A method for evaluating regional natural resource utilization efficiency considering both flow and stock

Abstract

This invention relates to the field of natural resource surveying, monitoring, and accounting, specifically to a method for evaluating the efficiency of regional natural resource utilization that considers both flow and stock. The method includes: determining the spatial boundaries of the target area and defining the baseline time point; classifying all natural resources within the target area that fall under the scope of natural resource accounting into two categories: stock resources and flow resources; calculating the corresponding physical quantities of each stock resource and each flow resource; integrating all physical quantities of natural resources to the district / county scale to form district / county-level statistical results; processing the district / county-level statistical results using the entropy weight method to obtain comprehensive stock indicators and comprehensive flow indicators; constructing heterogeneity constraint factors based on the "three zones and three lines" framework; and evaluating the efficiency of natural resource utilization in each district / county using the comprehensive stock indicator as input and the comprehensive flow indicator as output through an SBM model incorporating heterogeneity constraint factors. This invention improves the accuracy of the accounting results.

Description

Technical Field

[0001] This invention relates to the field of natural resource survey, monitoring, and accounting, and specifically to a method for evaluating the efficiency of regional natural resource utilization that takes into account both flow and stock. Background Technology

[0002] With the development of the global economy, the economic attributes of natural resources in the market economy are becoming increasingly prominent. Natural resources not only provide for human survival and well-being but also participate more extensively in market activities such as factor supply, financing and lending, and equity investment. Against this backdrop, evaluating the efficiency of natural resource utilization from the perspective of capital management has become an important requirement in current administrative management. Currently, related work mainly faces the following problems:

[0003] In the process of natural resource accounting and quantification, a systematic framework has not yet been formed, making it difficult to uniformly measure and standardize the assessment of diverse and heterogeneous natural resources (e.g., land is measured by area, and mineral resources by mass or volume). The differences in physical dimensions among different resource types result in a lack of reliable benchmarks for comprehensive evaluation, directly affecting the accuracy of accounting results and their cross-regional comparability.

[0004] Research on the dynamic relationship between natural resource stock and flow is significantly insufficient. Existing methods rarely effectively characterize the intrinsic connection between stock and flow, resulting in a lack of dynamic dimension support for natural resource utilization efficiency assessment, making it difficult to adapt to diverse natural resource management needs.

[0005] Existing methods for assessing the efficiency of natural resource utilization have not adequately considered the impact of binding factors such as ecological protection red lines, urban development boundaries, and permanent basic farmland boundaries. This makes it difficult for the assessment results to reflect the boundary effects of rigid constraints on resource utilization, and also weakens the practical guiding significance of the accounting conclusions for decision support. Summary of the Invention

[0006] To address the above problems, this invention provides a method for evaluating the efficiency of regional natural resource utilization that takes into account both flow and stock, comprising:

[0007] S1. Determine the spatial boundaries of the target area and specify the baseline time point for evaluating the target area;

[0008] S2. Divide all resources within the target area that fall under the scope of natural resource accounting into two categories: stock resources and flow resources, wherein:

[0009] The scope of natural resource accounting covers natural resources that can be directly or indirectly assessed by the market, as well as ecosystem services derived from them, which are also a type of natural resource.

[0010] Existing resources refer to resources that have physical entities, exist for a long time, are in a stable state, have clear reserve characteristics and physical attributes, and can be directly measured in units of measurement.

[0011] Flow resources refer to resources derived from existing resources, which are fluid, dynamically changing, and whose physical quantity is easily affected by external environmental factors.

[0012] S3. Calculate the corresponding physical quantity of each existing resource;

[0013] S4. Based on the GEP accounting method and biophysical process model, the corresponding physical quantities of each flow resource, excluding agricultural, forestry, animal husbandry and fishery outputs, are calculated; the physical quantities of agricultural, forestry, animal husbandry and fishery outputs are obtained from the annual statistical yearbooks of the target region.

[0014] S5. Integrate the physical quantities of all resources within the target area that fall under the scope of natural resource accounting to the district / county level to form district / county-level statistical results; the district / county-level statistical results include the physical quantities of each resource in each district / county within the target area;

[0015] S6. The entropy weight method is used to process the statistical results at the district and county levels to obtain the comprehensive stock index of each stock resource in each district and county within the target area, as well as the comprehensive flow index of each flow resource in each district and county within the target area.

[0016] S7. Construct heterogeneity constraint factors for each district and county within the target area based on the three zones and three lines;

[0017] S8. In each district and county, the efficiency of natural resource utilization is evaluated by using the comprehensive stock index as input and the comprehensive flow index as output through the SBM model that introduces heterogeneity constraint factors.

[0018] The beneficial effects of this invention are:

[0019] This invention provides a complete framework and corresponding steps for natural resource accounting and quantification that is applicable to administrative management needs and can be replicated in batches in different regions. While ensuring that the accounting items are as comprehensive as possible, it optimizes the sources of data and parameters in the accounting process, thereby improving the accuracy of the accounting results.

[0020] This invention effectively distinguishes between flow and stock in natural resources, forming a brand-new approach and scheme for evaluating the efficiency of natural resource utilization, and providing support for the efficient allocation and high-level protection of natural resources.

[0021] When evaluating utilization efficiency, this invention effectively solves the problems of difficult integration and comparison of multi-source heterogeneous data by systematically dimensionless processing of the original input-output indicators, thereby improving the accuracy and reliability of the comprehensive evaluation results and providing a unified benchmark for efficiency comparison and analysis in different regions and at different times.

[0022] This invention objectively quantifies the constraint intensity of the "three zones and three lines" spatial control policy and constructs a heterogeneous constraint factor embedded SBM model, which enables the model to truly reflect the differentiated restrictions of spatial policies on efficiency values, thereby greatly improving the real-world interpretability and policy fit of the evaluation results. Attached Figure Description

[0023] Figure 1 This is a flowchart of the method of the present invention;

[0024] Figure 2 This is a logical concept diagram of an embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] This invention is primarily developed to address the needs of municipal administrative departments for the capitalization management of natural resources. Existing natural resource accounting methods suffer from incomplete accounting categories and difficulty in unifying different accounting contents to the same dimensional dimension, resulting in insufficient accuracy of accounting results. Furthermore, in the capitalization management process, a complete utilization efficiency evaluation system is lacking, making it difficult to effectively quantify resource utilization status.

[0027] Therefore, this invention proposes a method for evaluating the efficiency of regional natural resource utilization that takes into account both flow and stock. This method first constructs a natural resource accounting framework, improving the accuracy of the accounting results by integrating multiple accounting categories. Based on this, it further distinguishes between flow resources and stock resources, using them as input and output indicators respectively, thereby comprehensively evaluating the efficiency of natural resource utilization within the region.

[0028] After explaining the overall concept of the present invention, in order to more clearly illustrate the purpose, technical solution and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0029] In some embodiments, such as Figure 1As shown, a method for evaluating the efficiency of regional natural resource utilization that takes into account both flow and stock includes the following steps:

[0030] S1. Determine the spatial boundaries of the target area and specify the baseline time point for evaluating the target area.

[0031] Specifically, the benchmark time point is specified down to the year.

[0032] S2. All resources within the target area that fall under the scope of natural resource accounting are divided into two categories: stock resources and flow resources, wherein:

[0033] The scope of natural resource accounting covers natural resources that can be directly or indirectly assessed by the market, as well as the ecosystem services derived from them.

[0034] Existing resources refer to resources that have physical entities, exist for a long time, are in a stable state, have clear reserve characteristics and physical attributes, and can be directly measured in units of measurement.

[0035] Flow resources refer to resources derived from existing resources, which are fluid, dynamic, and whose physical quantity is easily affected by external environmental factors.

[0036] Specifically, in the method of this invention, the accounting items for existing resources should cover common categories such as agricultural land, construction land, forest land, grassland, water bodies, and minerals, as well as other natural resources with significant local characteristics (such as marine resources in some areas). The accounting items for flow resources should include common service types such as agricultural, forestry, animal husbandry and fishery products, carbon sequestration and oxygen release, flood control, water conservation, water purification, air purification, climate regulation, and soil conservation, as well as other natural resources that reflect regional characteristics.

[0037] S3. Calculate the corresponding physical quantity of each existing resource.

[0038] In some embodiments, step S3 uses different metering units to calculate the physical quantity for different stock resources, including:

[0039] For mineral resources, extract the proven mineral types and their corresponding reserves from the mineral resource reserve database of the target area;

[0040] For land resources, geographic information system software is used to extract secondary land category classification information from the land change survey data of the target area at the benchmark time point, and the total area of ​​each secondary land category is calculated.

[0041] Regarding water resources, surface water and groundwater storage data are extracted from water resources bulletins published in the target area at the benchmark time point, and the total water resource storage of the target area is calculated by summarizing the data.

[0042] In some embodiments, in addition to common categories such as mineral resources, land resources and water resources, for other stock resources with significant local characteristics (such as marine resources in certain areas), physical quantity information is extracted at a reference time point in the target area by combining field surveys with existing databases.

[0043] S4. Based on the GEP accounting method and biophysical process model, the corresponding physical quantities of various flow resources other than agricultural, forestry, animal husbandry and fishery outputs are calculated; the physical quantities of agricultural, forestry, animal husbandry and fishery outputs are obtained from the annual statistical yearbooks of the target region.

[0044] S5. Integrate the physical quantities of all resources within the scope of natural resource accounting in the target area to the district / county level to form district / county-level statistical results; the district / county-level statistical results include the physical quantities of each resource in each district / county within the target area, wherein the resources are still classified according to stock resources and flow resources.

[0045] In some embodiments, step S5 includes:

[0046] S51. If the resource accounting results are presented in raster layer form, then use ArcGIS's Zonal Statistics tool to summarize and statistically analyze the resource at the district / county scale to obtain the physical quantity of the resource in each district / county within the target area. In particular, the physical quantity of flow resources is generally obtained through multi-source data inversion, and its accounting results are usually presented in raster layer form.

[0047] S52. If the resource accounting results are presented in the form of a vector layer, since its attribute table usually already contains district and county information, the ArcGIS Summary Statistics tool can be used to summarize and statistically analyze the resource at the district and county scale to obtain the physical quantity of the resource in each district and county within the target area.

[0048] S53. Summarize the physical quantities of all resources in each district and county within the target area to form district and county-level statistical results.

[0049] S6. The entropy weight method is used to process the statistical results at the district and county levels to obtain the comprehensive stock index of each stock resource in each district and county within the target area, as well as the comprehensive flow index of each flow resource in each district and county within the target area.

[0050] In some embodiments, step S6 includes:

[0051] S61. For the physical quantities of each existing resource and each flow resource, the minimum-maximum standardization method is used to perform dimensionless transformation to obtain the corresponding standard physical quantities. The calculation formula is as follows:

[0052]

[0053]

[0054] In the formula, x si Let y represent the physical quantity of the i=1,2,…,m stock resource in the s-th district / county. sj Let m represent the physical quantity of the flow resource j=1,2,…,n in the s-th district / county, m represent the quantity of stock resources within the scope of natural resource accounting in the target area, and n represent the quantity of flow resources within the scope of natural resource accounting in the target area. Let represent the standard physical quantity (dimensionless, range [0,1]) of the i-th stock resource in the s-th district / county. Let represent the physical quantity (dimensionless, range [0,1]) of the j-th flow resource in the s-th district / county.

[0055] S62. Introducing a smoothing factor and probabilistic transformation, the probabilistic values ​​of each resource are calculated using standard physical quantities to characterize the distribution characteristics of the standard physical quantities of each resource. The calculation formula is as follows:

[0056]

[0057]

[0058] In the formula, This represents the minimum value correction factor, used to prevent subsequent logarithmic calculations from being invalid when the standard physical quantity is 0; p si Let q represent the probabilistic value of the i-th existing resource in the s-th district / county. sj This represents the probabilistic value of the j-th flow resource in the s-th district / county. The probabilistic value reflects the distribution ratio of each natural resource across different districts / counties.

[0059] Specifically, The value is 10 −8 .

[0060] S63. Calculate the information entropy of each resource based on the probabilistic values ​​to quantify the dispersion of natural resources. The specific calculation method is as follows:

[0061]

[0062]

[0063] In the formula, The information entropy of the i-th existing resource (range [0,1]) represents the information entropy of the i-th existing resource. Let S represent the information entropy of the j-th flow resource (range [0,1]); S represents the number of districts and counties within the target area. The smaller the information entropy value, the greater the dispersion of the natural resource indicator and the more information it provides.

[0064] S64. According to the information entropy theory, the dispersion of a resource is negatively correlated with its weight. Based on this, an objective method for determining the entropy weight of each resource is established, and the specific calculation method is as follows:

[0065]

[0066]

[0067] In the formula, This represents the entropy weight of the i-th existing resource (range [0,1]). ); This represents the entropy weight of the j-th flow resource (range [0,1]). ).

[0068] S65. For each existing resource, multiply its standard physical quantity by its entropy weight to obtain a comprehensive stock index; for each flow resource, multiply its standard physical quantity by its entropy weight to obtain a comprehensive flow index; the specific calculation formula is as follows:

[0069]

[0070]

[0071] In the formula, This represents the i-th comprehensive stock index of the s-th district / county; This represents the j-th comprehensive flow indicator of the s-th district / county.

[0072] The above calculations yield comprehensive stock indicators and comprehensive flow indicators that integrate standardized processing and objective weight allocation, and can be directly substituted into the SBM model for calculation.

[0073] S7. Construct heterogeneity constraint factors for each district and county within the target area based on the three zones and three lines.

[0074] Specifically, the "three zones and three lines" refer to three types of spaces: ecological space, agricultural space, and urban space, and the three control lines corresponding to them: ecological protection red line, permanent basic farmland protection red line, and urban development boundary. This is the core framework of territorial spatial planning.

[0075] In some embodiments, step S7 includes:

[0076] S71. Quantify the ecological protection red line, permanent basic farmland protection red line, and urban development boundary in the three zones and three lines respectively to obtain the corresponding heterogeneity constraint factors. The calculation formula is as follows:

[0077]

[0078] In the formula, A 总,s This represents the total administrative area (km²) of the s-th district / county.2 A 生态,s This represents the area (km²) of the ecological protection red line in the s-th district / county. 2 ); f 1,s ∈[0,1] represents the ecological protection red line constraint factor (purely negative constraint) of the s-th district / county, that is, the first constraint factor of the s-th district / county; f 1,s The closer the value is to 0, the higher the proportion of the ecological protection red line, the stronger the spatial constraints on natural resource utilization, and the more limited the available space. For example, in areas with high ecological protection levels, development and utilization activities are often significantly restricted. Conversely, f 1,s The closer the value is to 1, the lower the proportion of ecological protection red lines and the weaker the spatial constraints on the use of natural resources.

[0079]

[0080] In the formula, A 农田,s This represents the area of ​​the permanent basic farmland protection red line in the s-th district / county (km²). 2 ); f 2,s ∈[0,1] represents the constraint factor (moderate negative constraint) of the permanent basic farmland protection red line of the s-th district / county, that is, the second constraint factor of the s-th district / county; β represents the moderate proportion threshold, which is set based on the regional agricultural development planning standards of each district / county. If β=0.3, it indicates that the proportion of the permanent basic farmland protection red line to the total area of ​​the administrative division is equal to 30%, which is a reasonable range to avoid extreme proportion deviations. When A 农田,s With A 总,s When the proportion is within β, f 2,s The value increases with the increase of the proportion, reflecting the supporting role of agricultural production; after exceeding β, f 2,s The value decreases as the proportion increases, reflecting the increased constraints on non-agricultural use.

[0081]

[0082] In the formula, A 城镇,s This represents the controlled area of ​​the urban development boundary of the s-th district / county (km²). 2 ), that is, the area of ​​urban construction land permitted for development within the urban development boundary; f 3,s ∈[0,1] represents the urban development boundary control constraint factor (moderate positive support) of the s-th district / county, i.e., the 3rd constraint factor of the s-th district / county; γ represents the reasonable proportion threshold, which is set based on urban planning standards. If γ=0.2, it indicates that the proportion of the urban development boundary to the total area of ​​the administrative division is equal to 20%, which is a reasonable range, balancing urban development and space encroachment. When the proportion is within γ, f 3,s The value increases with the increase of the proportion, reflecting the spatial support for urban development and improving the efficiency of construction land use; after exceeding γ, f 3,sThe value decreases as the proportion increases, reflecting the encroachment on ecological and agricultural space and the constraint on overall efficiency.

[0083] S72. Calculate the entropy weight for each type of constraint factor, using the following formula:

[0084]

[0085]

[0086]

[0087] In the formula, p t,s ∈[0,1] represents the standard value of the constraint factor of the s-th district / county at t=1,2,3, where ε represents the minimum correction coefficient, and e t Let w represent the information entropy of the t-th constraint factor. t Let w1 + w2 + w3 represent the entropy weight of the t-th constraint factor, where w1 + w2 + w3 = 1.

[0088] Specifically, ε takes the value 10. -8 .

[0089] This step uses the entropy weighting method to assign objective weights based on the dispersion of the three types of constraint factors across all districts and counties, thus avoiding the influence of subjective judgment on the importance of direction.

[0090] S73. Calculate the heterogeneity constraint factor based on the entropy weight of the constraint factor. The calculation formula is:

[0091]

[0092] In the formula, α s ∈[0,1] represents the heterogeneity constraint factor of the s-th district / county, α s The larger the value, the more abundant the space for natural resource utilization in the s-th district / county, the weaker the constraints, and the higher the effective scale of input and output; conversely, the smaller the value, the stronger the constraints and the lower the effective scale.

[0093] The heterogeneity constraint factor calculated above will be used as a constraint condition in the SBM model.

[0094] S8. In each district and county, the efficiency of natural resource utilization is evaluated by using the comprehensive stock index as input and the comprehensive flow index as output through the SBM model that introduces heterogeneity constraint factors.

[0095] In some embodiments, the Slack-Based Measure (SBM) model is a non-radial, non-directed Data Envelopment Analysis (DEA) method. In this model, slack variables are key decision variables in the optimization process. They are not pre-set parameters, and their values ​​are not calculated in advance through independent pre-calculation formulas. Instead, they are determined simultaneously as part of the optimal solution of the model by solving the linear programming (LP) problem of the model, obtaining the optimal efficiency value. This invention introduces heterogeneity constraints based on the traditional SBM model, aiming to solve the problem of efficiency evaluation bias caused by differences in technological level, regional characteristics, and resource endowment among decision-making units.

[0096] In the SBM model with the heterogeneity constraint factor, the objective function is expressed as:

[0097]

[0098] The constraints are expressed as follows:

[0099]

[0100]

[0101]

[0102]

[0103] In the formula, ρ s ∈[0,1] represents the natural resource utilization efficiency of the s-th district / county, ρ s The closer the value is to 1, the better the efficiency of natural resource utilization. This represents the input slack variable for the i-th input indicator (comprehensive stock indicator); Let λ represent the output slack variable of the j-th output indicator (comprehensive flow indicator). s This represents the weight coefficient of the s-th district / county. Input slack variables. Output slack variables Weighting coefficient λ s Natural resource utilization efficiency ρ s These are all decision variables automatically generated after solving the linear programming problem (including the objective function and all constraints) of the SBM model.

[0104] In particular, This is used to quantify the redundancy in which the actual investment in the i-th existing resource in the s-th district / county under evaluation exceeds the frontier optimal investment. This indicates no waste; This is used to quantify the gap between the actual output of the j-th flow resource in the s-th district / county to be evaluated and the frontier optimal output. This indicates that there is nothing lacking.

[0105] For example, based on business needs, the area under the jurisdiction of a city in Southwest China is identified as the target region, and an assessment of its natural resource utilization efficiency is conducted, specifically covering:

[0106] STEP 1: Determine the spatial boundary of the target area to provide a basis for subsequent data processing and mask analysis. This boundary is defined based on the vector files of the city's municipal boundaries and district / county-level boundaries. The baseline time for evaluating the target area is specified as 2023.

[0107] STEP2: Divide all natural resources within the target area that fall under the scope of natural resource accounting into two categories: stock resources and flow resources.

[0108] Specifically, the scope of natural resource accounting, stock resources, and flow resources are defined as follows:

[0109] The scope of natural resource accounting covers natural resources that can be directly or indirectly assessed by the market, as well as the ecosystem services derived from them. It is important to note that all accounting items in this embodiment of the invention must be limited to this scope. This is because the goal of this invention is to promote the marketization and efficient utilization of natural resources; therefore, accounting items must be accepted or recognized by the market. Items outside this scope will not be included in the accounting system. For example, although solar radiation, sunshine, barren mountains, and wasteland are broad categories of natural resources, they are not included in the accounting because they have low market acceptance and are difficult to quantify.

[0110] Stock resources refer to natural resources that have physical entities, exist for a long time, are relatively stable, have clear reserve characteristics and physical attributes, and can be directly measured in specific units of measurement.

[0111] Flow resources refer to natural resources derived from existing resources, which are fluid, relatively abstract, dynamic, and whose physical quantity is easily affected by external environmental factors.

[0112] Furthermore, STEP2 specifically includes:

[0113] The process involves identifying all natural resources within the target area that fall under the scope of natural resource accounting, and classifying them into either existing resources or flow resources. For example... Figure 2As shown in this embodiment, the existing resources of a city in the southwest region mainly include agricultural land, construction land, forest land, grassland, water resources, and mineral resources, and currently do not involve other resource categories such as marine resources. Flow resources mainly include agricultural, forestry, animal husbandry, and fishery outputs, carbon sequestration and oxygen release services, flood control services, water conservation services, water purification services, air purification services, climate regulation services, and soil conservation services, and currently do not include other natural resources with significant local characteristics.

[0114] STEP 3: Extract the physical quantities of various stock resources in the above-mentioned accounting subject system directly through specialized databases and other means.

[0115] Furthermore, STEP3 specifically includes:

[0116] For mineral resources, the proven reserves of mineral types and their corresponding reserves are extracted from the mineral resource reserve database of the target area. Specifically, for proven oil and gas reserves, the physical quantity is the Remaining Proved Technically Recoverable Reserves (RPTRR) recorded in the mineral reserve database; for proven solid mineral resources, the physical quantity is the proven reserves in the mineral resource reserve database; and for proven geothermal mineral water reserves, the physical quantity is the allowable extraction volume in the mineral resource reserve database.

[0117] For land resources, Geographic Information System (GIS) software is used to extract secondary land category classification information from the land change survey data of the target area at the benchmark time point, and to calculate the total area of ​​each secondary land category.

[0118] Specifically, agricultural land, construction land, forest land, and grassland all belong to land resources. This embodiment of the invention is based on the 2023 land use change survey data of a city in Southwest China. Using the "extract by attribute" function of GIS software, the area of ​​secondary land categories in each district and county within the city's jurisdiction is extracted, thereby obtaining the total area of ​​each secondary land category in each district and county of the city. The information on each secondary land category of the city is shown in Tables 1-3 below.

[0119] Table 1 Classification of Agricultural Land

[0120]

[0121] Table 2 Classification of Construction Land

[0122]

[0123] Table 3 Classification of Forest and Grassland

[0124]

[0125] Regarding water resources, surface water and groundwater storage data are extracted from water resources bulletins published in the target area at the benchmark time point, and the total water resource storage of the target area is calculated by summarizing the data.

[0126] In this embodiment, since there are no other natural resources with significant local characteristics in the target area, only the physical quantity of natural resources involved in the aforementioned steps is extracted.

[0127] STEP4: Based on the GEP (Gross Ecosystem Product) accounting method and biophysical process model, perform inversion calculation and extraction of the physical quantities of various flow resources other than agricultural, forestry, animal husbandry and fishery outputs; the physical quantity data of agricultural, forestry, animal husbandry and fishery outputs are obtained from the annual statistical yearbooks published by the local statistical bureau.

[0128] Specifically, GEP (Genomic Product) accounting is a systematic approach to quantifying the total value of final products and services provided by an ecosystem in a specific region for human well-being, comprising two core components: physical quantity accounting and value accounting. In the physical quantity inversion of natural resources, the physical quantity accounting module is the primary application, which calculates the actual output of various ecological products (natural resources) through standardized processes. Biophysical process models, based on principles of ecology, hydrology, and meteorology, use mathematical equations to simulate key processes of energy flow and material cycling (such as carbon, water, and nutrients) in ecosystems, thereby quantifying the relationship between ecosystem structure and function. This is the core technological support for GEP physical quantity inversion. The main processes for the synergistic application of these two approaches in natural resource physical quantity inversion include:

[0129] (1) Determine the accounting items for natural resources (such as forest stock volume and carbon storage);

[0130] (2) Collect basic data such as meteorological, topographical, and vegetation data;

[0131] (3) Select a suitable biophysical process model (e.g., use the InVEST model to calculate water production and the CENTURY model to estimate carbon storage).

[0132] (4) Perform model parameter calibration and verification (in conjunction with field observation data);

[0133] (5) Run the model to obtain the physical quantity results of natural resources;

[0134] (6) Incorporate the results into the GEP accounting system and carry out quality control and output of results.

[0135] To ensure the accuracy of the calculation results in STEP4 and to align with the concepts and scope of natural resource accounting, this embodiment makes the following improvements to the existing conventional inversion method:

[0136] First, GIS software was used to extract spatial vectors and vegetation types of forests, grasslands, and wetlands from the "National Forest, Grassland, and Wetland Survey and Monitoring Results" or the "National Forest, Grassland, and Wetland Desertification Census Results." The extracted spatial vectors and vegetation types were then used as input parameters in the calculations for three accounting items: water conservation services, flood control services, and air purification services, further improving the accuracy of the accounting results.

[0137] Second, when calculating the flow resource of flood storage services, only the flood storage volume in vegetation and farmland ecosystems is considered, while the flood storage volume of man-made facilities such as reservoirs is ignored.

[0138] STEP 5: Integrate the physical quantities of all natural resources within the target area that fall under the scope of natural resource accounting to the district / county level to form district / county-level statistical results.

[0139] Specifically, the statistical results at the district and county levels are presented in Table 4.

[0140] Table 4 Statistical Results at the District and County Level

[0141]

[0142] In the table, x si Let y represent the physical quantity of the i-th (i=1,2,…,m) stock resource in the s=1,2,…,S-th district / county. sj Let represent the physical quantity of the flow resource j=1,2,…,n in the s-th district / county, m represent the quantity of stock resources within the scope of natural resource accounting in the target area, n represent the quantity of flow resources within the scope of natural resource accounting in the target area, and S represent the number of districts / counties in the target area.

[0143] STEP 6: Process the district and county-level statistical results using the entropy weight method to obtain the comprehensive stock index of each stock resource in each district and county within the target area, as well as the comprehensive flow index of each flow resource in each district and county within the target area.

[0144] STEP 7: Construct heterogeneity constraint factors for each district and county within the target area based on the three zones and three lines;

[0145] STEP 8. In each district and county, using the comprehensive stock index as input and the comprehensive flow index as output, the efficiency of natural resource utilization is evaluated by introducing the SBM model with heterogeneity constraint factors.

[0146] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the efficiency of regional natural resource utilization that takes into account both flow and stock, characterized in that, include: S1. Determine the spatial boundaries of the target area and specify the baseline time point for evaluating the target area; S2. Divide all resources within the target area that fall under the scope of natural resource accounting into two categories: stock resources and flow resources, wherein: The scope of natural resource accounting covers natural resources that can be directly or indirectly assessed by the market, as well as the ecosystem services derived from them; Stock resources refer to resources that have physical entities, exist for a long time, are relatively stable in state, have clear reserve characteristics and physical attributes, and can be directly measured in a specific unit of measurement. Flow resources refer to resources derived from existing resources that are fluid, relatively abstract, dynamically changing, and whose physical quantity is easily affected by external environmental factors. Flow resource accounting items include common service types such as agricultural, forestry, animal husbandry and fishery products, carbon sequestration and oxygen release, flood control, water conservation, water purification, air purification, climate regulation, and soil conservation. S3. Calculate the corresponding physical quantity of each existing resource; S4. Based on the GEP accounting method and biophysical process model, the corresponding physical quantities of each flow resource, excluding agricultural, forestry, animal husbandry and fishery outputs, are calculated; the physical quantities of agricultural, forestry, animal husbandry and fishery outputs are obtained from the annual statistical yearbooks of the target region. S5. Integrate the physical quantities of all resources within the target area that fall under the scope of natural resource accounting to the district / county level to form district / county-level statistical results; the district / county-level statistical results include the physical quantities of each resource in each district / county within the target area; S6. The entropy weight method is used to process the statistical results at the district and county levels to obtain the comprehensive stock index of each stock resource in each district and county within the target area, as well as the comprehensive flow index of each flow resource in each district and county within the target area. S7. Construct heterogeneity constraint factors for each district and county within the target area based on the three zones and three lines; Step S7 includes: S71. Quantify the ecological protection red line, permanent basic farmland protection red line, and urban development boundary in the three zones and three lines respectively to obtain the corresponding constraint factors. The calculation formula is as follows: In the formula, A 总,s Let A represent the total area of ​​the administrative division of the s-th district / county. 生态,s f represents the area of ​​the ecological protection red line in the s-th district / county. 1,s ∈[0,1] represents the ecological protection red line constraint factor of the s-th district / county; A 农田,s f represents the area of ​​the permanent basic farmland protection red line in the s-th district / county; 2,s ∈[0,1] represents the constraint factor of the permanent basic farmland protection red line in the s-th district / county, and β represents the appropriate proportion threshold; A 城镇,s f represents the controlled area of ​​the urban development boundary of the s-th district / county. 3,s ∈[0,1] represents the urban development boundary control constraint factor of the s-th district / county, and γ represents the reasonable proportion threshold; S72. Calculate the entropy weight for each type of constraint factor, using the following formula: In the formula, p t,s ∈[0,1] represents the standard value of the constraint factor of the s-th district / county at t=1,2,3, where ε represents the minimum correction coefficient, and e t Let w represent the information entropy of the t-th constraint factor. t Let w1 + w2 + w3 = 1 represent the entropy weight of the t-th constraint factor; S represents the number of districts and counties within the target area. S73. Calculate the heterogeneity constraint factor based on the entropy weight of the constraint factor. The calculation formula is:

2. In the formula, α s ∈[0,1] represents the heterogeneity constraint factor of the s-th district / county; S8. In each district and county, the efficiency of natural resource utilization is evaluated by using the comprehensive stock index as input and the comprehensive flow index as output through the SBM model that introduces heterogeneity constraint factors. In the SBM model incorporating heterogeneity constraint factors, the objective function is: The constraints are: In the formula, ρ s ∈[0,1] represents the natural resource utilization efficiency of the s-th district / county. Represents i comprehensive stock indicators Slack variables, Represents the j-th comprehensive flow index The slack variable, λ s Let α represent the weight coefficient of the s-th district / county, where S represents the number of districts / counties within the target area. s ∈[0,1] represents the heterogeneity constraint factor of the s-th district / county. This represents the entropy weight of the i-th existing resource. denoted by , where represents the entropy weight of the j-th flow resource, m represents the quantity of stock resources within the target area that fall under the scope of natural resource accounting, and n represents the quantity of flow resources within the target area that fall under the scope of natural resource accounting.

3. The method for evaluating the efficiency of regional natural resource utilization that takes into account both flow and stock, as described in claim 1, is characterized in that... Mineral resources, land resources, and water resources are all existing resources. Step S3 uses different measurement units to calculate the physical quantity for different existing resources, including: For mineral resources, extract the proven mineral types and their corresponding reserves from the mineral resource reserve database of the target area; For land resources, geographic information system software is used to extract secondary land category classification information from the land change survey data of the target area at the benchmark time point, and the total area of ​​each secondary land category is calculated. Regarding water resources, surface water and groundwater storage data are extracted from water resources bulletins published in the target area at the benchmark time point, and the total water resource storage of the target area is calculated by summarizing the data.

4. The method for evaluating the efficiency of regional natural resource utilization that takes into account both flow and stock, as described in claim 1, is characterized in that... Step S5 includes: S51. If the resource accounting results are presented in the form of a raster layer, then use ArcGIS's Zonal Statistics tool to summarize and statistically analyze the resource at the county / district scale to obtain the physical quantity of the resource in each county / district within the target area. S52. If the resource accounting results are presented in the form of a vector layer, then use ArcGIS's Summary Statistics tool to summarize and statistically analyze the resource at the district / county level to obtain the physical quantity of the resource in each district / county within the target area; S53. Summarize the physical quantities of all resources in each district and county within the target area to form district and county-level statistical results.

5. The method for evaluating the efficiency of regional natural resource utilization that takes into account both flow and stock, as described in claim 1, is characterized in that... Step S6 includes: S61. For the physical quantities of each existing resource and each flow resource, the minimum-maximum standardization method is used to perform dimensionless transformation to obtain the corresponding standard physical quantities. The calculation formula is as follows: In the formula, x si Let y represent the physical quantity of the i=1,2,…,m stock resource in the s-th district / county. sj Let m represent the physical quantity of the flow resource j=1,2,…,n in the s-th district / county, m represent the quantity of stock resources within the scope of natural resource accounting in the target area, and n represent the quantity of flow resources within the scope of natural resource accounting in the target area. This represents the standard physical quantity of the i-th stock resource in the s-th district / county. This represents the physical quantity of the j-th flow resource in the s-th district / county; S62. Introducing a smoothing factor and probabilistic transformation, the probabilistic values ​​of each resource are calculated using standard physical quantities. The calculation formula is as follows: In the formula, p represents the minimum correction factor. si Let q represent the probabilistic value of the i-th existing resource in the s-th district / county. sj This represents the probabilistic value of the j-th flow resource in the s-th district / county; S63. Calculate the information entropy of each resource based on the probabilistic values. The calculation formula is: In the formula, This represents the information entropy of the i-th existing resource. Let S represent the information entropy of the j-th traffic resource, and S represent the number of districts and counties within the target area; S64. Calculate the entropy weight of each natural resource according to the information entropy theory. The calculation formula is as follows: In the formula, This represents the entropy weight of the i-th existing resource. This represents the entropy weight of the j-th traffic resource; S65. For each existing resource, multiply its standard physical quantity and entropy weight to obtain a comprehensive stock index; for each flow resource, multiply its standard physical quantity and entropy weight to obtain a comprehensive flow index.

Images