Carbon emission spatial autocorrelation evaluation method, model and system based on land gradient utilization model, and medium

Abstract

The invention relates to the technical field of land utilization change evaluation, in particular to a carbon emission spatial autocorrelation evaluation method, model and system based on a land gradient utilization model and a medium. Calculating the carbon absorption amount of the to-be-evaluated area from the natural vegetation dimension, the crop dimension and the water area dimension; calculating the carbon emission of the to-be-evaluated area from the energy consumption dimension, the industrial production process dimension, the waste treatment dimension, the agricultural dimension, the respiration dimension and the water area carbon volatilization dimension; determining a carbon emission net value of the to-be-evaluated region according to a difference value between the carbon absorption amount and the carbon emission amount; determining a global spatial autocorrelation index and a local spatial autocorrelation index of the to-be-evaluated region according to the carbon emission net value; and determining a carbon emission spatial autocorrelation evaluation result of the to-be-evaluated region according to the global spatial autocorrelation index and the local spatial autocorrelation index. The objective of the invention is to solve the problem of how to carry out spatial autocorrelation evaluation on areas with obvious vertical zone performance and altitude gradient performance.

Description

Technical Field

[0001] This application relates to the technical field of land use change evaluation, and particularly to an evaluation method, model, system and medium for carbon emission spatial autocorrelation based on a land gradient utilization model. Background Art

[0002] Spatial autocorrelation evaluation of carbon emissions is often used to measure the correlation between observed values of nearby statistical units, revealing the spatial dependence and interaction of the research object; and land use is one of the important driving factors of carbon emissions, and the spatial distribution and gradient change of land use directly affect the spatial autocorrelation of carbon emissions.

[0003] The purpose of spatial autocorrelation evaluation of carbon emissions is to reveal "how carbon emissions are distributed" in the region, while the evaluation of the carbon emission effect of land gradient utilization is used to reveal "why carbon emissions are distributed like this" in the region. In other words, spatial autocorrelation evaluation of carbon emissions is a diagnostic tool for diagnosing whether there are problems with carbon emissions in the region, while the evaluation of the carbon emission effect of land gradient utilization is an analysis tool for explaining the causes of the problems in the region. Therefore, combining spatial autocorrelation evaluation of carbon emissions with the evaluation of the carbon emission effect of land gradient utilization can more comprehensively analyze the relationship between the spatial distribution of carbon emissions and land use in the region, thus facilitating the formulation of relevant carbon reduction strategies.

[0004] However, the current calculation method of the carbon emission effect of land gradient utilization cannot be applied to regions with obvious vertical zonality and altitude gradients, such as the central and eastern regions of Yunnan, where various land use types such as cultivated land, forest land, grassland, water area and urban land are significantly affected by altitude, because it does not consider the characteristics of the spatial distribution and spatial changes of different land use types under the conditions of land vertical differentiation and gradient stratification in this type of region.

[0005] Therefore, using traditional carbon emission effect evaluation models to conduct spatial autocorrelation evaluation on regions with obvious vertical zonality and altitude gradients is prone to inaccurate evaluation problems. Summary of the Invention

[0006] The main purpose of this application is to provide an evaluation method for carbon emission spatial autocorrelation based on a land gradient utilization model, aiming to solve the problem of how to conduct spatial autocorrelation evaluation on regions with obvious vertical zonality and altitude gradients.

[0007] To achieve the above purpose, an evaluation method for carbon emission spatial autocorrelation based on a land gradient utilization model provided by this application includes:

[0008] Calculate the carbon absorption of the area to be evaluated from the dimensions of natural vegetation, crops, and water areas; and calculate the carbon emissions of the area to be evaluated from the dimensions of energy consumption, industrial production processes, waste treatment, agriculture, respiration, and carbon volatilization in water areas;

[0009] Determine the net carbon emission value of the area to be evaluated based on the difference between the carbon absorption and the carbon emissions;

[0010] Determine the global spatial autocorrelation index and the local spatial autocorrelation index of the area to be evaluated based on the net carbon emission value;

[0011] Determine the evaluation result of the spatial autocorrelation of carbon emissions in the area to be evaluated based on the global spatial autocorrelation index and the local spatial autocorrelation index.

[0012] Optionally, the step of calculating the carbon absorption of the area to be evaluated from the dimensions of natural vegetation, crops, and water areas includes:

[0013] (1.1) Dimension of natural vegetation

[0014] Obtain the carbon sequestration capacity per unit area of the target type of vegetation in the area to be evaluated and the land area corresponding to the target type of vegetation;

[0015] Calculate the carbon absorption of natural vegetation based on the carbon sequestration capacity per unit area and the land area;

[0016] Take the sum of the carbon absorption of natural vegetation corresponding to each target type of vegetation as the carbon absorption calculated from the dimension of natural vegetation;

[0017] (1.2) Dimension of crops

[0018] Obtain the biological yield of the target type of crops in the area to be evaluated, the carbon absorption rate for synthesizing unit organic matter of the target type of crops, and the water content of the target type of crops;

[0019] Calculate the carbon absorption by photosynthesis of the target type of crops based on the biological yield, the carbon absorption rate, and the water content;

[0020] Take the sum of the carbon absorption by photosynthesis corresponding to each target type of crops as the carbon absorption calculated from the dimension of crops;

[0021] (1.3) Dimension of water areas

[0022] Obtain the carbon sequestration rate per unit area of water areas, the water area, the carbon absorption by wet and dry deposition per unit area of water areas, and the total area of the area to be evaluated;

[0023] Calculate the carbon absorption amount of the water area according to the carbon sequestration rate per unit area of the water area, the water area, the carbon absorption amount of dry and wet deposition per unit area of the water area, and the total area of the area to be evaluated;

[0024] Take the carbon absorption amount of the water area as the carbon absorption amount calculated from the water area dimension.

[0025] Optionally, the steps of calculating the carbon emissions of the area to be evaluated from the dimensions of energy consumption, industrial production process, waste treatment, agriculture, respiration, and water area carbon volatilization specifically include:

[0026] (1.4) Dimension of energy consumption

[0027] Obtain the consumption amount, net calorific value, carbon dioxide emission factor, and methane emission factor of the target type of energy in the area to be evaluated. Calculate the carbon emissions from energy consumption according to the consumption amount, the net calorific value, the carbon dioxide emission factor, and the methane emission factor; and, obtain the consumption amount, carbon dioxide emission factor, and methane emission factor of the target type of biomass fuel in the area to be evaluated. Calculate the carbon emissions from the combustion of the target type of biomass fuel according to the consumption amount, the carbon dioxide emission factor, and the methane emission factor; Determine the sum of the carbon emissions from energy consumption corresponding to each target type of energy and the sum of the carbon emissions from the combustion of biomass fuels corresponding to each target type of biomass fuel as the carbon emissions calculated from the energy consumption dimension;

[0028] (1.5) Dimension of industrial production process

[0029] Obtain the production amount and carbon dioxide emission factor of the target type of industrial product in the area to be evaluated;

[0030] Calculate the industrial production carbon emissions of the target type of industrial product according to the production amount and the carbon dioxide emission factor;

[0031] Take the sum of the industrial production carbon emissions corresponding to each target type of industrial product as the carbon emissions calculated from the industrial production process dimension;

[0032] (1.6) Dimension of waste treatment

[0033] Obtain the annual garbage generation amount, annual garbage landfill treatment rate, annual methane recovery amount, oxidation factor, and methane generation potential coefficient of the target type of domestic waste landfill in the area to be evaluated. Calculate the methane emissions of the target type of domestic waste landfill according to the annual garbage generation amount, the annual garbage landfill treatment rate, the annual methane recovery amount, the oxidation factor, and the methane generation potential coefficient; and,

[0034] Obtain the incineration treatment volume, carbon content ratio, proportion of mineral carbon in the total carbon, waste combustion efficiency, and carbon dioxide conversion coefficient of the urban domestic waste in the area to be evaluated in the current year. Calculate the carbon dioxide generation amount from the incineration of domestic waste in the area to be evaluated based on the incineration treatment volume, the carbon content ratio, the proportion of mineral carbon in the total carbon, the waste combustion efficiency, and the carbon dioxide conversion coefficient; and,

[0035] Obtain the total amount of organic matter, maximum methane generation capacity, methane correction factor, and methane recovery amount in the domestic sewage in the area to be evaluated. Calculate the total methane amount generated from domestic sewage treatment based on the total amount of organic matter, the maximum methane generation capacity, the methane correction factor, and the methane recovery amount; and,

[0036] Obtain the total amount of organic matter in the biodegradable wastewater of the target industrial sector, the total amount of organic matter removed by the sludge method, the methane correction factor, and the methane recovery amount. Calculate the carbon emissions generated from industrial wastewater treatment based on the total amount of organic matter in the biodegradable wastewater, the total amount of organic matter removed by the sludge method, the methane correction factor, and the methane recovery amount;

[0037] Determine the carbon emissions calculated from the waste treatment dimension based on the sum of the methane emissions of each of the target type domestic waste landfills, the carbon dioxide generation amount from the incineration of domestic waste, the total methane amount generated from domestic sewage treatment, and the sum of the carbon emissions generated from industrial wastewater treatment of each of the target industrial sectors;

[0038] (1.7) Agricultural dimension

[0039] Obtain the input amount of the target type of agricultural production materials and the agricultural production material carbon emission factor in the area to be evaluated. Calculate the agricultural production carbon emissions corresponding to the target type of agricultural production materials based on the input amount and the agricultural production material carbon emission factor; and,

[0040] Obtain the planting area of the target type of rice and the methane emission factor in the area to be evaluated. Calculate the methane emissions of the target type of rice based on the rice planting area and the methane emission factor; and,

[0041] Obtain the number of the target type of animals, the enteric fermentation methane emission factor, and the manure management methane emission factor in the area to be evaluated. Calculate the enteric fermentation and manure management carbon emissions of the target type of animals based on the number, the enteric fermentation methane emission factor, and the manure management methane emission factor;

[0042] Determine the carbon emissions calculated from the agricultural dimension based on the sum of the agricultural production carbon emissions corresponding to each of the target type agricultural production materials, the sum of the methane emissions corresponding to each of the target type rice, and the sum of the carbon emissions from animal intestinal fermentation and manure management for each of the target type animals;

[0043] (1.8) Respiration dimension

[0044] Obtain the population quantity and human respiration carbon emission factor in the area to be evaluated, as well as the quantity of each target type of livestock and the livestock respiration carbon emission factor. Calculate the carbon emissions from human and livestock respiration in the area to be evaluated according to the population quantity, the human respiration carbon emission factor, the quantity of each target type of livestock, and the livestock respiration carbon emission factor;

[0045] Obtain the land area of the target type vegetation, the autotrophic respiration amount per unit area of plants, and the heterotrophic respiration carbon emissions in the area to be evaluated. Calculate the carbon emissions from plant autotrophic respiration and soil heterotrophic respiration of the target type vegetation according to the land area, the autotrophic respiration amount per unit area of plants, and the heterotrophic respiration carbon emissions;

[0046] Determine the carbon emissions calculated from the respiration dimension based on the carbon emissions from human and livestock respiration and the carbon emissions from plant autotrophic respiration and soil heterotrophic respiration corresponding to each of the target type vegetation;

[0047] (1.9) Water area carbon volatilization dimension

[0048] Obtain the area of rivers or lakes in the area to be evaluated and the carbon volatilization factor per unit area of rivers or lakes. Calculate the carbon volatilization amount of the water area, and use the carbon volatilization amount of the water area as the carbon emissions calculated from the water area carbon volatilization dimension.

[0049] Optionally, the carbon emission spatial autocorrelation evaluation result includes a global autocorrelation evaluation result and a local autocorrelation evaluation result;

[0050] The step of determining the carbon emission spatial autocorrelation evaluation result of the area to be evaluated according to the global spatial autocorrelation index and the local spatial autocorrelation index specifically includes:

[0051] Determine the global autocorrelation evaluation result of the area to be evaluated according to the magnitude relationship between the global spatial autocorrelation index and the preset global index threshold;

[0052] Determine the local autocorrelation evaluation result of the area to be evaluated according to the magnitude relationship between the local spatial autocorrelation index and the preset local index threshold; or,

[0053] Determine the local autocorrelation evaluation result according to the magnitude of the local spatial autocorrelation index.

[0054] Optionally, the calculation expression of the global spatial autocorrelation index is:

[0055]

[0056] In the formula, Z1 represents the value of the global spatial autocorrelation index, n is the number of units of the research unit, x i , x j are the carbon emissions of research unit i and research unit j respectively, is the average carbon emission in the research unit, S 2 is the variance, w ij is the spatial weight matrix.

[0057] Optionally, the calculation expression of the local spatial autocorrelation index is:

[0058]

[0059] In the formula, Z2 represents the value of the local spatial autocorrelation index, n is the number of units of the research unit, x i , x j are the carbon emissions of research unit i and research unit j respectively, is the average carbon emission in the research unit, S 2 is the variance, w ij is the spatial weight matrix.

[0060] Optionally, the carbon emission spatial autocorrelation evaluation result includes a global autocorrelation evaluation result and a local autocorrelation evaluation result. The global autocorrelation evaluation result includes whether there is an aggregation phenomenon in the space, whether there is a dispersion phenomenon in the space, and whether there is a spatial correlation in the space. The local autocorrelation evaluation result includes whether there is a high-value / low-value aggregation phenomenon in the space, whether there is an anomaly in the space, and whether there is no spatial correlation;

[0061] The steps of determining the carbon emission spatial autocorrelation evaluation result of the area to be evaluated according to the global spatial autocorrelation index and the local spatial autocorrelation index include:

[0062] When the global spatial autocorrelation index is positive, determine that the aggregation phenomenon evaluation result is that there is an aggregation phenomenon;

[0063] When the global spatial autocorrelation index is negative, determine that the aggregation phenomenon evaluation result is that there is a dispersion phenomenon;

[0064] When the global spatial autocorrelation index is zero, it is determined that there is no spatial correlation in the aggregation phenomenon evaluation result;

[0065] And, when the local spatial autocorrelation index is positive, it is determined that the aggregation degree evaluation result is high / low value aggregation;

[0066] When the local spatial autocorrelation index is negative, it is determined that the aggregation phenomenon evaluation result is a spatial anomaly;

[0067] When the local spatial autocorrelation index is zero, it is determined that there is no spatial correlation in the aggregation phenomenon evaluation result.

[0068] In addition, to achieve the above object, the present application further provides a carbon emission spatial autocorrelation evaluation model, and the carbon emission spatial autocorrelation evaluation model includes:

[0069] A land gradient utilization model for calculating the carbon absorption amount of the area to be evaluated from the dimensions of natural vegetation, crops, and water areas; and calculating the carbon emission amount of the area to be evaluated from the dimensions of energy consumption, industrial production process, waste treatment, agriculture, respiration, and water area carbon volatilization;

[0070] A carbon emission net value calculation module for determining the carbon emission net value of the area to be evaluated according to the difference between the carbon absorption amount and the carbon emission amount;

[0071] A spatial autocorrelation index calculation module for determining the global spatial autocorrelation index and the local spatial autocorrelation index of the area to be evaluated according to the carbon emission net value;

[0072] A carbon emission spatial autocorrelation evaluation module for determining the carbon emission spatial autocorrelation evaluation result of the area to be evaluated according to the global spatial autocorrelation index and the local spatial autocorrelation index.

[0073] In addition, to achieve the above object, the present application further provides a carbon emission spatial autocorrelation evaluation system, and the carbon emission spatial autocorrelation evaluation system includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, the steps of the carbon emission spatial autocorrelation evaluation method based on the land gradient utilization model described in any one of the above are implemented.

[0074] In addition, to achieve the above object, the present application further provides a computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the carbon emission spatial autocorrelation evaluation method based on the land gradient utilization model described in any one of the above are implemented.

[0075] This application has at least the following beneficial effects:

[0076] 1. By calculating the carbon absorption in the area to be assessed from three dimensions: natural vegetation, crops and waters; and calculating the carbon emissions in the area to be assessed from six dimensions: energy consumption, industrial production process, waste treatment, agriculture, respiration and water carbon volatilization, the carbon emission status of regional land gradient utilization under the intervention of natural and human activities can be accurately reflected, and then a regional land gradient utilization model with more obvious vertical zonality and altitude gradient can be constructed;

[0077] 2. Calculate the spatial autocorrelation index based on the net carbon emission value calculated by the carbon emission calculation model, more accurately analyze the relationship between the spatial distribution of carbon emissions and land use in the area to be evaluated, and achieve accurate carbon emission spatial autocorrelation assessment;

[0078] 3. The Moran index is used as a global spatial statistical index for regions with significant vertical zonality and altitude gradients. The spatial weight matrix that can be flexibly set by the Moran index can easily capture the continuous gradient changes of environmental variables in the region with altitude, as well as simulate terrain-driven or ecological process-driven changes, thereby more accurately evaluating spatial correlation.

[0079] 4. The local Moran index is used as a local spatial statistical index for areas with significant vertical zonality and altitude gradients to better identify hot spots (high-high value clustering areas), cold spots (low-low value clustering areas) or abnormal areas (high-low value alternating areas) in the local area. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 This is a flow chart of the first embodiment of the carbon emission spatial autocorrelation assessment method based on the land gradient utilization model of the present application;

[0081] Figure 2 A Moran index scatter plot of carbon emission space of land gradient utilization in the county of the central Yunnan urban agglomeration involved in the embodiment of the present application;

[0082] Figure 3 This is a schematic diagram of the spatial distribution of the local spatial autocorrelation index involved in the embodiment of the present application;

[0083] Figure 4 This is a schematic diagram of the architecture of the carbon emission spatial correlation assessment model involved in the carbon emission spatial autocorrelation assessment method based on the land gradient utilization model of this application;

[0084] Figure 5 This is a schematic diagram of the architecture of the hardware operating environment of the carbon emission spatial autocorrelation assessment system involved in the embodiment of the present application.

[0085] The realization, functional features, and advantages of the present application will be further described in conjunction with embodiments and with reference to the accompanying drawings. Detailed Embodiment

[0086] To better understand the above technical solution, the exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0087] First Embodiment

[0088] Referring to Figure 1 , in this embodiment, the method for evaluating the spatial autocorrelation of carbon emissions based on the land gradient utilization model includes the following steps:

[0089] S10, calculating the carbon absorption amount of the area to be evaluated from the dimensions of natural vegetation, crops, and water areas; and calculating the carbon emission amount of the area to be evaluated from the dimensions of energy consumption, industrial production process, waste treatment, agriculture, respiration, and water area carbon volatilization;

[0090] In the carbon emission and absorption amount model constructed in this embodiment, the carbon absorption amount is calculated from three dimensions: natural vegetation, crops, and water areas. The calculated carbon absorption amount is the carbon absorption amount of the terrestrial ecosystem in the area to be evaluated.

[0091] For the dimension of natural vegetation, the photosynthesis of natural vegetation is a process in which organisms use light energy for carbon fixation. When evaluating the carbon sink of terrestrial vegetation, the amount of CO2 absorbed by vegetation per unit time, that is, GPP, is usually used for characterization.

[0092] For the dimension of crops, during the growth period of crops, they will capture CO2 in the air through photosynthesis, synthesize carbohydrates, and release oxygen for their own growth and development. Using crop yields to calculate carbon absorption is a mature and feasible method.

[0093] For the dimension of water areas, water area carbon absorption is an indispensable link in the natural carbon cycle. It mainly dissolves CO2 into water through two ways: water area carbon fixation and wet and dry deposition, thus playing an important role in maintaining ecological balance.

[0094] In this embodiment, for the carbon emission amount in the carbon emission and absorption amount model, it is calculated from six dimensions: energy consumption, industrial production process, waste treatment, agriculture, respiration, and water area carbon volatilization. The calculated carbon emission amount is the carbon emission amount generated by the land gradient utilization in the area to be evaluated.

[0095] For the energy consumption dimension, the energy consumption dimension mainly includes carbon emissions from energy consumption and biomass fuels. Energy consumption is an important emission source of greenhouse gases, and traditional energy consumption represented by fossil energy is the main source of carbon emissions. In some specific embodiments, 20 energy types such as raw coal, washed clean coal, coke, gasoline, coal, petroleum, natural gas, etc. are the main accounting items. Biomass fuels are widely sourced, have high calorific values, low densities, and are easily combusted, and are the main energy sources in the daily life of rural residents.

[0096] For the industrial production process dimension, industrial production carbon emissions are an important emission source carried by construction land, including CO2 generated during the industrial production process, product use, and non-energy use of fossil energy carbon.

[0097] It should be noted that since carbon emissions from energy consumption have been separately accounted for in the energy consumption dimension, only carbon emissions caused by industrial production processes are considered.

[0098] For the waste treatment dimension, the greenhouse gases released during the waste treatment process will have a negative impact on the environment and exacerbate global warming. Due to the complex waste treatment process and the difficulty in obtaining data, rural domestic waste is usually discarded without treatment at will.

[0099] Optionally, in some specific embodiments, referring to the calculation suggestions put forward in the "Guidelines for Compiling Provincial Greenhouse Gas Inventories (Trial)", CH4 and CO2 generated from the treatment of urban domestic waste, domestic sewage, and industrial wastewater are selected for accounting, and carbon emissions from rural domestic waste are not considered.

[0100] For the agricultural dimension, agricultural carbon emissions come from carbon emissions caused by agricultural activities, including agricultural production carbon emissions, CH4 release from paddy fields, animal intestinal fermentation, and manure management.

[0101] Optionally, considering that the degree of agricultural mechanization in vertical gradient regions is not high, large-scale agriculture is not yet mature, and statistics on farmland irrigation, plowing, and the use of agricultural machinery and equipment are incomplete, therefore, agricultural production carbon emissions are considered to be accounted for from the input and use of production materials such as pesticides, fertilizers, and agricultural films.

[0102] For the respiration dimension, carbon emissions generated by respiration are an integral part of carbon emissions from land gradient utilization and have a significant impact on the carbon balance of terrestrial ecosystems.

[0103] Optionally, the carbon emissions from land gradient utilization of the area to be evaluated can be accounted for from aspects such as human and animal respiration, autotrophic respiration of vegetation, and heterotrophic respiration of soil.

[0104] For the dimension of water area carbon volatilization, water area carbon volatilization is a natural carbon release process. Optionally, water area carbon volatilization may cover river carbon volatilization and lake carbon volatilization.

[0105] S20. Determine the net carbon emission of the area to be evaluated according to the difference between the carbon absorption amount and the carbon emission amount.

[0106] S30. Determine the global spatial autocorrelation index and the local spatial autocorrelation index of the area to be evaluated according to the net carbon emission.

[0107] In this embodiment, subtracting the carbon emission amount from the carbon absorption amount can obtain the net carbon emission, and the net carbon emission is used to calculate the global spatial autocorrelation index and the local spatial autocorrelation index of the area to be evaluated. Among them, the net carbon emission can be positive or negative.

[0108] The spatial autocorrelation index includes the local autocorrelation index and the global autocorrelation index. It should be noted that spatial autocorrelation can reveal the spatial dependence and interaction of the research object by measuring the correlation between the observed values of nearby statistical units. In addition, spatial autocorrelation analysis can also help identify the spatial aggregation or dispersion patterns of land resources such as land use types, soil quality, and vegetation cover.

[0109] Optionally, the global autocorrelation index can be calculated by methods such as Moran's I, Geary's C, and Getis-Ord General G.

[0110] Optionally, the local autocorrelation index can be calculated by methods such as Local Moran's I, Local Geary's C, and Getis-Ord Local Gi*.

[0111] The specific index selection is not limited in this embodiment.

[0112] S40. Determine the evaluation result of the carbon emission spatial autocorrelation of the area to be evaluated according to the global spatial autocorrelation index and the local spatial autocorrelation index.

[0113] In this embodiment, the evaluation result of the carbon emission spatial autocorrelation is a data set, and this data set can be displayed in the user interface in a visual form.

[0114] There are various results for the evaluation result of the carbon emission spatial autocorrelation. This result is affected by the global spatial autocorrelation index and / or the local spatial autocorrelation index, and its specific content depends on the vertical zone and altitude gradient of the area to be evaluated.

[0115] In the technical solution provided in this embodiment, by calculating the carbon absorption amount in the area to be evaluated from three dimensions: the natural vegetation dimension, the crop dimension, and the water area dimension, and calculating the carbon emission amount in the area to be evaluated from six dimensions: the energy consumption dimension, the industrial production process dimension, the waste treatment dimension, the agricultural dimension, the respiration dimension, and the water area carbon volatilization dimension, a carbon emission and absorption calculation model that is more suitable for areas with obvious vertical zonality and altitude gradient is constructed; based on the net carbon emission calculated by the carbon emission and absorption calculation model, the global spatial autocorrelation index and the local spatial autocorrelation index are calculated, so as to more accurately analyze the relationship between the spatial distribution of carbon emissions and land use in the area to be evaluated, and realize accurate spatial autocorrelation assessment of areas with obvious vertical zonality and altitude gradient.

[0116] Second Embodiment

[0117] Based on the first embodiment, in this embodiment, for how to calculate the carbon absorption amount from the three dimensions of the natural vegetation dimension, the crop dimension, and the water area dimension, the calculation methods for the above three dimensions are given in this embodiment:

[0118] (1.1) Natural Vegetation Dimension

[0119] Obtain the carbon sink capacity per unit area corresponding to the target type of vegetation in the area to be evaluated and the land area corresponding to the target type of vegetation;

[0120] According to the carbon sink capacity per unit area and the land area, calculate the natural vegetation carbon absorption amount;

[0121] Take the sum of the natural vegetation carbon absorption amounts corresponding to each target type of vegetation as the carbon absorption amount calculated from the natural vegetation dimension;

[0122] Exemplarily, the calculation formula is as follows:

[0123]

[0124] In the formula, CS vegetation is the natural vegetation carbon absorption amount, GPP i is the carbon sink capacity per unit area of vegetation of target type i, and A i is the land area corresponding to the target type of vegetation.

[0125] Furthermore, the value of GPP i can be referred to as shown in Table 1 below:

[0126] Table 1. Natural Vegetation Carbon Absorption Parameters

[0127]

[0128] (1.2) Crop Dimension

[0129] Obtain the biological yield of the target type of crops in the area to be evaluated, the carbon absorption rate of the target type of crops for synthesizing unit organic matter, and the water content of the target type of crops;

[0130] Calculate the photosynthetic carbon absorption of the target type of crops according to the biological yield, the carbon absorption rate, and the water content;

[0131] Take the sum of the photosynthetic carbon absorption corresponding to each target type of crops as the carbon absorption calculated from the crop dimension;

[0132] Exemplarily, the calculation formula for crop carbon absorption is as follows:

[0133]

[0134] In the formula, CS crops is the crop carbon absorption; Y i is the biological yield of the target type of crop i, which is mainly obtained by dividing the economic yield (YE i ) of the corresponding crop by its economic coefficient (H i ); CSR i is the carbon absorption rate of the target type of crop i for synthesizing unit organic matter; P i is the water content of the target type of crop i.

[0135] Furthermore, the crop carbon absorption parameters are shown in Table 2 below:

[0136] Table 2. Crop carbon absorption parameters

[0137]

[0138] (1.3) Water area dimension

[0139] Obtain the carbon sequestration rate per unit area of water in the area to be evaluated, the water area, the carbon absorption of dry and wet deposition per unit area of water, and the total area of the study area;

[0140] Calculate the carbon absorption of the water area according to the carbon sequestration rate per unit area of water, the water area, the carbon absorption of dry and wet deposition per unit area of water, and the total area of the study area;

[0141] Take the carbon absorption of the water area as the carbon absorption calculated from the water area dimension.

[0142] Exemplarily, the calculation expression for the carbon absorption of the water area is as follows:

[0143]

[0144] In the formula, CSwater is the carbon absorption of water area; WSR is the carbon sequestration rate of water area per unit area; A water is the water area; WDD is the carbon absorption of wet and dry deposition of water area per unit area, and A is the total area of the area to be evaluated.

[0145] Furthermore, the parameters of water area carbon absorption and carbon emission are shown in Table 3 below:

[0146] Table 3. Parameters of water area carbon absorption and carbon emission

[0147]

[0148] The Third Embodiment

[0149] Based on any one of the embodiments, in this embodiment, for how to calculate the carbon emissions from six dimensions: energy consumption dimension, industrial production process dimension, waste treatment dimension, agricultural dimension, respiration dimension, and water area carbon volatilization dimension, the calculation methods for the above six dimensions are given in this embodiment:

[0150] (1.4) Energy consumption dimension

[0151] Obtain the consumption amount, net calorific value, carbon dioxide emission coefficient, and methane emission coefficient of the target type of energy in the area to be evaluated. According to the consumption amount, the net calorific value, the carbon dioxide emission coefficient, and the methane emission coefficient, calculate the carbon emissions from energy consumption; and, obtain the consumption amount, carbon dioxide emission factor, and methane emission factor of the target type of biomass fuel in the area to be evaluated. According to the consumption amount, the carbon dioxide emission factor, and the methane emission factor, calculate the carbon emissions from the combustion of the target type of biomass fuel; according to the sum of the carbon emissions from energy consumption corresponding to each target type of energy, and the sum of the carbon emissions from the combustion of biomass fuel corresponding to each target type of biomass fuel, determine the carbon emissions calculated from the energy consumption dimension;

[0152] In this embodiment, the energy consumption dimension is calculated from two sub - dimensions: carbon emissions from energy consumption and carbon emissions from the combustion of biomass fuel.

[0153] Exemplarily, the calculation expression of carbon emissions from energy consumption is as follows:

[0154]

[0155] In the formula, CE energy is the carbon emissions from energy consumption; E i is the consumption amount of the i - th type of target energy; NCV i is the net calorific value of the i - th type of target energy (also known as the average lower calorific value); EM iis the CO2 emission factor of the target type of energy i, which can be calculated by the product of the carbon content per unit calorific value and the carbon oxidation rate; CF i is the CH4 emission factor of the target type of energy i.

[0156] Furthermore, the values of the carbon emission parameters for each energy type can refer to Table 4 below:

[0157] Table 4. Carbon emission parameters for each energy type

[0158]

[0159]

[0160] Exemplarily, the calculation expression for the carbon emissions from the combustion of biomass fuel is as follows:

[0161]

[0162] In the formula, CE biomass is the carbon emissions from the combustion of biomass fuel; E i is the fuel consumption of the target type of biomass fuel i, including the consumption of straw (rice, wheat, corn, rapeseed, soybeans, and cotton) and firewood; EM i and CF i are the CO2 and CH4 emission factors of the target type of biomass fuel, respectively, and the specific values are shown in Table 5.

[0163] Furthermore, the calculation formulas for the direct combustion and open burning consumption of straw as fuel are as follows:

[0164]

[0165] In the formula, E is the straw combustion consumption, P k is the yield of the k-th type of crop, N k is the straw-to-grain ratio of the k-th type of crop, R is the straw burning ratio, and η is the combustion rate. The specific parameters are shown in Table 5:

[0166] Table 5. Straw consumption parameters

[0167]

[0168] (1.5) Industrial production process dimension

[0169] Obtain the production volume and carbon dioxide emission factor of the target type of industrial product in the area to be evaluated;

[0170] Calculate the industrial production carbon emissions of the target type of industrial product based on the production volume and the carbon dioxide emission factor;

[0171] The sum of the industrial production carbon emissions corresponding to each of the target type industrial products is taken as the carbon emissions calculated from the dimension of the industrial production process;

[0172] Exemplarily, the calculation formula for industrial production carbon emissions is as follows:

[0173]

[0174] In the formula, CE manu is the sum of the industrial production carbon emissions corresponding to each target type industrial product; Q i is the production volume of the target type industrial product i; EF i is the CO2 emission factor of the target type industrial product i;

[0175] Furthermore, the value of the carbon emission factor of the industrial production process can be referred to Table 6:

[0176] Table 6. Carbon emission factors of the industrial production process

[0177]

[0178] (1.6) Waste treatment dimension

[0179] Obtain the annual garbage generation amount, the annual garbage landfill treatment rate, the annual methane recovery amount, the oxidation factor, and the methane generation potential coefficient of the target type domestic waste landfill in the area to be evaluated. According to the annual garbage generation amount, the annual garbage landfill treatment rate, the annual methane recovery amount, the oxidation factor, and the methane generation potential coefficient, calculate the methane emissions of the target type domestic waste landfill; and,

[0180] Obtain the incineration treatment amount, the carbon content ratio, the proportion of mineral carbon in the total carbon, the garbage combustion efficiency, and the carbon dioxide conversion coefficient of the urban domestic waste in the area to be evaluated in the current year. According to the incineration treatment amount, the carbon content ratio, the proportion of mineral carbon in the total carbon, the garbage combustion efficiency, and the carbon dioxide conversion coefficient, calculate the carbon dioxide generation amount of the domestic waste incineration in the area to be evaluated; and,

[0181] Obtain the total amount of organic matter, the maximum methane generation capacity, the methane correction factor, and the methane recovery amount in the domestic sewage in the area to be evaluated. According to the total amount of organic matter, the maximum methane generation capacity, the methane correction factor, and the methane recovery amount, calculate the total amount of methane generated from domestic sewage treatment; and,

[0182] Obtain the total amount of organic matter in the biodegradable wastewater of the target industrial sector, the total amount of organic matter removed by the sludge method, the methane correction factor, and the methane recovery amount. Calculate the carbon emissions generated from the industrial wastewater treatment based on the total amount of organic matter in the biodegradable wastewater, the total amount of organic matter removed by the sludge method, the methane correction factor, and the methane recovery amount.

[0183] Determine the carbon emissions calculated from the waste treatment dimension based on the sum of the methane emissions from each of the target type of municipal solid waste landfills, the carbon dioxide generated from the incineration of municipal solid waste, the total amount of methane generated from the domestic sewage treatment, and the sum of the carbon emissions generated from the industrial wastewater treatment of each of the target industrial sectors.

[0184] In this embodiment, the carbon emissions from waste treatment are calculated based on methane and / or carbon dioxide generated from three sub-dimensions: municipal solid waste, domestic sewage, and industrial wastewater treatment.

[0185] Exemplarily, the carbon emission calculation formula for municipal solid waste landfill is as follows:

[0186]

[0187] P0 = LCF × DOC × DOC f × L × 16 / 12

[0188] In the formula, CE CH4 is the CH4 emission of carbon emissions from municipal solid waste landfill; W p is the amount of waste generated in the region in that year; W d is the waste landfill treatment rate; P0 is the CH4 generation potential of different types of municipal solid waste landfills (10 4 tCH4 / 10 4 t waste). LCF is the CH4 correction factor (ratio) of each type of municipal solid waste landfill; DOC is the biodegradable organic carbon; DOC f is the decomposition ratio of the biodegradable organic carbon (DOC); L is the proportion of CH4 in the gas generated from waste landfill; R is the CH4 recovery amount; OF is the oxidation factor.

[0189] It should be noted that the above carbon emission calculation model for municipal solid waste landfill is constructed based on the characteristics of municipal solid waste in the European and American regions, and there will be errors in evaluating the CH4 emissions from waste landfills in China. Therefore, it is necessary to make corrections. Thus, in this embodiment, LCF is introduced as the CH4 correction factor for each type of municipal solid waste landfill.

[0190] Furthermore, the values of each parameter can be referred to in Table 7 below:

[0191] Table 7. CH4 Emission Parameters for Municipal Solid Waste Landfill

[0192]

[0193] Exemplarily, the carbon emission calculation formula for municipal solid waste incineration is as follows:

[0194]

[0195] In the formula, is the carbon emission of municipal solid waste in the area to be evaluated in the current year; WI is the incineration treatment volume of municipal solid waste; WCP is the proportion of carbon content in municipal solid waste; MCP is the proportion of mineral carbon in the total carbon of domestic waste; IE is the combustion efficiency of municipal solid waste; 44 / 12 is the conversion coefficient for converting carbon to CO2.

[0196] Furthermore, for the parameter values in the carbon emission calculation formula of municipal solid waste incineration, refer to Table 8 below:

[0197]

[0198] Exemplarily, the carbon emission calculation formula for domestic sewage treatment is as follows:

[0199]

[0200] In the formula, is the methane emission of municipal solid waste in the area to be evaluated in the current year; BOD is the total amount of organic matter in domestic sewage; G is the maximum production capacity of CH4; MCF is the CH4 correction factor; R is the CH4 recovery amount.

[0201] It should be noted that since only the chemical oxygen demand (COD) data is statistically available in China, COD can be converted to BOD during calculation, and the conversion coefficient is 0.51.

[0202] Exemplarily, the carbon emission calculation formula for industrial wastewater treatment is as follows:

[0203]

[0204] In the formula, is the total amount of CH4 released from industrial wastewater treatment in the area to be evaluated; i is the target industrial sector; COD i is the total amount of biodegradable organic matter in the wastewater of the target industrial sector i; D i is the organic matter removed in the form of sludge by the target industrial sector i.

[0205] Furthermore, the specific values are shown in Table 9.

[0206] Table 9. Carbon Emission Parameters for Wastewater Treatment

[0207]

[0208] (1.7) Agricultural Dimension

[0209] Obtain the input quantity of the target type of agricultural production materials and the carbon emission factor of agricultural production materials in the area to be evaluated, and calculate the agricultural production carbon emissions corresponding to the target type of agricultural production materials according to the input quantity and the carbon emission factor of agricultural production materials; and,

[0210] Obtain the planting area of the target type of rice and the methane emission factor in the area to be evaluated, and calculate the methane emissions of the target type of rice according to the rice planting area and the methane emission factor; and,

[0211] Obtain the number of the target type of animals, the methane emission factor of enteric fermentation, and the methane emission factor of manure management in the area to be evaluated, and calculate the carbon emissions of enteric fermentation and manure management of the target type of animals according to the number, the methane emission factor of enteric fermentation, and the methane emission factor of manure management;

[0212] Determine the carbon emissions calculated from the agricultural dimension according to the sum of the agricultural production carbon emissions corresponding to each target type of agricultural production materials, the sum of the methane emissions corresponding to each target type of rice, and the sum of the carbon emissions of enteric fermentation and manure management of each target type of animals;

[0213] In this embodiment, in areas with obvious vertical zonality and altitude gradient, the degree of agricultural mechanization is usually not high. Therefore, the accounting is only carried out from the aspects of the input and use of production materials such as pesticides, fertilizers, and agricultural films, without considering the statistics of farmland irrigation, ploughing, and the use of agricultural machinery and equipment.

[0214] Exemplarily, the calculation expression of agricultural production carbon emissions is:

[0215]

[0216] In the formula, CE agriculture is the agricultural production carbon emissions; Q i is the input quantity of the target type of agricultural production material i; EF i is the carbon emission factor of the target type of agricultural production material i.

[0217] Furthermore, the values of the carbon emission factors of agricultural production materials are shown in Table 10 below:

[0218] Table 10. Carbon Emission Factors of Agricultural Production Materials

[0219]

[0220]

[0221] Exemplarily, the calculation expression for the methane emission of the target type of rice is as follows:

[0222]

[0223] In the formula, CE paddy is the total CH4 emission from the paddy field; m is the rice planting type; A m is the planting area of the target type of rice m; EF i is the CH4 emission factor corresponding to the target type of rice m.

[0224] Furthermore, for the value of the rice carbon emission factor, see Table 11 below:

[0225] Table 11. Values of Rice Carbon Emission Factors

[0226]

[0227] Exemplarily, the calculation expressions for the carbon emissions from animal enteric fermentation and manure management are as follows:

[0228]

[0229] In the formula, CE animal is the carbon emission from animal enteric fermentation and manure management; N k is the number of the target type of animal k; EF k1 is the CH4 emission factor corresponding to the enteric fermentation of the target type of animal k, and EF k2 is the CH4 emission factor corresponding to the manure management of the target type of animal k;

[0230] Furthermore, the value of the CH4 emission factor corresponding to the manure management of the target type of animal k can be as shown in Table 12 below:

[0231] Table 12. CH4 Emission Factors for Animal Enteric Fermentation and Manure Management

[0232]

[0233] (1.8) Respiration Dimension

[0234] Obtain the population number and human respiration carbon emission factor in the area to be evaluated, as well as the number of each target type of livestock and the livestock respiration carbon emission factor. Calculate the human and livestock respiration carbon emissions in the area to be evaluated based on the population number, the human respiration carbon emission factor, the number of each target type of livestock, and the livestock respiration carbon emission factor;

[0235] Obtain the land area of the target type of vegetation in the area to be evaluated, the autotrophic respiration amount per unit area of plants, and the heterotrophic respiration carbon emissions, and calculate the autotrophic respiration of the target type of vegetation and the soil heterotrophic respiration carbon emissions according to the land area, the autotrophic respiration amount per unit area of plants, and the heterotrophic respiration carbon emissions;

[0236] According to the carbon emissions from human and livestock respiration, and the autotrophic respiration of plants and soil heterotrophic respiration carbon emissions corresponding to each of the target types of vegetation, determine the carbon emissions calculated from the respiration dimension.

[0237] In this embodiment, the carbon emissions generated by respiration are an integral part of the carbon emissions from land gradient utilization and have a significant impact on the carbon balance of the terrestrial ecosystem. Therefore, in this embodiment, the carbon emissions from land gradient utilization in the central Yunnan urban agglomeration are accounted for from aspects such as human and livestock respiration, vegetation autotrophic respiration, and soil heterotrophic respiration.

[0238] Exemplarily, the calculation expression for the carbon emissions from human and livestock respiration is:

[0239]

[0240] In the formula, CE breathe is the carbon emissions generated by human and livestock respiration in the area to be evaluated; N h is the population quantity, N ai is the quantity of the target type of livestock i; EF h is the carbon emission factor of human respiration, EF ai is the carbon emission factor of the respiration of the target type of livestock i.

[0241] Furthermore, the specific values are shown in Table 13 below:

[0242] Table 13. Carbon emission factors for human and livestock respiration

[0243]

[0244] (1.9) Water area carbon volatilization dimension

[0245] Obtain the area of rivers or lakes in the area to be evaluated, and the carbon volatilization factor per unit area of rivers or lakes, calculate the carbon volatilization amount of the water area, and take the carbon volatilization amount of the water area as the carbon emissions calculated from the water area carbon volatilization dimension.

[0246] Exemplarily, the calculation expression for the carbon volatilization amount of the water area is as follows:

[0247]

[0248] In the formula, CE wateris the carbon volatilization amount of the water area in the area to be evaluated; A i is the area of rivers or lakes in the area to be evaluated; EF is the carbon volatilization factor per unit area of rivers or lakes.

[0249] Among them, the value of the carbon volatilization factor per unit area of rivers or lakes can be referred to Table 3 in the second embodiment.

[0250] The fourth embodiment

[0251] Based on any of the above embodiments, in this embodiment, the evaluation results of the spatial autocorrelation of carbon emissions include the global autocorrelation evaluation results and the local autocorrelation evaluation results. Among them, the global autocorrelation evaluation results are determined by the magnitude relationship between the global spatial autocorrelation index and the preset global index threshold; while the local autocorrelation evaluation results are determined according to the magnitude relationship between the local spatial autocorrelation index and the preset local index threshold or according to the magnitude of the local spatial autocorrelation index itself.

[0252] Specifically, the evaluation results of the spatial autocorrelation of carbon emissions include the global autocorrelation evaluation results and the local autocorrelation evaluation results. The global autocorrelation evaluation results include whether there is an aggregation phenomenon in the space, whether there is a dispersion phenomenon in the space, and whether there is a spatial correlation in the space.

[0253] Further and optionally, in this embodiment, when the global spatial autocorrelation index is greater than the preset global index threshold, it is determined that the aggregation phenomenon evaluation result is that there is an aggregation phenomenon; when the global spatial autocorrelation index is less than the preset global index threshold, it is determined that the aggregation phenomenon evaluation result is that there is a dispersion phenomenon; when the global spatial autocorrelation index is equal to the preset global index threshold, it is determined that the aggregation phenomenon evaluation result is that there is no spatial correlation.

[0254] It should be noted that different methods are used as the global spatial autocorrelation index, and the corresponding global index thresholds are also different. Exemplarily, assuming that the Moran index is used as the global spatial autocorrelation index, the global index threshold is set to 0. Assuming that the Geary index is used as the threshold, the global index threshold is set to 1; and if the Getis-Ord global G index is used as the global spatial autocorrelation index, the threshold is the expected value corresponding to this index.

[0255] In addition, it should also be noted that when the global spatial autocorrelation index is greater than the preset global index threshold and thus indicates that the land shows an aggregation phenomenon, the larger the global spatial autocorrelation index, the higher the degree of land aggregation, indicating that the land phenomenon is concentrated functional zoning, resource-dependent aggregation, and policy-driven contiguous development. This kind of land phenomenon usually appears in land areas with land use types such as cultivated land, forest land, grassland, water area, industry, agriculture, or ecological protection areas.

[0256] When the global spatial autocorrelation index is less than the preset global index threshold, indicating that the land shows a dispersed phenomenon, the smaller the global spatial autocorrelation index, the higher the degree of land dispersion, and the land phenomenon is characterized by functional isolation, ecological-development contradictions, and fragmented resource competition. This land phenomenon usually appears in areas such as urban land use types.

[0257] When the global spatial autocorrelation index is equal to the preset global index threshold, indicating that there is no spatial correlation in the land, the land phenomenon is characterized by multi-factor mixing, mixed use in the transition zone, homogenization or unplanned distribution. It is necessary to formulate relevant strategies to optimize carbon emissions in this area. This land phenomenon usually appears in areas such as the urban-rural fringe, industrial and mining, and transportation construction land.

[0258] Preferably, in this embodiment, the Moran index is used as the global spatial autocorrelation index.

[0259] It should be noted that compared with other global spatial statistical indices (such as the Geary index and the Getis-Ord global G index mentioned above), for regions with significant vertical zonality and altitude gradients, the Moran index can quantify the overall spatial autocorrelation (aggregation or dispersion) of the region, so as to judge whether the spatial pattern of vertical zonality is significant; in addition, the Moran index is more sensitive to the directionality and gradient changes in space. When environmental variables such as temperature and vegetation type show continuous gradient changes with altitude, the Moran index can capture the directional correlation between these continuous gradient changes through the spatial weight matrix. In addition, in vertical zonality regions, spatial correlation is more easily driven by ecological processes such as terrain or species diffusion, rather than simple straight-line distance, and the Moran index allows customizing the weight matrix to simulate terrain-driven or ecological process-driven, so as to more accurately evaluate spatial correlation.

[0260] The Geary index or the Getis-Ord global G index usually only reflects differences and is not sensitive to continuous gradient changes.

[0261] Specifically, when the Moran index is used as the global spatial autocorrelation index, the calculation expression of the global spatial autocorrelation index is:

[0262]

[0263] In the formula, Z1 represents the value of the global spatial autocorrelation index, n is the number of research units, x i 、x j are the carbon emissions of research unit i and research unit j respectively, is the average carbon emission in the research unit, S 2 is the variance, and w ij is the spatial weight matrix.

[0264] On the other hand, in this embodiment, the local autocorrelation evaluation result includes whether there is a high value / low value clustering phenomenon in the space, whether there is anomaly in the space, and whether there is no spatial correlation.

[0265] Further and optionally, when the local spatial autocorrelation index is greater than a preset local index threshold, the clustering degree assessment result is determined to be a high value / low value clustering; when the local spatial autocorrelation index is less than a preset local index threshold, the clustering phenomenon assessment result is determined to be a spatial anomaly; when the local spatial autocorrelation index is equal to the preset local index threshold, the clustering phenomenon assessment result is determined to be the absence of spatial correlation;

[0266] High-value / low-value clustering refers to the situation where the attribute values of a certain land unit and its adjacent areas are significantly higher / lower than the global average level. High-value clustering reflects the resource enrichment or functional polarization of the land, such as industrial land, urban development zones, or major agricultural production areas.

[0267] Spatial anomaly refers to a high value surrounded by low values (i.e., the attribute value of a certain land unit is high, but the attribute value of the adjacent area is low, referred to as high-low anomaly), or a low value surrounded by high values (i.e., the attribute value of a certain land unit is low, but the attribute value of the adjacent area is high, referred to as low-high anomaly).

[0268] The absence of spatial correlation means that the local spatial autocorrelation is not significant and the land attribute values have no statistical correlation with the neighboring areas.

[0269] The above method is applicable to methods such as the local Moran index or the Gittes-Ord local G index.

[0270] Alternatively, in this embodiment, the degree of local spatial autocorrelation is determined according to the magnitude of the local spatial autocorrelation index, wherein the local spatial autocorrelation index is positively correlated with the degree of local spatial autocorrelation.

[0271] This method is applicable to the local Geary index, where the smaller the local spatial autocorrelation index, the stronger the local spatial autocorrelation; the larger the local spatial autocorrelation index, the weaker the local spatial autocorrelation.

[0272] Preferably, the local Moran's index is selected as the local spatial autocorrelation index.

[0273] It should be noted that compared with other local spatial statistical indices (such as the aforementioned local Geary index and Gittes-Ord local G index), the local Moran index can better identify local hot spots (high-high value clustering areas), cold spots (low-low value clustering areas) or abnormal areas (high-low value alternating areas) in areas with significant vertical zonality and altitude gradients.

[0274] However, the Getis-Ord local G index is limited by the low flexibility of its weight matrix setting, and it is prone to ignoring low-value clusters or areas with alternating high and low values; the local Moran index is insensitive to continuous gradient changes in the region.

[0275] Specifically, taking the local Moran index as the local spatial autocorrelation index, the calculation expression of the local spatial autocorrelation index is:

[0276]

[0277] In the formula, Z2 represents the value of the local spatial autocorrelation index, n is the number of research units, x i 、x j are the carbon emissions of research unit i and research unit j respectively, is the average carbon emission in the research unit, S 2 is the variance, and w ij is the spatial weight matrix.

[0278] The Fifth Embodiment

[0279] Based on any of the above embodiments, in this embodiment, the terrain of the central Yunnan urban agglomeration in the central and eastern regions of Yunnan Province from 2000 to 2020 is used as the area to be evaluated, and the change of carbon emission spatial autocorrelation of the land gradient utilization type transfer characteristics of the terrain of the central Yunnan urban agglomeration is analyzed based on the above constructed model.

[0280] (1) Global Spatial Autocorrelation Analysis

[0281] See Tables 14 and 15:

[0282] Table 14. Changes in the net carbon emissions of the central Yunnan urban agglomeration from 2000 to 2020 (unit: 10,000 tons)

[0283]

[0284] Table 15. Changes in the net carbon emissions of different land use types in the central Yunnan urban agglomeration (10,000 tons)

[0285]

[0286] Based on this data, the global spatial autocorrelation results of the central Yunnan urban agglomeration shown in Table 16 are obtained, as well as the Moran index scatter plot of the carbon emission space of the county land gradient utilization in the central Yunnan urban agglomeration shown as Figure 2 .

[0287] Table 16. Global Spatial Autocorrelation Results of the Central Yunnan Urban Agglomeration

[0288]

[0289] Figure 2Intuitively shows the spatial correlation distribution characteristics of carbon emissions from land gradient use in the counties of the central Yunnan urban agglomeration. From 2000 to 2020, the linear fitting degree of carbon emissions from land gradient use in the study area was positively correlated spatially, and most of the observation points fell in the third quadrant, which means that the county-level carbon emissions showed an agglomeration distribution characteristic where low values were surrounded by low values, that is, the spatial units were homogeneous. Among them, in 2000, the number of counties in the first quadrant (high - high) was 5, the number of counties in the second quadrant (low - high) was 6, the number of counties in the third quadrant (low - low) was 33, and the number of counties in the fourth quadrant (high - low) was 5. In 2005, the number of counties in the first quadrant (high - high), second quadrant (low - high), third quadrant (low - low), and fourth quadrant (high - low) were 6, 7, 31, and 5 respectively. In 2010, the number of counties in the first quadrant (high - high), second quadrant (low - high), third quadrant (low - low), and fourth quadrant (high - low) were 7, 8, 29, and 5 respectively. In 2015, the number of counties in the first quadrant (high - high), second quadrant (low - high), third quadrant (low - low), and fourth quadrant (high - low) were 7, 8, 28, and 6 respectively. In 2020, the number of counties in the first quadrant (high - high), second quadrant (low - high), third quadrant (low - low), and fourth quadrant (high - low) were 8, 10, 26, and 5 respectively. In the past 20 years, the number of counties distributed in the first quadrant (high - high) and the second quadrant (low - high) has been increasing, the number of counties distributed in the third quadrant (low - low) has been continuously decreasing, and the fourth quadrant (high - low) has remained basically unchanged. This shows that the agglomeration distribution characteristic where high values are surrounded by high values in county-level carbon emissions has become more obvious, while the spatial association form where low values are surrounded by low values has weakened. At the same time, the phenomenon of heterogeneous distribution of spatial units has gradually emerged, mainly manifested as an increase in the spatial connection form where low-carbon emission areas are surrounded by high-carbon emission areas. These changes together reveal the complexity and dynamics of the spatial correlation distribution of carbon emissions from land gradient use at the county level.

[0290] (2) Local spatial autocorrelation analysis

[0291] Global spatial autocorrelation reveals that there is a strong spatial correlation in carbon emissions from land gradient use in the central Yunnan urban agglomeration, but the spatial relationship between county units is still vague. Therefore, in this embodiment, a local spatial autocorrelation model is used to further explore the local significant correlation relationship of carbon emissions from land gradient use in the central Yunnan urban agglomeration. The schematic diagram of the spatial distribution of the local spatial autocorrelation index is shown in Figure 3 .

[0292] In 2000, the carbon emissions from the gradient land use in the central Yunnan urban agglomeration showed obvious local spatial autocorrelation characteristics. Among them, the counties and districts with high-high correlation include Wuhua District, Panlong District, Guandu District and Xishan District. These 4 districts are mainly located in the main urban area of Kunming, with a high level of urbanization, a dense population and a concentration of construction land, resulting in a large amount of carbon emissions from the gradient land use and forming a high-value agglomeration area; the counties and districts with low-high correlation are Chenggong District, Fumin County and Songming County. The carbon emissions of these 3 counties and districts are relatively low themselves, but they are surrounded by areas with relatively large carbon emissions and maintain a cooperative relationship with them, forming a low-value isolated area; the counties and districts with low-low correlation include 6 counties and districts such as Luquan Yi and Miao Autonomous County, Yao'an County, Dayao County, Yuanmou County, Chuxiong City and Xinping Yi and Dai Autonomous County. They are located in 3 prefecture-level cities of Kunming, Chuxiong and Yuxi. Affected by the natural environment and geographical conditions, the leading industry is agricultural production, and they have similar carbon emission characteristics, jointly forming a low-value agglomeration area.

[0293] In 2005, the local spatial association pattern of carbon emissions from land gradient use in the central Yunnan urban agglomeration changed. The main manifestations were that Chuxiong City became a county with high-low association, Wuding County became a county with low-low association, and Xinping Yi and Dai Autonomous County broke away from the low-value agglomeration area. The reasons for these changes were as follows: From 2000 to 2005, the intensity of human activities in Chuxiong City increased, and the processes of industrialization and urbanization accelerated, leading to a sharp rise in carbon emissions and forming a high-low distribution pattern with surrounding low-carbon emission areas. The carbon emissions of Wuding County remained relatively stable and did not show a significant increase. However, the industrial development mode dominated by agricultural production enhanced its connection with surrounding counties and formed a low-low agglomeration distribution pattern. The growth of carbon emissions in Xinping Yi and Dai Autonomous County was not obvious, and its spatial correlation with the surrounding areas decreased, thus breaking away from the original low-value agglomeration area. In 2010, the main changes in the local spatial association pattern were that Chenggong District became a county with high-high association, Jinning County became a county with low-high association, and Yongren County and Xinping Yi and Dai Autonomous County became counties with low-low association. The spatial correlations of Wuding County, Chuxiong City, and Luquan Yi and Miao Autonomous County were not significant. The reasons were as follows: After the county was abolished and the district was established, Chenggong District showed a certain degree of synchronization and similarity in economic development and industrial structure with surrounding counties (Kunming urban area), resulting in an increase in carbon emissions from land gradient use and forming the characteristics of high-high association. From 2005 to 2010, the carbon emissions of Jinning County increased by 429,650 tons, and environmental problems were prominent compared with those 10 years ago. At the same time, the adjacent Xishan District and Chenggong District were both high-carbon emission areas, forming an obvious spatial contrast. Therefore, Jinning County showed a low-high clustering distribution. The carbon emissions of Yongren County and Xinping Yi and Dai Autonomous County remained at a low level, and were relatively close to the carbon emission intensities of surrounding counties, forming the characteristics of low-value agglomeration distribution. The spatial correlations between Wuding County, Chuxiong City, and Luquan Yi and Miao Autonomous County and surrounding counties weakened, making them no longer significant. In 2015, Chuxiong City became a county with high-low association, Yiliang County became a county with low-high association, and the spatial correlations of Jinning County and Xinping Yi and Dai Autonomous County were not significant. This indicated that from 2010 to 2015, the urbanization level of Chuxiong City developed rapidly, energy consumption increased, resulting in a significant increase in carbon emissions and forming an obvious contrast with surrounding low-carbon emission areas, thus forming a high-low agglomeration phenomenon. The increase in carbon emissions in Yiliang County was small, and it formed a low-high association distribution characteristic with high-carbon emission counties such as Guandu District and Chenggong District adjacent to it. The spatial correlations between Jinning County and Xinping Yi and Dai Autonomous County and surrounding counties weakened and were no longer significant. In 2020, the carbon emissions from land gradient use in Songming County increased, forming a high-high associated spatial agglomeration characteristic with surrounding counties. Luquan Yi and Miao Autonomous County became a low-value agglomeration area, and the spatial correlations between Fumin County, Songming County, and Yongren County and surrounding counties weakened and became no longer significant.

[0294] Overall, the northwestern region of the central Yunnan urban agglomeration is the main distribution area of the local spatial autocorrelation of carbon emissions from land gradient utilization. Some counties under the jurisdiction of Kunming City, Chuxiong City, and Yuxi City show significant local spatial autocorrelation characteristics. In contrast, the seven counties and cities in Qujing City and Honghe Prefecture are not obvious.

[0295] In addition, as an implementation solution, with reference to Figure 4 , this embodiment also proposes a carbon emission spatial autocorrelation evaluation model, and the carbon emission spatial autocorrelation evaluation model includes:

[0296] A land gradient utilization model 100 for calculating the carbon absorption amount of the area to be evaluated from the dimensions of natural vegetation, crops, and water areas; and calculating the carbon emission amount of the area to be evaluated from the dimensions of energy consumption, industrial production process, waste treatment, agriculture, respiration, and water area carbon volatilization;

[0297] A carbon emission net value calculation module 200 for determining the carbon emission net value of the area to be evaluated according to the difference between the carbon absorption amount and the carbon emission amount;

[0298] A spatial autocorrelation index calculation module 300 for determining the global spatial autocorrelation index and the local spatial autocorrelation index of the area to be evaluated according to the carbon emission net value;

[0299] A carbon emission spatial autocorrelation evaluation module 400 for determining the carbon emission spatial autocorrelation evaluation result of the area to be evaluated according to the global spatial autocorrelation index and the local spatial autocorrelation index.

[0300] As an implementation solution, Figure 5 This is a schematic diagram of the architecture of the hardware operating environment of the carbon emission spatial autocorrelation evaluation system involved in the embodiment solution of this application.

[0301] As Figure 5As shown in the figure, the carbon emission spatial autocorrelation evaluation system may include: a processor 1001, such as a CPU, a memory 1005, a user interface 1003, a network interface 1004, and a communication bus 1002. Among them, the communication bus 1002 is used to implement connection communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0302] Those skilled in the art can understand that Figure 5 the carbon emission spatial autocorrelation evaluation system architecture shown in the figure does not constitute a limitation on the carbon emission spatial autocorrelation evaluation system, and may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0303] As Figure 5 shown in the figure, the memory 1005, as a storage medium, may include an operating system, a network communication module, a user interface module, and a computer program. Among them, the operating system is a program that manages and controls the hardware and software resources of the carbon emission spatial autocorrelation evaluation system, and the operation of the computer program and other software or programs.

[0304] In Figure 5 the carbon emission spatial autocorrelation evaluation system shown in the figure, the user interface 1003 is mainly used to connect to the terminal and perform data communication with the terminal; the network interface 1004 is mainly used to connect to the background server and perform data communication with the background server; the processor 1001 may be used to call the computer program stored in the memory 1005.

[0305] In this embodiment, the carbon emission spatial autocorrelation evaluation system includes: a memory 1005, a processor 1001, and a computer program stored on the memory and executable on the processor, where:

[0306] When the processor 1001 calls the computer program stored in the memory 1005, the following operations are performed:

[0307] Calculate the carbon absorption amount of the area to be evaluated from the dimensions of natural vegetation, crops, and water areas; and calculate the carbon emission amount of the area to be evaluated from the dimensions of energy consumption, industrial production processes, waste treatment, agriculture, respiration, and water area carbon volatilization;

[0308] Determine the net carbon emission of the area to be evaluated according to the difference between the carbon absorption amount and the carbon emission amount;

[0309] Determine the global spatial autocorrelation index and the local spatial autocorrelation index of the area to be evaluated according to the net carbon emission;

[0310] Determine the evaluation result of the spatial autocorrelation of carbon emissions in the area to be evaluated according to the global spatial autocorrelation index and the local spatial autocorrelation index.

[0311] When the processor 1001 calls the computer program stored in the memory 1005, the following operations are performed:

[0312] (1.1) Natural vegetation dimension

[0313] Obtain the carbon sequestration capacity per unit area of the target type vegetation in the area to be evaluated and the land area corresponding to the target type vegetation;

[0314] Calculate the carbon absorption amount of natural vegetation according to the carbon sequestration capacity per unit area and the land area;

[0315] Take the sum of the carbon absorption amounts of natural vegetation corresponding to each target type vegetation as the carbon absorption amount calculated from the natural vegetation dimension;

[0316] (1.2) Crop dimension

[0317] Obtain the biological yield of the target type crops in the area to be evaluated, the carbon absorption rate for synthesizing unit organic matter of the target type crops, and the water content of the target type crops;

[0318] Calculate the photosynthetic carbon absorption amount of the target type crops according to the biological yield, the carbon absorption rate, and the water content;

[0319] Take the sum of the photosynthetic carbon absorption amounts corresponding to each target type crop as the carbon absorption amount calculated from the crop dimension;

[0320] (1.3) Water area dimension

[0321] Obtain the carbon sequestration rate per unit area of water area, the water area, the carbon absorption amount of dry and wet deposition per unit area of water area, and the total area of the area to be evaluated in the area to be evaluated;

[0322] Calculate the carbon absorption amount of the water area according to the carbon sequestration rate per unit area of water area, the water area, the carbon absorption amount of dry and wet deposition per unit area of water area, and the total area of the area to be evaluated;

[0323] Take the carbon absorption amount of the water area as the carbon absorption amount calculated from the water area dimension.

[0324] When the processor 1001 calls the computer program stored in the memory 1005, the following operations are performed:

[0325] (1.4) Energy consumption dimension

[0326] Obtain the consumption, net calorific value, carbon dioxide emission factor, and methane emission factor of the target type of energy in the area to be evaluated. Calculate the carbon emissions from energy consumption based on the consumption, net calorific value, carbon dioxide emission factor, and methane emission factor; and, obtain the consumption, carbon dioxide emission factor, and methane emission factor of the target type of biomass fuel in the area to be evaluated. Calculate the carbon emissions from biomass fuel combustion of the target type of biomass fuel based on the consumption, carbon dioxide emission factor, and methane emission factor; Determine the carbon emissions calculated from the energy consumption dimension based on the sum of the carbon emissions from energy consumption corresponding to each target type of energy and the sum of the carbon emissions from biomass fuel combustion corresponding to each target type of biomass fuel;

[0327] (1.5) Industrial production process dimension

[0328] Obtain the production volume and carbon dioxide emission factor of the target type of industrial product in the area to be evaluated;

[0329] Calculate the industrial production carbon emissions of the target type of industrial product based on the production volume and the carbon dioxide emission factor;

[0330] Take the sum of the industrial production carbon emissions corresponding to each target type of industrial product as the carbon emissions calculated from the industrial production process dimension;

[0331] (1.6) Waste treatment dimension

[0332] Obtain the annual garbage generation volume, annual garbage landfill treatment rate, annual methane recovery volume, oxidation factor, and methane generation potential coefficient of the target type of domestic waste landfill in the area to be evaluated. Calculate the methane emissions of the target type of domestic waste landfill based on the annual garbage generation volume, annual garbage landfill treatment rate, annual methane recovery volume, oxidation factor, and methane generation potential coefficient; and,

[0333] Obtain the incineration treatment volume, carbon content ratio, proportion of mineral carbon in the total carbon, waste combustion efficiency, and carbon dioxide conversion coefficient of the urban domestic waste in the area to be evaluated in the current year. Calculate the carbon dioxide generation amount from the incineration of domestic waste in the area to be evaluated based on the incineration treatment volume, carbon content ratio, proportion of mineral carbon in the total carbon, waste combustion efficiency, and carbon dioxide conversion coefficient; and,

[0334] Obtain the total amount of organic matter, the maximum methane production capacity, the methane correction factor, and the methane recovery amount in the domestic sewage of the area to be evaluated. Calculate the total amount of methane generated from domestic sewage treatment based on the total amount of organic matter, the maximum methane production capacity, the methane correction factor, and the methane recovery amount; and,

[0335] Obtain the total amount of organic matter in the degradable wastewater of the target industrial sector, the total amount of organic matter removed by the sludge method, the methane correction factor, and the methane recovery amount. Calculate the carbon emissions generated from industrial wastewater treatment based on the total amount of organic matter in the degradable wastewater, the total amount of organic matter removed by the sludge method, the methane correction factor, and the methane recovery amount;

[0336] Determine the carbon emissions calculated from the waste treatment dimension based on the sum of the methane emissions from each of the target type domestic waste landfills, the carbon dioxide generated from the incineration of domestic waste, the total amount of methane generated from domestic sewage treatment, and the sum of the carbon emissions generated from industrial wastewater treatment of each of the target industrial sectors;

[0337] (1.7) Agricultural dimension

[0338] Obtain the input amount of the target type of agricultural production materials and the agricultural production material carbon emission factor in the area to be evaluated. Calculate the agricultural production carbon emissions corresponding to the target type of agricultural production materials based on the input amount and the agricultural production material carbon emission factor; and,

[0339] Obtain the planting area of the target type of rice and the methane emission factor in the area to be evaluated. Calculate the methane emissions of the target type of rice based on the rice planting area and the methane emission factor; and,

[0340] Obtain the number of the target type of animals, the methane emission factor for enteric fermentation, and the methane emission factor for manure management in the area to be evaluated. Calculate the carbon emissions from enteric fermentation and manure management of the target type of animals based on the number, the methane emission factor for enteric fermentation, and the methane emission factor for manure management;

[0341] Determine the carbon emissions calculated from the agricultural dimension based on the sum of the agricultural production carbon emissions corresponding to each of the target type of agricultural production materials, the sum of the methane emissions corresponding to each of the target type of rice, and the sum of the carbon emissions from enteric fermentation and manure management of each of the target type of animals;

[0342] (1.8) Respiration dimension

[0343] Obtain the population quantity and human respiration carbon emission factor in the area to be evaluated, as well as the quantity of each target type of livestock and the livestock respiration carbon emission factor. Calculate the human and livestock respiration carbon emissions in the area to be evaluated according to the population quantity, the human respiration carbon emission factor, the quantity of each target type of livestock, and the livestock respiration carbon emission factor;

[0344] Obtain the land area of the target type of vegetation, the autotrophic respiration amount per unit area of plants, and the heterotrophic respiration carbon emissions in the area to be evaluated. Calculate the autotrophic respiration of plants and the heterotrophic respiration carbon emissions of the soil for the target type of vegetation according to the land area, the autotrophic respiration amount per unit area of plants, and the heterotrophic respiration carbon emissions;

[0345] Determine the carbon emissions calculated from the respiration dimension according to the human and livestock respiration carbon emissions and the autotrophic respiration of plants and the heterotrophic respiration carbon emissions of the soil corresponding to each target type of vegetation;

[0346] (1.9) Water area carbon volatilization dimension

[0347] Obtain the area of rivers or lakes in the area to be evaluated and the carbon volatilization factor per unit area of rivers or lakes. Calculate the carbon volatilization amount of the water area, and use the carbon volatilization amount of the water area as the carbon emissions calculated from the water area carbon volatilization dimension.

[0348] When the processor 1001 calls the computer program stored in the memory 1005, the following operations are performed:

[0349] Determine the global autocorrelation evaluation result of the area to be evaluated according to the magnitude relationship between the global spatial autocorrelation index and the preset global index threshold;

[0350] Determine the local autocorrelation evaluation result of the area to be evaluated according to the magnitude relationship between the local spatial autocorrelation index and the preset local index threshold; or,

[0351] Determine the local autocorrelation evaluation result according to the magnitude of the local spatial autocorrelation index.

[0352] When the processor 1001 calls the computer program stored in the memory 1005, the following operations are performed:

[0353] When the global spatial autocorrelation index is positive, determine that the aggregation phenomenon evaluation result is that there is an aggregation phenomenon;

[0354] When the global spatial autocorrelation index is negative, determine that the aggregation phenomenon evaluation result is that there is a dispersion phenomenon;

[0355] When the global spatial autocorrelation index is zero, it is determined that there is no spatial correlation in the aggregation phenomenon evaluation result;

[0356] Moreover, when the local spatial autocorrelation index is positive, it is determined that the aggregation degree evaluation result is high-value / low-value aggregation;

[0357] When the local spatial autocorrelation index is negative, it is determined that the aggregation phenomenon evaluation result is a spatial anomaly;

[0358] When the local spatial autocorrelation index is zero, it is determined that the aggregation phenomenon evaluation result is that there is no spatial correlation.

[0359] In addition, those of ordinary skill in the art can understand that all or part of the processes in the methods of implementing the above embodiments can be completed by instructing relevant hardware through a computer program. This computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the carbon emission spatial autocorrelation evaluation system to implement the process steps of the embodiments of the above method.

[0360] Therefore, the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements each step of the carbon emission spatial autocorrelation evaluation method based on the land gradient utilization model as described in the above embodiments.

[0361] Wherein, the computer-readable storage medium can be various computer-readable storage media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disc that can store program codes.

[0362] It should be noted that since the storage medium provided in the embodiments of the present application is the storage medium adopted for implementing the methods of the embodiments of the present application, those skilled in the art can understand the specific structure and deformation of the storage medium based on the methods introduced in the embodiments of the present application, so it will not be elaborated here. Any storage medium adopted for the methods of the embodiments of the present application belongs to the scope to be protected by the present application.

[0363] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0364] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0365] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0366] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0367] It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim listing several means, several of these means can be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not denote any order. These words can be interpreted as names.

[0368] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0369] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to cover these changes and modifications.

Claims

1. A method for evaluating the spatial autocorrelation of carbon emissions based on a land gradient utilization model, characterized in that The method includes the following steps: Calculating the carbon absorption amount of the area to be evaluated from the dimensions of natural vegetation, crops, and water areas; and calculating the carbon emission amount of the area to be evaluated from the dimensions of energy consumption, industrial production process, waste treatment, agriculture, respiration, and water area carbon volatilization; Determining the net carbon emission value of the area to be evaluated according to the difference between the carbon absorption amount and the carbon emission amount; Determining the global spatial autocorrelation index and the local spatial autocorrelation index of the area to be evaluated according to the net carbon emission value; Determining the evaluation result of the spatial autocorrelation of carbon emissions in the area to be evaluated according to the global spatial autocorrelation index and the local spatial autocorrelation index.

2. The method according to claim 1, wherein The step of calculating the carbon absorption amount of the area to be evaluated from the dimensions of natural vegetation, crops, and water areas includes: (1.1) Dimension of natural vegetation Obtaining the carbon sequestration capacity per unit area of the target type of vegetation in the area to be evaluated and the land area corresponding to the target type of vegetation; Calculating the carbon absorption amount of natural vegetation according to the carbon sequestration capacity per unit area and the land area; Taking the sum of the carbon absorption amounts of natural vegetation corresponding to each target type of vegetation as the carbon absorption amount calculated from the dimension of natural vegetation; (1.2) Dimension of crops Obtaining the biological yield of the target type of crops in the area to be evaluated, the carbon absorption rate for synthesizing unit organic matter of the target type of crops, and the water content of the target type of crops; Calculating the photosynthetic carbon absorption amount of the target type of crops according to the biological yield, the carbon absorption rate, and the water content; Taking the sum of the photosynthetic carbon absorption amounts corresponding to each target type of crops as the carbon absorption amount calculated from the dimension of crops; (1.3) Dimension of water area Obtaining the carbon sequestration rate per unit area of water in the area to be evaluated, the water area, the carbon absorption amount of dry and wet deposition per unit area of water, and the total area of the area to be evaluated; Calculating the carbon absorption amount of the water area according to the carbon sequestration rate per unit area of water, the water area, the carbon absorption amount of dry and wet deposition per unit area of water, and the total area of the area to be evaluated; Taking the carbon absorption amount of the water area as the carbon absorption amount calculated from the dimension of the water area.

3. The method according to claim 1, characterized in that, The step of calculating the carbon emission amount of the area to be evaluated from the dimensions of energy consumption, industrial production process, waste treatment, agriculture, respiration, and water area carbon volatilization specifically includes: (1.4) Dimension of energy consumption Obtain the consumption, net calorific value, carbon dioxide emission factor, and methane emission factor of the target type of energy in the area to be evaluated. Calculate the carbon emissions from energy consumption based on the consumption, net calorific value, carbon dioxide emission factor, and methane emission factor. Also, obtain the consumption, carbon dioxide emission factor, and methane emission factor of the target type of biomass fuel in the area to be evaluated. Calculate the carbon emissions from biomass fuel combustion of the target type of biomass fuel based on the consumption, carbon dioxide emission factor, and methane emission factor. Determine the carbon emissions calculated from the energy consumption dimension based on the sum of the carbon emissions from energy consumption corresponding to each target type of energy and the sum of the carbon emissions from biomass fuel combustion corresponding to each target type of biomass fuel. (1.5) Industrial production process dimension Obtain the production volume and carbon dioxide emission factor of the target type of industrial product in the area to be evaluated. Calculate the industrial production carbon emissions of the target type of industrial product based on the production volume and the carbon dioxide emission factor. Take the sum of the industrial production carbon emissions corresponding to each target type of industrial product as the carbon emissions calculated from the industrial production process dimension. (1.6) Waste treatment dimension Obtain the annual garbage generation volume, annual garbage landfill treatment rate, annual methane recovery volume, oxidation factor, and methane generation potential coefficient of the target type of domestic waste landfill in the area to be evaluated. Calculate the methane emissions of the target type of domestic waste landfill based on the annual garbage generation volume, annual garbage landfill treatment rate, annual methane recovery volume, oxidation factor, and methane generation potential coefficient. Also, Obtain the incineration treatment volume, carbon content ratio, proportion of mineral carbon in the total carbon, garbage combustion efficiency, and carbon dioxide conversion coefficient of the urban domestic waste in the area to be evaluated. Calculate the carbon dioxide generation amount from the incineration of domestic waste in the area to be evaluated based on the incineration treatment volume, carbon content ratio, proportion of mineral carbon in the total carbon, garbage combustion efficiency, and carbon dioxide conversion coefficient. Also, Obtain the total amount of organic matter, maximum methane generation capacity, methane correction factor, and methane recovery volume in the domestic sewage in the area to be evaluated. Calculate the total methane amount generated from domestic sewage treatment based on the total amount of organic matter, maximum methane generation capacity, methane correction factor, and methane recovery volume. Also, Obtain the total amount of organic matter in the biodegradable wastewater, the total amount of organic matter removed by sludge, methane correction factor, and methane recovery volume of the target industrial sector. Calculate the carbon emissions generated from industrial wastewater treatment based on the total amount of organic matter in the biodegradable wastewater, the total amount of organic matter removed by sludge, methane correction factor, and methane recovery volume. Determine the carbon emissions calculated from the waste treatment dimension based on the sum of the methane emissions of each of the target type domestic waste landfills, the carbon dioxide generation amount from the incineration of domestic waste, the total amount of methane generated from the treatment of domestic sewage, and the sum of the carbon emissions generated from the treatment of industrial wastewater in each of the target industrial sectors; (1.7) Agricultural dimension Obtain the input amount of the target type of agricultural production materials and the agricultural production material carbon emission factor in the area to be evaluated, and calculate the agricultural production carbon emissions corresponding to the target type of agricultural production materials according to the input amount and the agricultural production material carbon emission factor; and, Obtain the planting area of the target type of rice and the methane emission factor in the area to be evaluated, and calculate the methane emissions of the target type of rice according to the rice planting area and the methane emission factor; and, Obtain the number of the target type of animals, the enteric fermentation methane emission factor, and the manure management methane emission factor in the area to be evaluated, and calculate the carbon emissions from enteric fermentation and manure management of the target type of animals according to the number, the enteric fermentation methane emission factor, and the manure management methane emission factor; Determine the carbon emissions calculated from the agricultural dimension based on the sum of the agricultural production carbon emissions corresponding to each of the target type of agricultural production materials, the sum of the methane emissions corresponding to each of the target type of rice, and the sum of the carbon emissions from enteric fermentation and manure management of each of the target type of animals; (1.8) Respiration dimension Obtain the population number and the human respiration carbon emission factor in the area to be evaluated, as well as the number of each target type of livestock and the livestock respiration carbon emission factor, and calculate the carbon emissions from human and livestock respiration in the area to be evaluated according to the population number, the human respiration carbon emission factor, the number of each target type of livestock, and the livestock respiration carbon emission factor; Obtain the land area of the target type of vegetation, the autotrophic respiration amount per unit area of plants, and the heterotrophic respiration carbon emissions in the area to be evaluated, and calculate the carbon emissions from autotrophic respiration of plants and soil heterotrophic respiration of the target type of vegetation according to the land area, the autotrophic respiration amount per unit area of plants, and the heterotrophic respiration carbon emissions; Determine the carbon emissions calculated from the respiration dimension based on the carbon emissions from human and livestock respiration and the carbon emissions from autotrophic respiration of plants and soil heterotrophic respiration corresponding to each of the target type of vegetation; (1.9) Water area carbon volatilization dimension Obtain the area of rivers or lakes in the area to be evaluated and the carbon volatilization factor per unit area of rivers or lakes, and calculate the carbon volatilization amount of the water area, and use the carbon volatilization amount of the water area as the carbon emissions calculated from the water area carbon volatilization dimension.

4. The method according to claim 1, wherein The carbon emission spatial autocorrelation evaluation result includes a global autocorrelation evaluation result and a local autocorrelation evaluation result; The step of determining the carbon emission spatial autocorrelation evaluation result of the area to be evaluated according to the global spatial autocorrelation index and the local spatial autocorrelation index specifically includes: Determine the global autocorrelation evaluation result of the area to be evaluated according to the magnitude relationship between the global spatial autocorrelation index and the preset global index threshold; Determine the local autocorrelation evaluation result of the area to be evaluated according to the magnitude relationship between the local spatial autocorrelation index and the preset local index threshold; or, Determine the local autocorrelation evaluation result according to the magnitude of the local spatial autocorrelation index.

5. The method according to claim 1 or 4, characterized in that, The calculation expression of the global spatial autocorrelation index is: where Z1 represents the global spatial autocorrelation index value, n is the number of study units, and x i , x j are the carbon emissions of study unit i and study unit j respectively, is the average carbon emission in the study unit, S 2 is the variance, and w ij is the spatial weight matrix.

6. The method according to claim 1 or 4, characterized in that The calculation expression of the local spatial autocorrelation index is: wherein, Z2 represents the value of the local spatial autocorrelation index, n is the number of cells of the research unit, and x i , x j are the carbon emissions of research unit i and research unit j respectively, is the average value of carbon emissions in the research unit, S 2 is the variance, and w ij is the spatial weight matrix.

7. The method according to claim 1 or 4, characterized in that, The evaluation result of the spatial autocorrelation of carbon emissions includes the global autocorrelation evaluation result and the local autocorrelation evaluation result. The global autocorrelation evaluation result includes whether there is an aggregation phenomenon in the space, whether there is a dispersion phenomenon in the space, and whether there is a spatial correlation in the space. The local autocorrelation evaluation result includes whether there is a high-value / low-value aggregation phenomenon in the space, whether there is an anomaly in the space, and whether there is no spatial correlation; The steps of determining the evaluation result of the spatial autocorrelation of carbon emissions in the area to be evaluated according to the global spatial autocorrelation index and the local spatial autocorrelation index include: When the global spatial autocorrelation index is positive, determine that the aggregation phenomenon evaluation result is that there is an aggregation phenomenon; When the global spatial autocorrelation index is negative, determine that the aggregation phenomenon evaluation result is that there is a dispersion phenomenon; When the global spatial autocorrelation index is zero, determine that the aggregation phenomenon evaluation result is that there is no spatial correlation; And, when the local spatial autocorrelation index is positive, determine that the aggregation degree evaluation result is high-value / low-value aggregation; When the local spatial autocorrelation index is negative, determine that the aggregation phenomenon evaluation result is a spatial anomaly; When the local spatial autocorrelation index is zero, determine that the aggregation phenomenon evaluation result is that there is no spatial correlation.

8. An evaluation model for the spatial autocorrelation of carbon emission space, characterized in that, The evaluation model of the spatial autocorrelation of carbon emissions includes: The land gradient utilization model is used to calculate the carbon absorption amount of the area to be evaluated from the dimensions of natural vegetation, crops, and water areas; and, calculate the carbon emission amount of the area to be evaluated from the dimensions of energy consumption, industrial production process, waste treatment, agriculture, respiration, and carbon volatilization in water areas; The carbon emission net value calculation module is used to determine the carbon emission net value of the area to be evaluated according to the difference between the carbon absorption amount and the carbon emission amount; The spatial autocorrelation index calculation module is used to determine the global spatial autocorrelation index and the local spatial autocorrelation index of the area to be evaluated according to the carbon emission net value; The evaluation module of the spatial autocorrelation of carbon emissions is used to determine the evaluation result of the spatial autocorrelation of carbon emissions in the area to be evaluated according to the global spatial autocorrelation index and the local spatial autocorrelation index.

9. An evaluation system for the spatial autocorrelation of carbon emission space, characterized in that, The evaluation system of the spatial autocorrelation of carbon emissions includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it realizes the steps of the method for evaluating the spatial autocorrelation of carbon emissions based on the land gradient utilization model as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps of the method for evaluating the spatial autocorrelation of carbon emissions based on the land gradient utilization model as described in any one of claims 1 to 7 are implemented.

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