Carbon emission fine scale comparison method and system based on geographical weighted correlation analysis

Abstract

The invention discloses a geographic weighted correlation analysis-based carbon emission fine scale comparison method and system. The method comprises the following steps of: acquiring carbon emission data released by different mechanisms in the same region under the same scale; carrying out attribute calculation on the carbon emission data released by different mechanisms to generate space panel data of different mechanisms; based on the generated space panel data, calculating correlation among the carbon emission data released by different mechanisms from a global perspective by adopting a Pearson's correlation coefficient; when the correlation calculated from the global perspective meets a first threshold value, the correlation coefficient between the carbon emission data released by different mechanisms is calculated from the local perspective through a geographical weighted correlation analysis method, and the first threshold value is a preset numerical value or a numerical value range. According to the method, the geographical weighted correlation coefficients among the carbon emission data issued by different institutions are compared, the correlation strength is judged from a fine scale, and reference is provided for selecting proper carbon emission data and more efficiently implementing a carbon emission reduction policy.

Description

Technical Field

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

[0002] Carbon data refers to data related to carbon emissions, which can be used to measure greenhouse gases such as carbon dioxide generated by individuals, organizations or regions during the production and consumption processes, including different data such as direct carbon emissions, indirect carbon emissions, emission factors and carbon sinks. Different industries have their corresponding carbon emission data, and there are also corresponding carbon data at different geographical scales, such as national carbon data lists, provincial carbon data lists, city-level carbon data lists, county-level carbon data lists, etc. However, the carbon emission data released by environmental data statistical agencies are generally calculated from satellite raster image data and then compiled into tables. The data summarized by region and industry are inevitably different.

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

[0004] To overcome the deficiencies of the above-mentioned prior art, 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 the carbon emission data released by different institutions, the correlation strength is judged from 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 specification of the present invention, a fine-scale comparison method for carbon emissions based on geographically weighted correlation analysis is provided, including: Obtaining carbon emission data released by different institutions in the same region at the same scale; Respectively performing attribute calculations on the carbon emission data released by different institutions to generate spatial panel data of different institutions; Based on the generated spatial panel data, calculating the correlation between the carbon emission data released by different institutions from a global perspective using the Pearson correlation coefficient; When the correlation calculated from a global perspective satisfies a first threshold, calculating the correlation coefficient between the carbon emission data released by different institutions from a local perspective using the method of geographically weighted correlation analysis, where the first threshold is a preset value or value range.

[0006] As a further technical solution, the method further includes: when the correlation calculated from a global perspective does not meet the first threshold, the carbon emission data released by different institutions currently does not have the condition for fine-scale comparison, and a global weak correlation is output.

[0007] As a further technical solution, the method further includes: when the correlation coefficient calculated from a local perspective meets the second threshold, a local strong correlation is output, and the second threshold is a preset value or value range.

[0008] As a further technical solution, when the correlation coefficient calculated from a local perspective does not meet the second threshold, a local weak correlation is output.

[0009] As a further technical solution, when calculating the correlation coefficient between the carbon emission data released by two institutions from a local perspective by means of geographically weighted correlation analysis, the calculation formula is as follows: , where is the coordinate of position i, is the geographically weighted covariance between institution X and institution Y, , are the geographically weighted covariances of institution X and institution Y respectively, is the geographical weight of the observed value j for each specific position i, calculated through a kernel function based on the distance between i and j; is the geographically weighted mean of institution X; , are the sampling values of institution X and institution Y at position j respectively; , are the means of the sampling values of institution X and institution Y at position i respectively.

[0010] As a further technical solution, when calculating the correlation between the carbon emission data released by two institutions from a global perspective using the Pearson correlation coefficient, the calculation formula is as follows: , where , are the sampling values of institution X and institution Y at position i respectively, , are the means of the overall data of institution X and institution Y.

[0011] As a further technical solution, the method further includes: Based on the generated spatial panel data, spatial autocorrelation analysis is respectively performed on the carbon emission data released by different institutions, and the global Moran index and local Moran index are calculated; Obtain the carbon emission distribution characteristics of the current region according to the visualization results after spatial autocorrelation analysis.

[0012] According to one aspect of the specification of the present invention, there is provided a carbon emission fine-scale comparison system based on geographically weighted correlation analysis, including: 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 respectively performing attribute calculations on the carbon emission data released by different institutions 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; A fourth main module for calculating the correlation coefficient between the carbon emission data released by different institutions from a local perspective using the method of geographically weighted correlation analysis when the correlation calculated from a global perspective satisfies a first threshold, where the first threshold is a preset value or value range.

[0013] According to one aspect of the specification of the present invention, there is provided a carbon emission fine-scale comparison device based on geographically weighted correlation analysis, including a memory and a processor. 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.

[0014] According to one aspect of the specification of the present invention, there is provided a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the steps of the carbon emission fine-scale comparison method based on geographically weighted correlation analysis.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: By introducing the geographically weighted correlation analysis technology, the present invention judges the correlation strength of carbon emission data between different institutions from 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

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings used in the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1Schematic flowchart of the carbon emission fine-scale comparison method based on geographically weighted correlation analysis provided by an embodiment of the present invention.

[0018] Figure 2 Schematic structural diagram of the carbon emission fine-scale comparison system based on geographically weighted correlation analysis provided by an embodiment of the present invention.

[0019] Figure 3 Schematic flowchart of the carbon emission fine-scale comparison method based on geographically weighted correlation analysis provided by another embodiment of the present invention. Detailed implementation manners

[0020] To make the objectives, 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 with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. In addition, the technical features in each embodiment or a single embodiment provided by the present invention can be arbitrarily combined with each other to form a new technical solution. This combination is not restricted by the order of steps and / or the structural composition mode, but must be based on the fact that those of ordinary skill in the art can implement it. When the combination of the technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0021] An embodiment of the present invention provides a carbon emission fine-scale comparison method 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 respectively performed on the carbon emission data released by different institutions to generate spatial panel data of different institutions for subsequent correlation analysis and display; then, based on the generated spatial panel data, the Pearson correlation coefficient is used from a global perspective to calculate the correlation between the carbon emission data released by different institutions to obtain a correlation result from a global perspective, facilitating the determination of whether it is necessary to perform local fine-scale correlation analysis and comparison; finally, 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 by using the geographically weighted correlation analysis method, where the first threshold is a preset value or value range, and the correlation strength of the carbon emission data between different institutions is judged from a fine scale, providing a certain reference for selecting appropriate carbon emission data and more efficiently implementing carbon emission reduction policies.

[0022] The method described in the embodiment of the present invention introduces local geographically weighted correlation analysis to perform fine-scale comparison and visual display of data released by different institutions, thereby avoiding the problem in the prior art 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 from different institutions, thereby facilitating targeted selection of data providing institutions in different regions.

[0023] The technical solution of the present invention is further described in detail below through specific implementation methods. Figure 1 This is the process of the carbon emission fine-scale comparison method based on geographically weighted correlation analysis of the present invention, taking the carbon emission data of two institutions as an example, including the following steps: Step 1: Obtain carbon emission data released by two institutions in the same region at the same scale.

[0024] 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.

[0025] Step 2: Calculate the attributes of the carbon emission data released by the two institutions to generate spatial panel data of different institutions. Specifically, the matched data in step 1 is converted into raster data, and then the attributes of the two sets of data are calculated respectively to generate spatial panel data.

[0026] 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 correlation results from a global perspective.

[0027] Specifically, the Pearson correlation coefficient is used from a global perspective to explore the correlation between the two sets of 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.

[0028] The correlation coefficient calculated in this step is between 0 and 1. A value or range of values can be preset as a threshold for evaluating the correlation of the global angle. For example, if the threshold is set to 0.5, and the calculated correlation coefficient is greater than 0.5, it is considered to have 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 strong global correlation and there is no need to enter the local angle correlation analysis. Another example, if the threshold range is set to 0.4 - 0.6, and the calculated correlation coefficient exceeds the maximum value of this range, it is considered to have strong global correlation and can proceed to the next step of local fine-scale comparison; if it is lower than the minimum value of this range, it is considered not to have strong global correlation; if it falls within this range, it can be judged whether to conduct local angle correlation analysis according to actual needs or expert experience.

[0029] Step 4, when the correlation calculated from the global angle meets the preset requirements, calculate the correlation coefficient between the carbon emission data released by different institutions from the local angle by means of geographically weighted correlation analysis.

[0030] Calculate the correlation coefficient from the local angle by means of geographically weighted (GW) correlation analysis, and judge the correlation strength of the data from the fine scale. The expression can be defined as follows:

[0031] where is the coordinate of position i, is the GW covariance between X and Y, 、 is the GW covariance between X and Y, which can be calculated by the following formula:

[0032] where is the geographical weight of the observation value j for each specific position i, which can be calculated by a kernel function based on the distance between i and j; is the GW mean of X, which can be calculated by the following formula:

[0033] In the embodiment of the present invention, when the correlation coefficient calculated from the local angle meets the preset threshold, local strong correlation output is performed, and the preset threshold is a preset value or range of values. For example, the threshold is preset to 0.5. When the correlation coefficient calculated from the local angle is greater than 0.5, local strong correlation output and visualization display are performed.

[0034] For another example, if the threshold is preset to 0.4 - 0.6, and the correlation coefficient calculated by the local angle exceeds the maximum value of this range, it is considered to have local strong correlation and visualized output; if it is lower than the minimum value of this range, it is considered not to have local strong correlation; if it falls within this range, when selecting institutional data, it is judged whether to select the data of a certain institution according to actual needs or expert experience.

[0035] As a preferred embodiment, as Figure 3 shown, taking the data of two institutions as an example, the method described in the embodiment of the present invention further includes: Step S1, perform spatial autocorrelation analysis on the two sets of data respectively, and use GeoDa software to calculate the global Moran's I and local Moran's I. The calculation formula of the global Moran's I is as follows:

[0036] 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 points, and S0 is the sum of the spatial weights.

[0037] The calculation formula of the local Moran's I is as follows:

[0038] Step S2, according to the numerical values and curve results after the spatial autocorrelation analysis of the two sets of data, summarize their carbon emission distribution characteristics by using the positive and negative of the global Moran's I, LISA analysis, etc. Specifically, the above two coefficients are calculated through spatial autocorrelation analysis, and then it is judged whether it is an agglomerated / dispersed / random distribution according to the division of the numerical values.

[0039] The implementation basis of each embodiment of the present invention is realized through programmed processing by a device with a processor function. Therefore, in engineering practice, the technical solutions and functions of each embodiment of the present invention are encapsulated into various modules. Based on this actual situation, on the basis of the above embodiments, the embodiment of the present invention provides a carbon emission fine-scale comparison system based on geographically weighted correlation analysis, and this system is used to execute the carbon emission fine-scale comparison method based on geographically weighted correlation analysis in the above method embodiments.

[0040] See 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 calculating attributes of 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; a fourth main module for calculating the correlation coefficient between the carbon emission data released by different institutions from a local perspective using the method of geographically weighted correlation analysis when the correlation calculated from the global perspective meets a first threshold, where the first threshold is a preset value or value range.

[0041] For the carbon emission fine-scale comparison system based on geographically weighted correlation analysis provided by the embodiments of the present invention, for the data released by different institutions, it is difficult to obtain obvious difference features only through numerical comparison, and thus it is impossible to analyze the specific reasons, which may lead to the scenario where the spatial distribution characteristics of carbon emission data cannot be fully considered. By using Figure 2 several modules therein, by comparing the geographically weighted correlation coefficients between the carbon emission data released by different institutions, the correlation intensity is judged from the fine scale, providing a certain reference for selecting appropriate carbon emission data and more efficiently implementing carbon emission reduction policies.

[0042] It should be noted that the system embodiments provided by the present invention are used not only to implement the methods in the above method embodiments, but also to implement the methods in other method embodiments provided by the present invention. The difference is only in setting corresponding functional modules, and its principle is basically the same as that of the above system embodiments provided by the present invention. As long as those skilled in the art, based on the above system embodiments, refer to the specific technical solutions in other method embodiments, obtain corresponding technical means by combining technical features, and the technical solutions constituted by these technical means, and on the premise of ensuring the practicability of the technical solutions, improve the modules in the above system embodiments to obtain corresponding system-like embodiments for implementing the methods in other method-like embodiments.

[0043] Based on the same inventive concept as the foregoing embodiments, the embodiments of the present invention also provide a carbon emission fine-scale comparison device based on geographically weighted correlation analysis, including a memory and a processor. 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.

[0044] 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), etc., or may also be a volatile memory, such as a random-access memory (RAM). The memory is any other medium that can be used to carry or store the 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 the embodiment of the present invention may also be a circuit or any other device capable of implementing a storage function, for storing program instructions and / or data.

[0045] In an embodiment 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 devices, discrete gate or transistor logic devices, discrete hardware components, and can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention may be directly embodied as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0046] Based on the same inventive concept as the foregoing embodiments, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions cause the computer to execute the steps of the method for fine-scale comparison of carbon emissions based on geographically weighted correlation analysis.

[0047] The terms "comprising" and "having" and any variations thereof in the specification and claims of the present invention and the above drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate 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 in that: include: Obtain carbon emission data published by different institutions in the same region at the same scale; Calculate the attributes of 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; When the correlation calculated from a global perspective meets a first threshold, the correlation coefficient between carbon emission data released by different institutions is calculated from a local perspective using a geographically weighted correlation analysis method, wherein the first threshold is a preset value or value range.

2. The carbon emission fine-scale comparison method based on geographically weighted correlation analysis according to claim 1 is characterized in that: 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.

3. The carbon emission fine-scale 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 satisfies a second threshold, performing a local strong correlation output, wherein the second threshold is a preset value or value range.

4. The carbon emission fine-scale 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 satisfy the second threshold, a local weak correlation output is performed.

5. The carbon emission fine-scale comparison method based on geographically weighted correlation analysis according to claim 1 is characterized in that: When calculating the correlation coefficient between carbon emission data released by two institutions from a local perspective using the method of geographically weighted correlation analysis, 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 geographic weight of observation j for each specific location i, calculated using the 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.

6. The carbon emission fine-scale comparison method based on geographically weighted correlation analysis according to claim 1 is characterized in that: When using the Pearson correlation coefficient to calculate the correlation between carbon emission data released by two 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.

7. The carbon emission fine-scale comparison method 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 performed on carbon emission data released by different institutions, and the global and local Moran indexes are calculated; According to the visualization results after spatial autocorrelation analysis, the carbon emission distribution characteristics of the current area are obtained.

8. A fine-scale comparison system of carbon emissions based on geographically weighted correlation analysis, characterized in that: include: The first main module is used to obtain carbon emission data released 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 fourth main module is used to calculate the correlation coefficient between carbon emission data released by different institutions from a local perspective by 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.

9. A fine-scale comparison device for carbon emissions based on geographically weighted correlation analysis, characterized in that: It 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 7.

10. 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 as described in any one of claims 1 to 7.

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