Analysis method for promoting collaborative development of multiple sustainable development targets

Abstract

The invention discloses an analysis method for promoting collaborative development of multiple sustainable development targets. Comprising the steps of evaluating four sustainable development objectives including water (SDG6), energy (SDG7), grain (SDG2) and ecology (SDG15), building a double-layer fuzzy multi-objective optimization model with water resources as independent variables, solving the multi-objective optimization model and providing a water resource allocation strategy for achieving collaborative development of multiple sustainable development objectives. The method is simple, can assist in quickly constructing various sustainable development target optimization models, and provides necessary technical support for formulating a resource optimization allocation scheme.

Description

Technical Field

[0001] The present invention relates to technical fields such as systems planning, ecology, and sustainable development goals, and particularly to an analysis method for promoting the coordinated development of multiple sustainable development goals. Background Art

[0002] Sustainable Development Goals (SDGs) cover many aspects such as economy, society, and environment, and have become an important framework for evaluating the comprehensive development of countries and regions since their proposal. There are many trade-off effects among various sustainable development goals. The growth of one goal may lead to a decline in the benefits of another goal or the entire sustainable development goal system. In order to quantify the non-linear interaction effects among multiple sustainable development goals and avoid the systematic imbalance of "attending to one thing and losing sight of another" in policy implementation, it is necessary to separately model multiple sustainable development goals with a unified independent variable, and adopt a multi-objective optimization method for optimal resource allocation. At the same time, considering that the independent variable may be an unfixed parameter, it is introduced into the multi-objective optimization model in the form of fuzzy numbers, and a two-layer fuzzy multi-objective optimization model is constructed to provide a scientific basis for the coordinated development of multiple sustainable development goals and necessary technical support for formulating ecological resource optimization allocation measures under different scenarios.

[0003] Existing methods for the coordinated development of multiple sustainable development goals mainly adopt the analytic hierarchy process, weighted summation method, or constraint conversion method. Although they can handle the simple trade-offs among limited goals, there are three limitations: First, relying on subjective weight setting, it is difficult to objectively represent the dynamic game relationship among SDGs; second, linear superposition or dimensionality reduction is used for multiple goals, weakening the spatial heterogeneity and threshold mutation effect among multi-dimensional goals; third, a closed-loop feedback mechanism is not constructed, resulting in insufficient adaptability of the optimization scheme under policy intervention. This makes it difficult for existing models to support the precise coordination of multi-dimensional sustainable development goals. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide an analysis method for promoting the coordinated development of multiple sustainable development goals. By separately modeling multiple sustainable development goals, a two-layer fuzzy multi-objective optimization model is constructed, and different development scenarios are set to achieve optimal resource allocation; this method provides optimization analysis and decision-making support for the mutual trade-off effects among multiple sustainable development goals.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The analysis method for promoting the coordinated development of multiple sustainable development goals provided by the present invention includes the following steps:

[0007] S1. Construct a specific sustainable development goal function model, where the sustainable development goals include SDG2 - Zero Hunger, SDG6 - Clean Water and Sanitation, SDG7 - Affordable and Clean Energy, and SDG15 - Life on Land;

[0008] S2. According to the four constructed specific sustainable development goal functions, construct a two - layer fuzzy multi - objective optimization model with water resources as the independent variable, and set different development scenarios;

[0009] S3. According to the constructed two - layer multi - objective optimization model, solve the water resource allocation methods under different scenarios, so as to obtain the optimal water resource allocation method for the coordinated development of sustainable development goals.

[0010] Furthermore, the construction of the specific sustainable development goal model is carried out in the following way:

[0011] Use the per capita food availability to characterize the index value of SDG2 - Zero Hunger;

[0012] Use the water resource utilization efficiency to calculate the index value of SDG6 - Clean Water and Sanitation.

[0013] Use the proportion of clean energy to calculate the index value of SDG7 - Affordable and Clean Energy;

[0014] Use the proportion of forest area, cultivated land area and grassland area in the total land area to calculate the index value of SDG15 - Life on Land;

[0015] Furthermore, step S2 is specifically carried out according to the following steps:

[0016] S21. Construction of the two - layer fuzzy multi - objective optimization model

[0017] Construct a two - layer multi - objective optimization model with water resources as the independent variable. Among them, the upper - layer objective is to optimize the comprehensive benefits of multiple sustainable development goals and strive to maximize the overall system benefits; the lower - layer objective focuses on maximizing the benefits of each sustainable development goal. At the same time, considering that the river water volume is an uncertain value, the water resources are set as fuzzy number variables;

[0018] S22. Establishment of different scenarios

[0019] According to the established sustainable development goal models and the two - layer fuzzy multi - objective optimization model, consider 3 kinds of food demands, 3 kinds of water resource extraction efficiencies and 3 kinds of river water volumes, a total of 27 development scenarios;

[0020] Furthermore, step S3 is specifically carried out according to the following steps:

[0021] Based on solving the fuzzy multi-objective optimization model using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the optimal values of sustainable development goals are identified to determine the best development scenarios and water resource allocation methods.

[0022] Furthermore, the SDG2 - Zero Hunger sustainable development goal model is modeled and calculated as follows:

[0023]

[0024] In the formula, i represents different crop types, which respectively represent three main food crops: wheat, corn, and rice here; j represents different regions; SW ij and AW ij respectively represent the water extraction amounts (m 3 ) for irrigation from two different rivers; YA ij is the yield per unit area (kg / ha) of each food crop in different regions at different times; β is the water resource extraction efficiency; WPC ij is the water consumption per unit area of cultivated land (m 3 / ha); PO is the total population of the study area; FC is the per capita food consumption (kg) when the score of Sustainable Development Goal 2 is 100.

[0025] Furthermore, the SDG6 - Clean Water and Sanitation sustainable development goal model is modeled and calculated as follows:

[0026]

[0027] In the formula, i represents different crop types, which respectively represent five main food crops: wheat, corn, rice, cotton, and vegetables here; j represents different regions; SW ij and AW ij respectively represent the water extraction amounts (m 3 ) for irrigation from two different rivers; YA ij is the yield per unit area (kg / ha) of each food crop in different regions at different times; PA ij is the price per unit crop ($ / kg); β is the water resource extraction efficiency; WPC ij is the water consumption per unit area of cultivated land (m 3 / ha); MA is the maximum value of agricultural water resource utilization efficiency.

[0028] Furthermore, the SDG7 - Clean Energy sustainable development goal model is modeled and calculated as follows:

[0029]

[0030] SDG7 = CEP / TEP × 100

[0031] In the formula, CEP is the clean energy production volume; TEP is the total energy production volume; CWS is the water consumption for coal power generation (m 3 ); OWS is the water consumption for oil power generation (m 3 ); GWS is the water consumption for natural gas power generation (m 3 ); NWS is the water consumption for nuclear power generation (m 3 ); HWS is the water consumption for hydropower generation (m 3 ); GU is the power generation per unit water volume for natural gas power generation (KWh / m 3 ); NU is the power generation per unit water volume for nuclear power generation (KWh / m 3 ); HU is the power generation per unit water volume for hydropower generation (KWh / m 3 ); CU is the power generation per unit water volume for hydropower generation (KWh / m 3 ); NU is the power generation per unit water volume for nuclear power generation (KWh / m 3 ); HU is the power generation per unit water volume for hydropower generation (KWh / m 3 ); CU is the power generation per unit water volume for coal power generation (KWh / m 3 ); OU is the power generation per unit water volume for oil power generation (KWh / m 3 ).

[0032] Furthermore, the SDG15 - terrestrial biodiversity sustainable development goal model is modeled and calculated in the following way:

[0033]

[0034] In the formula, SW ij and AW ij respectively represent the water volumes extracted from two different rivers for irrigation (m 3 ); β is the water resource extraction efficiency; UWC is the water consumption per unit area of cultivated land (m 3 / ha / year); UWF is the water consumption per unit area of forest land (m 3 / ha / year); UWG is the water consumption per unit area of grassland (m 3 / ha / year); FAW is the water volume allocated by the Amu Darya River for the growth of forest land (m 3 ); FSW is the water volume allocated by the Amu Darya River for the growth of grassland (m 3 ); GAW is the water volume allocated by the Amu Darya River for the growth of forest land (m 3 ); GSW is the water volume allocated by the Amu Darya River for the growth of grassland (m 3 ); GSW is the water volume allocated by the Syr Darya River for the growth of grassland (m 3 ).

[0035] Furthermore, the double-layer fuzzy multi-objective optimization model is established according to the following method:

[0036]

[0037] ···

[0038]

[0039] The constraint conditions are:

[0040] C r [α ki ×x ki ≤w j ≥α j ,j = 1,2,···,n

[0041] x j ≥0,j = 1,2,···,n

[0042] In the formula, C knj is the coefficient of the decision variable x in the i-th decision-making department, j is the number of decision-making departments, α ki is the coefficient of the decision variable, i is multiple decision-making departments in the lower-level model, is the fuzzy parameter, α i is the confidence level, that is, the constraint set should be greater than or equal to a certain certainty, Cr is the confidence level, and is defined by the following formula:

[0043] C r [r≤ξ] = 1 / 2[Pos(r≤ξ)+Nec(r≤ξ)]

[0044] When the stability is equal to 1, the fuzzy event will definitely occur; when the stability is 0, the fuzzy event will not occur. When facing practical problems, decision-makers hope to make highly reliable decisions under high stability. Therefore, the stability should be greater than 0.5. Assuming that the fuzzy parameter can be represented by a trigonometric function, then the possibility, necessity, and credibility of the fuzzy parameter can be expressed as:

[0045]

[0046] Therefore, the fuzzy multi-objective optimization model can be expressed as:

[0047]

[0048] ···

[0049]

[0050] The constraint conditions are:

[0051]

[0052] x j ≥0, j = 1, 2, ···, n

[0053] For the coordinated development model of four Sustainable Development Goals (SDGs) including SDG2 - SDG6 - SDG7 - SDG15, its two - layer multi - objective optimization model can be expressed as:

[0054] MAXF = max(SDG2 + SDG6 + SDG7 + SDG15)

[0055] Where MAXF is the value of the top - layer Sustainable Development Goal, and SDG2, SDG6, SDG7, and SDG15 are the values of the four Sustainable Development Goal models in the second layer, respectively, where:

[0056]

[0057] SDG7 = CEP / TEP × 100

[0058]

[0059] Its constraint conditions are:

[0060]

[0061]

[0062] Where i represents different crop types, here respectively representing three main food crops: wheat, corn, and rice; j represents different regions; SW ij and AW ij respectively represent the water extraction for irrigation from two different rivers (m 3 ); YA ij is the yield per unit area of each food crop in different regions at different times (kg / ha); β is the water extraction efficiency; WPC ij is the water consumption per unit area of cultivated land (m 3 / ha); PO is the total population of the research area; FC is the per - capita food consumption (kg) when the score of Sustainable Development Goal 2 is 100; MA is the maximum value of agricultural water use efficiency; CEP is the clean energy production; TEP is the total energy production; CWS is the water consumption for coal - fired power generation (m 3 ); OWS is the water consumption for oil - fired power generation (m 3 ); GWS is the water consumption for gas - fired power generation (m 3 ); NWS is the water consumption for nuclear - powered power generation (m 3 ); HWS is the water consumption for hydropower generation (m 3 ); GU is the power generation per unit water volume for gas - fired power generation (KWh / m 3); NU is the power generation per unit water volume of nuclear power generation (KWh / m 3 ); HU is the power generation per unit water volume of hydropower generation (KWh / m 3 ); CU is the power generation per unit water volume of hydropower generation (KWh / m 3 ); NU is the power generation per unit water volume of nuclear power generation (KWh / m 3 ); HU is the power generation per unit water volume of hydropower generation (KWh / m 3 ); CU is the power generation per unit water volume of coal power generation (KWh / m 3 ); OU is the power generation per unit water volume of oil power generation (KWh / m 3 ); UWC is the water consumption per unit area of cultivated land (m 3 / ha / year); UWF is the water consumption per unit area of forest land (m 3 / ha / year); UWG is the water consumption per unit area of grassland (m 3 / ha / year); FAW is the water volume allocated to the growth of forest land by the Amu Darya River (m 3 ); FSW is the water volume allocated to the growth of grassland (m 3 ); GAW is the water volume allocated to the growth of forest land (m 3 ); GSW is the water volume allocated to the growth of grassland (m 3 ); GSW is the water volume allocated to the growth of grassland by the Syr Darya River (m 3 ).

[0063] Furthermore, the different development scenarios are established according to the following method:

[0064] Three levels of food demand, three levels of river water inflow, and three levels of river water resource extraction efficiency are designed, resulting in a total of 27 scenarios.

[0065] Table 1. Input parameters of different scenarios of the SDG2-SDG6-SDG7-SDG15 linkage system

[0066]

[0067] Table 2. Design of 27 scenarios

[0068]

[0069]

[0070] The beneficial effects of the present invention are as follows:

[0071] The analysis method for promoting the coordinated development of multiple sustainable development goals provided by the present invention includes the assessment of four sustainable development goals, namely water (SDG6) - energy (SDG7) - food (SDG2) - ecology (SDG15), the construction of a two-layer fuzzy multi-objective optimization model with water resources as the independent variable, the solution of the multi-objective optimization model, and the proposed water resource allocation strategy for achieving the coordinated development of multiple sustainable development goals. The method of the present invention is concise, which can assist in quickly constructing an optimization model for multiple sustainable development goals and provide necessary technical support for formulating a resource optimization allocation plan.

[0072] The data used in the present invention comes from the official statistics of the Food and Agriculture Organization of the United Nations and the open-source data of the United Nations Sustainable Development Goals Socio-Economic Database. Finally, multiple sustainable development goal models, a two-layer fuzzy multi-objective optimization model, and water resource planning and allocation strategies under different scenarios are completed, greatly reducing the interference of human factors in the implementation process and realizing the efficiency and scientific nature of resource optimization allocation.

[0073] The present invention constructs a quantitative model for four sustainable development goals, namely water (SDG6) - energy (SDG7) - food (SDG2) - ecology (SDG15), through open-source data and establishes a two-layer fuzzy multi-objective optimization model. On the basis of clear calculation logic, its algorithm model can be appropriately adjusted according to independent variables (water volume, land area, and other ecological resources) and constraint conditions to adapt to the development needs of different regions and achieve wide applicability.

[0074] The present invention uses a multi-objective optimization model to quantify the mutual relationship between multiple sustainable development goals, obtains the Pareto optimal solution set by using the non-dominated sorting genetic algorithm II (NSGA-II) and crowding degree calculation, adopts a reference point-guided elitist retention strategy to enhance the distribution and convergence of the Pareto front, and identifies the optimal value of the sustainable development goal with the highest comprehensive satisfaction from the solution set. On this basis, combined with the scenario analysis method, development scenarios under different climate conditions and water use demands are constructed, the best development plan is determined through multi-criteria decision-making evaluation, and finally, a water resource spatial equilibrium allocation strategy that takes into account system resilience and efficiency is proposed to reduce the subjectivity in the process of ecological resource optimization and realize the scientific nature of planning decisions.

[0075] Some of the other advantages, objectives, and features of the present invention will be described in the subsequent description, and this content can be naturally understood by those skilled in the art through the following research analysis or verification of specific implementation methods. The objectives, related advantages, and beneficial effects of the present invention can be achieved and verified through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to make the objectives, technical solutions, and effects of the present invention clearer, the following drawings are provided by the present invention for illustration:

[0077] Figure 1 It is a schematic diagram of the overall process of an embodiment of the present invention.

[0078] Figure 2 It is a schematic diagram of the water consumption ratio of each department under different scenarios of an embodiment of the present invention.

[0079] Figure 3 It is a schematic diagram of the grain yield under different scenarios of an embodiment of the present invention.

[0080] Figure 4 It is a schematic diagram of water volume allocation under the optimal scenario (S3) and the worst scenario (S25) of an embodiment of the present invention. Detailed implementation manners

[0081] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, describe in detail the specific implementation manners, structures, features and their effects of the present invention as follows.

[0082] As Figure 1 shown, the analysis method for promoting the coordinated development of multiple sustainable development goals provided in this embodiment, by constructing multiple sustainable development goal optimization models with ecological resources as independent variables, setting different development scenarios, and proposing water resource optimal allocation strategies, includes the following steps:

[0083] S1. Construction of specific sustainable development goal models

[0084] The sustainable development goal assessment in this embodiment includes the following contents:

[0085] Using the per capita grain availability to characterize the index value of SDG2 - Zero Hunger

[0086]

[0087] In the formula, i is different crop types, here respectively representing three main food crops of wheat, corn and rice; j is different regions; SW ij and AW ij respectively represent the water volume (m 3 ) extracted from two different rivers for irrigation; YA ij is the unit area yield (kg / ha) of each food crop in different regions at different times; β is the water resource extraction efficiency; WPC ij is the water consumption (m 3 / ha) per unit area of cultivated land; PO is the total population of the research area; FC is the per capita food consumption (kg) when the score of sustainable development goal 2 is 100.

[0088] Using the water resource utilization efficiency to calculate the indicator value of SDG6 - Clean Water and Sanitation

[0089]

[0090] wherein, i represents different crop types, here respectively representing five major food crops of wheat, corn, rice, cotton and vegetables; j represents different regions; SW ij and AW ij respectively represent the water extraction amounts for irrigation from two different rivers (m 3 ); YA ij is the yield per unit area of each food crop in different regions at different times (kg / ha); PA ij is the price of the unit crop ($ / kg); β is the water resource extraction efficiency; WPC ij is the water consumption per unit area of cultivated land (m 3 / ha); MA is the maximum value of the agricultural water resource utilization efficiency.

[0091] Using the proportion of clean energy to calculate the indicator value of SDG7 - Clean Energy;

[0092]

[0093] SDG7 = CEP / TEP × 100

[0094] wherein, CEP is the clean energy production; TEP is the total energy production; CWS is the water consumption for coal power generation (m 3 ); OWS is the water consumption for oil power generation (m 3 ); GWS is the water consumption for natural gas power generation (m 3 ); NWS is the water consumption for nuclear power generation (m 3 ); HWS is the water consumption for hydropower generation (m 3 ); GU is the power generation per unit water volume for natural gas power generation (KWh / m 3 ); NU is the power generation per unit water volume for nuclear power generation (KWh / m 3 ); HU is the power generation per unit water volume for hydropower generation (KWh / m 3 ); CU is the power generation per unit water volume for hydropower generation (KWh / m 3 ); NU is the power generation per unit water volume of nuclear power generation (KWh / m 3 ); HU is the power generation per unit water volume of hydropower generation (KWh / m 3 ); CU is the power generation per unit water volume of coal power generation (KWh / m 3 ); OU is the power generation per unit water volume of oil power generation (KWh / m 3 ).

[0095] Calculate the indicator value of SDG15 - Terrestrial Life using the proportions of forest area, arable land area, and grassland area in the total land area;

[0096]

[0097] Where, SW ij and AW ij respectively represent the water volumes (m 3 ) extracted from two different rivers for irrigation; β is the water resource extraction efficiency; UWC is the water consumption per unit area of arable land (m 3 / ha / year); UWF is the water consumption per unit area of forest land (m 3 / ha / year); UWG is the water consumption per unit area of grassland (m 3 / ha / year); FAW is the water volume allocated by the Amu Darya River for forest growth (m 3 ); FSW is the water volume allocated by the Amu Darya River for grassland growth (m 3 ); GAW is the water volume allocated by the Amu Darya River for forest growth (m 3 ); GSW is the water volume allocated by the Amu Darya River for grassland growth (m 3 ); GSW is the water volume allocated by the Syr Darya River for grassland growth (m 3 ).

[0098] S2. Construction of a two - layer fuzzy multi - objective optimization model with water resources as the independent variable

[0099] S2.1 Two - layer fuzzy multi - objective optimization model

[0100] In this embodiment, considering that the river water volume is an uncertain value, it is set as a fuzzy number. At the same time, this fuzzy number is introduced into the two - layer multi - objective optimization model to establish a two - layer fuzzy multi - objective optimization model with water resources as the independent variable and multiple specific sustainable development goals as the objective functions. The specific steps are as follows:

[0101]

[0102] ···

[0103]

[0104] The constraint conditions are:

[0105] C r [α ki ×x ki ≤w j ≥α j , j = 1, 2, ···, n

[0106] x j ≥0, j = 1, 2, ···, n

[0107] In the formula, C knj is the coefficient of the decision variable x in the i-th decision-making department, j is the number of decision-making departments, and α ki is the coefficient of the decision variable, and i are multiple decision-making departments in the lower-level model. is the fuzzy parameter, and α i is the confidence level, that is, the constraint set should be greater than or equal to a certain degree of certainty. Cr is the confidence level and is defined by the following formula:

[0108] C r [r ≤ ξ] = 1 / 2[Pos(r ≤ ξ) + Nec(r ≤ ξ)]

[0109] When the stability is equal to 1, the fuzzy event will definitely occur; when the stability is 0, the fuzzy event will not occur. When facing practical problems, decision-makers hope to make decisions with high reliability under high stability. Therefore, the stability should be greater than 0.5. Assuming that the fuzzy parameter can be represented by a trigonometric function, then the possibility, necessity, and credibility of the fuzzy parameter can be expressed as:

[0110]

[0111] Therefore, the fuzzy multi-objective optimization model can be expressed as:

[0112]

[0113] ···

[0114]

[0115] The constraint conditions are:

[0116]

[0117] x j ≥ 0, j = 1, 2, ···, n

[0118] For the coordinated development model of four sustainable development goals including SDG2 - SDG6 - SDG7 - SDG15, its two-layer multi-objective optimization model can be expressed as:

[0119] MAXF = max(SDG2 + SDG6 + SDG7 + SDG15)

[0120] In the formula, MAXF is the value of the top-level sustainable development goal, and SDG2, SDG6, SDG7, and SDG15 are the values of the four sustainable development goal models in the second layer, where:

[0121]

[0122] SDG7 = CEP / TEP × 100

[0123]

[0124] Its constraints are as follows:

[0125]

[0126]

[0127] In the formula, i represents different crop types, here respectively representing three major food crops: wheat, corn, and rice; j represents different regions; SW ij and AW ij respectively represent the water extraction volumes (m 3 ) for irrigation from two different rivers; YA ij is the yield per unit area (kg / ha) of each food crop in different regions at different times; β is the water extraction efficiency; WPC ij is the water consumption per unit area of cultivated land (m 3 / ha); PO is the total population of the research area; FC is the per capita food consumption (kg) when the score of Sustainable Development Goal 2 is 100; MA is the maximum value of agricultural water use efficiency; CEP is the clean energy production; TEP is the total energy production; CWS is the water consumption for coal power generation (m 3 ); OWS is the water consumption for oil power generation (m 3 ); GWS is the water consumption for natural gas power generation (m 3 ); NWS is the water consumption for nuclear power generation (m 3 ); HWS is the water consumption for hydropower generation (m 3 ); GU is the power generation per unit water volume for natural gas power generation (KWh / m 3 ); NU is the power generation per unit water volume for nuclear power generation (KWh / m 3 ); HU is the power generation per unit water volume for hydropower generation (KWh / m 3 ); CU is the power generation per unit water volume for hydropower generation (KWh / m 3 ); NU is the power generation per unit water volume for nuclear power generation (KWh / m 3 ); HU is the power generation per unit water volume for hydropower generation (KWh / m 3 ); CU is the power generation per unit water volume for coal power generation (KWh / m 3 ); OU is the power generation per unit water volume for oil power generation (KWh / m 3 ); UWC is the water consumption per unit area of cultivated land (m 3 / ha / year); UWF is the water consumption per unit area of forest land (m 3 / ha / year); UWG is the water consumption of grassland per unit area (m 3 / ha / year); FAW is the water volume allocated by the Amu Darya River for forest growth (m 3 ); FSW is the water volume allocated for grassland growth (m 3 ); GAW is the water volume allocated for forest growth (m 3 ); GSW is the water volume allocated for grassland growth (m 3 ); GSW is the water volume allocated by the Syr Darya River for grassland growth (m 3 ).

[0128] S2.2. Establishment of different development scenarios

[0129] Three levels of food demand, three levels of river water inflow, and three levels of river water resource extraction efficiency were designed, resulting in a total of 27 scenarios.

[0130] Table 1. Input parameters for different scenarios of the SDG2-SDG6-SDG7-SDG15 linkage system.

[0131]

[0132] Table 2. Design of 27 scenarios

[0133]

[0134] S3. Solving the optimal water resource allocation method

[0135] The solution of the two-layer fuzzy multi-objective optimization model in this embodiment is carried out as follows:

[0136] The NSGA-II algorithm is used for multi-objective optimization solution. The Pareto optimal solution set is obtained through fast non-dominated sorting and crowding degree calculation, and the optimal value of the sustainable development goal with the highest comprehensive satisfaction is identified from the solution set. On this basis, development scenarios under different water use demands are constructed by combining the scenario analysis method, and the best development plan is determined through multi-criteria decision-making evaluation, as Figure 2 and Figure 3 shown. Finally, a water resource spatial equilibrium allocation strategy that takes into account both system resilience and efficiency in the Aral Sea Basin is proposed, as Figure 4 shown.

[0137] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments by using the above-disclosed technical content within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. An analytical method for promoting the coordinated development of multiple sustainable development goals, characterized in that: Including the following steps: S1. According to the principles such as the Outline of Sustainable Development Goals and data availability, construct a specific sustainable development goal function model, where the sustainable development goals include SDG2 - Zero Hunger, SDG6 - Clean Water and Sanitation, SDG7 - Affordable and Clean Energy, and SDG15 - Life on Land; S2. According to the four constructed specific sustainable development goal functions, construct a two - layer fuzzy multi - objective optimization model with water resources as the independent variable, and set different development scenarios; S3. According to the constructed two - layer multi - objective optimization model, solve the water resource allocation methods under different scenarios, so as to obtain the optimal water resource allocation method for the coordinated development of sustainable development goals.

2. The analysis method for promoting the coordinated development of multiple sustainable development goals according to claim 1, wherein: The specific sustainable development goal model is established in the following way: Use the per capita food availability to characterize the index value of SDG2 - Zero Hunger; Use the water resource utilization efficiency to calculate the index value of SDG6 - Clean Water and Sanitation; Use the proportion of clean energy to calculate the index value of SDG7 - Affordable and Clean Energy; Use the proportion of forest area, cultivated land area and grassland area in the total land area to calculate the index value of SDG15 - Life on Land.

3. The analytical method for promoting the coordinated development of multiple sustainable development goals according to claim 1, wherein: The construction of the two - layer multi - objective optimization model under different scenarios in step S2 is carried out in the following way: S21. Construction of the two - layer fuzzy multi - objective optimization model Construct a two - layer multi - objective optimization model with water resources as the independent variable. Among them, the upper - level goal is to maximize the overall benefit of the system by optimizing the comprehensive benefits of multiple sustainable development goals; the lower - level goal focuses on maximizing the benefits of each sustainable development goal. Considering that the river water volume is an uncertain value, the water resources are set as fuzzy number variables; S22. Establishment of different scenarios According to the established sustainable development goal models and the two - layer fuzzy multi - objective optimization model, consider 3 kinds of food demands, 3 kinds of water resource extraction efficiencies and 3 kinds of river water volumes, a total of 27 development scenarios.

4. The analysis method for promoting the coordinated development of multiple sustainable development goals according to claim 1, characterized in that: The optimal water resource allocation method in step S3 is carried out according to the following steps: On the basis of using the Non - dominated Sorting Genetic Algorithm II (NSGA - II) to solve the fuzzy multi - objective optimization model, identify the optimal values of sustainable development goals, and determine the best development scenario and water resource allocation method.

5. The analytical method for promoting the coordinated development of multiple sustainable development goals according to claim 2, characterized in that: The construction of the SDG2 - Zero Hunger sustainable development goal model is calculated in the following way: Where \(i\) represents different crop types, which respectively represent the three major food crops of wheat, corn, and rice here; \(j\) represents different regions; \(SW\) ij and \(AW\) ij respectively represent the water extraction for irrigation from two different rivers (\(m\) 3 ); \(YA\) ij is the yield per unit area of each food crop at different times in different regions (\(kg / ha\)); \(\beta\) is the water resource extraction efficiency; \(WPC\) ij is the water consumption per unit area of cultivated land (\(m\) 3 / ha); \(PO\) is the total population of the study area; \(FC\) is the per capita food consumption (\(kg\)) when the score of Sustainable Development Goal 2 is 100.

6. The analytical method for promoting the coordinated development of multiple sustainable development goals according to claim 2, wherein: The construction of the SDG6 - Clean Water and Sanitation sustainable development goal model is calculated in the following way: In the formula, i represents different crop types, which respectively represent five major food crops: wheat, corn, rice, cotton, and vegetables; j represents different regions; SW ij and AW ij respectively represent the water volumes (m 3 ) extracted from two different rivers for irrigation; YA ij is the yield per unit area of each food crop in different regions at different times (kg / ha); PA ij is the price of the unit crop ($ / kg); β is the water resource extraction efficiency; WPC ij is the water consumption per unit area of cultivated land (m 3 / ha); MA is the maximum value of the agricultural water resource utilization efficiency.

7. The analysis method for promoting the coordinated development of multiple sustainable development goals according to claim 2, characterized in that: The construction of the SDG7 - Affordable and Clean Energy sustainable development goal model is calculated in the following way: SDG7 = CEPTEP×100 Where CEP is the clean energy production; TEP is the total energy production; CWS is the water consumption for coal power generation (m 3 ); OWS is the water consumption for oil power generation (m 3 ); GWS is the water consumption for natural gas power generation (m 3 ); NWS is the water consumption for nuclear power generation (m 3 ); HWS is the water consumption for hydropower generation (m 3 ); GU is the power generation per unit water volume for natural gas power generation (KWh / m 3 ); NU is the power generation per unit water volume for nuclear power generation (KWh / m 3 ); HU is the power generation per unit water volume for hydropower generation (KWh / m 3 ); CU is the power generation per unit water volume for hydropower generation (KWh / m 3 ); NU is the power generation per unit water volume for nuclear power generation (KWh / m 3 ); HU is the power generation per unit water volume for hydropower generation (KWh / m 3 ); CU is the power generation per unit water volume for coal power generation (KWh / m 3 ); OU is the power generation per unit water volume for oil power generation (KWh / m 3 ).

8. The analytical method for promoting the coordinated development of multiple sustainable development goals according to claim 2, wherein: The construction of the SDG15 - Life on Land sustainable development goal model is calculated in the following way: where, SW ij and AW ij respectively represent the water volumes (m 3 ) extracted from two different rivers for irrigation; β is the water resource extraction efficiency; UWC is the water consumption per unit area of cultivated land (m 3 / ha / year); UWF is the water consumption per unit area of forest land (m 3 / ha / year); UWG is the water consumption per unit area of grassland (m 3 / ha / year); FAW is the water volume (m 3 ) allocated by the Amu Darya River for the growth of forest land; FSW is the water volume (m 3 ) allocated by the Amu Darya River for the growth of grassland; GAW is the water volume (m 3 ) allocated by the Amu Darya River for the growth of forest land; GSW is the water volume (m 3 ) allocated by the Amu Darya River for the growth of grassland; GSW is the water volume (m 3 ) allocated by the Syr Darya River for the growth of grassland.

9. The analytical method for promoting the coordinated development of multiple sustainable development goals according to claim 3, characterized in that: The construction of the two - layer fuzzy multi - objective optimization model in step S21 is carried out in the following way: The constraint conditions are: C r [α ki ×x ki ≤w j ≥α j , j = 1, 2, ···, n x j ≥ 0, j = 1, 2, ···, n where C knj is the coefficient of the decision variable x in the i-th decision-making department, j is the number of decision-making departments, α ki is the coefficient of the decision variable, i is multiple decision-making departments in the lower-level model, is a fuzzy parameter, α i is the confidence level, that is, the constraint set should be greater than or equal to a certain certainty, Cr is the confidence level, which is defined by the following formula: C r [r ≤ ξ] = 1 / 2[Pos(r ≤ ξ) + Nec(r ≤ ξ)] When the stability is equal to 1, the fuzzy event will definitely occur; when the stability is 0, the fuzzy event will not occur. When facing practical problems, decision-makers hope to make highly reliable decisions under high stability. Therefore, the stability should be greater than 0.

5. Assuming that the fuzzy parameters can be represented by trigonometric functions, the possibility, necessity, and credibility of the fuzzy parameters can be expressed as: Therefore, the fuzzy multi-objective optimization model can be expressed as: The constraints are: x j ≥ 0, j = 1, 2, ···, n For the coordinated development model of four sustainable development goals, including SDG2-SDG6-SDG7-SDG15, its two-layer multi-objective optimization model can be expressed as: MAXF = max(SDG2 + SDG6 + SDG7 + SDG15) Where MAXF is the value of the top-level sustainable development goal, and SDG2, SDG6, SDG7, and SDG15 are the values of the four sustainable development goal models in the second layer, respectively, where: SDG7 = CEP / TEP × 100 Its constraints are: In the formula, i represents different crop types, here respectively representing three major food crops: wheat, corn, and rice; j represents different regions; SW ij and AW ij respectively represent the water volumes (m 3 ) extracted from two different rivers for irrigation; YA ij is the yield per unit area (kg / ha) of each food crop in different regions at different times; β is the water resource extraction efficiency; WPC ij is the water consumption per unit area of cultivated land (m 3 / ha); PO is the total population of the study area; FC is the per capita food consumption (kg) when the score of Sustainable Development Goal 2 is 100; MA is the maximum value of agricultural water resource utilization efficiency; CEP is the clean energy production; TEP is the total energy production; CWS is the water consumption for coal power generation (m 3 ); OWS is the water consumption for oil power generation (m 3 ); GWS is the water consumption for natural gas power generation (m 3 ); NWS is the water consumption for nuclear power generation (m 3 ); HWS is the water consumption for hydropower generation (m 3 ); GU is the power generation per unit water volume for natural gas power generation (KWh / m 3 ); NU is the power generation per unit water volume for nuclear power generation (KWh / m 3 ); HU is the power generation per unit water volume for hydropower generation (KWh / m 3 ); CU is the power generation per unit water volume for hydropower generation (KWh / m 3 ); NU is the power generation per unit water volume for nuclear power generation (KWh / m 3 ); HU is the power generation per unit water volume for hydropower generation (KWh / m 3 ); CU is the power generation per unit water volume for coal power generation (KWh / m 3 ); OU is the power generation per unit water volume for oil power generation (KWh / m 3 ); UWC is the water consumption per unit area of cultivated land (m 3 / ha / year); UWF is the water consumption per unit area of forest land (m 3 / ha / year); UWG is the water consumption per unit area of grassland (m 3 / ha / year); FAW is the water volume allocated by the Amu Darya River for forest growth (m 3 ); FSW is the water volume allocated for grassland growth (m 3 ); GAW is the water volume allocated for forest growth (m 3 ); GSW is the water volume allocated for grassland growth (m 3 ); GSW is the water volume allocated by the Syr Darya River for grassland growth (m 3 ).

10. The analysis method for promoting the coordinated development of multiple sustainable development goals according to claim 3, characterized in that: The construction of different scenarios in step S22 is carried out in the following way: Three levels of food demand, three levels of river inflow, and three levels of river water resource extraction efficiency are designed, with a total of 27 scenarios. Table 1. Input parameters of different scenarios of the SDG2-SDG6-SDG7-SDG15 linkage system. Table 2. Design of 27 scenarios 11. The analytical method for promoting the coordinated development of multiple sustainable development goals according to claim 4, wherein: The optimal water resource allocation method is carried out according to the following steps: The NSGA-II algorithm is used for multi-objective optimization solution. The Pareto optimal solution set is obtained through fast non-dominated sorting and crowding degree calculation, and the optimal value of the sustainable development goal with the highest comprehensive satisfaction is identified from the solution set. On this basis, combined with the scenario analysis method, development scenarios under different climate conditions and water use demands are constructed, and the best development plan is determined through multi-criteria decision-making evaluation. Finally, a spatial balance allocation strategy of water resources that takes into account system resilience and efficiency is proposed.

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