CN117034588A - Industrial carbon emission space simulation method and system based on noctilucent remote sensing and interest points - Google Patents

Abstract

The invention discloses a method and system for spatial simulation of industrial carbon emissions based on night-light remote sensing and points of interest. The method includes: obtaining industrial energy consumption and corresponding carbon emission factor coefficients; calculating the total annual carbon emissions of the industry; and obtaining industrial interest point data. and night light remote sensing data; calculate the normalized score; obtain the first indicator and construct a panel data matrix. The first indicator includes the total value of nighttime lighting and the total annual carbon emissions of the industry. The panel data matrix includes the The first indicator; calculate the weight of each first indicator; calculate the spatial simulation results of carbon emissions. The invention realizes the spatial simulation of regional industrial carbon emissions, improves the accuracy of spatial distribution results of carbon emissions, and reveals the distribution characteristics and rules of industrial carbon emissions in geographical space. The invention can be widely used in the technical field of carbon emission treatment.

Description

Industrial carbon emission spatial simulation method and system based on nighttime light remote sensing and points of interest

Technical field

The invention relates to the technical field of carbon emission processing, and in particular to an industrial carbon emission spatial simulation method and system based on nighttime remote sensing and points of interest.

Background technique

The current climate warming caused by increased carbon emissions has a profound impact on people's lives and health. In order to deal with the serious consequences of climate warming, a series of measures need to be taken to curb climate warming and reduce man-made carbon dioxide emissions. The carbon emission accounting system is mainly developed from a statistical perspective and targets three types of accounting objects: the regional level, the industry and enterprise level, and the product level. However, it lacks accurate systematic accounting, resulting in unclear carbon emission distribution, unclear mechanisms, and weak regulatory capabilities. At different levels, Massive carbon emission data from different scales and industries lack a unified benchmark and are difficult to integrate and utilize.

The traditional carbon emission accounting system based on statistical data has shortcomings. Statistical data only provides digital records of relevant elements in a specific area, and it is not comprehensive enough to conduct research only through statistical values. In related technologies, the research perspective of carbon emission accounting methods is too single, and the spatial distribution results of carbon emissions generated have low accuracy.

Contents of the invention

The following is an overview of the topics described in detail in this article. This summary is not intended to limit the scope of the claims.

Embodiments of the present invention provide a method and system for spatial simulation of industrial carbon emissions based on night-light remote sensing and points of interest, which can effectively improve the calculation accuracy of spatial distribution results of carbon emissions, thereby revealing the distribution characteristics of industrial carbon emissions in geographical space. and rules.

On the one hand, embodiments of the present invention provide a spatial simulation method of industrial carbon emissions based on nighttime remote sensing and points of interest, including the following steps:

Obtain industrial energy consumption and corresponding carbon emission factor coefficients;

Calculate the total annual carbon emissions of the industry based on the industrial energy consumption and the corresponding carbon emission factor coefficient;

Obtain industrial interest point data and night light remote sensing data;

Calculate a normalized score based on the industrial interest point data and the night light remote sensing data;

Obtain the first indicator and construct a panel data matrix. The first indicator includes the total value of nighttime lighting and the total annual carbon emissions of the industry. The panel data matrix includes the first indicator;

Calculate the weight of each first indicator according to the panel data matrix;

Calculate the spatial simulation results of carbon emissions based on the total annual carbon emissions of the industry, the normalized score and the weight.

Further, the calculation formula for the total annual carbon emissions of the industry is as follows:

In the formula, CE is the total annual carbon emissions of the industry, K _i is the carbon emission factor coefficient of energy i, E _i is the industrial energy consumption of energy i, and p is the number of energy types.

Further, calculating the normalized score based on the industrial interest point data and the night light remote sensing data includes the following steps:

Perform kernel density analysis on the industrial interest point data to obtain an industrial interest point kernel density raster image;

Analyze the luminous remote sensing data to obtain luminous remote sensing images;

The normalized score is calculated based on the industrial interest point kernel density raster image and the night-light remote sensing image. The calculation formula of the normalized score is as follows:

In the formula, I _jk is the normalized score of the first indicator mentioned in the j-th item in the k-th raster, and R _jk is the raster image pixel value of the first indicator mentioned in the j-th item in the k-th raster. , f is the total number of pixels of the raster image of the first indicator mentioned in the j-th item.

Further, obtaining the first indicator and constructing a panel data matrix includes the following steps:

Calculate the nighttime light remote sensing data to obtain the total nighttime lighting value;

Construct a panel data matrix. The calculation formula of the panel data matrix is as follows:

In the formula, r _ij is the panel data value of the first indicator mentioned in the j-th item in the i-th year, m is the total number of panel data years, and n is the total number of the first indicator.

Further, calculating the weight of each first indicator according to the panel data matrix includes the following steps:

Calculate standardized indicators based on the panel data matrix;

Calculate the entropy value of each of the first indicators according to the standardized indicators;

According to the entropy value, the weight of each first indicator is calculated.

Further, the calculation formula of the standardized index is as follows:

or,

In the formula, x _ij is the value of the first indicator mentioned in the j-th item after standardization in the i-th year, Max _j {r _ij } is the maximum value of the first indicator mentioned in the j-th item, and Min _j {r _ij } is the value of the first indicator mentioned in the j-th item. The minimum value of the first indicator mentioned in item j, r _ij is the panel data value of the first indicator mentioned in item j in year i.

Further, the calculation formula of the entropy value is as follows:

In the formula, e _j is the entropy value of the first indicator mentioned in the j-th item, p _ij is the index value proportion of the first indicator mentioned in the j-th item in the i-th year, and when p _ij =0, p _ij ·lnp _ij =0, n is the total number of the first indicators, k is the coefficient;

In the formula, m is the number of rows of the panel data matrix, that is, the total number of years of data;

In the formula, p _ij is the index value proportion of the first indicator mentioned in the j-th item in the i-th year, x _ij is the value of the first indicator mentioned in the j-th item in the i-th year after panel data standardization, and m is the panel data matrix. number of rows.

Further, the calculation formula of the weight is as follows:

In the formula, w _j is the weight value of the first indicator mentioned in the j-th item, e _j is the entropy value of the first indicator mentioned in the j-th item, and n is the total number of the first indicators.

Further, the calculation formula of the spatial simulation result of carbon emissions is as follows:

In the formula, F _k is the comprehensive weight value of the k-th grid, w _j is the weight of the first indicator mentioned in the j-th item, and I _jk is the normalized value of the first indicator mentioned in the j-th item in the k-th grid. value, n is the total number of the first indicators;

In the formula, CE _k is the carbon emissions in the k-th grid after spatialization, CE is the total annual carbon emissions of the industry in the research area where the grid is located, F _k is the comprehensive weight value of the k-th grid, and u is The total number of rasters.

On the other hand, embodiments of the present invention provide an industrial carbon emission spatial simulation system based on nighttime remote sensing and points of interest, including:

The first module is used to obtain industrial energy consumption and corresponding carbon emission factor coefficients;

The second module is used to calculate the total annual carbon emissions of the industry based on the industrial energy consumption and the corresponding carbon emission factor coefficient;

The third module is used to obtain industrial interest point data and night light remote sensing data;

The fourth module is used to calculate a normalized score based on the industrial interest point data and the night light remote sensing data;

The fifth module is used to obtain the first indicator and construct a panel data matrix. The first indicator includes the total value of nighttime lighting and the total annual carbon emissions of the industry. The panel data matrix includes the first indicator;

The sixth module is used to calculate the weight of each first indicator according to the panel data matrix;

The seventh module is used to calculate the spatial simulation results of carbon emissions based on the total annual carbon emissions of the industry, the normalized score and the weight.

The beneficial effects of the present invention are as follows:

Based on luminous remote sensing and point of interest data, this invention first obtains industrial energy consumption and corresponding carbon emission factor coefficients, calculates the total annual carbon emissions of the industry, then obtains industrial interest point data and luminous remote sensing data, calculates the normalized score, and then Obtain the first indicator, construct a panel data matrix, calculate the weight of each first indicator, and finally calculate the spatial simulation results of carbon emissions, realizing the spatial simulation of regional industrial carbon emissions and improving the accuracy of the spatial distribution results of carbon emissions. It reveals the distribution characteristics and rules of industrial carbon emissions in geographical space.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and obtained by the structure particularly pointed out in the written description, claims and appended drawings.

Description of the drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without exerting creative efforts.

Figure 1 is a flow chart of an industrial carbon emission spatial simulation method based on nighttime light remote sensing and points of interest provided by an embodiment of the present invention;

Figure 2 is a schematic diagram of an energy consumption carbon emission coefficient table according to an embodiment of the present invention;

Figure 3 is a flow chart for calculating the weight of each first indicator based on the panel data matrix in the spatial simulation method of industrial carbon emissions based on nighttime light remote sensing and points of interest provided by the embodiment of the present invention;

Figure 4 is a schematic diagram of the spatial indicators and weights of industrial carbon emissions in a certain region in 2020 provided by the example of the present invention;

Figure 5 is a schematic diagram of an industrial carbon emission spatial simulation system based on nighttime remote sensing and points of interest provided by an embodiment of the present invention.

Detailed ways

Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present invention and cannot be understood as limiting the present invention.

In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only set for the convenience of explanation. The order between the steps is not limited in any way. The execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art. sexual adjustment.

Unless otherwise defined, all technical and scientific terms used in the embodiments of the present invention have the same meanings as commonly understood by those skilled in the technical field of the present invention. The terms used in the embodiments of the present invention are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

As shown in Figure 1, the embodiment of the present invention provides a spatial simulation method of industrial carbon emissions based on nighttime light remote sensing and points of interest, which includes the following steps:

Step S11: Obtain industrial energy consumption and corresponding carbon emission factor coefficients.

Specifically, based on the textile industry, petroleum, coal and other fuel processing industry, chemical raw materials and chemical products manufacturing industry, non-metallic mineral products industry, automobile manufacturing industry, computer, communications and other electronic equipment manufacturing industry in a certain region from 2016 to 2020 7 types of industries, including electricity, heat production and supply industries, are used as targets to collect energy terminal consumption data of 7 types of industrial sectors in the region from 2016 to 2020. From the energy terminal consumption data of this industry, the industrial energy consumption and energy consumption can be obtained. The corresponding carbon emission factor coefficient.

Step S12: Calculate the total annual carbon emissions of the industry based on the industrial energy consumption and the corresponding carbon emission factor coefficient.

Specifically, carbon emissions are calculated using the carbon emission factor method. Based on the carbon emission coefficient table of various fossil energy sources in the "2006 Greenhouse Gas Emission Inventory" as shown in Figure 2, combined with regional energy terminal consumption data, raw coal and gasoline are selected. , diesel, fuel oil, heat, electricity and other energy terminal consumption to estimate the carbon dioxide emissions generated by regional industrial energy consumption. The calculation formula is as follows:

In the formula, CE is the total annual carbon emissions of the industry, the unit is t; K _i is the carbon emission factor coefficient of energy i, the unit is (10 ⁴ t carbon)/(10 ⁴ t standard coal), the K _i value comes from carbon emissions The default value of the calculation guide is that the original data unit is J. In order to be consistent with the statistical data unit, it is converted into standard coal. The conversion coefficient is 1×10 ⁴ t standard coal which is equal to 2.93×10 ⁵ GJ; E _i is the industry of energy i Energy consumption, based on standard coal, unit is 10 ⁴ t; p is the number of energy types.

Furthermore, the total carbon emissions of the seven industries are calculated year by year according to formula (1). It is calculated that the total carbon emissions of the seven categories of industries from 2016 to 2020 are 3272.83, 3569.63, 3662.27, 3749.86, and 37.761 million tons respectively.

Step S13: Obtain industrial interest point data and night light remote sensing data.

Specifically, by calling the Internet map software supplier's software development toolkit to crawl, the point-of-interest data of seven types of industries in a certain region in 2020 was obtained, and the NPP-VIIRS global nighttime remote sensing data from 2016 to 2020 was obtained.

Step S14: Calculate the normalized score based on the industrial interest point data and night light remote sensing data.

Specifically, calculating the normalized score based on industrial interest point data and nighttime light remote sensing data includes the following steps:

Perform kernel density analysis on industrial interest point data to obtain a kernel density raster image of industrial interest points;

Specifically, ArcGIS10.6 software was used to conduct kernel density analysis on the interest point data of each industry, and a kernel density raster image of industrial interest points was obtained;

Analyze night light remote sensing data to obtain night light remote sensing images;

Specifically, the nighttime light remote sensing data were preprocessed and cropped to obtain the NPP-VIIRS nighttime light remote sensing images in the area from 2016 to 2020;

Calculate the normalized score based on the industrial interest point kernel density raster image and night light remote sensing image;

Specifically, the raster pixel values of the industrial interest point kernel density raster image and the night-light remote sensing image are normalized to obtain the normalized score. The calculation formula of the normalized score is as follows:

In the formula, I _jk is the normalized score of the j-th first indicator in the k-th raster, R _jk is the raster image pixel value of the j-th first indicator in the k-th raster, and f is the raster image pixel value of the j-th first indicator in the k-th raster. The total number of pixels in the raster image of the first indicator of item j.

Step S15: Obtain the first indicator and construct a panel data matrix, where the first indicator includes the total value of nighttime lighting and the total annual carbon emissions of the industry, and the panel data matrix includes the first indicator.

Specifically, obtaining the first indicator and constructing a panel data matrix includes the following steps:

Calculate the night light remote sensing data to obtain the total value of night light;

Specifically, based on the nighttime remote sensing data, the pixel statistical data function of ArcGIS10.6 is used to calculate the total nighttime light value of the regional nightlight remote sensing;

Construct a panel data matrix;

Specifically, the total value of nighttime lighting and the total annual carbon emissions of each industry are combined to form a panel data matrix. The calculation formula of the panel data matrix is as follows:

In the formula, r _ij is the panel data value of the j-th first indicator in the i-th year, m is the total number of panel data years, and n is the total number of the first indicator.

Step S16: Calculate the weight of each first indicator based on the panel data matrix.

Specifically, the entropy weight method is used, which can determine the weight of the indicator based on the information it provides. The more information an indicator provides, the lower the entropy, and the greater the weight of the indicator; it is used as an important evaluation method. It can overcome the subjectivity of artificially determined weights and the negative impact caused by the overlap of multi-index variable information, and is highly objective. As shown in Figure 3, calculating the weight of each first indicator based on the panel data matrix includes the following steps:

Step S301: Calculate the standardized index according to the panel data matrix.

Specifically, the setting angles of various first indicators are different, resulting in differences in the changing trends and units of the data. In order to eliminate the influence of differences in indicator dimensions on the results, it is necessary to unify the main data into a unified value without measurement units. The extreme value method is used to unify the first indicator. Since nighttime lighting has a positive correlation with carbon emissions, the indicators in this example are all positive indicators. The calculation formula of the standardized indicator is as follows:

or,

In the formula, x _ij is the value of the j-th first indicator in the i-th year after standardization, Max _j {r _ij } is the maximum value of the j-th first indicator, and Min _j {r _ij } is the j-th first indicator. The minimum value of the indicator, r _ij is the panel data value of the j-th first indicator in the i-th year. Formula (4) is a positive indicator, indicating that the higher the value of the indicator, the more important the performance; Formula (5) is a negative indicator, indicating that the lower the value of the indicator, the more important the performance is.

Step S302: Calculate the entropy value of each first index according to the standardized index.

Specifically, the calculation formula of entropy value is as follows:

In the formula, e _j is the entropy value of the j-th first indicator, p _ij is the index value proportion of the j-th first indicator in the i-th year, and when p _ij =0, p _ij ·lnp _ij =0, n is the total number of first indicators, k is the coefficient;

In the formula, m is the number of rows of the panel data matrix, that is, the total number of years of data;

In the formula, p _ij is the index value proportion of the j-th first indicator in the i-th year, x _ij is the value of the j-th first indicator in the i-th year after panel data standardization, and m is the number of rows of the panel data matrix.

Step S303: Calculate the weight of each first indicator based on the entropy value.

Specifically, the weight of each first indicator is calculated according to formula (9). The spatial evaluation index system and weight of industrial carbon emissions are shown in Figure 4. The sum of the weights of the first indicator is 1, and the calculation formula of the weight is as follows:

In the formula, w _j is the weight value of the j-th first indicator, e _j is the entropy value of the j-th first indicator, and n is the total number of first indicators.

Step S17: Calculate the spatial simulation results of carbon emissions based on the total annual carbon emissions of the industry, the normalized score and the weight.

Specifically, the spatial simulation results of carbon emissions are calculated based on the total annual carbon emissions CE of the industry, the normalized score I _jk and the weight w _j . The calculation formula of the spatial simulation results of carbon emissions is as follows:

In the formula, F _k is the comprehensive weight value of the k-th grid, w _j is the weight of the j-th first indicator, I _jk is the normalized score of the j-th first indicator in the k-th grid, n is the total number of first indicators;

In the formula, CE _k is the carbon emissions in the k-th grid after spatialization, CE is the total annual carbon emissions of the industry in the research area where the grid is located, F _k is the comprehensive weight value of the k-th grid, and u is The total number of rasters.

The beneficial effects of implementing the embodiments of the present invention include: the present invention is based on traditional nighttime light remote sensing data, integrates geospatial big data of points of interest, realizes spatial simulation of carbon emissions through geospatial information technology, and focuses on spatial dimensions and specific At the industry scale, we establish high spatial resolution spatial grid data of carbon dioxide emissions, help establish emission inventories and spatial databases for specific regions, industries, and departments, and reveal the distribution characteristics and patterns of industrial carbon emissions in geographical space.

In addition, on the basis of statistical data, the embodiment of the present invention combines spatial geographical big data and geographical information spatial analysis technology to realize the spatial simulation of carbon emissions at the industrial level in the region, and can establish a regional industrial carbon emission spatial data set to provide industrial green services. It provides key basic data support for transformation and upgrading, optimizing industrial structure, and promoting low-carbon development of industries. It also has broad application prospects in fields such as space low-carbon control and precise carbon reduction.

As shown in Figure 5, embodiments of the present invention also provide an industrial carbon emission spatial simulation system based on nighttime remote sensing and points of interest, including:

The first module 51 is used to obtain industrial energy consumption and corresponding carbon emission factor coefficients;

The second module 52 is used to calculate the total annual carbon emissions of the industry based on the industrial energy consumption and the corresponding carbon emission factor coefficient;

The third module 53 is used to obtain industrial interest point data and night light remote sensing data;

The fourth module 54 is used to calculate the normalized score based on industrial interest point data and night light remote sensing data;

The fifth module 55 is used to obtain the first indicator and construct a panel data matrix. The first indicator includes the total value of nighttime lighting and the total annual carbon emissions of the industry. The panel data matrix includes the first indicator;

The sixth module 56 is used to calculate the weight of each first indicator based on the panel data matrix;

The seventh module 57 is used to calculate the spatial simulation results of carbon emissions based on the total annual carbon emissions of the industry, the normalized score and the weight.

It can be seen that the contents in the above-mentioned method embodiments are applicable to this system embodiment. The specific functions implemented by this system embodiment are the same as those in the above-mentioned method embodiments, and the beneficial effects achieved are also the same as those achieved by the above-mentioned method embodiments. same.

The above is a detailed description of the preferred implementation of the present invention, but the present invention is not limited to the above-mentioned embodiments. Those skilled in the art can also make various equivalent modifications or substitutions without violating the spirit of the present invention. Equivalent modifications or substitutions are included within the scope of the claims of the present invention.

Claims (10)

1. A spatial simulation method of industrial carbon emissions based on nighttime light remote sensing and points of interest, which is characterized by including the following steps: Obtain industrial energy consumption and corresponding carbon emission factor coefficients; Calculate the total annual carbon emissions of the industry based on the industrial energy consumption and the corresponding carbon emission factor coefficient; Obtain industrial interest point data and night light remote sensing data; Calculate a normalized score based on the industrial interest point data and the night light remote sensing data; Obtain the first indicator and construct a panel data matrix. The first indicator includes the total value of nighttime lighting and the total annual carbon emissions of the industry. The panel data matrix includes the first indicator; Calculate the weight of each first indicator according to the panel data matrix; Calculate the spatial simulation results of carbon emissions based on the total annual carbon emissions of the industry, the normalized score and the weight. 2. The spatial simulation method of industrial carbon emissions based on night-light remote sensing and points of interest according to claim 1, characterized in that the calculation formula of the total annual carbon emissions of the industry is as follows: In the formula, CE is the total annual carbon emissions of the industry, K _i is the carbon emission factor coefficient of energy i, E _i is the industrial energy consumption of energy i, and p is the number of energy types. 3. The industrial carbon emission spatial simulation method based on night-light remote sensing and points of interest according to claim 1, characterized in that the normalized score is calculated based on the industrial interest point data and the night-light remote sensing data, Includes the following steps: Perform kernel density analysis on the industrial interest point data to obtain an industrial interest point kernel density raster image; Analyze the luminous remote sensing data to obtain luminous remote sensing images; The normalized score is calculated based on the industrial interest point kernel density raster image and the night-light remote sensing image. The calculation formula of the normalized score is as follows: In the formula, I _jk is the normalized score of the first indicator mentioned in the j-th item in the k-th raster, and R _jk is the raster image pixel value of the first indicator mentioned in the j-th item in the k-th raster. , f is the total number of pixels of the raster image of the first indicator mentioned in the j-th item. 4. The industrial carbon emission spatial simulation method based on night-light remote sensing and points of interest according to claim 1, characterized in that obtaining the first indicator and constructing a panel data matrix includes the following steps: Calculate the nighttime light remote sensing data to obtain the total nighttime lighting value; Construct a panel data matrix. The calculation formula of the panel data matrix is as follows: In the formula, r _ij is the panel data value of the first indicator mentioned in the j-th item in the i-th year, m is the total number of panel data years, and n is the total number of the first indicator. 5. The industrial carbon emission spatial simulation method based on night-light remote sensing and points of interest according to claim 1, characterized in that calculating the weight of each first indicator according to the panel data matrix includes the following steps: Calculate standardized indicators based on the panel data matrix; Calculate the entropy value of each of the first indicators according to the standardized indicators; According to the entropy value, the weight of each first indicator is calculated. 6. The spatial simulation method of industrial carbon emissions based on night-light remote sensing and points of interest according to claim 5, characterized in that the calculation formula of the standardized index is as follows: or, In the formula, x _ij is the value of the first indicator mentioned in the j-th item after standardization in the i-th year, Max _j {r _ij } is the maximum value of the first indicator mentioned in the j-th item, and Min _j {r _ij } is the value of the first indicator mentioned in the j-th item. The minimum value of the first indicator mentioned in item j, r _ij is the panel data value of the first indicator mentioned in item j in year i. 7. The spatial simulation method of industrial carbon emissions based on nighttime light remote sensing and points of interest according to claim 5, characterized in that the calculation formula of the entropy value is as follows: In the formula, e _j is the entropy value of the first indicator mentioned in the j-th item, p _ij is the index value proportion of the first indicator mentioned in the j-th item in the i-th year, and when p _ij =0, p _ij ·lnp _ij =0, n is the total number of the first indicators, k is the coefficient; In the formula, m is the number of rows of the panel data matrix, that is, the total number of years of data; In the formula, p _ij is the index value proportion of the first indicator mentioned in the j-th item in the i-th year, x _ij is the value of the first indicator mentioned in the j-th item in the i-th year after panel data standardization, and m is the panel data matrix. number of rows. 8. The spatial simulation method of industrial carbon emissions based on night-light remote sensing and points of interest according to claim 5, characterized in that the calculation formula of the weight is as follows: In the formula, w _j is the weight value of the first indicator mentioned in the j-th item, e _j is the entropy value of the first indicator mentioned in the j-th item, and n is the total number of the first indicators. 9. The spatial simulation method of industrial carbon emissions based on night-light remote sensing and points of interest according to claim 1, characterized in that the calculation formula of the spatial simulation results of carbon emissions is as follows: In the formula, F _k is the comprehensive weight value of the k-th grid, w _j is the weight of the first indicator mentioned in the j-th item, and I _jk is the normalized value of the first indicator mentioned in the j-th item in the k-th grid. value, n is the total number of the first indicators; In the formula, CE _k is the carbon emissions in the k-th grid after spatialization, CE is the total annual carbon emissions of the industry in the research area where the grid is located, F _k is the comprehensive weight value of the k-th grid, and u is The total number of rasters. 10. An industrial carbon emission spatial simulation system based on nighttime remote sensing and points of interest, which is characterized by: The first module is used to obtain industrial energy consumption and corresponding carbon emission factor coefficients; The second module is used to calculate the total annual carbon emissions of the industry based on the industrial energy consumption and the corresponding carbon emission factor coefficient; The third module is used to obtain industrial interest point data and night light remote sensing data; The fourth module is used to calculate a normalized score based on the industrial interest point data and the night light remote sensing data; The fifth module is used to obtain the first indicator and construct a panel data matrix. The first indicator includes the total value of nighttime lighting and the total annual carbon emissions of the industry. The panel data matrix includes the first indicator; The sixth module is used to calculate the weight of each first indicator according to the panel data matrix; The seventh module is used to calculate the spatial simulation results of carbon emissions based on the total annual carbon emissions of the industry, the normalized score and the weight.