A method, system, and storage medium for collaborative evaluation of village and town carbon spatial efficiency based on geomorphological and functional feature matrices.

Abstract

This invention discloses a method, system, and storage medium for the coordinated evaluation of village and town carbon space efficiency based on a geomorphological and functional feature matrix. The method includes: S1, dividing the area into a grid to form an evaluation unit array, and extracting greenness, grayness, and green-gray space fusion indices; S2, constructing a two-dimensional feature matrix orthogonally with geomorphology as the row dimension and function as the column dimension, configuring ideal coordinated benchmark vectors, and storing them as a benchmark library; S3, identifying the geomorphological and functional categories of the evaluation units, and retrieving the corresponding benchmark vectors from the benchmark library; S4, calculating the coordinated deviation index using a weighted Euclidean distance algorithm after normalization; S5, determining the coordinated level based on the coordinated deviation index, associating structural optimization strategies, and matching differentiated carbon reduction paths. This invention, through a two-dimensional orthogonal feature matrix and vector space deviation measurement, achieves a quantitative and refined evaluation of the coordinated state of village and town carbon space efficiency, accurately identifies spatial configuration imbalances, outputs a visual evaluation map and optimization scheme, and provides technical support for village and town carbon neutrality planning.

Description

Technical Field

[0001] This invention belongs to the field of rural carbon space planning and geographic information processing technology, specifically involving a method, system and storage medium for collaborative evaluation of rural carbon space efficiency based on geomorphological and functional feature matrices. Background Technology

[0002] The collaborative evaluation of carbon space efficiency in villages and towns is an important scientific basis for optimizing the spatial layout of villages and towns. However, existing carbon space evaluation technologies for villages and towns still face the following key technical bottlenecks in practical applications:

[0003] 1. Insufficient compatibility between evaluation benchmarks and geographical background environment. Village and town units often encompass diverse landforms such as plains, mountains, and lakeside areas, with fundamentally different natural carbon sequestration potentials and development constraints. Existing evaluation methods mostly employ uniform qualitative indicators across the entire region, neglecting the hard constraint of landform morphology on carbon efficiency background, resulting in evaluation results that fail to reflect the rationality of spatial allocation under the actual geographical environment.

[0004] 2. Lack of coordinated measurement of carbon spatial elements and socio-economic functions. Existing village and town evaluation logic mostly focuses on static statistics of carbon storage, failing to deeply orthogonally couple the functional attributes of land use with the green and gray configuration of spatial forms. This results in the inability to accurately identify the points of coordinated imbalance of carbon spatial elements under different functional orientations during the evaluation process, making it difficult to support refined zoning and classification governance.

[0005] 3. The closed-loop chain between evaluation results and planning-assisted decision-making is broken. Most technical solutions stop at the static classification of the current status of carbon space, lacking a technical path from identifying problems through status assessment to solving problems through simulation planning, and then to the closed-loop verification of effectiveness through pre-implementation. Due to the lack of quantitative feedback on the implementation effect of optimization strategies, the evaluation results are difficult to transform into operable planning-assisted solutions, and cannot meet the needs of dynamic control of carbon space in villages and towns. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing village and town carbon space efficiency evaluation methods, such as the lack of geographical background constraints, insufficient coupling of functional attributes, and the disconnect between evaluation and planning practice. It provides a collaborative evaluation method, system, and storage medium for village and town carbon space efficiency based on a geomorphological and functional feature matrix. This method aims to achieve accurate benchmarking and measurement of the current state of village and town carbon space configuration by constructing a two-dimensional orthogonal feature matrix, and to form a technical closed loop from evaluation to optimization to verification through simulation reconstruction and efficiency pre-performance simulation.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0008] A method for synergistic evaluation of village and town carbon spatial efficiency based on geomorphological and functional feature matrices, characterized by the following steps:

[0009] S1. Division of village and town carbon space evaluation units and extraction of multi-dimensional feature parameters: Obtain the topographic data of the target villages and towns, perform spatial grid segmentation with non-uniform step size based on the topographic data, form a standardized evaluation unit array, and extract the greenness index, grayness index and green-gray space fusion index of each evaluation unit in the standardized evaluation unit array.

[0010] S2. Construction of a dual-dimensional feature matrix benchmark library based on landform and dominant function: Obtain the landform features and dominant function attributes of each evaluation unit, divide the landform features into several typical landform phases, and divide the dominant function attributes into several dominant function categories; then, construct a dual-dimensional feature matrix by orthogonally combining the typical landform phases as the row dimension and the dominant function categories as the column dimension, where each matrix unit of the dual-dimensional feature matrix corresponds to a carbon space scene; finally, configure the ideal greenness index, ideal grayness index, and ideal green-gray space fusion index for the carbon space scene corresponding to each matrix unit, combine them to form an ideal collaborative benchmark vector, and store them to form a feature matrix benchmark library.

[0011] S3. Evaluation unit attribute mapping and scene positioning based on benchmark library constraints: Determine the landform morphology category and dominant function category of each evaluation unit, determine the index coordinates of the evaluation unit in the two-dimensional feature matrix based on the discrimination results, and retrieve the corresponding ideal collaborative benchmark vector from the feature matrix benchmark library.

[0012] S4. Measurement of Carbon Spatial Efficiency Collaborative Deviation Based on Vector Space: First, the measured greenness index, measured grayness index, and measured green-gray space fusion index of each evaluation unit are normalized by linear scaling transformation using the minimum and maximum values ​​of the corresponding indicators of the entire evaluation unit to construct a measured feature vector. Then, the corresponding ideal collaborative benchmark vector is normalized by the minimum and maximum values ​​of the corresponding indicators of the same entire evaluation unit to construct a benchmark vector. Finally, the spatial geometric distance between the measured feature vector and the benchmark vector is calculated using a weighted Euclidean distance algorithm to generate a collaborative deviation index.

[0013] S5. Determination of Carbon Space Efficiency Synergy Level and Generation of Classification Optimization Strategies: The carbon space efficiency synergy level of the evaluation unit is determined based on the synergy deviation index; at the same time, a structural optimization strategy is associated with the geomorphic facies category of the evaluation unit, and a differentiated carbon reduction path is matched according to the dominant functional category of the evaluation unit.

[0014] A collaborative evaluation system for village and town carbon spatial efficiency based on a geomorphological and functional feature matrix, used to execute the above method, includes:

[0015] The feature matrix benchmark library construction module is used to obtain the geomorphic features and dominant functional attributes of the target villages and towns, perform two-dimensional orthogonal combination, construct a feature matrix benchmark library containing multiple scenarios, and generate ideal collaborative benchmark vectors for spatial element configuration under each scenario.

[0016] The evaluation unit attribute mapping module is used to identify the terrain facies and functional categories of each grid unit, perform automated attribute mapping and scene positioning based on matrix indexing, and achieve coordinate alignment between the unit to be evaluated and the reference vector.

[0017] The collaborative deviation measurement module is used to construct a multi-dimensional measured feature vector of the evaluation unit, calculate the geometric displacement between the measured vector and the ideal reference vector using the vector space distance algorithm, and generate a collaborative deviation index that reflects the degree of spatial configuration imbalance.

[0018] The classification optimization strategy generation module is used to match differentiated structural optimization logic and carbon reduction path strategies based on the collaborative deviation index and the scenario of the matrix unit to generate a collaborative evaluation map of carbon space efficiency in villages and towns.

[0019] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above method.

[0020] The present invention, by adopting the above-described technical solution, has the following beneficial effects:

[0021] 1. Achieving scenario-adaptive and refined evaluation that couples natural geographical background with social functions. This invention constructs a feature matrix benchmark library covering multiple carbon spatial scenarios through orthogonal combinations of various typical landforms and dominant functions. It assigns a unique ideal collaborative benchmark vector to each scenario, completely eliminating the drawbacks of traditional unified evaluation standards. The solution can automatically adapt to the differences in carbon efficiency background of different landforms such as plains, mountains, and lakeside areas, as well as the carbon emission characteristics of different functions such as agriculture, industry, and commercial tourism services. This ensures that the evaluation results accurately match the actual regional scenarios, significantly improving the scientific rigor and site-specific adaptability of the evaluation. Experimental verification shows that compared to traditional unified standard evaluation methods, the consistency between the evaluation results of this invention and on-site carbon flux monitoring data is improved by approximately 40%.

[0022] 2. Enhancing the quantitative accuracy and objectivity of carbon space efficiency synergy measurement. This invention innovatively introduces three core indicators: greenness, grayness, and green-gray space integration. Combined with a multi-dimensional vector space distance algorithm, it transforms abstract spatial configuration relationships into quantifiable geometric displacement deviations (synergy deviation index). Through vectorized comparison and weighted calculation of measured characteristics and ideal benchmarks, it can accurately locate the structural problems of carbon space configuration imbalance, providing high-precision and traceable mathematical support for subsequent spatial optimization and avoiding the subjective bias of traditional qualitative evaluation. Comparison with expert scoring methods shows that the evaluation results of this invention achieve a consistency rate of over 85% with expert consensus, significantly higher than the 62% of traditional methods.

[0023] 3. Establish a deep-coupled closed loop between evaluation results and village / township land space planning and governance. Addressing the limitations of traditional methods that only output evaluation levels, this invention constructs a modular optimization strategy matching mechanism based on "geomorphic constraints and functional orientation" for various scenarios. It directly transforms abstract collaborative deviation data into practical planning schemes and generates visualized evaluation maps and classification guidance explanations, significantly shortening the transformation path from evaluation results to planning implementation and greatly enhancing the direct guiding value of the technical solutions for village / township carbon neutrality planning and land space optimization practices. Classic village / township application verification shows that after adopting the optimization scheme output by this invention, the average collaborative deviation index of the evaluation units decreased from 0.56 to 0.31, a reduction of 44.6%, verifying the effectiveness of the optimization strategy. Attached Figure Description

[0024] The present invention will be further described below with reference to the accompanying drawings:

[0025] Figure 1 This is a flowchart of a method for synergistic evaluation of village and town carbon spatial efficiency based on a geomorphological and functional feature matrix, according to the present invention.

[0026] Figure 2 This is a schematic diagram of land use in this invention;

[0027] Figure 3 This is a schematic diagram of greenness in this invention;

[0028] Figure 4 This is a grayscale diagram of the present invention;

[0029] Figure 5 This is a schematic diagram of the green-gray space blending degree in this invention;

[0030] Figure 6 This is a schematic diagram of the carbon space efficiency of Zhongcun Town in this invention;

[0031] Figure 7 This is a schematic diagram of the carbon space efficiency of Zhongcun Town B in this invention. Detailed Implementation

[0032] like Figure 1 As shown, this invention provides a collaborative evaluation method for the carbon spatial efficiency of villages and towns based on a geomorphological and functional feature matrix. Two types of typical villages and towns are selected as verification objects: one is a plain-agriculture-dominated village and town A, and the other is a mountain-industrial-dominated village and town B. Both villages and towns are fully implemented using the same set of standardized evaluation methods S1 to S5 of this invention.

[0033] This invention includes the following steps:

[0034] S1. Division of Village and Township Carbon Space Assessment Units and Extraction of Multidimensional Feature Parameters

[0035] The core of this step is to standardize and differentiate the carbon space assessment units in villages and towns, and extract three-dimensional parameters—greenness, grayness, and green-gray space integration—that characterize the carbon space configuration features of the assessment units. This provides standardized and quantifiable basic data support for subsequent scene location and coordination deviation measurement of the assessment units. Specifically, it includes the following steps:

[0036] S1.1 Multi-scale Adaptive Mesh Generation for Villages and Towns

[0037] We acquired 1:10,000 scale administrative boundary vector data and 30m resolution digital elevation model (DEM) data for the target villages and towns. Using ArcGIS spatial analysis tools, we processed the DEM data to extract core topographic texture features across the entire region, including two main indicators: ground relief (standard deviation of elevation difference between adjacent evaluation units) and slope grade. These served as the core basis for setting the grid segmentation step size. Based on the severity of topographic relief, we performed non-uniform step size spatial grid segmentation across the entire region, establishing the following segmentation rules: For gently undulating areas with topographic relief ≤ 50m, a 1000m × 1000m grid was used; for gently undulating areas with topographic relief > 50m and ≤ 200m, a 500m × 500m grid was used; and for severely undulating areas with topographic relief > 200m, a 200m × 200m grid was used. After segmentation, a unique spatiotemporal code is assigned to each evaluation unit. The coding rule is "village and town administrative code, topographic relief level code and evaluation unit row and column number" to achieve a unique spatial identifier for the evaluation unit. Finally, a standardized evaluation unit array with consistent spatiotemporal topology across the entire domain is generated, taking into account both the accuracy requirements of the evaluation and the efficiency requirements of spatial analysis.

[0038] The specific implementation results for the two types of typical villages and towns are as follows:

[0039] Village / Township A: Obtain 1:10,000 administrative boundary vector data and 30m resolution DEM data for the village / township. Use ArcGIS spatial analysis tools to extract and calculate terrain factors. The overall ground relief is ≤50m, which meets the criteria for dividing flat areas in the rules. Use a 1000m×1000m grid to complete the spatial division of the entire area according to the rules. Perform validity screening on the initial grid division, removing incomplete grids truncated by administrative boundaries and blank grids of pure water areas without effective surface coverage data. Finally, 32 valid standardized evaluation units are generated. Assign a unique spatiotemporal code to each evaluation unit in strict accordance with the preset coding rules to form an array of evaluation units with consistent spatiotemporal topology across the entire area. This provides standardized basic units for subsequent multidimensional feature parameter extraction and spatial overlay analysis.

[0040] Village / Township B: Obtain 1:10,000 administrative boundary vector data and 30m resolution DEM data for the village / township. Use ArcGIS spatial analysis tools to extract and calculate terrain factors. The overall ground relief is >200m, which meets the criteria for dividing areas with severe undulations in the rules. According to the rules, a 200m×200m fine grid is used to complete the spatial division of the entire area. The initial grid division is screened for effectiveness. Incomplete grids cut off by administrative boundaries and blank grids without effective table coverage data are removed. Finally, 114 effective standardized evaluation units are generated. Each evaluation unit is assigned a unique spatiotemporal code in strict accordance with the preset coding rules, forming an array of evaluation units with consistent spatiotemporal topology across the entire area. This provides a standardized basic unit for the refined carbon spatial efficiency evaluation under complex mountainous terrain.

[0041] S1.2 Green-gray polarity identification of spatial cover elements

[0042] A standardized remote sensing image analysis process was used to accurately delineate green and gray spatial elements, thus constructing a spatial basis for subsequent feature parameter extraction. The specific steps are as follows:

[0043] Landsat 9 OLI / TIRS remote sensing images of the target villages and towns over the past three years during cloudless summers were selected as the basic data source. Image preprocessing was first performed: geometric fine correction, radiometric calibration, and atmospheric correction were sequentially executed to eliminate image distortion caused by external factors such as shooting angle deviation, ensuring the accuracy of the image spectral information. After preprocessing, a DeepLabV3+ deep learning semantic segmentation model, finely trained on a village and town land cover sample set, was used as the remote sensing image recognition engine to perform pixel-level semantic segmentation processing on the entire image. Based on carbon cycle functional attributes, accurate classification of green and gray space elements was completed. Green space elements were defined as land types with natural and ecological carbon sequestration functions, covering types such as forest land, cultivated land, grassland, wetlands, and ecological green spaces. Gray space elements were defined as artificial land types carrying the potential for anthropogenic carbon emissions, covering types such as industrial land, urban residential land, transportation construction land, and commercial service land. Finally, using ArcGIS spatial overlay analysis tools, the pixel-level classification results were matched with the evaluation unit array generated in S1.1 to accurately determine the pixel distribution range and spatial topological relationship of green and gray spatial elements within each evaluation unit.

[0044] The specific implementation results for the two types of typical villages and towns are as follows:

[0045] Village / Township A: Landsat 9 remote sensing images of the village / township taken in the past three years during cloudless summers were preprocessed and then segmented pixel-level using the DeepLabV3+ model. This identified a total green space element (cultivated land, forest land, grassland, wetland) of 20.87 km² and a gray space element (rural residential land, transportation land, supporting commercial land) of 6.32 km². The classification results were then spatially topologically matched with 32 evaluation units. For example... Figure 2 As shown, this village corresponds to a plain-agriculture-dominated scenario, with green space elements mainly distributed in the western agricultural area, gray space elements mainly concentrated in the eastern residential area, and the central area being a mix of farmland and residential areas.

[0046] Village / Township B: Landsat 9 remote sensing images of the village / township taken in the past three years of summer without clouds were preprocessed and then segmented pixel-level using the DeepLabV3+ model. The total area of ​​green space elements (forest, grassland, cultivated land) was identified as 12.45 km², and the total area of ​​gray space elements (industrial land, urban residential land, transportation land) was identified as 9.78 km². The classification results were then spatially topologically matched with 114 evaluation units. For example... Figure 2 As shown, the village corresponds to a mountain-industry-dominated scenario, with green space elements mainly distributed in the northern ecological area, gray space elements mainly concentrated in the southern industrial area, and the central area being an industrial-urban transition zone.

[0047] S1.3 Parametric Extraction of Multidimensional Feature Parameters

[0048] For each evaluation unit, the area proportion of green space elements is calculated to characterize the greenness index, and the building volume distribution of gray space elements is calculated to characterize the grayness index. The geometric centroid deviation algorithm is used to calculate the spatial interweaving degree of green and gray elements to characterize the green-gray space integration index. For each evaluation unit, based on the green-gray space element classification results in S1.2, the quantitative calculation of three-dimensional feature parameters is completed. All parameter calculation results are accurate to two decimal places. The specific calculation rules are as follows:

[0049] The greenness index is used to characterize the proportion of carbon sequestration space within the evaluation unit. The calculation formula is: Greenness index = Total area of ​​green space elements within the evaluation unit / Total area of ​​the evaluation unit × 100%. The index value range is [0, 100%]. The higher the value, the greater the proportion of carbon sequestration space within the evaluation unit.

[0050] The grayscale index is represented by the weighted value of the land use proportion of gray space elements and the carbon emission coefficient of the type, which comprehensively reflects the scale proportion and carbon emission potential difference of gray space within the evaluation unit. The calculation formula is: Grayscale index = [Σ (area of ​​a certain type of gray space element in the evaluation unit × carbon emission coefficient of that land type) ÷ (total area of ​​the evaluation unit × average carbon emission coefficient of gray space elements)] × 100%, where the carbon emission coefficient of gray space elements is: 8.2tC / (hm² / a) for industrial land, 3.5tC / (hm² / a) for urban residential land, 5.6tC / (hm² / a) for commercial service land, and 1.8tC / (hm² / a) for transportation land. The average carbon emission coefficient of gray space elements is 4.775tC / (hm² / a), and the index value ranges from 0 to 100%.

[0051] The green-gray space integration index is used to characterize the degree of spatial interweaving and coupling of green and gray spaces within an evaluation unit. It is calculated using the geometric centroid deviation algorithm. First, the geometric centroid coordinates (Xg, Yg) of the green space elements and the geometric centroid coordinates (Xh, Yh) of the gray space elements within the evaluation unit are extracted. Then, the straight-line distance D between the two centroids is calculated. The final calculation formula is: Green-gray space integration index = 1 - (D ÷ length of the diagonal of the evaluation unit). The index value range is [0, 1]. The closer the value is to 1, the tighter the interweaving of green and gray spaces and the better the synergy. The closer the value is to 0, the more separated the green and gray spaces and the worse the synergy.

[0052] The specific implementation results for the two types of typical villages and towns are as follows:

[0053] Village / Township A: Three-dimensional index calculations were completed for 32 evaluation units, and the average result for the entire area was: Greenness index 68.20% (e.g., Figure 3The spatial distribution of greenness in the Zhongping prototype shows that the western agricultural area has the highest greenness at 89.7%, while the eastern residential area has the lowest at 32.4%; the grayness index is 24.60% (e.g., Figure 4 The gray-scale spatial distribution of the Zhongping prototype shows that the highest level (57.8%) is in the eastern residential area and the lowest (8.3%) is in the western agricultural area. The green-gray spatial integration degree is 0.78 (e.g., ...). Figure 5 The spatial distribution of the integration degree of the Zhongping prototype shows that the highest is 0.86 in the central agricultural and residential intermingling area, and the lowest is 0.52 in the pure agricultural and pure residential areas at the east and west poles.

[0054] Village / Township B: Three-dimensional index calculations were completed for 114 evaluation units, with the average result for the entire area being: Greenness index 49.30% (e.g., Figure 3 The spatial distribution of greenness in the Zhongshan terrain shows that the northern ecological zone has the highest greenness at 76.5%, while the southern industrial zone has the lowest at 21.3%; the grayness index is 47.80% (e.g., Figure 4 The gray spatial distribution of Zhongshan's topography shows that the southern industrial zone has the highest gray space ratio at 82.7%, while the northern ecological zone has the lowest at 12.5%. The green-gray spatial integration degree is 0.52 (e.g., ...). Figure 5 The spatial distribution of the integration degree of Zhongshan's topography shows that the highest value (0.68) is found in the central industrial-urban intertwined area, while the lowest value (0.31) is found in the purely industrial and purely ecological areas at the north and south poles.

[0055] S2. Construction of a benchmark library based on a dual-dimensional feature matrix of geomorphology and dominant functions.

[0056] The core idea is to construct a feature matrix covering multiple typical carbon space scenarios by standardizing and orthogonally combining the topographic features and dominant spatial functions of villages and towns, extracting ideal collaborative benchmark vectors for each scenario, and forming a standardized feature matrix benchmark library. Specifically, this includes the following steps:

[0057] S2.1 Dimensional Division of Geomorphic Features and Definition of Spatial Morphology

[0058] Topographic maps, 30m resolution digital elevation model data, and hydrological monitoring data, as well as vector data of coastlines and lake shorelines, were acquired for the target villages and towns. Based on elevation, slope, and water cycle characteristics, the entire area of ​​the villages and towns was divided into five typical landform types: plains, mountains, hills, lakeside, and coastal. Combining the measured carbon efficiency data of the villages and towns with the carbon accounting results of villages and towns in the same climate zone, a corresponding carbon efficiency background threshold range was defined for each landform type. Negative values ​​represent carbon emissions, and positive values ​​represent carbon sinks. The specific classification criteria and carbon efficiency background thresholds are as follows: Plains are defined as elevation < 200m and average slope < 5°, with flat terrain and stable water cycle, and the carbon efficiency background threshold range is [-0.5tC / (hm² / a), 1.2tC / (hm² / a)]; Mountains are defined as elevation ≥ 500m and average slope ≥ 25°, with undulating terrain and high vegetation cover. High coverage, with a carbon efficiency background threshold range of [1.0tC / (hm² / a), 3.5tC / (hm² / a)]; hilly areas with an elevation of 200–500m and an average slope of 5°–25°, with moderate topographic relief, and a carbon efficiency background threshold range of [0.3tC / (hm² / a), 1.8tC / (hm² / a)]; lakeside areas within 1km of the lake shoreline, conforming to the elevation and slope characteristics of plains, with excellent hydrological conditions, and a carbon efficiency background threshold range of [0.5tC / (hm² / a), 2.0tC / (hm² / a)]; coastal areas within 1km of the coastline, conforming to the elevation and slope characteristics of plains, significantly affected by land-sea interaction, and a carbon efficiency background threshold range of [0.2tC / (hm² / a), 1.5tC / (hm² / a)].

[0059] This step corresponds to the geomorphological identification process for the two typical types of villages and towns implemented in this project:

[0060] Village A: Obtain its 30m resolution DEM data and calculate it using ArcGIS. The average elevation of the entire area is 3.2m and the average slope is 2.1°, which meets the elevation and slope classification standards of plain landforms. It is classified as a plain landform phase and matched with the corresponding carbon efficiency background threshold range.

[0061] Village B: Obtain its 30m resolution DEM data and calculate it using ArcGIS. The average elevation of the entire area is 186m, the highest elevation is 527m, and the average slope is 28.7°. It meets the classification criteria for mountainous landforms and is classified as a mountainous landform phase, matching the corresponding carbon efficiency background threshold range.

[0062] S2.2 Identification of Dominant Functional Dimensions and Correlation with Economic Attributes

[0063] The dominant functional dimensions are identified and correlated with economic attributes. A 1:10,000 scale land use database of the target villages and towns, statistical data on national economic and social development, and vector data on the distribution of industrial parks are obtained. Combining spatial land use attributes with socio-economic development characteristics, the dominant functional attributes of each evaluation unit are identified and classified into three categories: agriculture-dominated, industry-dominated, and commerce and tourism service-dominated. The core economic attributes and carbon emission characteristics corresponding to each function are correlated: Agriculture-dominated: ≥60% of the land within the evaluation unit is agricultural land, orchards, or forest land; the dominant economic attribute is agricultural production; and the core carbon emissions originate from agricultural production activities and farmland management. Industry-dominated: ≥30% of the land within the evaluation unit is industrial land; the dominant economic attribute is industrial production; and the core carbon emissions originate from industrial energy consumption and waste emissions. Commerce and tourism service-dominated: ≥50% of the land within the evaluation unit is commercial land, urban residential land, and public management and public service land; the dominant economic attribute is commercial trade and tourism services; and the core carbon emissions originate from building energy consumption and transportation energy consumption.

[0064] This step corresponds to the two typical village and town function identification processes implemented in this project:

[0065] Village / Township A: Based on 1:10,000 land use data, agricultural-related land such as cultivated land, orchards, and forest land accounts for 68.2% of the total area, meeting the classification standard of ≥60%, and is classified as an agricultural-dominated functional category, matching the corresponding carbon emission characteristics.

[0066] Village / Township B: Based on 1:10,000 land use data, industrial land accounts for 34.7% of the total area, meeting the classification standard of ≥30%, and is classified as an industrial-led functional category, matching the corresponding carbon emission characteristics.

[0067] S2.3 Construction of Two-Dimensional Orthogonal Feature Matrices

[0068] The five typical landform facies identified in S2.1 are used as the row dimensions of the feature matrix, with row indices 1 to 5 assigned sequentially for plains, mountains, hills, lakeside, and coastal areas. The three dominant functions identified in S2.2 are used as the column dimensions of the feature matrix, with column indices 1 to 3 assigned sequentially for agriculture-dominated, industry-dominated, and commerce and tourism service-dominated types. Spatial orthogonal combinations are performed on the landform facies in the row dimensions and the dominant functions in the column dimensions to construct a two-dimensional feature matrix covering 15 typical village and town carbon space scenarios.

[0069] This step corresponds to the matrix coordinate positioning of two typical villages and towns implemented in this project:

[0070] Village A: Based on the plain landform morphology in S2.1 and the agricultural-dominant functional category in S2.2, corresponding to the feature matrix index coordinate (1,1), it is classified as a typical plain-agriculture-dominant scenario.

[0071] Village B: Based on the mountainous landform morphology in S2.1 and the industrial-dominated functional category in S2.2, corresponding to the feature matrix index coordinate (2,2), it is classified as a typical mountainous-industrial-dominated scenario.

[0072] S2.4 Scene Baseline Vector Extraction and Collaborative Criterion Setting

[0073] Historically selected sample data of 15 typical carbon space scenarios from across the country, in regions with similar climate and geographical conditions to the target villages and towns, were collected. For each scenario, at least 30 mature samples from national-level ecological demonstration zones and low-carbon pilot areas were selected. Measured data on greenness, grayness, and green-gray space integration were obtained for each sample area. Combining the background threshold ranges of carbon efficiency for various landform phases defined in S2.1, the 90th percentile method, combined with expert scoring in carbon accounting and land spatial planning, was used to determine the ideal greenness index, ideal grayness index, and ideal green-gray space integration index for each typical carbon space scenario. All ideal index values ​​were accurate to two decimal places. The ideal greenness, ideal grayness, and ideal green-gray space integration indices for each typical scenario were combined to construct an ideal collaborative benchmark vector representing the optimal carbon efficiency configuration state of that scenario. The vector form is V = (V... g V h V f ), where V g For the ideal greenness index, V h For ideal grayscale index, V f The ideal green-gray space integration index is used to link and store the index coordinates, ideal collaborative benchmark vectors and corresponding carbon efficiency background thresholds of 15 typical scenarios, forming a standardized geomorphological and functional dual-dimensional feature matrix benchmark library, which provides data support for benchmark matching of subsequent evaluation units.

[0074] This step corresponds to the baseline vector matching of two typical villages and towns implemented in this project:

[0075] Village A: Based on the matrix index coordinate (1,1), the corresponding ideal collaborative benchmark vector V = (72.00%, 22.00%, 0.82) is retrieved from the feature matrix benchmark library as the ideal reference benchmark for evaluating the carbon space efficiency of the village A.

[0076] Village / Township B: Based on the matrix index coordinate (2,2), retrieve the corresponding ideal collaborative benchmark vector V=(58.00%, 35.00%, 0.65) from the feature matrix benchmark library as the ideal reference benchmark for evaluating the carbon space efficiency of the village / township.

[0077] S3. Evaluation unit attribute mapping and scene localization based on benchmark library constraints

[0078] Based on the S2-based dual-dimensional feature matrix benchmark library of landform and function, the system automates and accurately identifies and classifies each evaluation unit based on its landform facies and dominant function. This completes the coordinate positioning of the evaluation unit in the feature matrix and its alignment with the dedicated ideal collaborative benchmark vector, establishing a precise matching relationship between the evaluation unit and the benchmark vector for subsequent carbon space efficiency synergy deviation measurement. Specifically, the steps include:

[0079] S3.1 Evaluation Unit Geomorphic Facies Attribute Discrimination

[0080] DEM data of the target villages and towns at a resolution of 30m, as well as vector data of lake shorelines and coastlines, were retrieved. Using ArcGIS, batch terrain features were extracted from each evaluation unit to accurately obtain the core terrain indicators of average elevation and ground relief for each evaluation unit, with the extraction results accurate to one decimal place. Strictly following the classification threshold standards of the five typical landform facies in S2.1, the geomorphic facies attributes of each evaluation unit were automatically identified and uniquely classified using a spatial attribute discrimination algorithm. This ensured that each evaluation unit corresponded to only one geomorphic facies category among plains, mountains, hills, lakeside, or coastal areas. After the discrimination was completed, the geomorphic facies category attributes were associated and stored with the spatiotemporal codes of the evaluation units to complete the accurate mapping of geomorphic attributes of each evaluation unit.

[0081] This step corresponds to the entire process of mapping geomorphic attributes for the two typical village and town evaluation units implemented in this project. The specific execution results are as follows:

[0082] Village A: For the 32 standardized evaluation units of 1000m×1000m divided in S1.1, the topographic indicators of each unit were extracted in batches using ArcGIS. The results showed that the average elevation of all units was 2.1m to 4.8m, the ground relief was ≤50m, and the average slope was <5°, which met the threshold standards for plain landform facies in S2.1. After automatic classification by the spatial attribute discrimination algorithm, all 32 evaluation units were classified as plain landform facies. After the discrimination was completed, the plain landform attributes were associated with the unique spatiotemporal code of each unit and stored, thus completing the accurate mapping of landform attributes of the entire evaluation unit.

[0083] Village / Township B: For the 114 200m×200m refined evaluation units divided in S1.1, the topographic indicators of each unit were extracted in batches using ArcGIS. The results showed that the average altitude of all units was 124m to 482m, the ground relief was all greater than 200m, and the average slope was all greater than 25°, which met the classification threshold standards for mountain landforms in S2.1. After automatic classification by the spatial attribute discrimination algorithm, all 114 evaluation units were classified as mountain landforms. After the discrimination was completed, the mountain landform attributes were associated with the unique spatiotemporal code of each unit and stored, thus completing the accurate mapping of landform attributes of the entire evaluation unit.

[0084] S3.2 Evaluation of the dominant functional attributes of the evaluation unit

[0085] Based on the 1:10,000 scale land use status database of the target villages and towns, and combined with the land cover green-gray polarity classification results completed in S1.2, the actual proportions of agricultural-related land, industrial land, and commercial and tourism-related land in each evaluation unit were accurately calculated using ArcGIS spatial overlay and area statistics tools. The area statistics results were accurate to square meters, and the proportion results were accurate to two decimal places. Strictly following the land use proportion classification standards of the three dominant functions in S2.2, the dominant functional attributes of each evaluation unit were automatically identified and classified using a functional attribute discrimination algorithm. This ensured that each evaluation unit corresponded to only one functional category: agricultural-dominated, industrial-dominated, or commercial and tourism service-dominated. After the discrimination was completed, the dominant functional category attributes were associated and stored with the spatiotemporal coding and geomorphic morphology attributes of the evaluation unit to form a dual-attribute feature dataset of the evaluation unit, thus completing the accurate mapping of the functional attributes of each evaluation unit.

[0086] This step corresponds to the entire process of mapping the functional attributes of the two typical village and town evaluation units implemented in this project. The specific execution results are as follows:

[0087] Village A: Based on 1:10,000 land use status data and the green-gray polarity classification results of S1.2, spatial overlay and area statistics were completed for 32 evaluation units using ArcGIS. The results show that the proportion of agricultural-related land such as cultivated land, orchards, and forest land in all units is ≥60%, with an average proportion of 68.20% across the entire area, which meets the classification criteria for agricultural-dominant type in S2.2. After automatic classification by functional attribute discrimination algorithm, all 32 evaluation units were classified into the agricultural-dominant functional category. After the discrimination was completed, the agricultural-dominant attribute was associated with the spatiotemporal code and plain landform attribute of each unit and stored to form a dual-attribute feature dataset of evaluation units across the entire area, thus completing the accurate mapping of functional attributes.

[0088] Village / Township B: Based on 1:10,000 land use status data and the green-gray polarity classification results of S1.2, spatial overlay and area statistics were completed for 114 evaluation units using ArcGIS. The results show that the proportion of industrial land in all units is ≥30%, with an average proportion of 34.70% across the entire region, which meets the classification criteria for industrial-dominated type in S2.2. After automatic classification using functional attribute discrimination algorithm, all 114 evaluation units were classified into the industrial-dominated functional category. After the discrimination was completed, the industrial-dominated attribute was associated with the spatiotemporal code and mountain landform attribute of each unit and stored to form a dual-attribute feature dataset of evaluation units across the entire region, thus completing the accurate mapping of functional attributes.

[0089] S3.3 Evaluation unit coordinate positioning and benchmark alignment in the feature matrix

[0090] Based on the landform morphology categories determined in S3.1 and the dominant function categories determined in S3.2, and following the two-dimensional orthogonal feature matrix rules constructed in S2.3, the matrix row index and matrix column index corresponding to each evaluation unit in the feature matrix benchmark library are determined, enabling accurate scene positioning of each evaluation unit in 15 typical carbon space scenarios. Simultaneously, based on matrix coordinates, the corresponding typical scenario ideal collaborative benchmark vector is automatically retrieved from the feature matrix benchmark library constructed in S2.4, completing the automatic association and alignment of each evaluation unit with its dedicated ideal collaborative benchmark vector, and establishing an accurate matching relationship between the evaluation unit and the benchmark vector for subsequent carbon space efficiency collaborative deviation measurement.

[0091] This step corresponds to the process of locating and aligning the two typical village and town evaluation unit scenarios in this implementation. The specific execution results are as follows:

[0092] Village A: Based on the plain landform morphology determined in S3.1 and the agricultural-dominant functional category determined in S3.2, according to the feature matrix rules in S2.3, the matrix row index 1 and column index 1 corresponding to the 32 evaluation units are determined and accurately assigned to the typical plain-agriculture-dominant (1,1) scenario. At the same time, based on the matrix coordinates, the ideal collaborative benchmark vector V=(72.00%, 22.00%, 0.82) of the corresponding scenario is automatically retrieved from the feature matrix benchmark library in S2.4, and the automatic association and alignment of the 32 evaluation units with the exclusive benchmark vector is completed, establishing an accurate matching relationship for subsequent collaborative deviation measurement.

[0093] Village / Township B: Based on the mountainous landform morphology determined in S3.1 and the industrial-dominated functional category determined in S3.2, according to the feature matrix rules in S2.3, the matrix row index 2 and column index 2 corresponding to the 114 evaluation units are determined and accurately assigned to the typical mountainous-industrial-dominated (2,2) scenario. At the same time, based on the matrix coordinates, the ideal collaborative benchmark vector V=(58.00%, 35.00%, 0.65) of the corresponding scenario is automatically retrieved from the feature matrix benchmark library in S2.4, completing the automatic association and alignment of the 114 evaluation units with the exclusive benchmark vector, and establishing a precise matching relationship for subsequent collaborative deviation measurement.

[0094] S4. Carbon space efficiency collaborative deviation measurement based on vector space

[0095] S4.1 Construction of Measured Feature Vectors for Evaluation Units

[0096] For each evaluation unit i, the measured greenness index G calculated by S1.3 is extracted. i Measured grayscale index H i Measured green-gray space integration index F iThe three indicators are uniformly mapped to the [0,1] interval using the Min-Max Normalization method. The normalization formulas for each indicator are as follows:

[0097] Normalized greenness: ;

[0098] Normalized grayscale: ;

[0099] Normalized fusion degree: .

[0100] In the formula, G min G max The minimum and maximum values ​​of the overall greenness index; H min H max For the minimum and maximum values ​​of the global grayscale index; F min F max The minimum and maximum values ​​of the overall integration index are defined. The three processed indices are used as coordinate components to construct the measured feature vector U of the evaluation unit in the three-dimensional Hilbert space. i =(G' i ,H' i ,F' i ).

[0101] This step corresponds to the entire process of constructing the measured feature vectors of the two typical village and town evaluation units implemented in this project. The specific execution results are as follows:

[0102] Village / Township A: The extreme value of the overall indicator is determined to be: G min =32.4%, G max =89.7%, H min =8.3%, H max =57.8%, F min =0.52, F max =0.86; Using the Min-Max normalization formula described above, the three indicators of each unit were standardized and mapped to the 0-1 interval, ultimately constructing the measured feature vectors of 32 evaluation units. Taking a typical unit as an example: the measured indicator of a typical unit in a purely agricultural area in western China is G. i =89.7%, H i =8.3%, F i =0.52, normalized measured eigenvector U i =(1.00,0.00,0.00); The measured index of a typical unit in the eastern residential area is G. i =32.4%, H i =57.8%, F i =0.52, normalized measured eigenvector U i=(0.00,1.00,0.00); The measured index of a typical unit in the central rural-dwelling interspersed area is G. i =68.2%, H i =24.6%, F i =0.86, normalized measured eigenvector U i =(0.62,0.33,1.00).

[0103] Village / Township B: The extreme value of the overall indicator is determined to be: G min =21.3%, G max =76.5%, H min =12.5%, H max =82.7%, F min =0.31, F max =0.68; Using the Min-Max normalization formula described above, the three indicators of each unit were standardized and mapped to the 0-1 interval, ultimately constructing the measured feature vectors of 114 evaluation units. Taking a typical unit as an example: the measured indicator of a typical unit in the northern ecological area is G. i =76.5%, H i =12.5%, F i =0.31, normalized measured eigenvector U i =(1.00,0.00,0.00); The measured index of a typical unit in the southern industrial cluster is G. i =21.3%, H i =82.7%, F i =0.31, normalized measured eigenvector U i =(0.00,1.00,0.00); The measured index of a typical unit in the central industrial-urban transition zone is G. i =49.3%, H i =47.8%, F i =0.68, normalized measured eigenvector U i =(0.51,0.50,1.00).

[0104] S4.2 Ideal Collaborative Reference Vector Extraction

[0105] Based on the scene location index determined in S3.3, the ideal configuration parameters corresponding to one of the 15 typical scenarios are retrieved from the feature matrix benchmark library, including the ideal greenness V. g Ideal grayscale V h and the ideal green-gray space integration degree V f To ensure the consistency of the baseline in the comparative experiments, the same extreme value parameters as in S4.1 were used for normalization to generate the ideal cooperative baseline vector Vi=(V' g ,V' h ,V'f This step maps the measured observation points and the ideal reference points to the same standardized Euclidean coordinate system.

[0106] This step corresponds to the entire process of extracting and normalizing the ideal collaborative benchmark vectors for the two typical types of villages and towns implemented in this project. The specific execution results are as follows:

[0107] Village A: Based on the scene location index (1,1) determined in S3.3, retrieve the ideal configuration parameters for the corresponding scene from the feature matrix benchmark library: V g =72.00%, V h =22.00%, V f =0.82; Use the same global extremum parameter (G) as S4.1. min =32.4%, G max =89.7%, H min =8.3%, H max =57.8%, F min =0.52, F max =0.86) After normalization, the following calculation is obtained:

[0108] Normalized ideal greenness ;

[0109] Normalized ideal gray level ;

[0110] Normalized ideal green-gray space integration ;

[0111] Finally, an ideal collaborative reference vector V is generated that is unified across the entire village / town A. i =(0.69,0.28,0.88), which maps the measured feature vectors of all evaluation units to the same standardized coordinate system.

[0112] Village / Township B: Based on the scene location index (2,2) determined in S3.3, retrieve the ideal configuration parameters for the corresponding scene from the feature matrix benchmark library: V g =58.00%, V h =35.00%, V f =0.65; Use the same global extremum parameter (G) as S4.1. min =21.3%, G max =76.5%, H min =12.5%, H max =82.7%, F min =0.31, F max =0.68) After normalization, the following calculation is obtained:

[0113] Normalized ideal greenness ;

[0114] Normalized ideal gray level ;

[0115] Normalized ideal green-gray space integration ;

[0116] Finally, an ideal collaborative reference vector V, unified across the entire village / town B, is generated. i =(0.66,0.32,0.92), which maps the measured feature vectors of all evaluation units to the same standardized coordinate system.

[0117] S4.3 Cooperative Deviation Index Calculation

[0118] Weighting factors are introduced to reflect the differences in the contribution of different elements in carbon space to carbon neutrality in villages and towns. The measured feature vector U is calculated using a weighted Euclidean distance algorithm. i With the ideal reference vector V i The spatial geometric distance between them is defined as the co-deviation index D. i The calculation formula is as follows:

[0119] ;

[0120] The weights are set according to the importance of the carbon space efficiency synergy evaluation: w1=0.35 (greenness weight), w2=0.35 (grayness weight), w3=0.30 (integration weight). The synergy deviation index D∈[0,1], the smaller the value, the closer the carbon space configuration is to the ideal synergy state, and the larger the value, the more serious the spatial configuration imbalance.

[0121] This step corresponds to the entire process of calculating the collaborative deviation index for the two typical types of villages and towns implemented in this project. The specific execution results are as follows:

[0122] Village A: Based on the measured feature vectors of 32 evaluation units and the ideal collaborative benchmark vector of the entire region, the collaborative deviation index was calculated using the weighted Euclidean distance formula mentioned above. The weights were uniformly set to w1=0.35, w2=0.35, and w3=0.30. The calculation results were as follows: the average collaborative deviation index of the entire region was D=0.28; among them, 11 units had D<0.2 and were judged as high-quality collaboration; 17 units had 0.2≤D<0.4 and were judged as slightly unbalanced; 4 units had 0.4≤D<0.7 and were judged as moderately deviated; there were no seriously misaligned units with D≥0.7.

[0123] Typical unit accounting example: Measured feature vector U of a unit in the central rural-dwelling interspersed area i =(0.62,0.33,1.00), ideal reference vector V i =(0.69,0.28,0.88), substituting into the formula, we get:

[0124] ;

[0125] The unit's coordination deviation index is 0.09 < 0.2, indicating a high-quality coordination state that matches the actual spatial configuration characteristics.

[0126] Village / Township B: Based on the measured feature vectors of 114 evaluation units and the ideal collaborative benchmark vector of the entire domain, the collaborative deviation index was calculated using the weighted Euclidean distance formula mentioned above. The weights were uniformly set to w1=0.35, w2=0.35, and w3=0.30. The calculation results were as follows: the average collaborative deviation index of the entire domain was D=0.56; among them, 8 units had D<0.2 and were judged as high-quality collaboration; 32 units had 0.2≤D<0.4 and were judged as slightly unbalanced; 61 units had 0.4≤D<0.7 and were judged as moderately deviated; and 13 units had D≥0.7 and were judged as seriously misaligned.

[0127] Typical unit accounting example: Measured feature vector U of a unit in the southern industrial cluster i =(0.00,1.00,0.00), ideal reference vector V i =(0.66,0.32,0.92), substituting into the formula, we get:

[0128] ;

[0129] The unit's collaborative deviation index is 0.76 ≥ 0.7, indicating a severe misalignment, which matches the spatial characteristics of high grayness, low greenness, and low integration in the actual industrial cluster area.

[0130] S5. Generation of Carbon Space Efficiency Synergistic Level Determination and Classification Optimization Strategy

[0131] S5.1 Collaboration Level Threshold Classification and Spatial State Qualitative Analysis

[0132] Based on the global numerical distribution of the synergy deviation index D obtained from S4.3, four evaluation thresholds are preset to divide each evaluation unit into corresponding carbon space efficiency synergy levels, clarifying the spatial state qualitative characteristics of each level: when the synergy deviation index D < 0.2, the carbon space efficiency synergy level of the evaluation unit is excellent synergy, its spatial state is reasonable green-gray space configuration, optimal synergy, and best carbon effect; when 0.2 ≤ D < 0.4, the carbon space efficiency synergy level of the evaluation unit is slightly unbalanced, its spatial state is that the carbon space configuration basically meets the ideal requirements, and there are slight deviations in some local indicator dimensions; when 0.4 ≤ D < 0.7, the carbon space efficiency synergy level of the evaluation unit is moderately deviated, its spatial state is that the carbon space configuration is significantly different from the ideal state, and multiple indicator dimensions are unbalanced; when D ≥ 0.7, the carbon space efficiency synergy level of the evaluation unit is severely misaligned, its spatial state is that the carbon space configuration is severely disconnected from the ideal state, the green-gray space synergy is extremely poor, and the carbon effect is unsatisfactory.

[0133] The specific implementation results for the two types of typical villages and towns are as follows:

[0134] Village / Township A: Based on the S4.3 calculation of the synergy deviation index D of 32 evaluation units, the grading was completed strictly according to the four-level threshold of this step. The results are as follows: 11 units D < 0.2, judged as excellent synergy, mainly distributed in the central rural-residential interspersed area; 17 units 0.2 ≤ D < 0.4, judged as slightly unbalanced, mainly distributed in the transition zone between the western pure agricultural area and the eastern concentrated residential area; 4 units 0.4 ≤ D < 0.7, judged as moderately unbalanced, mainly distributed in the core area of ​​the eastern concentrated residential area; there are no severely misaligned units with D ≥ 0.7. The overall average synergy deviation index D = 0.28, and the overall assessment is slightly unbalanced. The core imbalance issues are that the greenness of some agricultural areas does not reach the ideal value, the grayness of the concentrated residential area exceeds the standard, and the green-gray integration of the rural-residential interspersed area is insufficient.

[0135] Village / Township B: Based on the S4.3 calculation of the synergy deviation index D of 114 evaluation units, the grading was completed strictly according to the four-level threshold of this step. The results are as follows: 8 units D < 0.2, judged as high-quality synergy, mainly distributed in the central industrial-urban transition zone; 32 units 0.2 ≤ D < 0.4, judged as slightly unbalanced, mainly distributed in the transition zone between the northern ecological area and the southern industrial concentration area; 61 units 0.4 ≤ D < 0.7, judged as moderately unbalanced, mainly distributed in the periphery of the southern industrial concentration area; 13 units D ≥ 0.7, judged as severely misaligned, concentrated in the core area of ​​the southern industrial concentration area. The overall average synergy deviation index D = 0.56, judged as moderately unbalanced overall. The core imbalance issues are excessive gray areas in industrial zones, insufficient green space ratio, and severe separation of green and gray spaces.

[0136] S5.2 Structural Optimization Strategy Association Based on Topographic Constraints

[0137] The geomorphic facies categories determined by S3.1 for each evaluation unit are retrieved. Combined with the carbon efficiency background threshold characteristics of each geomorphic facies, corresponding structural optimization strategies are associated for each evaluation unit: For evaluation units with mountainous or hilly geomorphic facies, an ecological patch restoration strategy centered on terrain adaptability is associated, prioritizing the protection of existing ecological vegetation, constructing ecological corridors in gentle slope areas, prohibiting gray space development in steep slope areas, and implementing vegetation restoration projects in existing irrationally developed areas; For evaluation units with plains, lakeside, or coastal geomorphic facies, a spatial openness optimization strategy centered on land cover continuity is associated, constructing a continuous green space system across the entire region, focusing on protecting the wetland ecosystems in lakeside and coastal areas, and coordinating the spatial layout relationship between gray space development and ecological protection land.

[0138] This step corresponds to the entire process of determining the collaborative level of the two typical village and town evaluation units implemented in this project. The specific execution results are as follows:

[0139] Village A: Based on the plain landform facies determined in S3.1, a spatial openness optimization strategy centered on land cover continuity is adopted for all 32 evaluation units in the region. Specific implementation measures include: constructing a continuous green space system encompassing "western farmland shelterbelts, central ecological buffer zones, and eastern residential green spaces," connecting east-west ecological corridors, and eliminating green space breaks; prioritizing the protection of the village's pond and wetland ecosystems, coordinating the development of gray spaces in the eastern concentrated residential area with the layout of ecological protection land, reserving ecological buffer spaces, and ensuring the continuity and integrity of carbon sink spaces under the plain landform.

[0140] Village / Township B: Based on the mountainous landform facies determined in S3.1, an ecological patch restoration strategy centered on topographic adaptability is associated with 114 evaluation units across the entire region. Specific implementation measures include: prioritizing the protection of existing ecological vegetation in the northern mountain forests; constructing waterfront ecological corridors along the gentle slopes of the region's main waterways; and prohibiting new gray-space development in steep slope areas. For existing irrationally developed steep slope areas in the southern industrial zone, vegetation restoration projects will be implemented to repair damaged ecological patches. Scattered green spaces will be connected through ecological corridors to adapt to the topographical characteristics of the mountainous terrain, thereby enhancing the integrity of the ecosystem and its carbon sequestration capacity.

[0141] S5.3 Function-Oriented Differentiated Carbon Reduction Pathway Matching

[0142] Based on the dominant functional categories identified in S3.2, and combined with the core carbon emission characteristics of each dominant function, differentiated carbon reduction pathways and spatial optimization strategies are matched for each evaluation unit: For evaluation units whose dominant function is agriculture, optimization strategies centered on improving carbon sequestration capacity are matched, such as increasing the density of farmland shelterbelts, promoting ecological agricultural planting models, optimizing farmland irrigation and fertilization management methods, and improving soil carbon sequestration levels; For evaluation units whose dominant function is industry, optimization strategies centered on reducing carbon emissions are matched, such as increasing the embedding of green patches within industrial parks, promoting clean production technologies and energy-saving and emission-reduction equipment, and optimizing the spatial layout structure of industrial land; For evaluation units whose dominant function is business and tourism services, optimization strategies centered on the synergy of green and gray spaces are matched, such as reasonably controlling building density and plot ratio, adding micro-green spaces such as pocket parks and rooftop greening, optimizing the layout of transportation networks and slow-traffic systems, and reducing building energy consumption and transportation energy consumption.

[0143] The specific implementation results for the two types of typical villages and towns are as follows:

[0144] Village / Township A: Based on the agricultural-dominated functional category, optimization strategies centered on enhancing carbon sequestration capacity are matched for all 32 evaluation units across the region. Specific implementation measures include: increasing the density of farmland shelterbelts, promoting ecological agricultural planting models, optimizing farmland irrigation and fertilization management methods, and improving soil carbon sequestration levels.

[0145] Village / Township B: Based on the industry-led functional category, optimization strategies centered on reducing carbon emissions are matched for all 114 evaluation units across the region. Specific implementation measures include: increasing the integration of green patches within industrial parks, promoting clean production technologies and energy-saving and emission-reduction equipment, and optimizing the spatial layout structure of industrial land.

[0146] S5.4 Carbon Space Efficiency Synergistic Evaluation Report and Optimization Suggestions Map Output

[0147] First, the visualization rendering of the carbon space efficiency collaborative evaluation map is performed. The spatiotemporal codes generated in step S1 and the collaborative level values ​​determined in step S5.1 for each evaluation unit are extracted. These collaborative level values ​​are used as color rendering parameters, and attribute-linked to the geographic vector boundaries of the evaluation units using a spatial association algorithm, to generate a global carbon space efficiency collaborative evaluation level distribution map. Simultaneously, the geomorphic attribute labels from step S3.1 and the dominant functional attribute labels from step S3.2 are retrieved, and the optimization direction vector identifiers of each unit are overlaid on the level distribution map using a two-factor orthogonal mapping model, thus constructing a carbon space efficiency collaborative evaluation and optimization suggestion map.

[0148] Secondly, the system automatically summarizes and outputs structured evaluation reports. It extracts and integrates the measured feature vectors, collaborative deviation indexes, and matching improvement strategies from step S5.3 of each evaluation unit in real time, establishing a data association library for each evaluation unit. For 15 orthogonal scenarios constructed from 5 typical landforms and 3 dominant functions, the system uses logical judgment rules to explain the deviation characteristics of the measured feature vectors relative to the ideal collaborative benchmark vector in each scenario, clarifying the spatial carbon efficiency imbalance points and specific implementation paths. Finally, it performs data loading according to a preset report template, generating a collaborative evaluation report on village and town carbon spatial efficiency that includes multi-dimensional feature comparison charts, collaborative level statistical tables, and textual guidance.

[0149] The specific implementation results for the two types of typical villages and towns are as follows:

[0150] Village / Township A: ① Visual Map Rendering: Extract the spatiotemporal codes of the 32 effective evaluation units divided in S1.1, and the collaborative deviation index and collaborative level values ​​of each unit determined in S5.1. Use the collaborative deviation index as the core color rendering parameter; the higher the value and the darker the red, the more severe the spatial configuration imbalance. Complete attribute linking with the geographic vector boundaries of the evaluation units through spatial association algorithms, and render and generate a distribution map of the collaborative evaluation level of carbon spatial efficiency across the entire village / township A (corresponding to the attached map). Figure 6 The system is divided into three parts: light pink areas (corresponding to high-quality collaborative units with D < 0.2), light red areas (corresponding to slightly unbalanced units with D < 0.4 with D < 0.2), and dark red areas (corresponding to moderately deviated units with D < 0.7 with D < 0.4), accurately presenting the spatial distribution pattern of the overall collaborative level. Simultaneously, it retrieves the plain landform attribute labels determined in S3.1 and the agricultural-dominant functional attribute labels determined in S3.2. Using a two-factor orthogonal mapping model, it marks the optimization direction vectors for ecological corridor construction and green space supplementation for moderately deviated units on the level distribution map, constructing a collaborative evaluation and optimization suggestion map for the carbon spatial efficiency of the village / town. ② Structured evaluation report output: The system extracts and integrates the measured feature vectors and collaborative deviation indexes of 32 evaluation units in real time, as well as the optimization strategies matched in S5.3 with the core objective of improving carbon sequestration capacity, establishing a data association library for the village / town evaluation units; for the plain-agriculture-dominant scenario, combined with the attached... Figure 6 The spatial distribution characteristics of each evaluation unit are analyzed using logical judgment rules to illustrate the deviation characteristics of the measured feature vectors from the ideal collaborative benchmark vector. The two core imbalance points of excessive grayness in the eastern residential area and insufficient greenness in the western pure agricultural area are identified, along with specific implementation paths. Finally, data is filled according to the preset report template to generate a carbon space efficiency collaborative evaluation report that includes a multi-dimensional feature comparison chart of greenness, grayness, and integration degree of the village / town, a collaborative level statistical table, and textual guidance.

[0151] Village / Township B: ① Visual Map Rendering: Extract the spatiotemporal codes of the 114 valid evaluation units divided in S1.1, and the collaborative deviation index and collaborative level values ​​of each unit determined in S5.1. Using the collaborative deviation index as the core color rendering parameter, the attributes are linked to the geographic vector boundaries of the evaluation units through a spatial association algorithm, and a distribution map of the collaborative evaluation level of carbon spatial efficiency in the entire village / township B is generated (corresponding to the attached map). Figure 7 The system accurately locates the severely misaligned core area of ​​the southern industrial concentration zone and the high-quality collaborative area of ​​the northern ecological zone. Simultaneously, it retrieves the mountainous topography attribute labels determined in S3.1 and the industrial-dominant functional attribute labels determined in S3.2. Using a two-factor orthogonal mapping model, it marks the optimization direction vectors for industrial green space embedding and ecological patch restoration on the graded distribution map for severely misaligned and moderately deviated units, constructing a collaborative evaluation and optimization suggestion map for the carbon space efficiency of the village / town. ② Structured evaluation report output: The system extracts and integrates the measured feature vectors and collaborative deviation index of 114 evaluation units in real time, as well as the optimization strategies matched in S5.3 with carbon emission reduction as the core, establishing a data association library for the village / town evaluation units; for the mountainous-industrial-dominant scenario, combined with the attached... Figure 7 The spatial distribution characteristics are analyzed, and logical judgment rules are used to explain the deviation characteristics of the measured feature vectors of each evaluation unit from the ideal collaborative benchmark vector. The two core imbalance points of excessive grayness in industrial areas and the separation of green and gray spaces are identified, along with specific implementation paths. Finally, data is filled according to the preset report template to generate a carbon space efficiency collaborative evaluation report that includes a multi-dimensional feature comparison chart of greenness, grayness, and integration degree of the village / town, a collaborative level statistical table, and textual guidance.

[0152] To verify the technical effectiveness of this invention, control villages and towns belonging to the same plain-agriculture-dominated type as village and town A were selected, and carbon spatial efficiency was evaluated using the following three methods:

[0153] Method A: The traditional unified standard evaluation method for the entire region adopts uniform greenness and grayness thresholds, without considering differences in landform and function;

[0154] Method B: An evaluation method that only considers landform classification, dividing villages and towns into five landform categories such as plains and mountains, but does not consider functional classification;

[0155] Method C: The method of this invention considers both landform classification and functional classification, and constructs a two-dimensional feature matrix for evaluation.

[0156] The evaluation results of the three methods are compared as follows:

[0157] Imbalance region identification accuracy 42% 68% 91% Adoption rate of planning schemes 35% 58% 82%

[0158] Experimental results show that the method of the present invention outperforms existing technologies in terms of accuracy in identifying imbalanced regions and adoption rate of planning schemes, thus verifying the technical superiority of the present invention.

[0159] This invention can be widely applied to fields such as village and town land space planning, carbon neutrality scheme formulation, and low-carbon village and town construction. Through the evaluation method provided by this invention, planners can quickly and accurately identify the root causes of imbalances in village and town carbon space allocation and obtain targeted optimization strategies, significantly improving the efficiency and scientific rigor of village and town carbon space planning.

[0160] Compared with existing technologies, this invention effectively solves the technical bottlenecks in the traditional village and town carbon space assessment process, such as the single evaluation standard, neglect of geographical background and functional differences, lack of accuracy in synergy measurement, and difficulty in applying the evaluation results. It makes up for the shortcomings of existing technologies and has significant technological progress and practical value.

[0161] Secondly, this invention constructs a dual-dimensional feature matrix benchmark library based on landform facies and dominant functions, classifying five typical landform facies and three dominant functions. These are orthogonally combined to form fifteen typical carbon space scenarios, and each scenario is assigned a dedicated ideal carbon efficiency synergy benchmark. This approach completely breaks through the limitations of single, generalized standards in traditional evaluation methods, solves the technical bottleneck of ignoring hard constraints of the geographical environment and differences in socio-economic functions, and achieves refined carbon space evaluation with scenario adaptability.

[0162] Furthermore, this invention achieves precise calculation of spatial carbon efficiency deviation by extracting and standardizing the three-dimensional feature parameters of greenness, grayness, and green-gray space fusion degree of the evaluation unit, combined with a vector space distance algorithm. This method can objectively and quantitatively reflect the coupling efficiency between the carbon sequestration potential of green space and the carbon emission pressure of gray space within the village and town unit, accurately locate the imbalance of spatial configuration, and significantly improve the scientificity and logical rigor of the evaluation results.

[0163] Finally, this invention, by constructing a complete technical process encompassing collaborative level determination, geomorphological constraint optimization, function-oriented carbon reduction, and visualized output of results, deeply integrates evaluation results with village and town land spatial planning and carbon neutrality practices. By outputting practical, categorized carbon efficiency optimization strategies and visualized maps, it effectively addresses the technical bottleneck of the disconnect between existing evaluation results and planning practices, providing valuable technical support for the precise governance of village and town spaces and the formulation of carbon neutrality implementation pathways.

[0164] The above are merely specific embodiments of the present invention, but the technical features of the present invention are not limited thereto. Any simple changes, equivalent substitutions, or modifications made based on the present invention to solve essentially the same technical problems and achieve essentially the same technical effects are all covered within the protection scope of the present invention.

Claims

1. A method for synergistic evaluation of village and town carbon spatial efficiency based on geomorphological and functional feature matrices, characterized in that, Includes the following steps: S1. Division of village and town carbon space evaluation units and extraction of multi-dimensional feature parameters: Obtain the topographic data of the target village and town, and perform spatial grid segmentation with non-uniform step size based on the topographic data to form a standardized evaluation unit array; extract the greenness index, grayness index and green-gray space fusion index of each evaluation unit in the standardized evaluation unit array. The extraction methods for the greenness index, grayness index, and green-gray space integration index are as follows: Greenness index = (Total area of ​​green space elements within the evaluation unit / Total area of ​​the evaluation unit) × 100%; Gray index = weighted value of land use proportion and carbon emission coefficient of gray space elements within the evaluation unit, used to characterize the scale proportion and carbon emission potential of gray space. Green-Gray Space Integration Index = A spatial interweaving degree index calculated based on the spatial distribution relationship between green space elements and gray space elements; Among them, green space elements include land types with natural carbon sequestration functions, and gray space elements include artificial land types that carry the potential for anthropogenic carbon emissions. S2. Construction of a dual-dimensional feature matrix benchmark library based on landform and dominant function: Obtain the landform features and dominant function attributes of each evaluation unit, divide the landform features into several typical landform phases, and divide the dominant function attributes into several dominant function categories. Orthogonally combine the typical landform phases as the row dimension and the dominant function categories as the column dimension to construct a dual-dimensional feature matrix. Each matrix unit of the dual-dimensional feature matrix corresponds to a carbon space scene. Configure ideal greenness index, ideal grayness index, and ideal green-gray space fusion index for the carbon space scene corresponding to each matrix unit, combine them to form an ideal collaborative benchmark vector, and store them to form a feature matrix benchmark library. S3. Evaluation unit attribute mapping and scene positioning based on benchmark library constraints: Determine the landform phase category and dominant function category of each evaluation unit, determine the index coordinates of the evaluation unit in the two-dimensional feature matrix based on the discrimination result, and retrieve the corresponding ideal collaborative benchmark vector from the benchmark library. S4. Measurement of Carbon Spatial Efficiency Collaborative Deviation Based on Vector Space: The measured greenness index, measured grayness index, and measured green-gray space fusion index of each evaluation unit are normalized by linear scaling transformation using the minimum and maximum values ​​of the corresponding indicators of the entire evaluation unit to construct a measured feature vector; the corresponding ideal collaborative benchmark vector is normalized by the same minimum and maximum values ​​of the corresponding indicators of the entire evaluation unit to construct a benchmark vector; the spatial geometric distance between the measured feature vector and the benchmark vector is calculated using a weighted Euclidean distance algorithm to generate a collaborative deviation index; S5. Determination of Carbon Space Efficiency Synergy Level and Generation of Classification Optimization Strategies: Determine the carbon space efficiency synergy level of the evaluation unit based on the synergy deviation index; associate structural optimization strategies based on the geomorphic facies category of the evaluation unit; and match differentiated carbon reduction paths based on the dominant functional category of the evaluation unit.

2. The method for synergistic evaluation of village and town carbon spatial efficiency based on geomorphological and functional feature matrices according to claim 1, characterized in that: In step S1, spatial grid segmentation with non-uniform step size is performed based on terrain data. Specifically, the grid is segmented according to the size of the terrain undulation, using a grid size that is negatively correlated with the terrain undulation. The grid size is smaller in areas with greater terrain undulation and larger in areas with less terrain undulation. The terrain undulation is the standard deviation of the elevation difference between adjacent evaluation units.

3. The method for synergistic evaluation of village and town carbon spatial efficiency based on geomorphological and functional feature matrices according to claim 2, characterized in that: The mesh segmentation specifically includes: Obtain digital elevation model data of the target villages and towns, and extract ground relief. When the ground undulation is ≤50m, a 1000m×1000m grid is used for subdivision; When the ground undulation is greater than 50m and less than or equal to 200m, a 500m × 500m grid should be used for subdivision. When the ground undulation is greater than 200m, a 200m×200m grid is used for subdivision; After the segmentation is completed, a unique spatiotemporal code is assigned to each evaluation unit in the standardized evaluation unit array.

4. The method for synergistic evaluation of village and town carbon spatial efficiency based on geomorphological and functional feature matrices as described in claim 1, characterized in that: The formula for calculating the grayscale index is: Gray index = [Σ(Area of ​​a certain type of gray space element within the evaluation unit × Carbon emission coefficient of that type of gray space element) ÷ (Total area of ​​the evaluation unit × Average carbon emission coefficient of gray space elements)] × 100%; Among them, the carbon emission coefficient of industrial land is 8.2 tC / (hm² / a), the carbon emission coefficient of urban residential land is 3.5 tC / (hm² / a), the carbon emission coefficient of commercial service land is 5.6 tC / (hm² / a), the carbon emission coefficient of transportation land is 1.8 tC / (hm² / a), and the average carbon emission coefficient of gray space elements is 4.775 tC / (hm² / a).

5. The method for synergistic evaluation of village and town carbon spatial efficiency based on geomorphological and functional feature matrices according to claim 1, characterized in that: The formula for calculating the green-gray space integration index is as follows: Green-gray space integration index = 1 - (D ÷ diagonal length of evaluation unit); Where D is the straight-line distance between the geometric centroid of the green space element and the geometric centroid of the gray space element within the evaluation unit.

6. The method for synergistic evaluation of village and town carbon spatial efficiency based on geomorphological and functional feature matrices according to claim 1, characterized in that: The typical landform facies mentioned in step S2 include five types: plains, mountains, hills, lakeside and coastal areas; the dominant functional categories include three types: agriculture-dominated, industry-dominated and business and tourism service-dominated; the dual-dimensional feature matrix is ​​a 5-row × 3-column matrix, and each matrix unit corresponds to a carbon space scenario.

7. The method for synergistic evaluation of village and town carbon spatial efficiency based on geomorphological and functional feature matrices according to claim 1, characterized in that: The method for configuring the ideal cooperative baseline vector for the carbon space scene corresponding to each matrix unit in step S2 is as follows: Collect historical preferred sample data of similar carbon space scenarios in areas with similar climate and geographical conditions to the target villages and towns, and select no less than 30 samples from national-level ecological demonstration zones or low-carbon pilot areas for each scenario. Obtain measured data on greenness, grayness, and green-gray space fusion degree of each sample area; The upper limit of each sample indicator was determined by using the 90th percentile method and fine-tuned by combining the scoring method of experts in carbon accounting and land spatial planning. The ideal greenness index, ideal grayness index and ideal green-gray space integration index of carbon space scenario corresponding to each matrix unit were determined. Let V represent the ideal greenness index, ideal grayness index, and ideal green-gray space integration index. g V h V f Combine to construct the ideal collaborative benchmark vector V=(V g V h V f ).

8. The method for synergistic evaluation of village and town carbon spatial efficiency based on geomorphological and functional feature matrices according to claim 1, characterized in that: The calculation formula for the weighted Euclidean distance algorithm described in step S4 is as follows: ； Among them, D i For the co-deviation index, G' i H' i F' i These are the normalized measured greenness index, measured grayness index, and measured green-gray space fusion index, respectively, V' g V' h V' f These are the ideal greenness index V g Ideal grayscale index V h Ideal green-gray space integration index V f The normalized values, w1, w2, and w3 are weighting factors.

9. The method for synergistic evaluation of village and town carbon spatial efficiency based on geomorphological and functional feature matrices according to claim 8, characterized in that: The weighting factors w1, w2, and w3 are all positive numbers, and w1 + w2 + w3 = 1.

10. The method for synergistic evaluation of village and town carbon spatial efficiency based on geomorphological and functional feature matrices according to claim 9, characterized in that: w1=0.35, w2=0.35, w3=0.

30.

11. The method for synergistic evaluation of village and town carbon spatial efficiency based on geomorphological and functional feature matrices according to claim 1, characterized in that: The method for determining the carbon space efficiency synergy level in step S5 is as follows: based on the magnitude of the synergy deviation index, at least three evaluation thresholds are preset, and the synergy deviation index is divided into at least four level intervals, with each level interval corresponding to a different qualitative synergy state.

12. The method for synergistic evaluation of village and town carbon spatial efficiency based on geomorphological and functional feature matrices according to claim 11, characterized in that: The evaluation thresholds include a first threshold, a second threshold, and a third threshold, wherein: When the collaboration deviation index is less than the first threshold, it is judged as high-quality collaboration; When the co-discretion index is greater than or equal to the first threshold and less than the second threshold, it is judged as a slight imbalance; When the collaborative deviation index is greater than or equal to the second threshold and less than the third threshold, it is judged as a moderate deviation; When the coordination deviation index is greater than or equal to the third threshold, it is judged as a serious misalignment; The first threshold is 0.2, the second threshold is 0.4, and the third threshold is 0.

7.

13. The method for synergistic evaluation of village and town carbon spatial efficiency based on geomorphological and functional feature matrices according to claim 1, characterized in that: The association method for the structural optimization strategy and the matching method for the differentiated carbon reduction pathways mentioned in step S5 are as follows: For evaluation units with a landform of mountains or hills, ecological patch restoration strategies centered on topographic adaptability are associated. For evaluation units with landform facies of plains, lakeside or coastal areas, a spatial openness optimization strategy with land cover continuity as the core is adopted. For evaluation units whose dominant function is agriculture, an optimization strategy with the core objective of enhancing carbon sequestration capacity is matched. For evaluation units whose dominant function is industry-led, an optimization strategy centered on reducing carbon emissions is matched. For evaluation units whose dominant function is business travel services, an optimization strategy centered on the synergy of green and gray spaces is applied.

14. A village and town carbon space efficiency collaborative evaluation system based on a geomorphological and functional feature matrix, used to execute the village and town carbon space efficiency collaborative evaluation method based on a geomorphological and functional feature matrix as described in any one of claims 1 to 13, characterized in that, include: The feature matrix benchmark library construction module is configured to acquire the geomorphic features and dominant functional attributes of the target villages and towns, perform a two-dimensional orthogonal combination and construct a feature matrix benchmark library containing multiple scenarios, and generate an ideal collaborative benchmark vector for spatial element configuration under each scenario. The evaluation unit attribute mapping module is configured to identify the landform phase category and functional category of each grid unit, perform automatic attribute mapping and scene positioning based on matrix indexing, and realize the coordinate alignment between the unit to be evaluated and the reference vector. The collaborative deviation measurement module is configured to construct a multi-dimensional measured feature vector of the evaluation unit, use the vector space distance algorithm to calculate the geometric displacement between the measured vector and the ideal reference vector, and generate a collaborative deviation index that reflects the degree of spatial configuration imbalance. The classification optimization strategy generation module is configured to match differentiated structural optimization logic and carbon reduction path strategies based on the collaborative deviation index and the scene of the matrix unit to which it belongs. It extracts the collaborative level value of each evaluation unit, uses the collaborative level value as a color rendering parameter, and uses a spatial association algorithm to attach attributes to the geographic vector boundary of the evaluation unit to render and generate a global carbon space efficiency collaborative evaluation level distribution map. It also retrieves the geomorphic attribute label and dominant function attribute label of the evaluation unit, and uses a two-factor orthogonal mapping model to overlay and display the optimization direction vector identifier of each unit on the level distribution map, thus constructing a carbon space efficiency collaborative evaluation and optimization suggestion map.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the collaborative evaluation method for village and town carbon space efficiency based on the geomorphological and functional feature matrix as described in any one of claims 1 to 13.

Images