Method for identifying key pollution control and carbon reduction areas based on cumulative emission-area relationship

Abstract

This invention discloses a method for identifying key control areas for pollution reduction and carbon reduction based on the cumulative emission-area relationship, belonging to the field of ecological environment governance technology. This method uses spatial units as the basic analysis object, sequentially completing the steps of spatial unit construction, emission intensity index acquisition, spatial unit descending order sorting, cumulative emission-area ratio construction, automatic threshold identification, and key control area identification. Thresholds are determined through algorithms such as curve slope and curvature analysis, and units with a cumulative area ratio not exceeding the threshold are designated as key control areas. This invention constructs a standardized automatic identification process, establishes quantitative threshold identification rules, adapts to different emission index systems, and improves the ability for refined spatial control. While ensuring emission coverage, it significantly reduces the area of ​​control areas. The identification results are objective and repeatable, and it is applicable to the delineation of key control areas for pollution reduction and carbon reduction in various regions.

Description

Technical Field

[0001] This invention relates to the field of ecological environment management and spatial data analysis technology, specifically a method for identifying key control areas for pollution reduction and carbon reduction based on the cumulative emission-area relationship. Background Technology

[0002] Against the backdrop of regional ecological environment governance and the advancement of "dual carbon" goals, the accurate identification of key control areas for pollution reduction and carbon reduction is a key technical support for achieving differentiated and refined environmental management. Existing related identification technologies are mainly divided into three categories: zoning methods based on administrative divisions, spatial identification methods based on the emission intensity of single pollutants or greenhouse gases, and methods based on collaborative evaluation indicators. At the same time, some technologies involve ranking analysis or cumulative contribution analysis, but a complete technical process oriented towards spatial units has not yet been formed.

[0003] Existing technologies lack a systematic analysis framework for the contribution of spatial units in the identification and application of key control areas for pollution reduction and carbon reduction. There is no standardized spatial unit ranking and analysis process, nor has a quantitative identification rule based on the relationship between cumulative emission contribution and spatial area been established. This results in a lack of scientificity and systematicness in the correlation analysis between emissions and space, and it is impossible to form effective support for the delineation of key control areas from the data level.

[0004] When delineating key control areas for pollution reduction and carbon reduction using existing technologies, the scope of control is often determined by experience-based thresholds. This approach lacks objective quantitative basis, which not only makes the identification of key control areas highly subjective, but also results in significant differences in delineation outcomes among different personnel and in different scenarios, resulting in insufficient repeatability. Furthermore, it is difficult to balance emission coverage with the area of ​​the control zone, and thus fails to meet the current technical requirements for precision and standardization in environmental management.

[0005] Therefore, a method for identifying key control areas for pollution reduction and carbon reduction based on the cumulative emission-area relationship was proposed to address the above issues. Summary of the Invention

[0006] 1. The technical problem to be solved by the present invention

[0007] The purpose of this invention is to propose a method for identifying key control areas for pollution reduction and carbon reduction based on the cumulative emissions-area relationship, in order to solve the following problems existing in the prior art: (1) The existing identification of key control areas for pollution reduction and carbon reduction lacks a systematic technical process and quantitative identification rules, and relies heavily on experience thresholds, resulting in insufficient objectivity and repeatability of the identification results.

[0008] (2) Existing identification methods are difficult to balance emission coverage and key control area area, and spatial accuracy and control precision need to be improved.

[0009] 2. Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a method for identifying key control areas for pollution reduction and carbon reduction based on cumulative emissions-area relationship, comprising the following steps: S1: Spatial unit construction, dividing the study area into multiple spatial units with unique identifiers and area attributes. These spatial units can be regular or irregular grid units. The division scale of the spatial units should match the area and control precision requirements of the study area. Preferred adaptation rules are shown in the table below:

[0010] The spatial unit scale is positively correlated with the area of ​​the study region and negatively correlated with the control accuracy. A balance should be struck between data availability and analytical accuracy.

[0011] S2: Acquisition of emission intensity indicators. Integrating greenhouse gas emission data and air pollutant emission data within the study area, the comprehensive emission intensity indicator for each spatial unit is calculated. This emission intensity indicator characterizes the comprehensive emission contribution level of the spatial unit; preferably, the coordinated control emission reduction equivalent (ER-eq) indicator is used. The calculation formula is as follows:

[0012]

[0013] In the formula: ER eq Indicates the emission reduction equivalent of coordinated control; R CO2 and R P Q represents the weighting coefficients for CO2 and air pollutants, respectively; CO2 and Q P These represent the CO2 and atmospheric pollutant grid unit emissions, respectively; α represents the CO2 equivalent weighting coefficient; β, γ, δ… represent the atmospheric pollutant (SO2, NOx, PM…) equivalent weighting coefficients. It is important to emphasize that there are multiple methods for determining the weighting coefficients; the following method is preferred:

[0014] S3: Spatial unit sorting. All spatial units are sorted in descending order according to the emission intensity index to form a spatial unit sorting sequence.

[0015] S4: Constructing the cumulative relationship: Based on the sorting sequence, sequentially accumulate the area and emissions of spatial units to calculate the cumulative area ratio and cumulative emission ratio, generating a cumulative emission ratio - cumulative area ratio relationship curve; Calculation of cumulative emission ratio:

[0016] Cumulative area percentage calculation:

[0017] S5: Automatic threshold identification. The cumulative emission ratio-cumulative area ratio relationship curve is subjected to morphological feature quantitative analysis. Threshold identification is performed based on the morphological changes of the cumulative relationship curve. The identification rules include, but are not limited to, curve slope change analysis, curvature change analysis and inflection point detection algorithms. Through the progressive identification rules of slope mutation detection, curvature extreme value positioning and inflection point accurate determination, the cumulative area ratio threshold corresponding to the key control area is automatically determined. The threshold is identified through the following specific steps: (1) Detection of abrupt changes in slope; The slope between adjacent data points is calculated as follows:

[0018] In the formula, For the first The emission percentage corresponding to each spatial unit; For the first The cumulative area percentage corresponding to each spatial unit; This represents the local slope between adjacent data points.

[0019] Calculate the difference between adjacent slopes as follows:

[0020] Statistical analysis of the entire set of slope differences Calculate its mean and standard deviation And the threshold for determining slope abrupt changes is set as follows:

[0021] in, λ is the threshold for determining abrupt slope changes; λ is an adjustment coefficient, preferably ranging from 1.0 to 2.0. When... > When the slope changes abruptly, the interval is determined to be an interval of sudden slope change and is used as a candidate interval for subsequent curvature analysis.

[0022] (2) Curvature calculation and extreme value location; After determining the interval of abrupt slope change, the curvature of the curve is calculated. Let the cumulative emission percentage versus cumulative area percentage relationship curve be represented as a function: Y = f(X) Its curvature calculation formula is:

[0023] Where f'(X) is the first derivative of the curve; f''(X) is the second derivative of the curve; and K is the curvature of the curve at that point.

[0024] In the case of discrete data, approximate calculations can be performed using the finite difference method:

[0025]

[0026] After calculating the curvature value of each candidate point, the point corresponding to the maximum curvature value is selected as the core candidate point for the threshold.

[0027] (3) Inflection point determination Perform polynomial fitting on the curve segments near the candidate points and calculate the second derivative of the fitting function.

[0028] When the sign of the second derivative changes, the corresponding position is determined to be a characteristic inflection point of the curve.

[0029] The cumulative area percentage corresponding to this inflection point is the threshold area for key control areas.

[0030] S6: Key control area identification. Spatial units with a cumulative area ratio not exceeding the threshold are designated as key control areas for pollution reduction and carbon reduction, realizing automated spatial identification of key control areas without relying on experience thresholds.

[0031] Preferably, the slope mutation detection in S5 specifically involves: calculating the slope values ​​of adjacent data points on the cumulative emission ratio-cumulative area ratio relationship curve, solving for the difference between adjacent slope values, and marking the interval as a slope mutation interval when the difference exceeds a preset quantization threshold, which is then used as a threshold candidate interval.

[0032] Preferably, the curvature extremum localization in S5 specifically involves: performing curvature calculation on all data points within the slope abrupt change interval, and selecting the point corresponding to the maximum curvature value as the core candidate point for the threshold. The curvature calculation is completed based on the first and second derivatives of the curve.

[0033] As a preferred embodiment, the precise determination of the inflection point in S5 specifically involves: performing function fitting on the curve segments surrounding the core candidate point of the threshold, solving for the second derivative of the fitted function, and determining the point where the sign of the second derivative changes as the characteristic inflection point of the relationship curve. The cumulative area ratio corresponding to this characteristic inflection point is the area threshold of the key control area.

[0034] Preferably, in S4, a geographic information system or data processing software is used to calculate the cumulative area ratio and the cumulative emission ratio, and the software is used to draw a smooth curve showing the relationship between the cumulative emission ratio and the cumulative area ratio. The cumulative area ratio is the ratio of the accumulated area to the total area of ​​the study area, and the cumulative emission ratio is the ratio of the accumulated emissions to the total emissions of the study area.

[0035] Preferably, in step S5, multiple feature inflection points are identified through multi-level progressive identification rules, and the cumulative area ratio corresponding to the feature inflection point with the highest cumulative emission ratio is selected as the area threshold of the key control area.

[0036] S7: Threshold stability verification. To verify the stability of threshold recognition, repeated calculations were performed using different spatial unit scales.

[0037] Calculate the threshold change rate: R = |T i T ref | / T ref The threshold is considered stable when R ≤ 10%.

[0038] A computer-readable storage medium storing a computer program, which, when executed by a processor, sequentially performs the steps of spatial unit construction, emission intensity index acquisition, spatial unit sorting, cumulative relationship construction, automatic threshold identification, and key control area identification. Furthermore, it can use data processing software such as Geographic Information System (GIS) and Excel to complete cumulative percentage calculation and curve plotting, thereby achieving automated identification of key control areas.

[0039] Compared with existing technologies, the method for identifying key control areas for pollution reduction and carbon reduction based on cumulative emissions-area relationship provided by this invention has the following beneficial effects: (1) To automate and standardize the identification process and improve repeatability; This solution establishes a continuous and unified data processing flow from spatial unit construction to key control area identification. It integrates spatial sorting, cumulative relationship analysis, and threshold identification into standardized steps, eliminating the problem of fragmented processes in existing technology identification processes. This makes the identification of key control areas for pollution reduction and carbon reduction a repeatable and implementable standardized operation. Different scenarios and different operators can obtain consistent results based on this process, which greatly improves the standardization of technology application.

[0040] (2) Establish quantitative threshold identification rules and abandon the drawbacks of experience-based judgment; This solution, based on the changing shape of the curve relating cumulative emission percentage to cumulative area percentage, uses quantitative algorithms such as slope analysis, curvature analysis, and inflection point detection to automatically identify thresholds, establishing scientific quantitative identification rules. Compared to existing technologies that rely on empirical thresholds to delineate key areas, this solution fundamentally solves the problem of strong subjectivity in the judgment results, ensuring that the determination of the scope of key control areas is supported by objective data, and improving the scientific rigor and accuracy of the identification results.

[0041] (3) It has strong adaptability and is compatible with different emission index systems; This approach uses emission intensity indicators of spatial units as the core analytical basis and does not restrict the specific calculation method of emission intensity indicators. It is compatible with different types of emission data, such as greenhouse gases and air pollutants, as well as different indicator systems, such as various collaborative evaluations and single emission intensity. It overcomes the limitation of some existing methods in terms of single adaptability, and can flexibly combine emission indicators according to different research areas and different governance needs, thus possessing a wide range of practical application scenarios.

[0042] (4) Enhance the ability to manage space with precision, taking into account both emission coverage and area optimization; This solution uses spatial units as the basic analysis object and supports various spatial division methods such as regular grids and irregular units. Compared with the low spatial accuracy problem of existing technologies based on administrative divisions, it achieves more refined spatial analysis. At the same time, by determining the scope of the control area through cumulative emissions-area relationship analysis, it can significantly reduce the area proportion of key control areas while ensuring the main emission contribution of the covered area. This allows environmental management resources to be precisely focused on high-emission areas, improving the targeting and efficiency of pollution reduction and carbon reduction control. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of spatial unit sorting in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the relationship between the cumulative emission percentage and the cumulative area percentage in Jinan (1km×1km grid) in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of the relationship between the cumulative emission ratio and the cumulative area ratio in Shandong Province in Embodiment 2 of the present invention. Figure 5 This is a schematic diagram of the relationship between the cumulative emission ratio and the cumulative area ratio in Henan Province in Embodiment 2 of the present invention. Figure 6 This is a schematic diagram illustrating the relationship between the cumulative emission percentage and the cumulative area percentage in Xinjiang Uygur Autonomous Region, as shown in Embodiment 2 of the present invention. Figure 7 This is a schematic diagram of the relationship between the cumulative emission percentage and the cumulative area percentage in Jinan (2km×2km grid) in Embodiment 3 of the present invention; Figure 8 This is a schematic diagram of the relationship between the cumulative emission percentage and the cumulative area percentage in Jinan (3km×3km grid) in Embodiment 3 of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0045] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0046] Based on Example 1, Examples 2 and 3 will be described in detail. Example 1, please refer to Figures 1 to 3 As shown: To address the problems mentioned in the technical solutions, this application provides a method for identifying key pollution reduction and carbon reduction control areas based on cumulative emissions-area relationship. To achieve the above objective, the present invention provides the following technical solution: a method for identifying key pollution reduction and carbon reduction control areas based on cumulative emissions-area relationship, comprising the following steps: S1: Spatial unit construction. In this embodiment, the Jinan city area, about 10,000 km², is taken as the research object. Since it is a city-level area, a regular grid with a spatial resolution of 1km×1km is adopted, which is divided into about 10,000 spatial units.

[0047] S2: Acquisition of Emission Intensity Indicators. Emission intensity indicators can be derived from different calculation methods or indicator systems; this invention does not rely on a specific formula. This embodiment selects the emission intensity indicator ER-eq as a typical representative to illustrate the specific operation process of this invention. Multi-source emission data, including the integrated emission inventory of air pollutants and greenhouse gases in Jinan City, are collected. The emissions of various pollutants and greenhouse gases are summed and statistically analyzed for each spatial unit, and then normalized according to the following method to form the emission intensity indicators for each spatial unit.

[0048] S3: Spatial unit sorting. Using a geographic information system or software such as Excel, spatial units are sorted from highest to lowest emission intensity. Figure 2 As shown.

[0049] S4: Constructing the cumulative relationship: Based on the sorting sequence, the area and emissions of spatial units are accumulated sequentially to calculate the cumulative area ratio and cumulative emission ratio, generating a cumulative emission ratio-cumulative area ratio relationship curve, such as... Figure 3 As shown.

[0050] S5: Automatic threshold identification, for Figure 3Curvature analysis was performed on the curve representing the cumulative emission percentage versus the cumulative area percentage to identify the inflection point. The calculation results showed that the cumulative emission percentage was 0.91 and the cumulative area percentage was 0.1. Therefore, the threshold for the cumulative emission percentage in the key control area was determined to be 91%, corresponding to a cumulative area percentage of 10%.

[0051] S6: Key Control Area Identification. Spatial units with a cumulative area proportion not exceeding a threshold are designated as key control areas for pollution reduction and carbon reduction, achieving automated spatial identification of key control areas without dependence on empirical thresholds. Results show that this method identified key control areas covering 10% of the city's area, contributing over 90% of the city's CO2 emissions, with CO, SO2, NOx, and VOCs accounting for 85%, 72%, 68%, and 69%, respectively. This indicates that while covering the main emission-contributing areas, this method significantly reduces the area of ​​key control areas, demonstrating good application effectiveness.

[0052] Example 2: Multi-area scenario verification, please refer to... Figures 4 to 6 As shown: To verify the applicability of the method of this invention under different regional emission structure conditions, this embodiment selects three representative provincial regions in eastern, central, and western China as research objects: Shandong Province, Henan Province, and Xinjiang Uygur Autonomous Region. These regions exhibit significant differences in industrial structure, energy structure, and spatial distribution of emissions, thus effectively reflecting the emission characteristics of different regions.

[0053] Since the above-mentioned areas are all within the scope of provincial-level research, a 10 km × 10 km regular grid was used to divide the study area into spatial units, and a spatial unit database was constructed.

[0054] S1: Spatial unit construction. Regular grids were used to divide the regions of Shandong Province, Henan Province and Xinjiang Uygur Autonomous Region. Each spatial unit was assigned a unique identifier and its area attribute was calculated to form the basic data of spatial units in the study area.

[0055] S2: Acquisition of emission intensity indicators: Collect greenhouse gas emission data and air pollutant emission data for each region, and statistically analyze the emissions for each spatial unit. In this embodiment, the comprehensive emission intensity index ER-eq is used to characterize the comprehensive emission contribution level of the spatial unit.

[0056] The emission data comes from the MEIC (Multi-resolution Emission Inventory for China) gridded emission inventory developed by Tsinghua University, with a spatial resolution of 10 km × 10 km.

[0057] S3: Spatial unit sorting. Using geographic information system software or Excel software, spatial units are sorted from largest to smallest according to the emission intensity index ER-eq to form a spatial unit sorting sequence.

[0058] S4: Cumulative relationship construction. Based on the sorting results, the emissions and area of ​​spatial units are accumulated sequentially to calculate the cumulative emission ratio and cumulative area ratio, and a cumulative emission ratio-cumulative area ratio relationship curve is constructed.

[0059] The cumulative emissions-area relationship curves for Shandong, Henan, and Xinjiang Uygur Autonomous Region are as follows: Figure 4 , Figure 5 and Figure 6 As shown.

[0060] S5: Automatic threshold identification. Curve analysis is performed on the cumulative emission percentage versus cumulative area percentage curve to identify characteristic inflection points. The calculation results are shown in Table 1. Table 1 shows the emission statistics for different regions;

[0061] The results show that different regions exhibit a clear phenomenon of emission concentration, meaning that the vast majority of emission-contributing areas can be covered within a relatively small spatial range.

[0062] S6: Key control area identification. Based on the above thresholds, spatial units with a cumulative area ratio not exceeding the corresponding threshold are designated as key control areas for pollution reduction and carbon reduction.

[0063] The identification results show that approximately 22% of the area in Shandong Province accounts for about 88% of the emission contribution; approximately 20% of the area in Henan Province accounts for about 87% of the emission contribution; and approximately 12% of the area in Xinjiang Uygur Autonomous Region accounts for about 92% of the emission contribution.

[0064] The above results demonstrate that the method of the present invention can stably identify key areas with high emission contributions under different regional emission structure conditions, and achieve accurate identification of key control areas.

[0065] Example 3: Threshold stability verification, please refer to... Figure 3 , Figure 7 , Figure 8 As shown: To verify the stability of the identification results of the method of the present invention, this embodiment uses different spatial resolutions to perform calculations on the same study area.

[0066] Taking Jinan City in Example 1 as the research object, a 1 km × 1 km ( Figure 3 ), 2 km × 2 km ( Figure 7 ), 3km × 3 km ( Figure 8The cumulative emission-area relationship curves were constructed and the thresholds were calculated using spatial resolution. The results are shown in Table 2. Table 2 shows the statistics for different grid spatial resolutions;

[0067] The results show that the differences in the threshold area of ​​key control areas identified under different spatial resolutions are relatively small. Using a 1 km resolution as the baseline threshold, the threshold change rate is calculated using the following formula: R = |T i T ref | / T ref The calculation results show that the threshold change rates under each spatial resolution condition are 0.0000, 0.0000 and 0.1000, respectively, with the maximum change rate being 0.1000 (10%).

[0068] The above results show that the area threshold of the key control area identified by the method of the present invention varies little under different spatial resolution conditions, indicating that the method has good stability and robustness.

[0069] Summary of Examples. As can be seen from Examples 1 to 3, the key control area identification method based on the cumulative emission-area relationship curve proposed in this invention can stably identify emission concentration areas under different regional scales and emission structure conditions, and cover the main emission contribution areas within a relatively small spatial range, demonstrating good applicability and stability.

[0070] It should be noted that the term "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0071] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for identifying key control areas for pollution reduction and carbon reduction based on cumulative emissions-area relationship, characterized in that, Includes the following steps: S1: Spatial unit construction, dividing the study area into multiple spatial units with unique identifiers and area attributes, wherein the spatial units are regular grid units or irregular grid units; S2: Emission intensity index acquisition: Integrate greenhouse gas emission data and air pollutant emission data within the study area, calculate the synergistic control emission reduction equivalent of each spatial unit as the emission intensity index, which is used to characterize the comprehensive emission contribution level of the spatial unit; S3: Spatial unit sorting, sorting all spatial units in descending order according to the emission intensity index to form a spatial unit sorting sequence; S4: Constructing the cumulative relationship: Based on the sorting sequence, the area and emissions of the spatial units are accumulated sequentially to calculate the cumulative area ratio and the cumulative emission ratio, and generate the cumulative emission ratio-cumulative area ratio relationship curve. S5: Automatic threshold identification. The cumulative emission ratio-cumulative area ratio relationship curve is subjected to morphological feature quantitative analysis. Threshold identification is performed based on the morphological changes of the cumulative relationship curve. The identification rules include, but are not limited to, curve slope change analysis, curvature change analysis and inflection point detection algorithms. Through the progressive identification rules of slope mutation detection, curvature extreme value positioning and inflection point accurate determination, the cumulative area ratio threshold corresponding to the key control area is automatically determined. S6: Key control area identification. Spatial units with a cumulative area ratio not exceeding the threshold are designated as key control areas for pollution reduction and carbon reduction, realizing automated spatial identification of key control areas without relying on experience thresholds.

2. The method for identifying key control areas for pollution reduction and carbon reduction based on cumulative emissions-area relationship as described in claim 1, characterized in that, The slope mutation detection described in S5 specifically involves: calculating the slope values ​​of adjacent data points on the cumulative emission ratio-cumulative area ratio relationship curve, solving for the difference between adjacent slope values, and marking the interval as a slope mutation interval when the difference exceeds a preset quantization threshold, which is then used as a threshold candidate interval.

3. The method for identifying key control areas for pollution reduction and carbon reduction based on cumulative emissions-area relationship as described in claim 2, characterized in that, The curvature extremum localization described in S5 specifically involves: performing curvature calculation on all data points within the slope abrupt change interval, and selecting the point corresponding to the maximum curvature value as the core candidate point for the threshold. The curvature calculation is based on the first and second derivatives of the curve.

4. The method for identifying key control areas for pollution reduction and carbon reduction based on cumulative emissions-area relationship as described in claim 3, characterized in that, The precise determination of the inflection point in S5 specifically involves: performing function fitting on the curve segments surrounding the core candidate point of the threshold, solving for the second derivative of the fitted function, and determining the point where the sign of the second derivative changes as the characteristic inflection point of the relationship curve. The cumulative area ratio corresponding to this characteristic inflection point is the area threshold of the key control area.

5. The method for identifying key control areas for pollution reduction and carbon reduction based on cumulative emissions-area relationship according to claim 1, characterized in that, In S4, a geographic information system or data processing software is used to calculate the cumulative area ratio and the cumulative emission ratio, and the software is used to draw a smooth curve showing the relationship between the cumulative emission ratio and the cumulative area ratio. The cumulative area ratio is the ratio of the accumulated area to the total area of ​​the study area, and the cumulative emission ratio is the ratio of the accumulated emissions to the total emissions of the study area.

6. The method for identifying key control areas for pollution reduction and carbon reduction based on cumulative emissions-area relationship as described in claim 1, characterized in that, In S5, multiple feature inflection points are identified through multi-level progressive identification rules, and the cumulative area ratio corresponding to the feature inflection point with the highest cumulative emission ratio is selected as the area threshold of the key control area.

7. The method for identifying key control areas for pollution reduction and carbon reduction based on cumulative emissions-area relationship according to claim 1, characterized in that, The key control areas for pollution reduction and carbon reduction identified by this method cover ≥85% of the core emission contributions in the study area, while their area accounts for ≤25% of the total area of ​​the study area.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for identifying key pollution reduction and carbon reduction control areas based on cumulative emissions-area relationship as described in any one of claims 1-7.

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