of obtaining an emission reduction strategy database and selecting a prediction strategy from the emission reduction strategy database based on the agricultural characteristics of the target area includes: According to the crop planting area and livestock and poultry breeding scale, identifying the planting area of ​​different types of crops and the breeding scale of different types of animals in the target area; Based on the planting areas of different types of crops and the breeding scales of different types of animals in the target area, the crops with a planting area greater than a preset area threshold are classified as characteristic crops, and the animals with a breeding scale greater than a preset threshold are classified as characteristic breeding animals; In the emission reduction strategy database, a strategy related to the characteristic crops and the characteristic livestock is selected as the prediction strategy; The constructing of a multi-flow fusion material flow analysis model to calculate the non-carbon dioxide greenhouse gas emission reduction in the target area after executing each of the prediction strategies includes: A multi-flow fusion material flow analysis model based on planting volume, breeding volume, storage volume, processing volume, transportation volume and circulation volume is constructed. The multi-flow fusion material flow analysis model includes: Abs=i (1,1) PL1AL (1,1) EF (1,1) +i (1,2) PL2AL (1,2) EF (1,2) +...+i (1,a) PL a AL (1,a) EF (1,a) +...+ i (2,1) F1AL (2,1) EF (2,1) +...+i (2,b) F b AL (2,b) EF (2,b) +...+i (3,c) S c AL (3,c) EF (3,c) +...+ i (4,d) PR d AL (4,d) EF (4,d) +...+i (5,e) TR e AL (5,e) EF (5,e) +...+i (6,f) CI f AL (6,f) EF (6,f) Emi=j1PL1CA1+...+j a PL a CA a Re=R'-Abs-Emi Where R' is the annual average non-CO2 greenhouse gas emissions of the target area, Abs is the non-CO2 greenhouse gas emissions, Emi is the non-CO2 greenhouse gas absorption, i is the emission adjustment coefficient of the prediction strategy, a, b, c, d, e, f are the number of planting types, breeding types, storage types, processing types, transportation types and recycling types, respectively, PL a is the planting area of ​​the ath crop, F b is the number of animals of type b raised, S c is the scale of the cth type of storage, PR d is the scale of the dth processing plant, TR e is the capacity of the e-th means of transport, CI f is the circulation amount of the fth cycle, AL is the activity level, EF is the emission factor; j is the absorption adjustment coefficient of the prediction strategy, CA a is the average carbon absorption rate of vegetation of the ath crop, and Re is the non-carbon dioxide greenhouse gas emission reduction in the target area after executing each of the prediction strategies.

2. The method for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources according to claim 1, characterized in that: The calculating the cost curve corresponding to the prediction strategy includes: According to the prediction strategy, the corresponding input cost, planting economic change and breeding economic change are calculated; Calculate the total cost of the prediction strategy based on the input cost, the planting economic change and the breeding economic change; The time for executing the prediction strategy is used as the horizontal axis data, and the total cost of the prediction strategy is used as the vertical axis data to generate a cost curve corresponding to the prediction strategy.

3. The method for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources according to claim 1, characterized in that: The agricultural source non-CO2 greenhouse gas emission reduction potential assessment result of the target area is obtained according to the non-CO2 greenhouse gas emission reduction amount corresponding to the prediction strategy and the cost curve, including: The assessment results of the non-CO2 greenhouse gas emission reduction potential from agricultural sources in the target area are calculated by the following formula: Wherein, Pot is the potential assessment parameter for reducing non-CO2 greenhouse gas emissions from agricultural sources in the target area, y is the year predicted by the multi-stream fusion material flow analysis model, Y is the total number of years predicted by the multi-stream fusion material flow analysis model, Re(y) is the amount of non-CO2 greenhouse gas emissions reduced in the target area in the yth year after the implementation of the prediction strategy, f(y) is the cost curve corresponding to the prediction strategy, and k1 and k2 are preset weight coefficients respectively.

4. The method for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources according to claim 1, characterized in that: The step of obtaining an emission reduction strategy database and selecting a prediction strategy from the emission reduction strategy database based on the agricultural characteristics of the target area includes: Obtain the area of ​​agricultural vacant land in the target area; In the emission reduction strategy database, a waste treatment plant strategy that is consistent with the agricultural vacant land area is selected as the prediction strategy.

5. A system for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources, characterized in that: include: A data acquisition module, used to acquire agricultural activity data of a target area in historical years, and obtain the annual average non-carbon dioxide greenhouse gas emissions of the target area based on the agricultural activity data; A strategy selection module, used to obtain an emission reduction strategy database, and select a prediction strategy from the emission reduction strategy database based on the agricultural characteristics of the target area; An emission reduction simulation module is used to construct a multi-flow fusion material flow analysis model to calculate the non-carbon dioxide greenhouse gas emission reduction in the target area after executing each of the prediction strategies; A cost calculation module, used to calculate the cost curve corresponding to the prediction strategy; An emission reduction assessment module, used for obtaining an assessment result of the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources in the target area according to the non-CO2 greenhouse gas emission reduction amount corresponding to the prediction strategy and the cost curve; The step of obtaining agricultural activity data of the target area in historical years and obtaining the annual average non-carbon dioxide greenhouse gas emissions of the target area based on the agricultural activity data includes: Collecting agricultural activity data of the target area in historical years, wherein the agricultural activity data includes crop planting area, fertilizer application amount, irrigation amount, and livestock and poultry breeding scale; Identifying non-CO2 greenhouse gas emission sources in the target area, wherein the non-CO2 greenhouse gas emission sources include methane and nitrous oxide; Calculate the non-CO2 greenhouse gas emissions in historical years by using the emission factor method, and average the non-CO2 greenhouse gas emissions in the historical years to obtain the annual average non-CO2 greenhouse gas emissions in the target area; The step of obtaining an emission reduction strategy database and selecting a prediction strategy from the emission reduction strategy database based on the agricultural characteristics of the target area includes: According to the crop planting area and livestock and poultry breeding scale, identifying the planting area of ​​different types of crops and the breeding scale of different types of animals in the target area; Based on the planting areas of different types of crops and the breeding scales of different types of animals in the target area, the crops with a planting area greater than a preset area threshold are classified as characteristic crops, and the animals with a breeding scale greater than a preset threshold are classified as characteristic breeding animals; In the emission reduction strategy database, a strategy related to the characteristic crops and the characteristic livestock is selected as the prediction strategy; The constructing of a multi-flow fusion material flow analysis model to calculate the non-carbon dioxide greenhouse gas emission reduction in the target area after executing each of the prediction strategies includes: A multi-flow fusion material flow analysis model based on planting volume, breeding volume, storage volume, processing volume, transportation volume and circulation volume is constructed. The multi-flow fusion material flow analysis model includes: Abs=i (1,1) PL1AL (1,1) EF (1,1) +i (1,2) PL2AL (1,2) EF (1,2) +...+i (1,a) PL a AL (1,a) EF (1,a) +...+ i (2,1) F1AL (2,1) EF (2,1) +...+i (2,b) F b AL (2,b) EF (2,b) +...+i (3,c) S c AL (3,c) EF (3,c) +...+ i (4,d) PR d AL (4,d) EF (4,d) +...+i (5,e) TR e AL (5,e) EF (5,e) +...+i (6,f) CI f AL (6,f) EF (6,f) Emi=j1PL1CA1+...+j a PL a CA a Re=R'-Abs-Emi Where R' is the annual average non-CO2 greenhouse gas emissions of the target area, Abs is the non-CO2 greenhouse gas emissions, Emi is the non-CO2 greenhouse gas absorption, i is the emission adjustment coefficient of the prediction strategy, a, b, c, d, e, f are the number of planting types, breeding types, storage types, processing types, transportation types and recycling types, respectively, PL a is the planting area of ​​the ath crop, F b is the number of animals of type b raised, S c is the scale of the cth type of storage, PR d is the scale of the dth processing plant, TR e is the capacity of the e-th means of transport, CI f is the circulation amount of the fth cycle, AL is the activity level, EF is the emission factor; j is the absorption adjustment coefficient of the prediction strategy, CA a is the average carbon absorption rate of vegetation of the ath crop, and Re is the non-carbon dioxide greenhouse gas emission reduction in the target area after executing each of the prediction strategies.

6. A system for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a method for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources as described in any one of claims 1 to 4.

7. A system for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources, characterized in that: It includes a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the method for assessing the potential for reducing non-CO2 greenhouse gas emissions from agricultural sources as described in any one of claims 1 to 4.

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