Fine-scale comparison method and system of carbon emissions based on geographically weighted correlation analysis

Abstract

The present invention discloses a method and system for fine-scale comparison of carbon emissions based on geographically weighted correlation analysis, the method comprising: obtaining carbon emission data released by different institutions in the same region at the same scale; performing attribute calculations on the carbon emission data released by different institutions respectively to generate spatial panel data of different institutions; based on the generated spatial panel data, using the Pearson correlation coefficient to calculate the correlation between the carbon emission data released by different institutions from a global perspective; when the correlation calculated from the global perspective meets a first threshold, using the geographically weighted correlation analysis method to calculate the correlation coefficient between the carbon emission data released by different institutions from a local perspective, wherein the first threshold is a preset value or value range. The present invention judges the strength of the correlation from a fine scale by comparing the geographically weighted correlation coefficients between the carbon emission data released by different institutions, thereby providing a reference for selecting appropriate carbon emission data and more efficiently implementing carbon emission reduction policies.

Description

Technical Field

[0001] The present invention belongs to the field of spatial comparison of carbon emission data, and in particular relates to a carbon emission fine-scale comparison method and system based on geographically weighted correlation analysis. Background Art

[0002] Carbon data refers to data related to carbon emissions. It can be used to measure greenhouse gases such as carbon dioxide produced by individuals, organizations, or regions during production and consumption. This includes direct carbon emissions, indirect carbon emissions, emission factors, and carbon sinks. Different industries have corresponding carbon emissions data, as do different regional scales, such as national, provincial, city, and county carbon inventories. However, carbon emissions data released by environmental statistics agencies are generally compiled and tabulated using satellite raster imagery. Therefore, data aggregated by region and industry will inevitably differ.

[0003] For data released by different institutions, it is difficult to obtain obvious difference characteristics through numerical comparison alone, and it is therefore impossible to analyze the specific reasons. This may lead to the inability to fully consider the spatial distribution characteristics of carbon emission data, making it more difficult to choose which data to use as a reference for arrangements such as carbon emission reduction. Summary of the Invention

[0004] In order to overcome the shortcomings of the above-mentioned existing technologies, the present invention provides a fine-scale comparison method and system for carbon emissions based on geographically weighted correlation analysis. By comparing the geographically weighted correlation coefficients between carbon emission data released by different institutions, the strength of the correlation is judged at a fine scale, providing a certain reference for selecting appropriate carbon emission data and more efficiently implementing carbon emission reduction policies.

[0005] According to one aspect of the present invention, a method for fine-scale comparison of carbon emissions based on geographically weighted correlation analysis is provided, comprising:

[0006] Obtain carbon emission data published by different institutions in the same region at the same scale;

[0007] Attribute calculations are performed on carbon emission data released by different institutions to generate spatial panel data of different institutions;

[0008] Based on the generated spatial panel data, the Pearson correlation coefficient is used to calculate the correlation between carbon emission data released by different institutions from a global perspective;

[0009] When the correlation calculated from a global perspective meets a first threshold, the correlation coefficient between the carbon emission data released by different institutions is calculated from a local perspective using a geographically weighted correlation analysis method, where the first threshold is a preset value or value range.

[0010] As a further technical solution, the method also includes: when the correlation calculated from a global perspective does not meet a first threshold, the carbon emission data currently released by different institutions do not meet the conditions for fine-scale comparison, and a global weak correlation output is performed.

[0011] As a further technical solution, the method further includes: when the correlation coefficient calculated from the local angle meets a second threshold, outputting a local strong correlation, where the second threshold is a preset value or value range.

[0012] As a further technical solution, when the correlation coefficient calculated from the local angle does not meet the second threshold, a local weak correlation output is performed.

[0013] As a further technical solution, when calculating the correlation coefficient between the carbon emission data released by two institutions from a local perspective using the geographically weighted correlation analysis method, the calculation formula is as follows:

[0014] ,

[0015] in are the coordinates of position i, is the geographically weighted covariance between institution X and institution Y, 、 is the geographically weighted covariance of institution X and institution Y, is the geographical weight of observation j for each specific location i, calculated using a kernel function based on the distance between i and j; is the geographically weighted mean of institution X; 、 are the sampling values ​​of mechanism X and mechanism Y at position j respectively; 、 are the means of the sampling values ​​of mechanism X and mechanism Y at position i, respectively.

[0016] As a further technical solution, when using the Pearson correlation coefficient to calculate the correlation between the carbon emission data released by two institutions from a global perspective, the calculation formula is as follows:

[0017] ,

[0018] in 、 are the sampling values ​​of mechanism X and mechanism Y at position i, 、 is the mean of the overall data of institution X and institution Y.

[0019] As a further technical solution, the method further includes:

[0020] Based on the generated spatial panel data, spatial autocorrelation analysis is conducted on the carbon emission data released by different institutions, and the global and local Moran indices are calculated.

[0021] According to the visualization results of spatial autocorrelation analysis, the carbon emission distribution characteristics of the current area are obtained.

[0022] According to one aspect of the present invention, a fine-scale carbon emission comparison system based on geographically weighted correlation analysis is provided, comprising:

[0023] The first main module is used to obtain carbon emission data published by different institutions in the same region at the same scale;

[0024] The second main module is used to calculate the attributes of carbon emission data released by different institutions and generate spatial panel data of different institutions;

[0025] The third main module is used to calculate the correlation between carbon emission data released by different institutions from a global perspective using the Pearson correlation coefficient based on the generated spatial panel data;

[0026] The fourth main module is used to calculate the correlation coefficient between carbon emission data released by different institutions from a local perspective using the geographically weighted correlation analysis method when the correlation calculated from a global perspective meets a first threshold, wherein the first threshold is a preset value or value range.

[0027] According to one aspect of the present invention, a device for fine-scale comparison of carbon emissions based on geographically weighted correlation analysis is provided, comprising a memory and a processor, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the method for fine-scale comparison of carbon emissions based on geographically weighted correlation analysis.

[0028] According to one aspect of the present invention, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the steps of the carbon emission fine-scale comparison method based on geographically weighted correlation analysis.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] This paper introduces geographically weighted correlation analysis technology to judge the correlation strength of carbon emission data between different institutions at a fine scale, providing a certain reference for selecting appropriate carbon emission data and more efficiently implementing carbon emission reduction policies. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction will be given below to the drawings used in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 A schematic flow chart of a fine-scale carbon emissions comparison method based on geographically weighted correlation analysis provided in an embodiment of the present invention.

[0033] Figure 2 A schematic diagram of the structure of a fine-scale carbon emission comparison system based on geographically weighted correlation analysis provided by an embodiment of the present invention.

[0034] Figure 3 A schematic flow chart of a fine-scale carbon emission comparison method based on geographically weighted correlation analysis provided in yet another embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, the technical features in the various embodiments or single embodiments provided by the present invention are arbitrarily combined with each other to form a new technical solution. This combination is not restricted by the sequence of steps and / or structural composition mode, but must be based on the ability of ordinary technicians in this field to implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that this combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0036] An embodiment of the present invention provides a fine-scale comparison method for carbon emissions based on geographically weighted correlation analysis. First, carbon emission data released by different institutions in the same region at the same scale are obtained; then, attribute calculations are performed on the carbon emission data released by different institutions respectively to generate spatial panel data of different institutions for subsequent correlation analysis and display; then, based on the generated spatial panel data, the correlation between the carbon emission data released by different institutions is calculated using the Pearson correlation coefficient from a global perspective to obtain a correlation result from a global perspective, which is convenient for determining whether it is necessary to perform a local fine-scale correlation analysis and comparison; finally, when the correlation calculated from the global perspective meets a first threshold, the correlation coefficient between the carbon emission data released by different institutions is calculated from a local perspective using the geographically weighted correlation analysis method, wherein the first threshold is a preset value or value range. By judging the correlation strength between the carbon emission data of different institutions from a fine scale, a certain reference is provided for selecting appropriate carbon emission data and more efficiently implementing carbon emission reduction policies.

[0037] The method described in the embodiment of the present invention introduces local geographically weighted correlation analysis to perform fine-scale comparison and visualization of data released by different institutions, avoiding the problem of the existing technology that it is difficult to obtain obvious difference characteristics from only numerical comparison of carbon emission data provided by different institutions. It helps to analyze the difference characteristics of carbon emission data provided by different institutions, thereby facilitating targeted selection of data providers in different regions.

[0038] The technical solution of the present invention is further described in detail below through specific implementation methods. Figure 1 The process of the fine-scale carbon emission comparison method based on geographically weighted correlation analysis of the present invention, taking the carbon emission data of two institutions as an example, includes the following steps:

[0039] Step 1: Obtain carbon emission data published by two institutions in the same region at the same scale.

[0040] After obtaining carbon emission data released by two institutions in the same region at the same scale, the spatial scale and time scale of the obtained data are compared to ensure a complete match.

[0041] Step 2: Calculate the attributes of the carbon emission data released by the two institutions to generate spatial panel data of different institutions.

[0042] Specifically, the data after matching in step 1 is converted into raster data, and then attribute calculations are performed on the two sets of data to generate spatial panel data.

[0043] Step 3: From a global perspective, the Pearson correlation coefficient is used to calculate the correlation between the carbon emission data released by the two institutions to obtain the global correlation results.

[0044] Specifically, the Pearson correlation coefficient is used to explore the correlation between the two sets of data from a global perspective. The calculation formula is as follows:

[0045] ,

[0046] in 、 are the sampling values ​​of mechanism X and mechanism Y at position i, 、 is the mean of the overall data of institution X and institution Y.

[0047] The correlation coefficient calculated in this step is between 0 and 1. A numerical value or numerical range can be pre-set as a threshold value to evaluate the global angle correlation. For example, if the threshold is set to 0.5, the calculated correlation coefficient is greater than 0.5, which is considered to have a strong global correlation and can proceed to the next step of local fine-scale comparison; if the calculated correlation coefficient is less than 0.5, it is considered not to have a strong global correlation and there is no need to proceed to the local angle correlation analysis. For another example, if the threshold range is set to 0.4-0.6, the calculated correlation coefficient exceeds the maximum value of the range, which is considered to have a strong global correlation and can proceed to the next step of local fine-scale comparison; if it is lower than the minimum value of the range, it is considered not to have a strong global correlation; if it falls within the range, whether to perform local angle correlation analysis can be determined based on actual needs or expert experience.

[0048] Step 4: When the correlation calculated from a global perspective meets the preset requirements, the correlation coefficient between the carbon emission data released by different institutions is calculated from a local perspective using the geographically weighted correlation analysis method.

[0049] From a local perspective, the correlation coefficient is calculated using the Geographically Weighted (GW) correlation analysis method. The correlation strength of the data is determined at a fine scale. The expression can be defined as follows:

[0050]

[0051] in are the coordinates of position i, is the GW covariance between X and Y, 、 is the GW covariance of X and Y, which can be calculated as follows:

[0052]

[0053] in is the geographical weight of observation j for each specific location i, which can be calculated by the kernel function based on the distance between i and j; is the GW mean of X, which can be calculated as follows:

[0054]

[0055] In an embodiment of the present invention, when the correlation coefficient calculated from the local angle meets a preset threshold, a local strong correlation is output. The preset threshold is a preset value or range of values. For example, if the preset threshold is 0.5, when the correlation coefficient calculated from the local angle is greater than 0.5, a local strong correlation is output and visualized.

[0056] For another example, if the threshold is pre-set to 0.4-0.6, then if the correlation coefficient calculated from the local angle exceeds the maximum value of the range, it is considered to have strong local correlation and is visualized; if it is lower than the minimum value of the range, it is considered not to have strong local correlation; if it falls within the range, when selecting institutional data, whether to select the data of a certain institution is determined based on actual needs or expert experience.

[0057] As a preferred embodiment, Figure 3 As shown, taking the data of two institutions as an example, the method according to the embodiment of the present invention further includes:

[0058] Step S1: perform spatial autocorrelation analysis on the two sets of data respectively, and use GeoDa software to calculate the global Moran index and the local Moran index. The global Moran index calculation formula is as follows:

[0059]

[0060] where z i and z j are the samples at positions i and j, is the spatial weight between positions i and j, n is the size of the sampling point, and S0 is the sum of the spatial weights.

[0061] The calculation formula of the local Moran index is as follows:

[0062]

[0063] In step S2, based on the numerical values ​​and curves obtained from the spatial autocorrelation analysis of the two data sets, the carbon emission distribution characteristics are summarized using methods such as the global Moran index and LISA analysis. Specifically, the two coefficients mentioned above are calculated through spatial autocorrelation analysis, and then the numerical values ​​are used to determine whether the distribution is clustered, discrete, or random.

[0064] The implementation of each embodiment of the present invention is based on programmed processing performed by a device with processor functionality. Therefore, in practical engineering, the technical solutions and functions of each embodiment of the present invention are packaged into various modules. Based on this reality, and in addition to the aforementioned embodiments, an embodiment of the present invention provides a fine-scale carbon emissions comparison system based on geographically weighted correlation analysis. This system is used to implement the fine-scale carbon emissions comparison method based on geographically weighted correlation analysis described in the aforementioned method embodiments.

[0065] See also Figure 2 The system includes: a first main module for obtaining carbon emission data released by different institutions in the same region at the same scale; a second main module for performing attribute calculations on the carbon emission data released by different institutions respectively to generate spatial panel data of different institutions; a third main module for calculating the correlation between the carbon emission data released by different institutions from a global perspective using the Pearson correlation coefficient based on the generated spatial panel data; and a fourth main module for calculating the correlation coefficient between the carbon emission data released by different institutions from a local perspective using the geographically weighted correlation analysis method when the correlation calculated from the global perspective meets a first threshold, wherein the first threshold is a preset value or value range.

[0066] The carbon emission fine-scale comparison system based on geographically weighted correlation analysis provided by the embodiment of the present invention is for data released by different institutions. It is difficult to obtain obvious difference characteristics through numerical comparison alone, and thus it is impossible to analyze the specific reasons. This may lead to the inability to fully consider the spatial distribution characteristics of carbon emission data. Figure 2 By comparing the geographically weighted correlation coefficients between carbon emission data released by different institutions, the strength of the correlation can be judged at a fine scale, providing a certain reference for selecting appropriate carbon emission data and implementing carbon emission reduction policies more efficiently.

[0067] It should be noted that the system embodiments provided by the present invention are not only used to implement the methods in the above-mentioned method embodiments, but also used to implement the methods in other method embodiments provided by the present invention. The only difference lies in the setting of corresponding functional modules, and the principles thereof are basically the same as the principles of the above-mentioned system embodiments provided by the present invention. As long as those skilled in the art refer to the specific technical solutions in other method embodiments on the basis of the above-mentioned system embodiments, obtain corresponding technical means and technical solutions composed of these technical means by combining technical features, and on the premise of ensuring the practicality of the technical solutions, improve the modules in the above-mentioned system embodiments to obtain corresponding system class embodiments for implementing the methods in other method class embodiments.

[0068] Based on the same inventive concept as the aforementioned embodiment, an embodiment of the present invention also provides a carbon emission fine-scale comparison device based on geographically weighted correlation analysis, comprising a memory and a processor, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the carbon emission fine-scale comparison method based on geographically weighted correlation analysis.

[0069] In an embodiment of the present invention, the memory may be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), or a volatile memory (volatile memory), such as a random-access memory (RAM). The memory is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory in an embodiment of the present invention may also be a circuit or any other device that can implement a storage function, for storing program instructions and / or data.

[0070] In the embodiments of the present invention, the processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention may be directly implemented and executed by a hardware processor, or by a combination of hardware and software modules within the processor.

[0071] Based on the same inventive concept as the aforementioned embodiment, an embodiment of the present invention also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable the computer to execute the steps of the carbon emission fine-scale comparison method based on geographically weighted correlation analysis.

[0072] The terms "including" and "having" and any variations thereof in the description and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions, for example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to the steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A fine-scale comparison method of carbon emissions based on geographically weighted correlation analysis, characterized by: include: Obtain carbon emission data published by different institutions in the same region at the same scale; Attribute calculations are performed on carbon emission data released by different institutions to generate spatial panel data of different institutions; Based on the generated spatial panel data, the Pearson correlation coefficient is used to calculate the correlation between carbon emission data released by different institutions from a global perspective. The calculation formula is as follows: ， in 、 are the sampling values ​​of mechanism X and mechanism Y at position i, 、 is the mean of the overall data of institution X and institution Y; When the correlation calculated from a global perspective meets a first threshold, the correlation coefficient between the carbon emission data released by different institutions is calculated from a local perspective using a geographically weighted correlation analysis method, where the first threshold is a preset value or value range, and the calculation formula is as follows: ， in are the coordinates of position i, is the geographically weighted covariance between institution X and institution Y, 、 is the geographically weighted covariance of institution X and institution Y, is the geographical weight of observation j for each specific location i, calculated using a kernel function based on the distance between i and j; is the geographically weighted mean of institution X; 、 are the sampling values ​​of mechanism X and mechanism Y at position j respectively; 、 are the means of the sampling values ​​of mechanism X and mechanism Y at position i, respectively.

2. The fine-scale carbon emission comparison method based on geographically weighted correlation analysis according to claim 1 is characterized in that: The method further includes: when the correlation calculated from a global perspective does not meet a first threshold, the carbon emission data currently released by different institutions do not meet the conditions for fine-scale comparison, and a global weak correlation output is performed.

3. The fine-scale carbon emission comparison method based on geographically weighted correlation analysis according to claim 1 is characterized in that: The method further includes: when the correlation coefficient calculated from the local angle meets a second threshold, outputting a local strong correlation, where the second threshold is a preset value or value range.

4. The fine-scale carbon emission comparison method based on geographically weighted correlation analysis according to claim 2 is characterized in that: When the correlation coefficient calculated from the local angle does not meet the second threshold, a local weak correlation output is performed.

5. The fine-scale comparison method for carbon emissions based on geographically weighted correlation analysis according to claim 1 is characterized in that: The method further comprises: Based on the generated spatial panel data, spatial autocorrelation analysis is conducted on the carbon emission data released by different institutions, and the global and local Moran indices are calculated. According to the visualization results of spatial autocorrelation analysis, the carbon emission distribution characteristics of the current area are obtained.

6. A fine-scale carbon emission comparison system based on geographically weighted correlation analysis, characterized by: include: The first main module is used to obtain carbon emission data published by different institutions in the same region at the same scale; The second main module is used to calculate the attributes of carbon emission data released by different institutions and generate spatial panel data of different institutions; The third main module is used to calculate the correlation between carbon emission data released by different institutions from a global perspective using the Pearson correlation coefficient based on the generated spatial panel data. The calculation formula is as follows: ， in 、 are the sampling values ​​of mechanism X and mechanism Y at position i, 、 is the mean of the overall data of institution X and institution Y; The fourth main module is used to calculate the correlation coefficient between carbon emission data released by different institutions from a local perspective using a geographically weighted correlation analysis method when the correlation calculated from a global perspective meets a first threshold, wherein the first threshold is a preset value or value range, and the calculation formula is as follows: ， in are the coordinates of position i, is the geographically weighted covariance between institution X and institution Y, 、 is the geographically weighted covariance of institution X and institution Y, is the geographical weight of observation j for each specific location i, calculated using a kernel function based on the distance between i and j; is the geographically weighted mean of institution X; 、 are the sampling values ​​of mechanism X and mechanism Y at position j respectively; 、 are the means of the sampling values ​​of mechanism X and mechanism Y at position i, respectively.

7. A carbon emission fine-scale comparison device based on geographically weighted correlation analysis, characterized in that: The method comprises a memory and a processor, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the carbon emission fine-scale comparison method based on geographically weighted correlation analysis as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, which enable the computer to execute the steps of the carbon emission fine-scale comparison method based on geographically weighted correlation analysis according to any one of claims 1 to 5.

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