A method for comprehensive assessment and zoning governance of natural resource sustainability for SDGs

Abstract

This invention discloses a comprehensive assessment and regional governance method for the sustainability of natural resources oriented towards the SDGs, belonging to the field of resource and environmental management technology. It addresses the problem that existing natural resource assessment methods fail to deeply explore the systemic coupling relationships between resources. The invention includes constructing a sustainable development indicator system for natural resources within assessment units, building multiple types of indicator data; determining three-level, three-category labels for the indicator data, and calculating the dynamic weights of the indicator data; identifying weak resources within the assessment units and calculating the weakness index; determining the comprehensive SDG contribution within the assessment units; using the weakness index and comprehensive SDG contribution as key features to construct a dataset for all assessment units; performing cluster analysis on the dataset to determine the governance label for each assessment unit; and matching differentiated governance schemes based on the governance label. This invention overcomes the problems of fragmented assessment objects and single perspectives in existing studies, achieving a leap from local, static analysis to systemic, dynamic diagnosis.

Description

Technical Field

[0001] This invention relates to the field of resource and environmental management technology, specifically to a comprehensive assessment and zoning management method for the sustainability of natural resources oriented towards the SDGs. Background Technology

[0002] Current assessments of sustainable development at the natural resource level are significantly inadequate, impacting the sustainable management of natural resources.

[0003] In terms of research subjects, existing studies conduct separate studies on various natural resources. For example, they may only study a single element such as water and soil resources, or list multiple natural resources without studying the mutual influence between different types of natural resources. However, the overall natural environment is a unified whole, and separate studies cannot effectively assess the inherent coupling relationships that exist in the entire natural system. Obviously, this will also affect the effectiveness of subsequent sustainable management of natural resources.

[0004] In terms of research methods, due to the separation of research subjects, existing research methods mostly rely on static weights and simple weighted averages. Obviously, they cannot construct an internal response logic framework to reveal internal causality, and cannot reflect dynamic changing factors such as resource scarcity and policy priorities. As a result, the evaluation results are out of touch with the actual management needs.

[0005] Finally, since existing research methods all use weighted scoring to determine management priorities, they have failed to effectively translate the assessment results into clear and actionable zoning governance paths. This results in an ineffective integration of assessment and management, making it difficult to provide effective decision support for the sustainable management of natural resources. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a comprehensive assessment and zonal governance method for the sustainability of natural resources oriented towards SDGs. This method solves the technical problem that existing natural resource assessment methods fail to deeply explore the systematic coupling relationships between resources, resulting in fragmented assessments of different resources and making it difficult to provide effective decision support for the sustainable management of natural resources.

[0007] A comprehensive assessment and zoning governance approach for natural resource sustainability oriented towards the SDGs includes the following steps:

[0008] Step 1: Construct corresponding indicator data for the natural resources involved in each assessment unit, and determine the natural resource sustainable development indicator system of the assessment unit in combination with the Sustainable Development Goals to obtain the corresponding system table. The system table includes resource type, benchmark SDGs and indicator data, wherein the resource type and benchmark SDGs are one-to-one or one-to-many, and the benchmark SDGs and indicator data are one-to-one or one-to-many.

[0009] Step 2: The indicator data is sequentially graded and classified to determine three levels and three categories of labels, and the dynamic weight of the indicator data is calculated based on the three levels and three categories of labels; the grading refers to determining the logical level according to the definition and semantics of the indicator data, and the logical level includes pressure, state and response; the classification refers to further determining the economic and ecological attributes of the indicator data after determining the logical level, and the economic and ecological attributes include resource endowment, utilization efficiency and protection and restoration.

[0010] Step 3: Calculate the comprehensive score of each natural resource type within the assessment unit and identify the weak resources, then calculate the weak index of the weak resources;

[0011] Step 4: Determine all SDG sub-objectives within the assessment unit, and sum the weighted index data mapped to each sub-objective to obtain the contribution of the corresponding sub-objective. Then, sum the contributions of all sub-objectives to obtain the overall SDG contribution.

[0012] Steps 3 and 4 are not in any particular order;

[0013] Step 5: Using the bottleneck index and the contribution of the comprehensive SDGs as key features, construct a dataset for all assessment units, perform cluster analysis on the dataset to determine the governance label of each assessment unit, and match differentiated governance schemes according to the governance label.

[0014] Further, step 1 includes:

[0015] Step 11: Construct an indicator system covering different types of natural resources for the assessment unit and construct multiple indicator data for each type of resource. The resource types include arable land, orchards, forest land, grassland, water and wetlands, land, and energy. The assessment unit refers to the basic spatial management unit or administrative unit for the sustainable assessment of natural resources.

[0016] Step S12: Standardize the constructed indicator data and convert it into dimensionless values.

[0017] Furthermore, in step 2, the pressure refers to the corresponding indicator that directly quantifies the amount of extraction, emissions, or disturbance intensity of human socio-economic activities on the natural resource system; the state refers to the corresponding indicator that describes the objective condition, stock level, or quality level of the resource system at a specific point in time under pressure; and the response refers to the corresponding indicator that quantifies the policy measures, engineering actions, or management inputs taken by the social system to prevent resource degradation, reduce pressure, or improve the state.

[0018] Furthermore, in step 2, the resource endowment category is strongly correlated with the state level. When classifying, if the state indicator describes the natural quantity or baseline quality of the resource, it is classified into this category. The utilization efficiency category is correlated with pressure and some state and response levels. When classifying, if the pressure indicator originates from the utilization method in the production or consumption process, it is classified into this category. If the state indicator is the economic output or efficiency result generated after utilization, it is classified into this category. If the response indicator aims to improve resource conversion efficiency or optimize utilization technology, it is classified into this category. The protection and restoration category is strongly correlated with the pressure and response levels. When classifying, if the pressure indicator directly leads to ecological damage or quality decline and needs to be addressed through restoration, it is classified into this category. If the response indicator is a direct ecological protection, pollution control, or degradation restoration action, it is classified into this category.

[0019] Furthermore, the formula for calculating the dynamic weight in step 2 is as follows:

[0020]

[0021] In the formula, For indicator data The basic weights, For indicator data Policy priority coefficient For indicator data Resource scarcity coefficient For indicator data The attenuation coefficient, , This is the adjustment coefficient;

[0022] in, For indicator data Policy priority coefficient For indicator data The resource scarcity coefficient is calculated based on the three-level, three-category labels of the indicator data. According to indicator data The timeliness is determined.

[0023] Furthermore, based on indicator data The three-level tags select the corresponding set of keywords from a pre-defined keyword library associated with each logical level, while simultaneously extracting indicator data. The system identifies and expands keyword sets based on their types. Keyword sets are then combined and matched to generate keyword pairs. Text mining is used to statistically analyze the frequency of related expressions appearing in policy documents, and the ratio of each keyword pair to the total frequency of all natural resource-related terms is calculated to determine the frequency of such expressions. ;

[0024] According to indicator data The three types of labels determine their The calculation requires selecting the categories of the baseline value and the actual value of the region. If it is a resource endowment category, select the ideal / safe baseline value; if it is a utilization efficiency category, select the advanced / average efficiency value; if it is a protection and restoration category, select the governance needs / planning target value. Finally, the calculation is completed by combining the formula of baseline value / actual value of the region.

[0025] The time decay coefficient The specific calculation formula is as follows:

[0026]

[0027] In the formula, Based on the base decay rate, derived from indicator data The three types of labels have been determined. For indicator data The corresponding year is the time elapsed since the assessment base year.

[0028] Further, step 3 includes:

[0029] Step 31: For each type of natural resource, sum the standardized values ​​of all corresponding indicator data in a weighted manner to obtain the overall resource score:

[0030]

[0031] in, This represents the comprehensive resource score for the Kth type of natural resource. For the standard value of the i-th type of indicator data of the K-th type of natural resource, is the dynamic weight of the i-th type of indicator data for the K-th type of natural resource, and n is all the indicator data items corresponding to the K-th type of natural resource.

[0032] Step 32: Sort the comprehensive resource scores of all natural resource types, identify the 1-2 natural resource types with the lowest scores as the bottleneck resources restricting the overall sustainable development of the corresponding assessment unit, i.e., the region. Then calculate the bottleneck index of the bottleneck resources to quantify the degree of constraint. The formula for calculating the bottleneck index is as follows:

[0033]

[0034] In the formula, The overall sustainability score for this resource deficiency is calculated. The highest overall resource score among all resources. The basic weights of the resources with shortcomings in the indicator system are determined using the analytic hierarchy process (AHP).

[0035] Furthermore, in step 4... The formula for calculating the contribution of each sub-target is as follows:

[0036] In the formula, To map to the The first under the sub-target Standardized values ​​of each indicator data To map to the The first under the sub-target Dynamic weights of individual indicator data To map to the All indicator data items under the sub-target.

[0037] The formula for calculating the contribution of the comprehensive SDGs is as follows:

[0038]

[0039] In the formula, To assess the total number of SDG sub-targets covered by the unit.

[0040] Further, step 5 includes:

[0041] Step 51: Using the weakness index and the contribution of the comprehensive SDGs as key features, construct a two-dimensional dataset for all evaluation units;

[0042] Step 52: Use a clustering algorithm to cluster the two-dimensional dataset, dividing all evaluation units into multiple clusters, i.e., categories;

[0043] Step 53: Determine the center point coordinates of each cluster, and assign a corresponding governance partition label to each cluster based on the quadrant position of the center point coordinates in the two-dimensional coordinate system.

[0044] Step 54: Determine the corresponding governance scheme from the structured governance scheme knowledge base based on the governance partition labels of the evaluation unit.

[0045] Furthermore, the structured governance scheme knowledge base is obtained through the systematic sorting and refinement of regional planning, academic research results, and local best governance practices. The governance scheme includes governance zoning types, leading strategies, key projects, and policy tools.

[0046] The beneficial effects of this invention include:

[0047] Based on the three dimensions of resource endowment, utilization efficiency, and protection and restoration, and combined with the logical framework of pressure, state, and response, three types of three-level diagnostic models were constructed. This enabled multi-dimensional and multi-level integrated diagnosis of natural resources, overcoming the problems of fragmented assessment objects and single perspectives in existing studies, and achieving a leap from local and static analysis to systemic and dynamic diagnosis.

[0048] By employing the short-board effect assessment method based on the quantitative output of the diagnostic model, key limiting resources that constrain regional sustainable development are accurately identified, and a short-board index is calculated to determine governance priorities. This avoids the average distribution of governance resources, achieves adaptive governance of resources, and ensures overall governance effectiveness.

[0049] By constructing a dynamic weighted SDGs contribution assessment model, the scarcity of resources, policy priorities, and spatiotemporal change factors are quantified and integrated into the assessment system. This enables the model to respond sensitively to dynamic changes in regional conditions and management needs, thereby enhancing the practical application effect and policy guidance significance of the assessment results.

[0050] Overcoming the limitations of existing technologies that focus on resource assessment and are difficult to match with actual governance, this paper adopts an objective partitioning method based on K-means clustering and an automatic matching mechanism based on a rule engine to directly transform complex multi-indicator assessment results into four clear and actionable partitioning governance paths: priority protection, optimized utilization, remediation governance, and appropriate development. This achieves an effective transition from scientific assessment to final decision-making, greatly enhancing the practical value and implementation effectiveness of the method. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating a method for comprehensive assessment and zoning governance of natural resource sustainability oriented towards SDGs, as described in an embodiment of this application.

[0052] Figure 2 This is a schematic diagram of the three-level, three-category diagnostics involved in the embodiments of this application.

[0053] Figure 3 This is a schematic diagram of natural resource zoning management based on two-dimensional coordinates, which is involved in the embodiments of this application.

[0054] Figure 4 This refers to the comprehensive sustainability score of various resources in a certain city in 2023, which is involved in the embodiments of this application. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0056] The following is in conjunction with the appendix Figure 1Specific embodiments of the present invention will be described in detail;

[0057] Each specific implementation of this invention targets a predetermined evaluation set containing multiple evaluation units. For example, in one implementation, the evaluation set could be all prefecture-level cities under the jurisdiction of a province, or all cities within a planning area. All evaluation units mentioned in the method refer to all spatial objects within the evaluation set targeted in that implementation.

[0058] Step 1: Construct corresponding indicator data for the natural resources involved in each assessment unit, and determine the natural resource sustainable development indicator system of the assessment unit in combination with the Sustainable Development Goals to obtain the corresponding system table. The system table includes resource type, benchmark SDGs and indicator data, wherein the resource type and benchmark SDGs are one-to-one or one-to-many, and the benchmark SDGs and indicator data are one-to-one or one-to-many.

[0059] The assessment unit is the basic spatial object for implementing this method, specifically including but not limited to administrative regions such as counties, cities, and provinces, or management areas such as river basins and ecological function zones. All subsequent data processing, calculation analysis, and result output in this embodiment are performed using the assessment unit as the basic unit. An indicator system covering seven natural resource types—arable land, orchards, forest land, grassland, water and wetlands, land, and energy—is constructed. Here, "land" refers to construction land.

[0060] Step 11, Target Mapping and Resource Type Definition:

[0061] Seventeen Sustainable Development Goals (SDGs) and their specific sub-goals (such as SDG 2.1, 6.3, 15.1, etc.) were identified as the basis for the classification. Based on the concept of a community of life and combined with management practices, the natural resources of the entire region were systematically divided into seven types: arable land, orchards, forest land, grassland, water and wetlands, land, and energy.

[0062] Step 12: Multi-source indicator screening and initial screening:

[0063] 1. Source Aggregation: Indicators from four major sources are collected and integrated extensively.

[0064] International standards: SDG global indicator framework and assessment systems of relevant international organizations (such as FAO, UNEP).

[0065] Industry standards: Technical standards and core indicators recognized by the industry, such as land surveys, environmental statistics, energy statistics, and water conservancy censuses, as well as statistical yearbooks.

[0066] Policy documents: Targeted and performance indicators clearly defined in documents such as ecological protection red lines and land spatial planning.

[0067] Academic consensus: High-frequency and mature indicators used in authoritative academic research in the field of resources and environment.

[0068] 2. Initial screening principle: Select initial indicators that simultaneously possess "scientific validity" (clear concept, measurable), "data availability" and "policy relevance" to form an initial indicator pool.

[0069] Step S13, System Validation and Final Determination:

[0070] Through expert review meetings, the initially constructed system underwent multiple rounds of evaluation to ensure that: 1) there were no major omissions in indicator coverage; 2) the PSR logic chain was complete and self-consistent; 3) the indicator definitions were unambiguous; and 4) the data acquisition path was clear.

[0071] The specific indicator system is shown in Tables 1 and 2. Each indicator is precisely aligned with a specific sub-target of the SDGs (such as SDGs 2.1, 6.3, 7.1, 11.7, 15.1, etc.), ensuring international comparability and policy relevance of the assessment. The system composition refers to the seven types of natural resources categorized. This column defines the object of the assessment, ensuring its systematic and comprehensive nature. Alignment with SDGs refers to the specific Sustainable Development Goals and sub-target numbers directly contributed to or mapped by this row of indicators (such as SDG 15.1). This column clarifies the international norms and policy anchoring of the assessment work and serves as the index for calculating the "SDG contribution." The indicator layer refers to the name of the actionable statistical or monitoring item used for specific quantitative measurement under a particular resource type (such as forest cover). This column provides the lowest-level, collectable data items required for the assessment. The SDGs in the table are not consecutive because this embodiment only extracts SGDs related to the natural resources sector, i.e., benchmark SDGs, and does not cover all SDGs, such as SDG 2.2: Eliminate all forms of malnutrition by 2030, including achieving the international targets related to stunting and wasting in children under five years of age by 2025, and addressing the nutritional needs of adolescent girls, pregnant women, lactating women, and the elderly. These types of indicators cannot be quantified using data from the natural resources sector.

[0072] Table 1. Index System for Cultivated Land, Orchards, Forest Land, Grassland, Water Areas, and Wetlands:

[0073]

[0074] Table 2. Indicator System for Land and Energy:

[0075]

[0076] The indicator type is a mathematical symbol used for data standardization. "+" indicates a positive indicator, meaning that the larger the indicator value, the better the sustainability; "-" indicates a negative indicator, meaning that the larger the indicator value, the worse the sustainability.

[0077] To eliminate the incomparability caused by differences in dimensions and properties among the indicators in the constructed indicator system, the collected raw indicator values ​​need to be standardized and converted into dimensionless values. Standardization is performed based on the indicator type, including:

[0078] For positive indicators (marked with "+"): the larger the value, the better the sustainability. The calculation formula is:

[0079] =

[0080] For negative indicators (marked with "-"): A higher value indicates worse sustainability. The calculation formula is:

[0081] =

[0082] in As an indicator The standardized value, Its original value, and These are the upper and lower reference limits for the indicator, respectively. The determination of these limits will prioritize international / industry standards, and secondarily adopt the actual extreme values ​​or planning target values ​​within the regional context.

[0083] After this processing, all standardized values All values ​​fall between [0,1] and can be directly used for subsequent comprehensive calculations.

[0084] Step 2: Classify and categorize the indicator data sequentially to determine three levels and three categories of labels; calculate the dynamic weights of the indicator data based on these labels; for example... Figure 2 As shown, the grading refers to determining the logical level based on the definition and semantics of the indicator data. The logical level includes pressure, state, and response. The classification refers to further determining the economic and ecological attributes of the indicator data after determining the logical level. The economic and ecological attributes include resource endowment, utilization efficiency, and protection and restoration.

[0085] Specifically, the logical hierarchy is defined as follows:

[0086] 1. Pressure (P) quantifies the amount of extraction, emissions, or disturbance to natural resource systems caused by human socio-economic activities. Related keywords / characteristics include: consumption, emissions, occupied area, growth rate, intensity, and load.

[0087] Examples: Annual timber harvesting volume (human exploitation of forests), water consumption per 10,000 yuan of GDP (the intensity of water consumption by economic activities).

[0088] 2. State S: Under pressure, the objective condition, stock level, or quality level of the resource system at a specific point in time. Keywords / characteristics include: coverage, per capita amount, possession, concentration, proportion, and quality grade.

[0089] Examples: forest coverage rate (the spatial stock of forest resources), per capita water resources (the natural endowment of water resources).

[0090] 3. Response R quantifies the policy measures, engineering actions, or management inputs taken by social systems (macro-management, businesses, the public) to prevent resource degradation, alleviate stress, or improve the state. Key terms / characteristics include: governance rate, investment amount, construction area, increase, adoption rate, and number of policies.

[0091] Examples: wastewater treatment rate (the coverage rate of treatment facilities built to improve water quality), afforestation area (ecological engineering projects taken to increase forest resources).

[0092] Specifically, the economic and ecological attributes are defined as follows:

[0093] 1. Resource Endowment Category: Primarily strongly correlated with the State (S) level. Describes the natural background, inherent stock, or spatial distribution of resources, which is the material basis for sustainable development.

[0094] If the status indicators describe the natural quantity or baseline quality of resources, they are classified into this category. (e.g., per capita arable land area (quantitative baseline), forest coverage rate (spatial baseline), per capita water resources (natural endowment)).

[0095] 2. Utilization Efficiency Category: Associated with the stress (P) and partial state (S), response (R) levels. Describes the intensity, methods, and economic benefits of human utilization and transformation of resources.

[0096] When classifying, stress indicators are categorized as follows: if the stress indicator originates from the utilization method in the production or consumption process (e.g., water consumption per 10,000 yuan of GDP); if the status indicator is the economic output or efficiency result generated after utilization (e.g., timber output value); and if the response indicator aims to improve resource conversion efficiency or optimize utilization technology (e.g., the rate of decline in construction land use area per unit of GDP).

[0097] 3. Protection and Restoration Category: Strongly correlated with the Stress (P) and Response (R) levels. Describes behaviors and effects related to resource conservation, protection, management, and restoration.

[0098] When classifying, if the stress indicator directly leads to ecological damage or quality decline, requiring restoration to address it, then it is classified into this category (e.g., increased area of ​​sandy land); if the response indicator is a direct action for ecological protection, pollution control, or degradation restoration, then it is classified into this category (e.g., controlling the area of ​​soil erosion).

[0099] Here, we will briefly explain the judgment process using two indicator data as examples:

[0100] Indicator Data 1: Annual Timber Harvesting Volume; describes the amount of forest resources extracted by humans, which fits the characteristics of stress (P); and this stress originates from the utilization behavior of forestry production, therefore it belongs to the utilization efficiency category. Final Label: Stress (P) - Utilization Efficiency Category.

[0101] Indicator Data 2: Area of ​​Soil Erosion Controlled; This describes engineering measures taken to improve land conditions, meeting the characteristics of a Response (R). Furthermore, this action is a direct ecological restoration, therefore it belongs to the Protection and Restoration category. Final Label: Response (R) - Protection and Restoration.

[0102] The specific division results are shown in Table 3-5:

[0103] Table 3. Classification of Cultivated Land, Gardens, and Forest Land into Three Levels and Three Categories:

[0104]

[0105] Table 4. Classification of Grassland, Water and Wetland, and Energy in Three Levels and Three Categories:

[0106]

[0107] Table 5. Land Classification by Level and Category:

[0108]

[0109] The formula for calculating the dynamic weight of the indicator data based on the three-level, three-category labels is as follows:

[0110]

[0111] In the formula, For indicator data The basic weights, For indicator data Policy priority coefficient For indicator data Resource scarcity coefficient For indicator data Time decay coefficient (γ<1) , This is the adjustment coefficient;

[0112] in, For indicator data Policy priority coefficient For indicator data The resource scarcity coefficient is calculated based on the three-level, three-category labels of the indicator data. According to indicator data The timeliness is determined.

[0113] Specifically, the calculation process for the three types of coefficients is as follows:

[0114] According to indicator data The three-level tags select the corresponding set of keywords from a pre-defined keyword library associated with each logical level, while simultaneously extracting indicator data. The system identifies and expands keyword sets based on their types. Keyword sets are then combined and matched to generate keyword pairs. Text mining is used to statistically analyze the frequency of related expressions appearing in policy documents, and the ratio of each keyword pair to the total frequency of all natural resource-related terms is calculated to determine the frequency of such expressions. .

[0115] The keyword library associated with each logical level is constructed by semantic extension based on the definition of the corresponding logical level. The keywords corresponding to pressure include consumption, emission, occupation, growth, and intensity. The keywords corresponding to state include coverage, per capita amount, ownership, concentration, proportion, and quality. The keywords corresponding to response include governance, investment, construction, improvement, popularization, and policy.

[0116] The refining index data Keywords in the text and expanding to generate a set of type keywords refer to determining indicator data through semantic processing. The core vocabulary and then expanded to all related vocabulary.

[0117] For example, the core keyword extracted from the proportion of high-standard farmland construction area is farmland, and the extended combination includes high-standard farmland, farmland, agriculture, etc. The corresponding third-level tag is response, and the corresponding hierarchical keyword set includes governance, investment, construction, improvement, popularization, policy, facilities, etc. The combined keyword pairs include farmland construction, farmland quality improvement, agricultural facilities, etc.

[0118] According to indicator data The three types of labels determine their The calculation requires selecting the categories of the baseline value and the actual value of the region. If it is a resource endowment category, select the ideal / safe baseline value; if it is a utilization efficiency category, select the advanced / average efficiency value; if it is a protection and restoration category, select the governance needs / planning target value. Finally, the calculation is completed by combining the formula of baseline value / actual value of the region.

[0119] The time decay coefficient Used to evaluate indicator data Timeliness refers to the impact of time; specifically, , The basic attenuation rate is determined by the index data. The three types of labels were determined as follows: If it is a pressure indicator such as pollutant emissions, considering that the cumulative environmental impact caused by historical emissions may exist for a long time and decay slowly, the corresponding value is higher, and the value is 0.9.

[0120] If it is a state indicator such as forest cover, since it reflects the stock result of the combined effects of human activities and natural succession over the past years, the decay is moderate, and the corresponding value is moderate, with a value of 0.85.

[0121] For response-oriented indicators such as pollution control investment, given that past investment results may have become entrenched or the technology may be outdated, the assessment should focus more on recent and continuous investments, which have the fastest decay and corresponding lower values, such as 0.7.

[0122] The value is the length of time between the year corresponding to the indicator data and the evaluation benchmark year. At the same time, it is uniformly stipulated that data with a maximum retrospective period of 10 years are included in the calculation, and the weight of data exceeding the period is set to zero to prevent the value from being diluted indefinitely.

[0123] Specifically, taking the proportion of high-standard farmland construction area as an example, its three-level, three-category label is: Response-Protection and Restoration.

[0124] (1) Policy priority coefficient The calculation process is as follows:

[0125] The analysis revealed the frequency of keywords related to farmland, such as farmland construction, farmland quality improvement, and agricultural facilities, in the statistical policy document. The total frequency of these keywords was 18. The total frequency of all resource and environmental keywords in the document was 150.

[0126] The calculation yielded: (Area of ​​high-standard farmland construction) = 18 / 150 = 0.12.

[0127] (2) Resource scarcity coefficient The calculation process is as follows: Select the ideal / safety background value;

[0128] The scarcity here should be understood as the degree of lack of such response measures, and the calculation coefficient needs to take into account the relative progress of the high-standard farmland construction response. The annual target set in the plan here, such as the average annual percentage target required by the "National High-Standard Farmland Construction Plan", is set at 1.5%.

[0129] Regional actual value: The proportion of high-standard farmland construction area in a certain city in 2023 was 6.73%.

[0130] SCi (High-standard farmland) = 1.5 / 6.73 ≈ 0.223;

[0131] The calculated SCi1 indicates that the city's construction efforts significantly exceeded the target requirements (the actual value was 4.5 times the target). This coefficient will significantly reduce the weight of this indicator because its response has been very sufficient.

[0132] (3) Time decay coefficient The calculation process is as follows:

[0133]

[0134] in, The value is 0.7 based on the response type. In this embodiment, the evaluation base year is 2023, and the indicator data... This represents the latest percentage of high-standard farmland construction area in 2023; the difference between the two years is... The value is 0.

[0135] Step 3: Calculate the comprehensive score of each natural resource type in the assessment unit and identify the weak resources, and then calculate the weak index of the weak resources.

[0136] Specifically, for each type of natural resource, the standardized values ​​of all corresponding indicator data are weighted and summed to obtain the overall resource score:

[0137]

[0138] in, This represents the comprehensive resource score for the Kth type of natural resource. For the standard value of the i-th type of indicator data of the K-th type of natural resource, represents the dynamic weight of the i-th type of indicator data for the K-th type of natural resource, and n represents all indicator data items corresponding to the K-th type of natural resource.

[0139] The comprehensive resource scores of all natural resource types are ranked, and the 1-2 types of natural resource types with the lowest scores are identified as the bottleneck resources that restrict the sustainable development of the corresponding assessment unit, i.e., the region as a whole. Then, the bottleneck index of the bottleneck resources is calculated to quantify the degree of constraint of the bottleneck resources. The larger the final bottleneck index value, the more serious the bottleneck effect of the resource type, and the higher its priority in subsequent regional governance.

[0140] Specifically, the formula for calculating the shortest board index is as follows:

[0141]

[0142] In the formula, The overall sustainability score for this resource deficiency is calculated. The highest overall resource score among all resources. The basic weights of the weakest resources in the indicator system are determined using the analytic hierarchy process, and the determination process is as follows:

[0143] Step 1: Establish a hierarchical structure model:

[0144] Objective layer (A): Sustainable management of regional natural resources; Criterion layer (B): Direct pairwise comparison of each type of natural resource; Solution layer (C): None.

[0145] Step 2: Construct the judgment matrix:

[0146] An expert panel of 15 experts in resources, environment, and planning was invited to conduct pairwise importance comparisons of the seven resource types using the 1-9 scale.

[0147] Table 6 is provided to simplify the demonstration, showing a significantly simplified example of three classes.

[0148] Table 6 Example of a judgment matrix formed by expert consensus:

[0149]

[0150] Matrix Interpretation: "Farmland" is considered slightly more important than "Forestland" (Table 3); "Farmland" is considered between equal and slightly more important than "Water Area" (Table 2); "Forestland" is considered between equal and slightly less important than "Water Area" (Table 1 / 2).

[0151] Step 3: Calculate the weight vector (taking the square root method as an example):

[0152] 1. Calculate the geometric mean of the elements in each row. :

[0153] =(1 3 2)^(1 / 3) = 6^(1 / 3) ≈ 1.817

[0154] =(1 / 3 1 1 / 2)^(1 / 3)≈0.550

[0155] =(1 / 2 2 1)^(1 / 3) = 1^(1 / 3) = 1.000

[0156] 2. Normalize the M vector to obtain the corresponding weight vector. :

[0157] SUM(M)=1.817+0.550+1.000=3.367

[0158] =1.817 / 3.367≈0.540

[0159] =0.550 / 3.367≈0.163

[0160] =1.000 / 3.367≈0.297

[0161] This is the preliminary weighting of the three types of resources: arable land, forest land, and water area.

[0162] Step 4: Consistency Check (a crucial step to ensure the logical consistency of expert judgment):

[0163] Calculate the largest eigenvalue of the judgment matrix :

[0164] Multiply the judgment matrix A by the weight vector W to obtain a new vector. .

[0165] =1*0.540+3*0.163+2*0.297=1.637

[0166] =(1 / 3)*0.540+1*0.163+(1 / 2)*0.297=0.490

[0167] =(1 / 2)*0.540+2*0.163+1*0.297=0.892

[0168] The average value = (1.637 / 0.540 + 0.490 / 0.163 + 0.892 / 0.297) / 3 ≈ 3.010

[0169] Calculate the consistency index (CI):

[0170] =(3.010-3) / (3-1)=0.005

[0171] Query the average random consistency index RI:

[0172] When n=3, RI=0.58 (standard value).

[0173] Calculate the consistency ratio (CR):

[0174] CR = CI / RI = 0.005 / 0.58 ≈ 0.009

[0175] Judgment: Since CR=0.009<0.10, the judgment matrix passes the consistency test, and the calculated weights (0.540, 0.163, 0.297) are acceptable and logically consistent.

[0176] By repeating steps two, three, and four above for the judgment matrix of the seven types of natural resources, a complete set of basic weight tables that pass the consistency test can be obtained.

[0177] Step 4: Determine the content of all benchmarked SDGs sub-objectives (such as SDGs 15.3) within the assessment unit, and calculate the weighted sum of the indicator data mapped to each sub-objective to obtain the contribution of the corresponding sub-objective. Then, sum the contributions of all sub-objectives to obtain the comprehensive SDGs contribution.

[0178] Specifically, the first The formula for calculating the contribution of each sub-target is as follows:

[0179] In the formula, To map to the The first under the sub-target Standardized values ​​of each indicator data To map to the The first under the sub-target Dynamic weights of individual indicator data To map to the All indicator data items under the sub-target.

[0180] The formula for calculating the contribution of the comprehensive SDGs is as follows:

[0181]

[0182] In the formula, To assess the total number of SDG sub-targets covered by the unit.

[0183] Step 5: Using the bottleneck index and the contribution of the comprehensive SDGs as key features, construct a dataset for all assessment units, perform cluster analysis on the dataset to determine the governance label of each assessment unit, and match differentiated governance schemes according to the governance label.

[0184] Step S51: Objective partitioning based on K-means clustering:

[0185] The K-means clustering algorithm is used to achieve objective and automated partitioning of governance areas, which is completely data-driven and avoids the subjectivity of manually setting thresholds.

[0186] The Shortest Board Index (SII) output from step 3 (reflecting the constraint strength of critical resources) and the comprehensive SDGs contribution output from step 4 ( (Reflecting the overall sustainability level of the resource system) are used as two key features to construct a two-dimensional dataset for all assessment units.

[0187] The above two-dimensional dataset is input into the K-means clustering algorithm, with a preset cluster size of 4. The algorithm automatically finds the natural grouping of data points through iterative calculation, and finally divides all evaluation units into 4 categories (clusters).

[0188] After clustering, the coordinates of the centroid of each cluster are analyzed. Based on the quadrant position of the centroid in the two-dimensional coordinate system of comprehensive SDG contribution-bottom index, each cluster is assigned a corresponding governance partition label, completing the mapping from clustering results to governance partitions, such as... Figure 3 As shown.

[0189] Among them, located in high Clusters in the high SII quadrant are marked as priority protected areas; those located in the high... Clusters in the lower SII quadrant are marked as optimized utilization areas; those located in the lower... Clusters in the high SII quadrant are marked as remediation and treatment zones; those in the low SII quadrant are... Clusters in the lower SII quadrant are marked as moderately developed areas.

[0190] Step 52: Building a knowledge base for structured governance solutions:

[0191] To achieve automatic matching of governance solutions, a standardized and structured knowledge base of governance solutions needs to be pre-built. This knowledge base is derived from a systematic review and refinement of regional planning, academic research findings, and local best practices in governance, and a complete set of solution templates is preset for each governance zone. The core content of the knowledge base is shown in Table 7:

[0192] Table 7. Examples of Knowledge Base Content:

[0193]

[0194] Step 53: Automatic matching and generation of governance paths:

[0195] Using the partition label (such as the remediation governance area) of each evaluation unit obtained in step 51 as the input key, a pre-programmed rule engine (which is essentially a key-value pair mapping) retrieves and calls (i.e. matches) the complete set of governance schemes that uniquely correspond to it from the governance scheme knowledge base constructed in step 52.

[0196] A customized governance report is automatically generated for each assessment unit. This report directly integrates the leading strategies, key project lists, and policy toolkits matched from the knowledge base, forming a clear and actionable regional governance path, providing direct and precise decision support for regional natural resource management and sustainable development investment.

[0197] To verify the feasibility and effectiveness of this embodiment, the method was implemented using the entire province as an example. The evaluation set included all 21 prefectures and cities in the province, with 2023 as the base year (the base period data for some growth indicators was 2019).

[0198] This embodiment evaluates all 21 cities and prefectures in a province. Data from all 21 cities and prefectures in the province was collected, and the weakness index and SDG contribution of each city were calculated, resulting in 21 data points. K-means clustering was performed on these 21 points, objectively dividing them into four groups, namely four governance type zones, thereby generating differentiated governance plans for each city.

[0199] Specifically, taking a certain city as an example, the raw data of various indicators for that city in 2023 were collected, and the comprehensive sustainability score of various resources for that city in 2023 was calculated according to the comprehensive sustainable development assessment method. Figure 4 As shown (the base period data for growth indicators is 2019). Figure 4 The bar chart clearly reveals the significant differences in the sustainability status of different types of natural resources in a certain city. Land resources (0.577) and orchard resources (0.553) scored the highest and also showed a relative advantage compared to other assessment units in the province. This indicates that the city performs well in terms of intensive use of construction land, coordination of industrial development, and construction and management of parks and green spaces. Forest resources (0.549) and energy resources (0.549) are tied, showing good performance. This reflects the solid achievements the city has made in ecological construction (increasing forest coverage and building nature reserves) and the clean transformation of its energy structure. Grassland resources (0.474) are at a medium level, indicating that its ecosystem status and governance efforts are in a stable intermediate state. Water resources (0.435) face significant pressure, with the core constraint being the extremely scarce per capita water resources. Cultivated land resources are currently the weakest link in the system. Its extremely low score (0.215) is mainly due to multiple pressures such as "the per capita arable land area is far below the warning line", "the rate of non-agriculturalization of arable land is high" and "the yield per unit area of ​​grain needs to be improved".

[0200] A certain city was designated as a restoration and remediation zone based on its two-dimensional data coordinates (SII=0.088, C_Total=3.352).

[0201] Specifically, the calculation of the short-board index for a certain city is shown in Table 8:

[0202] Table 8. Example of calculating the short-board index for a certain city:

[0203]

[0204] The basic weights of the weakest resource in the table are calculated based on all seven categories of natural resources, and therefore differ from the basic weights calculated for the three categories of natural resources described above.

[0205] The overall sustainability level of natural resources in a certain city is moderate (SDGS contribution: 3.352), but significant shortcomings exist, with arable land resources (overall score: 0.215) being a key constraint. Meanwhile, water resources (overall score: 0.435) also face considerable pressure, requiring systematic restoration and comprehensive management. Examples of management pathways are shown in Table 9.

[0206] Table 9 Examples of Governance Paths:

[0207]

[0208] The embodiments described above merely illustrate specific implementation methods of this application, and while the descriptions are detailed and specific, they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the technical solution of this application, and these modifications and improvements all fall within the scope of protection of this application.

Claims

1. A comprehensive assessment and zoning management method for the sustainability of natural resources oriented towards the SDGs, characterized in that, Includes the following steps: Step 1: Construct corresponding indicator data for the natural resources involved in each assessment unit, and determine the natural resource sustainable development indicator system of the assessment unit in combination with the Sustainable Development Goals to obtain the corresponding system table. The system table includes resource type, benchmark SDGs and indicator data, wherein the resource type and benchmark SDGs are one-to-one or one-to-many, and the benchmark SDGs and indicator data are one-to-one or one-to-many. Step 2: The indicator data is sequentially graded and classified to determine three levels and three categories of labels, and the dynamic weight of the indicator data is calculated based on the three levels and three categories of labels; the grading refers to determining the logical level according to the definition and semantics of the indicator data, and the logical level includes pressure, state and response; the classification refers to further determining the economic and ecological attributes of the indicator data after determining the logical level, and the economic and ecological attributes include resource endowment, utilization efficiency and protection and restoration. Step 3: Calculate the comprehensive score of each natural resource type within the assessment unit and identify the weak resources, then calculate the weak index of the weak resources; Step 4: Determine all SDG sub-objectives within the assessment unit, and sum the weighted index data mapped to each sub-objective to obtain the contribution of the corresponding sub-objective. Then, sum the contributions of all sub-objectives to obtain the overall SDG contribution. Steps 3 and 4 are not in any particular order; Step 5: Using the weakness index and the contribution of the comprehensive SDGs as key features, construct a dataset for all assessment units, perform cluster analysis on the dataset to determine the governance label of each assessment unit, and match differentiated governance solutions based on the governance label; The formula for calculating the dynamic weight in step 2 is as follows: ； In the formula, For indicator data The basic weights, For indicator data Policy priority coefficient For indicator data Resource scarcity coefficient For indicator data The attenuation coefficient, For indicator data The corresponding year is the length of time from the assessment base year. , This is the adjustment coefficient; in, For indicator data Policy priority coefficient For indicator data The resource scarcity coefficient is calculated based on the three-level, three-category labels of the indicator data. According to indicator data The timeliness is determined; In step 4 The formula for calculating the contribution of each sub-target is as follows: ； In the formula, To map to the The first under the sub-target Standardized values ​​of each indicator data To map to the The dynamic weight of the nth indicator data under the sub-objective. For all indicator data items mapped to the j-th sub-target; The formula for calculating the contribution of the comprehensive SDGs is as follows: ； In the formula, To assess the total number of SDG sub-targets covered by the unit.

2. The method for comprehensive assessment and zoning management of natural resource sustainability oriented towards SDGs as described in claim 1, characterized in that, The resource types include arable land, orchards, forest land, grassland, water and wetlands, land, and energy. The assessment unit refers to the basic spatial management unit or administrative unit for the sustainable assessment of natural resources. The indicator data need to be standardized and uniformly converted into dimensionless values.

3. The method for comprehensive assessment and zoning management of natural resource sustainability oriented towards SDGs as described in claim 1, characterized in that, In step 2, the pressure refers to the corresponding indicator that directly quantifies the amount of extraction, emissions, or disturbance intensity of human socio-economic activities on the natural resource system; the state refers to the corresponding indicator that describes the objective condition, stock level, or quality level of the resource system at a specific point in time under the influence of pressure; and the response refers to the corresponding indicator that quantifies the policy measures, engineering actions, or management inputs taken by the social system to prevent resource degradation, reduce pressure, or improve the state.

4. The method for comprehensive assessment and zoning management of natural resource sustainability oriented towards SDGs as described in claim 1, characterized in that, In step 2, the resource endowment category is strongly correlated with the state level. When classifying, if the state indicator describes the natural quantity or baseline quality of the resource, it is classified into this category. The utilization efficiency category is correlated with pressure and some state and response levels. When classifying, if the pressure indicator originates from the utilization method in the production or consumption process, it is classified into this category. If the state indicator is the economic output or efficiency result generated after utilization, it is classified into this category. If the response indicator aims to improve resource conversion efficiency or optimize utilization technology, it is classified into this category. The protection and restoration category is strongly correlated with the pressure and response levels. When classifying, if the pressure indicator directly leads to ecological damage or quality decline and needs to be addressed through restoration, it is classified into this category. If the response indicator is a direct ecological protection, pollution control, or degradation restoration action, it is classified into this category.

5. A comprehensive assessment and zoning management method for natural resource sustainability oriented towards SDGs as described in claim 1, characterized in that, According to indicator data The three-level tags select the corresponding set of keywords from a pre-defined keyword library associated with each logical level, while simultaneously extracting indicator data. The system identifies and expands keyword sets based on their types. Keyword sets are then combined and matched to generate keyword pairs. Text mining is used to statistically analyze the frequency of related expressions appearing in policy documents, and the ratio of each keyword pair to the total frequency of all natural resource-related terms is calculated to determine the frequency of such expressions. ; According to indicator data The three types of labels determine their The calculation requires the classification of the baseline value and the actual value of the region. If it is a resource endowment category, select the ideal / safe baseline value; if it is a utilization efficiency category, select the advanced / average efficiency value; if it is a protection and restoration category, select the governance needs / planning target value. Finally, the calculation is completed by combining the formula of baseline value / actual value of the region. The The calculation formula is as follows: ； In the formula, Based on the base decay rate, derived from indicator data The three types of labels have been determined. For indicator data The corresponding year is the time elapsed since the assessment base year.

6. A comprehensive assessment and zoning management method for natural resource sustainability oriented towards SDGs, as described in claim 1, is characterized in that... Step 3 includes: Step 31: For each type of natural resource, sum the standardized values ​​of all corresponding indicator data in a weighted manner to obtain the overall resource score: ； in, This represents the comprehensive resource score for the Kth type of natural resource. For the Kth type of natural resource Standard values ​​for class indicator data For the Kth type of natural resource The dynamic weights of the class indicator data, where n represents all indicator data items corresponding to the Kth class of natural resource types; Step 32: Sort the comprehensive resource scores of all natural resource types, identify the 1-2 natural resource types with the lowest scores as the bottleneck resources restricting the overall sustainable development of the corresponding assessment unit, i.e., the region. Then calculate the bottleneck index of the bottleneck resources to quantify the degree of constraint. The formula for calculating the bottleneck index is as follows: ； In the formula, The overall sustainability score for this resource deficiency is calculated. The highest overall resource score among all resources. The basic weights of the resources with shortcomings in the indicator system are determined using the analytic hierarchy process (AHP).

7. A comprehensive assessment and zoning management method for natural resource sustainability oriented towards SDGs, as described in claim 1, is characterized in that... Step 5 includes: Step 51: Using the weakness index and the contribution of the comprehensive SDGs as key features, construct a two-dimensional dataset for all evaluation units; Step 52: Use a clustering algorithm to cluster the two-dimensional dataset, dividing all evaluation units into multiple clusters, i.e., categories; Step 53: Determine the center point coordinates of each cluster, and assign a corresponding governance partition label to each cluster based on the quadrant position of the center point coordinates in the two-dimensional coordinate system. Step 54: Determine the corresponding governance scheme from the structured governance scheme knowledge base based on the governance partition labels of the evaluation unit.

8. A comprehensive assessment and zoning management method for natural resource sustainability oriented towards SDGs, as described in claim 7, is characterized in that... The structured governance scheme knowledge base is obtained through the systematic sorting and refinement of regional planning, academic research results, and local best governance practices. The governance scheme includes governance zoning types, leading strategies, key projects, and policy tools.

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