Method for selecting index combination for constructing industrial park carbon emission evaluation model

Abstract

The invention provides a method for selecting an index combination used for constructing an industrial park carbon emission evaluation model, and the method comprises the steps: according to a correlation analysis result, through a stepwise regression analysis method, calculating the index combination from a night light index, a park scale index and a social economic index, and selecting an index combination in which sub-indexes determined to have no correlation with the total amount of carbon emission based on the correlation analysis result are deleted as an industrial park carbon emission evaluation index combination. Therefore, the invention provides the method for selecting the index combination for constructing the industrial park carbon emission evaluation model, and personalized carbon emission evaluation model index selection can be carried out for a specific industrial park.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental management, and particularly relates to a method for selecting an index combination for constructing an industrial park carbon emission assessment model. Background Art

[0002] In the early stage of the research on intensive land use, the research object was agricultural land, and then it was extended to non-agricultural land such as urban construction land. Many scholars have expounded on its connotation from different perspectives. Some scholars believe that intensive land use is affected by various factors such as land use structure and spatial layout, so comprehensive consideration should be given when evaluating intensive land use; some scholars have proposed that close cooperation among land input capital, labor, and technology is required to achieve the best land use effect. The evaluation related to intensive land use covers a wide range of aspects, which are mainly reflected in: the selection of the index system, the selection of methods, and the practical application of research results. The related indicators vary according to research objectives and regions. The more widely used ones include land use structure, land use intensity, land use efficiency, and land use sustainability.

[0003] Establishing a scientific, reasonable, clear-leveled, and highly operable evaluation index system is the central link for carrying out intensive land use evaluation work well. Scholars have explored, established, and improved the intensive land use evaluation index system from different perspectives in order to objectively, truly, and comprehensively reflect the intensive land use level and provide a reference for scientifically formulating land resource management policies.

[0004] The concept of "evaluating heroes by per mu output" originated from the "per mu yield" in agriculture. Briefly speaking, it means "not determining the standard by scale, but evaluating heroes by per mu output". "Evaluating heroes by per mu output" takes the input and output of per mu of unit land as the evaluation standard. Through comprehensive evaluation of per mu efficiency and differential allocation of resource elements, advanced models are established to obtain the maximum output benefit at the smallest resource and environmental cost and promote high-quality development. The direct goal of establishing an intensive land use evaluation system of "evaluating heroes by per mu output" is to establish a guiding mechanism for high-quality development of the manufacturing industry. From a macro perspective, it can specifically evaluate the resource utilization efficiency of different regions, parks, and industries, and each region can also refer to the evaluation results to formulate local high-quality development guiding or restrictive goals; from a micro perspective, it can provide precise and preferential support for enterprises that are environmentally friendly, resource-saving, and leading in efficiency, establish benchmarks for high-quality development, and encourage more enterprises to follow the path of high-quality development. Currently, under the framework of the "evaluating heroes by per mu output" system reform, the intensive land use evaluation index systems of various provinces in China are relatively mature and widely applied.

[0005] Carbon emissions are the main driver of anthropogenic climate change, so the issue of carbon emissions in industrial activities has attracted attention. Industrial parks are important engines for regional economic development and important carriers for reshaping the industrial economic geography. With a large investment intensity per mu and a high output intensity, they often overlook the negative external effects of increased carbon emissions. A large number of parks still have the situation of high pollution, high energy consumption, and high emissions, and these parks urgently need to transform towards low-carbon and zero-carbon. Therefore, as the main source of urban carbon emissions, estimating the carbon emissions of urban industrial parks is of great significance for reducing carbon emissions and reasonably planning urban industrial development.

[0006] However, most of the current research on carbon emission inversion modeling focuses on the regional / provincial / city level, with little research on the industrial park scale, and there is a lack of rapid and accurate carbon emission monitoring and assessment technologies for industrial parks. Traditional carbon emission estimation methods usually rely on on-site surveys and statistical data. Although these methods provide rich information, they have inherent limitations, such as limited accessibility, high difficulty in data collection, and limited spatial coverage. With the combination of satellite images and remote sensing technology becoming increasingly important in the research of the ecological environment, although some scholars have carried out relevant research, for example: inversing land use through satellite remote sensing data, overlaying traffic and industrial patterns, and planning low-carbon spatial patterns; or using night light and land use data to measure carbon emissions is also common. However, the carbon emission assessment at the entity level is not precise enough to balance the profit and loss relationship between the development of industrial-dominated industrial parks and carbon emissions. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a method for selecting an index combination for constructing an industrial park carbon emission assessment model to be able to perform personalized selection of the index of the carbon emission assessment model for a specific industrial park in view of the above-mentioned defects existing in the prior art.

[0008] According to the present invention, there is provided a method for selecting an index combination for constructing an industrial park carbon emission assessment model, including:

[0009] Obtain the night-time lighting indicators of the industrial park. Among them, the sub-indicators of the night-time lighting indicators include total lighting intensity, average lighting intensity, normalized lighting intensity, and lighting intensity per unit area. The obtaining of the night-time lighting indicators of the industrial park includes: obtaining satellite remote sensing data of the area corresponding to the industrial park; correcting the RGB bands of the satellite remote sensing data and obtaining the pixel information of the pixels within the area corresponding to the industrial park, where the pixel information includes: pixel value and number of pixels; calculating the sum of the pixel values of all pixels within the area corresponding to the industrial park to obtain the total lighting intensity; calculating the ratio of the sum of the pixel values of all pixels within the area corresponding to the industrial park to the number of pixels to obtain the average lighting intensity; calculating the sum of the ratios of the pixel value of a single pixel within the area corresponding to the industrial park to the pixel value of the maximum pixel to obtain the normalized lighting intensity; calculating the ratio of the sum of the pixel values of all pixels within the area corresponding to the industrial park to the area of the region to obtain the lighting intensity per unit area.

[0010] Obtain the park scale indicators. The sub-indicators of the park scale indicators of the industrial park include: total park area, area of land used for logistics and warehousing, area of land used for commercial service facilities, and area of industrial land.

[0011] Obtain the social and economic indicators. The sub-indicators of the social and economic indicators of the industrial park include: number of enterprises, average annual number of employees, total annual tax revenue, and total annual fixed asset investment.

[0012] Calculate the total carbon emissions of the industrial park, including: calculating the product of the consumption of fossil fuels in the industrial park, the carbon content of the fossil fuels, and the carbon oxidation rate to obtain the carbon emissions from fossil fuels in the industrial park; calculating the product of the consumption of electricity in the industrial park and the carbon dioxide emission factor of electricity supply to obtain the carbon emissions from electricity in the industrial park; calculating the product of the consumption of heat in the industrial park and the carbon dioxide emission factor of heat supply to obtain the carbon emissions from heat in the industrial park; calculating the product of the heat of hot water in the industrial park and the global warming potential value of methane to obtain the carbon emissions from waste treatment in the industrial park; based on the sum of the carbon emissions from fossil fuels, the carbon emissions from electricity, the carbon emissions from heat, and the carbon emissions from waste treatment, obtain the total carbon emissions of the industrial park.

[0013] Calculate the correlation coefficients between the sub - indicators of the night - light index, the sub - indicators of the park - scale index, and the sub - indicators of the socio - economic index and the total carbon emissions respectively using the linear regression algorithm; calculate the importance degrees of the sub - indicators of the night - light index to the total carbon emissions, the sub - indicators of the park - scale index to the total carbon emissions, and the sub - indicators of the socio - economic index to the total carbon emissions respectively using the random forest algorithm.

[0014] According to the results of the correlation analysis, through the step - by - step regression analysis method, select the index combination that deletes the sub - indicators determined to have no correlation with the total carbon emissions based on the results of the correlation analysis from the night - light index, the park - scale index, and the socio - economic index as the industrial park carbon emission assessment index combination, including:

[0015] Construct a step - by - step regression model.

[0016] Add the sub - indicators of the night - light index, the park - scale index, and the socio - economic index that are correlated with the total carbon emissions to the step - by - step regression model in descending order of their importance degrees to the total carbon emissions.

[0017] After adding each current sub - indicator, calculate the correlation coefficient between the currently added sub - indicator combination and the step - by - step regression model.

[0018] If the correlation coefficient is less than the preset threshold, delete the current sub - indicator.

[0019] If the correlation coefficient is greater than or equal to the preset threshold, continue to add the next sub - indicator until all the sub - indicators of the night - light index, the park - scale index, and the socio - economic index are traversed.

[0020] Select the sub - indicator combination with the largest correlation coefficient.

[0021] Preferably, the formulas for calculating the total light intensity (I), average light intensity (M), normalized light intensity (N), and light intensity per unit area (K) are as follows:

[0022]

[0023] Among them, DN i is the pixel value of each pixel in the area corresponding to the i - th industrial park, n is the number of pixels in the area corresponding to the i - th industrial park, DN max is the maximum pixel value in the area corresponding to the i - th industrial park, and s is the total area of the area corresponding to the i - th industrial park.

[0024] Preferably, the calculation formula for the carbon emissions of fossil fuels in the industrial park is as follows:

[0025] ECO 2-燃料 = ΣAD j × CC j × OF j × 44 ÷ 12

[0026] Where, ECO 2-燃料 is the CO2 emissions of the main fossil fuels in the industrial park, with the unit of ton; j is the type of fossil fuel; AD j is the consumption of the j-th fuel, with the unit of ton for solid and liquid fuels, and 10,000 Nm 3 for gas; CC j is the carbon content of the j-th fuel, with the unit of ton carbon / ton fuel for solid fuels, and ton carbon / 10,000 Nm 3 for gas; OF j is the carbon oxidation rate of fuel j, with the value range of 0 to 1.

[0027] Preferably, the calculation formula for the carbon emissions of electricity in the industrial park is as follows:

[0028] ECO 2-净电 = AD 电力 × EI 电

[0029] Where, ECO 2-净电 is the CO2 emissions implicit in the net purchased electricity in the industrial park, with the unit of ton CO2; AD 电力 is the net purchased electricity consumption of the enterprise, with the unit of MWh; EI 电 is the CO2 emission factor of electricity supply, taking 0.1031, with the unit of ton CO2 / MWh.

[0030] Preferably, the calculation formula for the carbon emissions of heat in the industrial park is as follows:

[0031] ECO 2-净热 = AD 热力 × EI 热

[0032] Where, ECO 2-净热 is the CO2 emissions implicit in the net purchased heat in the industrial park, with the unit of ton CO2; AD 热力 is the net purchased heat consumption of the enterprise, including hot water heat and steam heat, with the unit of GJ; EI 热 is the CO2 emission factor of heat supply, taking 0.11, with the unit of ton CO2 / GJ;

[0033] AD 热水 = Maw × (Tw - 20) × 4.1868 × 10 -3

[0034] AD 蒸汽 = Mast × (Enst - 83.74) × 10 -3

[0035] Wherein, AD 热水 is the heat of hot water, with the unit of GJ; AD 蒸汽 is the heat of steam, with the unit of GJ; Maw is the mass of hot water, with the unit of ton of hot water; Mast is the mass of steam, with the unit of ton of steam; Tw is the specific heat of water under normal temperature and pressure, with the unit of kJ / (kg·°C); Enst is the enthalpy of each kilogram of steam at the corresponding temperature and pressure of the steam, with the unit of kJ / kg.

[0036] Preferably, the calculation formula for the carbon emissions of the industrial park is as follows:

[0037] EGHG 废水 = ECH 4-废水 × GWPCH4 × 10 -3

[0038] Wherein, EGHG 废水 is the carbon dioxide emission equivalent generated during the anaerobic treatment of wastewater, with the unit of ton of carbon dioxide equivalent (tCO2); GWPCH4 is the global warming potential (GWP) value of methane,

[0039] taking 21;

[0040] ECH 4-废水 = (TOW - S) × EF - R

[0041] Wherein: ECH 4-废水 is the heat of hot water, with the unit of GJ; TOW is the total amount of organic matter removed by the anaerobic treatment of wastewater; S is the total amount of organic matter removed in the form of sludge (kilogram COD); EF is the methane emission factor; R is the methane recovery amount.

[0042] Preferably, the number of pixels n in the area corresponding to the industrial park and the pixel value DN of each pixel, i.e., the gray value Gray, are calculated as follows:

[0043] Gray = 0.1140 * B + 0.5870 * G + 0.2989 * R

[0044] Wherein, R is the brightness of the red band in the RGB three bands, G is the brightness of the green band in the RGB three bands, and B is the brightness of the blue band in the RGB three bands.

[0045] The present invention provides a method for selecting an index combination for constructing a carbon emission assessment model of an industrial park, which can perform personalized selection of carbon emission assessment model indexes for a specific industrial park. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 is a schematic flowchart of a method for constructing an industrial park carbon emission assessment model according to an embodiment of the present invention;

[0048] Figure 2 is a schematic processing flowchart of a method for constructing an industrial park carbon emission assessment model according to an embodiment of the present invention;

[0049] Figure 3 is a schematic flowchart of a method for constructing another industrial park carbon emission assessment model according to an embodiment of the present invention;

[0050] Figure 4 is a schematic diagram of the carbon emission sources of a method for constructing another industrial park carbon emission assessment model according to an embodiment of the present invention;

[0051] Figure 5 is a schematic flowchart of a method for constructing yet another industrial park carbon emission assessment model according to an embodiment of the present invention;

[0052] Figure 6 is a schematic diagram of the importance degree of model indicators of a method for constructing yet another industrial park carbon emission assessment model according to an embodiment of the present invention;

[0053] Figure 7 is a schematic flowchart of a method for constructing still another industrial park carbon emission assessment model according to an embodiment of the present invention;

[0054] Figure 8 is a structural block diagram of a device for constructing an industrial park carbon emission assessment model according to an embodiment of the present invention;

[0055] Figure 9 is a schematic hardware structure diagram of a computer device according to an embodiment of the present invention.

[0056] Figure 10 is the research technical route in a specific example of the present invention.

[0057] Figure 11 is the data of the third land use survey in Chengdu in a specific example of the present invention.

[0058] Figure 12It is the satellite data adopted in the specific example of the present invention.

[0059] Figure 13 It is the NPP inversion method process adopted in the specific example of the present invention.

[0060] Figure 14 It is the map of the classification results of the industrial functional areas in Chengdu adopted in the specific example of the present invention.

[0061] Figure 15 It is the NPP calculation result in Chengdu in the specific example of the present invention.

[0062] Figure 16 It is the comparison chart of the total carbon emissions statistics and the total carbon emissions per unit area statistics of the industrial functional areas in Chengdu in the specific example of the present invention.

[0063] Figure 17 It is the total carbon emissions of the industrial functional areas in Chengdu in the specific example of the present invention.

[0064] Figure 18 It is the total carbon emissions per unit area of the industrial functional areas in Chengdu in the specific example of the present invention.

[0065] Figure 19 It is the comparison chart of the total carbon emissions statistics and the total carbon emissions per unit area statistics of the industrial-dominated industrial functional areas in Chengdu in the specific example of the present invention.

[0066] Figure 20 It is the comparison chart of the total carbon sink volume and the carbon sink volume per unit area of the first-class industrial-dominated industrial functional areas in Chengdu in the specific example of the present invention.

[0067] Figure 21 It is the comparison chart of the net carbon emissions statistics and the net carbon emissions per unit area statistics of the industrial-dominated industrial functional areas in Chengdu in the specific example of the present invention.

[0068] Figure 22 It is the comparison chart of the net carbon emissions statistics and the net carbon emissions per unit area statistics of the first-class industrial-dominated industrial functional areas in Chengdu in the specific example of the present invention.

[0069] Figure 23 It is the comparison chart of the net carbon emissions statistics and the net carbon emissions per unit area statistics of the second- and third-class industrial-dominated industrial functional areas in Chengdu in the specific example of the present invention.

[0070] Figure 24 It is the comparison of the land development rate of the industrial-dominated industrial functional areas in the specific example of the present invention.

[0071] Figure 25 It is the comparison of the land supply rate of the industrial-dominated industrial functional areas in the specific example of the present invention.

[0072] Figure 26 It is the comparison of the land completion rate of the industry-dominated industrial functional area in the specific example of the present invention.

[0073] Figure 27 It is the comparison of the industrial land rate of the industry-dominated industrial functional area in the specific example of the present invention.

[0074] Figure 28 It is the comparison of the comprehensive plot ratio of the industry-dominated industrial functional area in the specific example of the present invention.

[0075] Figure 29 It is the comparison of the building density of the industry-dominated industrial functional area in the specific example of the present invention.

[0076] Figure 30 It is the comparison of the comprehensive plot ratio of the industrial land in the industry-dominated industrial functional area in the specific example of the present invention.

[0077] Figure 31 It is the comparison of the building coefficient of the industrial land in the industry-dominated industrial functional area in the specific example of the present invention.

[0078] Figure 32 It is the comparison of the fixed asset investment intensity of the industrial land in the industry-dominated industrial functional area in the specific example of the present invention.

[0079] Figure 33 It is the regression result of sub-indicators such as land development rate, land to-be-developed rate, land supply rate, and land completion rate on net carbon emissions in the specific example of the present invention.

[0080] Figure 34 It is the impact of the degree of land use on carbon emissions in the specific example of the present invention.

[0081] Figure 35 It is the impact of the land use structure on carbon emissions in the specific example of the present invention.

[0082] Figure 36 It is the impact of the sub-indicators of land use intensity on carbon emissions in the specific example of the present invention.

[0083] Figure 37 It is the impact of land use intensity on carbon emissions in the specific example of the present invention.

[0084] Figure 38 It is the impact of the sub-indicators of land use efficiency on carbon emissions in the specific example of the present invention.

[0085] Figure 39 It is the impact of land use efficiency on carbon emissions in the specific example of the present invention.

[0086] Figure 40 It is the impact of the performance of land use supervision on carbon emissions in the specific example of the present invention.

[0087] Figure 41 It is the impact of the sub - index of land use degree in the specific example of the present invention on carbon emissions.

[0088] Figure 42 It is the impact of land use degree in the specific example of the present invention on carbon emissions.

[0089] Figure 43 It is the impact of the land use structure status in the specific example of the present invention on carbon emissions.

[0090] Figure 44 It is the impact of the sub - index of land use intensity in the specific example of the present invention on carbon emissions.

[0091] Figure 45 It is the impact of land use intensity in the specific example of the present invention on carbon emissions.

[0092] Figure 46 It is the impact of the sub - index of land use benefit in the specific example of the present invention on carbon emissions.

[0093] Figure 47 It is the impact of land use benefit in the specific example of the present invention on carbon emissions.

[0094] Figure 48 It is the impact of land use supervision performance in the specific example of the present invention on carbon emissions.

[0095] Figure 49 It is the impact of the sub - index of land use degree in the specific example of the present invention on carbon emissions.

[0096] Figure 50 It is the impact of land use degree in the specific example of the present invention on carbon emissions.

[0097] Figure 51 It is the impact of the land use structure status in the specific example of the present invention on carbon emissions.

[0098] Figure 52 It is the impact of the sub - index of land use intensity in the specific example of the present invention on carbon emissions.

[0099] Figure 53 It is the impact of land use intensity in the specific example of the present invention on carbon emissions.

[0100] Figure 54 It is the impact of the sub - index of land use benefit in the specific example of the present invention on carbon emissions.

[0101] Figure 55 It is the impact of land use benefit in the specific example of the present invention on carbon emissions.

[0102] Figure 56 It is the impact of land use supervision performance on carbon emissions.

[0103] Figure 57 It is the coupling relationship between the land intensive use level and carbon emissions in the industrial-dominated industrial functional area in a specific example of the present invention.

[0104] Figure 58 It is the coupling relationship between the land intensive use level and carbon emissions in a certain type of industrial-dominated industrial functional area in a specific example of the present invention.

[0105] Figure 59 It is the coupling relationship between the land intensive use level and carbon emissions in the second and third types of industrial-dominated industrial functional areas in a specific example of the present invention.

[0106] It should be noted that the drawings are used to illustrate the present invention rather than limit it. Note that the drawings showing the structure may not be drawn to scale. And in the drawings, the same or similar elements are labeled with the same or similar reference numerals. Detailed implementation manners

[0107] The technical solutions of the present invention will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0108] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0109] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, the terms "installed", "connected", "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0110] In addition, the technical features involved in different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0111] The present invention uses, for example, the list statistical analysis method to estimate the total carbon emissions of typical industrial functional areas, and further uses multiple regression modeling to estimate the carbon emissions efficiency per unit area of land. The evaluation results of carbon emissions efficiency and land intensive use efficiency are correlated and analyzed to reveal the relationship between the intensive use efficiency of industrial land and carbon emissions efficiency in the core starting area.

[0112] The present invention provides a method for selecting an index combination for constructing an industrial park carbon emissions assessment model, which can perform personalized selection of indexes for the carbon emissions assessment model of a specific industrial park.

[0113] Specifically, in this embodiment, a method for constructing an industrial park carbon emissions assessment model is provided, which can be used for the above-mentioned computer, such as a computer. Figure 1 FIG. is a flowchart of the method for constructing an industrial park carbon emissions assessment model according to an embodiment of the present invention, in which the method for selecting an index combination for constructing an industrial park carbon emissions assessment model proposed by the present invention is adopted. Specifically, as Figure 1 shown, the process includes the following steps:

[0114] Step S101, obtain the night light index, park scale index and socio-economic index of the industrial park.

[0115] Specifically, in the embodiment of the present invention, in order to quantitatively evaluate the carbon emissions in the actual production of the industrial park, taking an industrial park in City B, Province A as an example, 20 industrial parks with different industrial types are selected for questionnaire surveys. The selected industrial parks are mainly industrial, with a high degree of land use intensity and relatively high carbon emissions, and are highly representative, but not limited thereto. According to the questionnaire survey results, indexes related to the actual production of the industrial park are selected for modeling, including the night light index, park scale index and socio-economic index. Among them, the night light index can reflect the lighting usage situation of the industrial park, and the lighting usage situation is correlated with carbon emissions; the park scale index can also reflect the development status of the industrial park. Generally, the better the development status of the industrial park, the larger the scale, and at the same time, the relatively higher the carbon emissions. Therefore, the park scale index is correlated with carbon emissions; the socio-economic index reflects the economic situation of the industrial park, and the economic situation is closely related to the development status. Therefore, the socio-economic index is correlated with carbon emissions.

[0116] In some alternative embodiments, such as Figure 2As shown in the figure, the embodiments of the present invention utilize the satellite remote sensing data obtained by the thermal infrared imager carried by SDGSAT-1 to calculate the night light index of the industrial park. The sub-indices of the night light index include: total light intensity, average light intensity, normalized light intensity, and light intensity per unit area. According to the relevant data of the industrial park, the park scope and land utilization rate of the industrial park are obtained, so as to obtain the relevant data of the park scale index. The sub-indices of the park scale index include: total park area, area of land used for logistics and warehousing, area of land used for commercial service facilities, and area of industrial land. The selection types and acquisition methods of the above sub-indices may vary in different cities. Only for example, but not limited thereto.

[0117] Step S102, calculate the total carbon emissions of the industrial park.

[0118] Specifically, in the embodiments of the present invention, through a questionnaire survey of 20 industrial parks, it is summarized that the production of industrial parks generally involves processes such as energy production, transportation, manufacturing, and waste treatment. Industrial parks usually generate carbon dioxide during the processes of fossil fuel energy supply, electricity supply, heat supply, and waste treatment. Therefore, as Figure 2 shown, the embodiments of the present invention obtain the consumption of fossil fuels, electricity, and heat, as well as the amount of waste treatment in the industrial park. Among them, fossil fuels include natural gas and liquefied petroleum gas, but not limited thereto. The embodiments of the present invention convert the consumption of fossil fuels, electricity, and heat, as well as the amount of waste treatment into carbon dioxide emissions according to the pre-obtained carbon emission conversion parameters, so as to obtain the total carbon emissions of the industrial park.

[0119] In some optional embodiments, the embodiments of the present invention can establish a data management system to collect and store the social economy, park scale, input-output, and high-resolution night light data of the industrial park, so as to determine the relevant values of the night light index, park scale index, social economy index, and total carbon emissions.

[0120] Step S103, analyze the correlations between the night light index and the total carbon emissions, between the park scale index and the total carbon emissions, and between the social economy index and the total carbon emissions.

[0121] Specifically, in the embodiments of the present invention, night light indicators, park scale indicators, and socio-economic indicators are selected according to the correlation between the actual production situation of the industrial park and carbon emissions. However, it is not determined whether the sub-indicators included in each indicator are truly related to carbon emissions. Therefore, the present invention analyzes the correlations between the night light indicators and the total carbon emissions, between the park scale indicators and the total carbon emissions, and between the socio-economic indicators and the total carbon emissions, so as to determine whether the selected sub-indicators are related to carbon emissions. If they are not related, the sub-indicators are deleted, and only the related sub-indicators are retained.

[0122] Step S104, select an indicator combination from the night light indicators, park scale indicators, and socio-economic indicators according to the correlation analysis results, and construct a carbon emission assessment model based on the indicator combination.

[0123] Specifically, in the embodiments of the present invention, the sub-indicators in the night light indicators, park scale indicators, and socio-economic indicators are sequentially input into the stepwise regression model to select the indicator combination most relevant to carbon emissions, and then a carbon emission assessment model is constructed based on the indicator combination. Among them, the stepwise regression analysis method is based on linear regression. Variables are introduced one by one. After introducing a new variable, the old variables already selected in the regression model are tested one by one, and the variables considered meaningless are deleted until no new variable is introduced and no old variable is deleted, so as to ensure that each variable in the regression model is meaningful.

[0124] In some alternative embodiments, the embodiments of the present invention use the collected data to analyze the carbon emission pattern and determine the main accounting method of carbon emissions, construct a carbon emission estimation model, and the obtained carbon emission assessment model can analyze the trend of carbon emissions in the industrial park over time.

[0125] The method for constructing a carbon emission assessment model of an industrial park provided by the embodiments of the present invention obtains the night light indicators, park scale indicators, socio-economic indicators, and total carbon emissions of the industrial park, analyzes the correlations between the night light indicators and the total carbon emissions, between the park scale indicators and the total carbon emissions, and between the socio-economic indicators and the total carbon emissions, and selects an indicator combination from the night light indicators, park scale indicators, and socio-economic indicators according to the correlation analysis results to construct a carbon emission assessment model. By analyzing the correlations between various indicators in the actual production of the industrial park and carbon emissions, the present invention can construct a carbon emission assessment model based on the selected indicators strongly related to carbon emissions, so as to accurately assess the carbon emissions in the actual production of the industrial park, and further provide a scientific basis for balancing the profit and loss relationship between the development of the industrial park and carbon emissions.

[0126] In this embodiment, a method for constructing a carbon emission assessment model of an industrial park is provided, which can be used in the above-mentioned computer, such as a computer.Figure 3 is a flowchart of a method for constructing an industrial park carbon emission assessment model according to an embodiment of the present invention. As Figure 3 shown, the process includes the following steps:

[0127] Step S301, obtain the night light index, park scale index, and socioeconomic index of the industrial park.

[0128] Specifically, the above step S301 includes:

[0129] Step S3011, obtain satellite remote sensing data including the area corresponding to the industrial park.

[0130] Specifically, in the embodiment of the present invention, satellite remote sensing data of the area corresponding to the industrial park obtained by the thermal infrared imager carried by SDGSAT-1 is obtained. Generally, the night lights in urban areas gradually decrease from the city center to the outskirts of the city, and industrial parks are often distributed in the suburbs with lower brightness. Therefore, higher requirements are put forward for the resolution of satellite remote sensing data. Coarse-resolution satellite remote sensing data is difficult to accurately monitor carbon emissions at the industrial park scale. Therefore, high-resolution night light data is of great significance for the monitoring of industrial parks. In the embodiment of the present invention, the spatial resolution of the SDGSAT-1 satellite remote sensing data in a certain year is 30m, which is higher than that of most other night light images. It can measure surface human activities with high precision and resolution and has advantages in the field of industrial park carbon emission inversion, thus providing a scientific reference for optimizing industrial layout, urban carbon emission reduction, and carbon neutrality.

[0131] Step S3012, correct the RGB bands of the satellite remote sensing data and obtain the pixel information of the pixels in the area corresponding to the industrial park. The pixel information includes: pixel value and pixel count.

[0132] Specifically, in the embodiment of the present invention, the SDGSAT-1 satellite remote sensing data is stored in RGB three bands. For the subsequent construction of the carbon emission inversion model, the Gamma correction algorithm is used to correct the RGB bands of the satellite, and the pixel count n of the pixels in the area corresponding to different industrial parks among 20 industrial parks and the pixel value DN (i.e., gray value Gray) of each pixel are obtained. The calculation formula is as follows:

[0133] Gray = 0.1140 * B + 0.5870 * G + 0.2989 * R

[0134] where R is the brightness of the red band in the RGB three bands, G is the brightness of the green band in the RGB three bands, and B is the brightness of the blue band in the RGB three bands.

[0135] Step S3013: Calculate the sum of the pixel values of all pixels within the area corresponding to the industrial park to obtain the total light intensity; calculate the ratio of the sum of the pixel values of all pixels within the area corresponding to the industrial park to the number of pixels to obtain the average light intensity; calculate the sum of the ratios of the pixel value of a single pixel within the area corresponding to the industrial park to the pixel value of the maximum pixel to obtain the normalized light intensity; calculate the ratio of the sum of the pixel values of all pixels within the area corresponding to the industrial park to the area of the region to obtain the light intensity per unit area.

[0136] Specifically, in the embodiments of the present invention, the formulas for calculating the total light intensity (I), average light intensity (M), normalized light intensity (N), and light intensity per unit area (K) based on the number of pixels and pixel values within a certain industrial park are as follows:

[0137]

[0138] Where, DN i is the pixel value of each pixel within the area corresponding to the i-th industrial park, n is the number of pixels within the area corresponding to the i-th industrial park, DN max is the maximum pixel value within the area corresponding to the i-th industrial park, and s is the total area of the area corresponding to the i-th industrial park.

[0139] In some alternative embodiments, there are differences in the night light indicators of industrial parks of different industrial types, but they all have a high correlation with carbon emissions, and the information provided by different indicators for carbon emissions will vary. The night light indicators of industrial parks are obtained in the embodiments of the present invention to invert the carbon emissions of this industrial park. In addition, by combining SDGSAT-1 satellite remote sensing data with data such as land use, energy consumption, and social economy, the carbon emissions of a specific region or area can be comprehensively understood, and the accuracy of the carbon emission model can be improved.

[0140] Step S302: Calculate the total carbon emissions of the industrial park.

[0141] Specifically, the above step S302 includes:

[0142] Step S3021: Calculate the product of the consumption of fossil fuels, the carbon content of fossil fuels, and the carbon oxidation rate in the industrial park to obtain the carbon emissions from fossil fuels in the industrial park.

[0143] Specifically, in the embodiments of the present invention, carbon emission accounting indicators, indicator calculation methods, and parameters applicable to industrial parks are summarized and sorted in advance through the historical production capacity data and relevant literature of industrial parks, and based on this, the carbon emissions of industrial parks are accounted. During the production period of industrial parks, it is inevitable to use fossil fuels for energy supply, such as natural gas, liquefied petroleum, and other types of fuels, but not limited thereto. The embodiments of the present invention obtain the consumption of different types of fossil fuels in industrial parks, and combined with the carbon emission conversion parameters of fossil fuels, can calculate the carbon emissions of fossil fuels in industrial parks. The calculation formula is as follows:

[0144] ECO 2-燃料 = ∑AD j ×CC j ×OF j ×44÷12

[0145] Wherein, ECO 2-燃料 is the CO2 emission of the main fossil fuels in the industrial park, with the unit of ton; j is the type of fossil fuel; AD j is the consumption of the jth fuel, with the unit of ton for solid and liquid, and 10,000 Nm 3 for gas; CC j is the carbon content of the jth fuel, with the unit of ton carbon / ton fuel for solid, and ton carbon / 10,000 Nm 3 for gas; OF j is the carbon oxidation rate of fuel j, and the value range is 0 to 1.

[0146] Step S3022: Calculate the product of the electricity consumption of the industrial park and the carbon dioxide emission factor of electricity supply to obtain the carbon emissions of electricity in the industrial park.

[0147] Specifically, in the embodiments of the present invention, during the production period of industrial parks, electricity is also used for energy supply. Therefore, by obtaining the electricity consumption of industrial parks and combining the carbon emission conversion parameters of electricity, the carbon emissions of electricity in industrial parks can be calculated. The calculation formula is as follows:

[0148] ECO 2-净电 = AD 电力 ×EI 电

[0149] Wherein, ECO 2-净电 is the CO2 emission implicit in the net purchased electricity of the industrial park, with the unit of ton CO2; AD 电力 is the net purchased electricity consumption of the enterprise, with the unit of MWh; EI 电 is the CO2 emission factor of electricity supply, taking 0.1031, with the unit of ton CO2 / MWh.

[0150] Step S3023: Calculate the product of the heat consumption in the industrial park and the carbon dioxide emission factor of heat supply to obtain the heat carbon emissions of the industrial park.

[0151] Specifically, in the embodiments of the present invention, during the production period of the industrial park, heat energy supply is also used, including hot water supply and steam supply. Therefore, by obtaining the heat consumption in the industrial park and combining it with the carbon emission conversion parameters of heat, the heat carbon emissions of the industrial park can be calculated. The calculation formula is as follows:

[0152] ECO 2-净热 = AD 热力 × EI 热

[0153] Where, ECO 2-净热 is the CO2 emission implicit in the net purchased heat in the industrial park, with the unit of ton CO2; AD 热力 is the net purchased heat consumption of the enterprise, including hot water heat and steam heat, with the unit of GJ; EI 热 is the CO2 emission factor of heat supply, taking 0.11, with the unit of ton CO2 / GJ.

[0154] AD 热水 = Maw × (Tw - 20) × 4.1868 × 10 -3

[0155] AD 蒸汽 = Mast × (Enst - 83.74) × 10 -3

[0156] Where, AD 热水 is the hot water heat, with the unit of GJ; AD 蒸汽 is the steam heat, with the unit of GJ; Maw is the mass of hot water, with the unit of ton hot water; Mast is the mass of steam, with the unit of ton steam; Tw is the specific heat of water at normal temperature and pressure, with the unit of kJ / (kg·°C); Enst is the heat enthalpy per kilogram of steam at the corresponding temperature and pressure of steam, with the unit of kJ / kg.

[0157] Step S3024: Calculate the product of the hot water heat in the industrial park and the global warming potential value of methane to obtain the waste treatment carbon emissions of the industrial park.

[0158] Specifically, in the embodiments of the present invention, during the production period of the industrial park, the generated waste is also treated, and carbon dioxide will be generated during the treatment process. For example, carbon dioxide generated during the anaerobic treatment of wastewater, but not limited thereto. Therefore, the present invention also takes into account the waste treatment carbon emissions when calculating carbon emissions, making the carbon emission assessment of the industrial park more accurate. The calculation formula is as follows:

[0159] EGHG 废水 = ECH 4-废水 ×GWPCH4×10 -3

[0160] wherein, EGHG 废水 is the carbon dioxide emission equivalent generated during the anaerobic treatment of wastewater, with the unit of ton carbon dioxide equivalent (tCO2); GWPCH4 is the global warming potential (GWP, Global Warming Potential) value of methane, taking 21.

[0161] ECH 4-废水 = (TOW - S) × EF - R

[0162] wherein: ECH 4-废水 is the heat of hot water, with the unit of GJ; TOW is the total amount of organic matter removed during the anaerobic treatment of wastewater (kilogram COD); S is the total amount of organic matter removed in the form of sludge (kilogram COD); EF is the methane emission factor (kilogram methane / kilogram COD), taking 0.25; R is the methane recovery amount (kilogram methane), taking 0.5.

[0163] Step S3025, based on the sum of the carbon emissions of fossil fuels, and / or the carbon emissions of electricity, and / or the carbon emissions of heat, and / or the carbon emissions of waste treatment, obtain the total carbon emissions of the industrial park.

[0164] Specifically, in the embodiments of the present invention, according to the actual production situation of the industrial park, determine whether there is fossil fuel energy supply, electricity energy supply, heat energy supply or waste treatment, and calculate according to steps S3021 - S3024. Based on the sum of the carbon emissions of fossil fuels, electricity, heat and waste treatment, obtain the total carbon emissions of the industrial park. There are differences in the carbon emissions of industrial parks of different industrial types. In the embodiments of the present invention, 20 industrial parks are divided into 7 types according to industrial type. Among them, the industrial parks in the semiconductor industry have the largest average carbon emissions. For 4 semiconductor industrial parks, the average annual emissions are 767,509 tons of CO2, and correspondingly, their night light intensity is also relatively large, with a DN value of 1,614,491. Followed by aviation manufacturing and basic industries, with average daily carbon emissions of 766,598 and 622,469 tons of CO2 respectively. Among them, the night light intensity of the aviation manufacturing industry is the largest among all industrial areas, with a DN value of 1,768,226, and that of the basic industry is 1,484,435. The annual average carbon emissions of composite material processing are the least, only 96,107 tons of CO2. Correspondingly, its night light intensity is also relatively small, being 243,925, only greater than that of modern agriculture, which is 223,787. This embodiment also statistically analyzes the carbon emission sources of industrial parks of different industrial types, such as Figure 4As shown, the carbon emissions of small and medium-sized processing enterprises and composite material processing mainly come from electricity, accounting for 56.38% and 48.35% respectively. The carbon emissions of semiconductor and pharmaceutical processing mainly come from heat, accounting for 70.80% and 40.83% respectively. The carbon emissions of equipment manufacturing, aviation manufacturing, and modern agriculture mainly come from gas, accounting for 62.83%, 51.86%, and 56.35% respectively. Among the carbon emissions of different industrial types, the highest proportion of fuel is in the aviation manufacturing industry, accounting for 24.50%.

[0165] Step S303: Analyze the correlations between the night light index and the total carbon emissions, between the park scale index and the total carbon emissions, and between the social and economic index and the total carbon emissions. For details, please refer to Figure 1 Step S103 of the embodiment shown, which will not be elaborated here.

[0166] Step S304: Select an index combination from the night light index, the park scale index, and the social and economic index according to the correlation analysis results, and construct a carbon emission assessment model based on the index combination. For details, please refer to Figure 1 Step S104 of the embodiment shown, which will not be elaborated here.

[0167] The construction method of the industrial park carbon emission assessment model provided by the embodiments of the present invention obtains the night light index, the park scale index, the social and economic index, and the total carbon emissions of the industrial park, analyzes the correlations between the night light index and the total carbon emissions, between the park scale index and the total carbon emissions, and between the social and economic index and the total carbon emissions, and selects an index combination from the night light index, the park scale index, and the social and economic index according to the correlation analysis results to construct a carbon emission assessment model. By analyzing the correlations between various indexes and carbon emissions in the actual production of the industrial park, the present invention can construct a carbon emission assessment model based on the selected indexes that are strongly correlated with carbon emissions, so as to accurately evaluate the carbon emissions in the actual production of the industrial park, and further provide a scientific basis for balancing the profit and loss relationship between the development of the industrial park and carbon emissions.

[0168] In this embodiment, a construction method of an industrial park carbon emission assessment model is provided, which can be used in the above computer Figure 5 is a flowchart of the construction method of the industrial park carbon emission assessment model according to the embodiment of the present invention, as Figure 5 shown, and the process includes the following steps:

[0169] Step S501: Obtain the night light index, the park scale index, and the social and economic index of the industrial park. For details, please refer to Figure 3 Step S301 of the embodiment shown, which will not be elaborated here.

[0170] Step S502: Calculate the total carbon emissions of the industrial park. For details, please refer toFigure 3 Step S302 of the illustrated embodiment will not be elaborated herein.

[0171] Step S503, by analyzing the correlations between the night-time lighting index and the total carbon emissions, between the park scale index and the total carbon emissions, and between the socio-economic index and the total carbon emissions.

[0172] Specifically, the above-mentioned step S503 includes:

[0173] Step S5031, using the linear regression algorithm to calculate the correlation coefficients between the sub-indices of the night-time lighting index and the total carbon emissions, between the sub-indices of the park scale index and the total carbon emissions, and between the sub-indices of the socio-economic index and the total carbon emissions, respectively.

[0174] Specifically, in the embodiment of the present invention, the sub-indices of the night-time lighting index include: total light intensity I, average light intensity M, normalized light intensity N, and light intensity per unit area K; the sub-indices of the park scale index include: total park area, area of land used for logistics and warehousing, area of land used for commercial service facilities, and area of industrial land; the sub-indices of the socio-economic index include: number of enterprises, average annual employment, total annual tax revenue, and total annual fixed asset investment. In the embodiment of the present invention, the linear regression algorithm is used to sequentially determine the correlation coefficients between the total carbon emissions and the total light intensity I, average light intensity M, normalized light intensity N, light intensity per unit area K, total park area, area of land used for logistics and warehousing, area of land used for commercial service facilities, area of industrial land, number of enterprises, average annual employment, total annual tax revenue, and total annual fixed asset investment. Among them, the calculation of the correlation coefficient is a conventional technical means in the art and will not be elaborated herein. Among the night-time lighting indices, the linear relationship between the normalized light intensity N and carbon emissions is the best, and the correlation coefficient R 2 = 0.67, p < 0.001; among the park scale indices, the linear relationship between the area of industrial land and carbon emissions is the best, and the correlation coefficient R 2 = 0.82, p < 0.001; among the socio-economic indices, the linear relationship between the total annual fixed asset investment and carbon emissions is the best, and the correlation coefficient R 2 = 0.90, p < 0.001.

[0175] Step S5032, using the random forest algorithm to calculate the importance degrees of the sub-indices of the night-time lighting index on the total carbon emissions, the sub-indices of the park scale index on the total carbon emissions, and the sub-indices of the socio-economic index on the total carbon emissions, respectively.

[0176] Specifically, in the embodiments of the present invention, although all the selected indicators have a good linear relationship with carbon emissions, not all indicators are equally important for carbon emissions. Therefore, in the embodiments of the present invention, the random forest algorithm is used to calculate the importance degrees of the sub-indicators of the night light indicators for the total carbon emissions, the sub-indicators of the park scale indicators for the total carbon emissions, and the sub-indicators of the socio-economic indicators for the total carbon emissions, so as to be able to judge the redundancy relationship between variables. Among them, the calculation of the importance degree is a conventional technical means in the art and will not be elaborated here. There are differences in the importance degrees of sub-indicators for carbon emissions in different ranges. Among the night light indicators, the importance degrees of each indicator from high to low are: normalized light intensity N > total light intensity I > average light intensity M > light intensity per unit area K. Among the park scale indicators, the importance degrees of each indicator from high to low are: industrial land area > logistics and warehousing land area > commercial service facilities land > total park area. Among the socio-economic indicators, the importance degrees of each indicator from high to low are: annual total tax > annual fixed asset investment > number of enterprises > average annual employment. When comprehensively analyzing the night light indicators, park scale indicators and socio-economic indicators, the importance degrees of each indicator from high to low are: annual total tax > annual fixed asset investment > number of enterprises > total light intensity I > industrial land area > average annual employment > logistics and warehousing land area > light intensity per unit area K > normalized light intensity N > commercial service facilities land > total park area > average light intensity M.

[0177] Step S504, select an indicator combination from the night light indicators, park scale indicators and socio-economic indicators according to the correlation analysis results, and construct a carbon emission assessment model based on the indicator combination.

[0178] Specifically, the above step S504 includes:

[0179] Step S5041, construct a stepwise regression model.

[0180] Step S5042, add the sub-indicators that are correlated with the total carbon emissions among the night light indicators, park scale indicators and socio-economic indicators to the stepwise regression model in descending order of the importance degrees of the sub-indicators for the total carbon emissions.

[0181] Specifically, in the embodiments of the present invention, it has been verified that the above 12 sub-indicators are all correlated with carbon emissions, but the importance degrees of sub-indicators for carbon emissions are different in different ranges. In order to select a suitable sub-indicator combination to evaluate the total carbon emissions of industrial parks in the embodiments of the present invention, each sub-indicator is input into the stepwise regression model in descending order of the importance degree obtained from the above comprehensive analysis. Among them, the stepwise regression model is a conventional technical means in the art and will not be elaborated here.

[0182] Step S5043: After adding each current sub - indicator, calculate the correlation coefficient between the currently added sub - indicator combination and the stepwise regression model.

[0183] Specifically, in the embodiments of the present invention, when directly establishing a model between the night - time light index and carbon emissions, the model effect is poor. Therefore, the embodiments of the present invention select the stepwise regression analysis method to analyze the sub - indicators. When constructing models using different indicator combinations, the prediction accuracy can be effectively improved. Among them, the stepwise regression model is a process of screening variables in regression analysis. Using stepwise regression can construct a regression model from a set of candidate variables, enabling the model to automatically identify influential variables. In the embodiments of the present invention, after adding each current sub - indicator, a new sub - indicator combination can be formed in the stepwise regression model, and at the same time, the correlation coefficient R between the sub - indicator combination in the stepwise regression model and carbon emissions can be calculated. 2 , representing the degree of interpretability or contribution rate of the sub - indicator combination to carbon emissions.

[0184] Step S5044: If the correlation coefficient is less than the preset threshold, delete the current sub - indicator; if the correlation coefficient is greater than or equal to the preset threshold, continue to add the next sub - indicator until all sub - indicators of the night - time light index, sub - indicators of the park scale index, and sub - indicators of the socio - economic index are traversed.

[0185] Specifically, in the embodiments of the present invention, after calculating that the correlation coefficient between the sub - indicator combination and carbon emissions is less than the preset threshold, the current sub - indicator is deleted. The embodiments of the present invention set the preset threshold to 0.5, that is, the sub - indicators with a correlation coefficient R 2 less than 0.5 are considered meaningless to carbon emissions. If it is greater than 0.5, the next sub - indicator is added until all sub - indicators are traversed.

[0186] Step S5045: Select the stepwise regression model corresponding to the sub - indicator combination with the largest correlation coefficient as the carbon emission assessment model.

[0187] Specifically, in the embodiments of the present invention, different sub - indicator combinations can be obtained through stepwise regression of sub - indicators. In order to accurately evaluate the carbon emissions of industrial parks, the stepwise regression model corresponding to the sub - indicator combination with the largest correlation coefficient is selected as the carbon emission assessment model. As Figure 6 shown in (b) below, in the stepwise regression model obtained in the embodiments of the present invention, the model of the annual fixed - asset investment, industrial land area, and night - time unit - area light intensity K has good generalization ability, and the correlation coefficient R 2 is 0.931. The relative importance of the annual fixed - asset investment in the model is the highest, and the contribution rate is 48.6%. In addition, as Figure 6 shown in (a) below, the model of the industrial land area, logistics and warehousing land area, and unit - area light intensity K also has good generalization ability, and the correlation coefficient R2 is 0.921. The relative importance of the industrial land area in the model is the highest, with a contribution rate of 64.4%. The modeling equations of the two models are shown in the following table:

[0188] Table 1 Carbon Emission Assessment Model

[0189]

[0190] The construction method of the industrial park carbon emission assessment model provided by the embodiments of the present invention obtains the night light index, park scale index, social and economic index and total carbon emission of the industrial park, analyzes the correlations between the night light index and the total carbon emission, between the park scale index and the total carbon emission, and between the social and economic index and the total carbon emission, and selects an index combination from the night light index, park scale index and social and economic index according to the correlation analysis results to construct a carbon emission assessment model. By analyzing the correlations between various indexes and carbon emissions in the actual production of the industrial park, the present invention can construct a carbon emission assessment model based on the selected indexes strongly correlated with carbon emissions, so as to accurately evaluate the carbon emissions in the actual production of the industrial park, and further provide a scientific basis for balancing the profit and loss relationship between the development of the industrial park and carbon emissions.

[0191] In this embodiment, a construction method of an industrial park carbon emission assessment model is provided, which can be used for the above computer. Figure 7 is a flowchart of the construction method of the industrial park carbon emission assessment model according to the embodiment of the present invention, as Figure 7 shown. The process includes the following steps:

[0192] Step S701, obtain the night light index, park scale index and social and economic index of the industrial park. For details, please refer to Figure 5 step S501 of the shown embodiment, which will not be elaborated here.

[0193] Step S702, calculate the total carbon emission of the industrial park. For details, please refer to Figure 5 step S502 of the shown embodiment, which will not be elaborated here.

[0194] Step S703, analyze the correlations between the night light index and the total carbon emission, between the park scale index and the total carbon emission, and between the social and economic index and the total carbon emission. For details, please refer to Figure 5 step S503 of the shown embodiment, which will not be elaborated here.

[0195] Step S704, select an index combination from the night light index, park scale index and social and economic index according to the correlation analysis results, and construct a carbon emission assessment model based on the index combination. For details, please refer to Figure 5Step S504 of the illustrated embodiment will not be elaborated herein.

[0196] Step S705: Obtain the index values of each sub-index in the sub-index combination corresponding to the current industrial park; input the index values into the carbon emission assessment model to obtain the total carbon emissions of the current industrial park.

[0197] Specifically, in the embodiment of the present invention, when it is necessary to evaluate the total carbon emissions of an industrial park, it is only necessary to obtain the index values of each sub-index in the sub-index combination obtained in the embodiment of the present invention. For example, if the carbon emission assessment model is constructed according to the annual fixed asset investment, industrial land area, and unit area light intensity K in the embodiment of the present invention, it is only necessary to obtain the index values of the annual fixed asset investment, industrial land area, and unit area light intensity K of the industrial park, and input the index values of the annual fixed asset investment, industrial land area, and unit area light intensity K into the carbon emission assessment model to predict the total carbon emissions of the current industrial park. This is only an example and is not limited thereto.

[0198] In some alternative embodiments, evaluating the carbon emissions of industrial parks through the sub-index combination has lower costs compared to directly monitoring the carbon dioxide emissions within the industrial park, and can also ensure the accuracy and reliability of the evaluation.

[0199] The present invention provides a method for constructing a carbon emission assessment model for industrial parks. By obtaining the night light index, park scale index, socio-economic index, and total carbon emissions of the industrial park, analyzing the correlations between the night light index and the total carbon emissions, between the park scale index and the total carbon emissions, and between the socio-economic index and the total carbon emissions, selecting an index combination from the night light index, park scale index, and socio-economic index to construct a carbon emission assessment model according to the correlation analysis results, and evaluating the carbon emissions of the current industrial park according to the carbon emission assessment model. By analyzing the correlations between various indices and carbon emissions in the actual production of industrial parks, the present invention can construct a carbon emission assessment model based on the selected indices that are strongly correlated with carbon emissions, thereby accurately evaluating the carbon emissions in the actual production of industrial parks, and further providing a scientific basis for balancing the profit and loss relationship between the development of industrial parks and carbon emissions.

[0200] In this embodiment, a device for constructing a carbon emission assessment model for industrial parks is also provided. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be elaborated again. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0201] This embodiment provides a device for constructing a carbon emission assessment model for an industrial park, as Figure 8 shown, including:

[0202] An index acquisition module 801, configured to acquire the night light index, park scale index, and socio-economic index of the industrial park;

[0203] A total carbon emission calculation module 802, configured to calculate the total carbon emission of the industrial park;

[0204] A correlation analysis module 803, configured to analyze the correlation between the night light index and the total carbon emission, between the park scale index and the total carbon emission, and between the socio-economic index and the total carbon emission;

[0205] A model construction module 804, configured to select an index combination from the night light index, park scale index, and socio-economic index according to the correlation analysis result, and construct a carbon emission assessment model based on the index combination.

[0206] In some alternative embodiments, the index acquisition module 801 includes:

[0207] A night light index acquisition unit, configured to acquire satellite remote sensing data including the area corresponding to the industrial park; correct the RGB bands of the satellite remote sensing data, and acquire the pixel information of the pixels within the area corresponding to the industrial park, where the pixel information includes: pixel value and pixel quantity; calculate the sum of the pixel values of all pixels within the area corresponding to the industrial park to obtain the total light intensity; calculate the ratio of the sum of the pixel values of all pixels within the area corresponding to the industrial park to the pixel quantity to obtain the average light intensity; calculate the sum of the ratios of the pixel value of a single pixel within the area corresponding to the industrial park to the pixel value of the maximum pixel to obtain the normalized light intensity; calculate the ratio of the sum of the pixel values of all pixels within the area corresponding to the industrial park to the area of the region to obtain the light intensity per unit area.

[0208] A park scale index acquisition unit, configured to acquire the sub-indices of the park scale index of the industrial park, where the sub-indices of the park scale index include: total park area, area of logistics and warehousing land use, area of commercial service facility land use, and area of industrial land use.

[0209] A socio-economic index acquisition unit, configured to acquire the sub-indices of the socio-economic index of the industrial park, where the sub-indices of the socio-economic index include: number of enterprises, average annual number of employees, total annual tax revenue, and total annual fixed asset investment.

[0210] In some alternative embodiments, the total carbon emission calculation module 802 includes:

[0211] A fossil fuel carbon emissions calculation unit, which is used to calculate the product of the consumption of fossil fuels, the carbon content of fossil fuels, and the carbon oxidation rate in the industrial park, so as to obtain the fossil fuel carbon emissions of the industrial park.

[0212] An electricity carbon emissions calculation unit, which is used to calculate the product of the electricity consumption in the industrial park and the carbon dioxide emission factor of electricity supply, so as to obtain the electricity carbon emissions of the industrial park.

[0213] A heat carbon emissions calculation unit, which is used to calculate the product of the heat consumption in the industrial park and the carbon dioxide emission factor of heat supply, so as to obtain the heat carbon emissions of the industrial park.

[0214] A waste treatment carbon emissions calculation unit, which is used to calculate the product of the hot water heat in the industrial park and the global warming potential value of methane, so as to obtain the waste treatment carbon emissions of the industrial park.

[0215] A total carbon emissions calculation unit, which is used to obtain the total carbon emissions of the industrial park based on the sum of the fossil fuel carbon emissions, and / or the electricity carbon emissions, and / or the heat carbon emissions, and / or the waste treatment carbon emissions.

[0216] In some alternative embodiments, the correlation analysis module 803 includes:

[0217] A correlation analysis unit, which is used to calculate the correlation coefficients between the sub-indicators of the night light index and the total carbon emissions, between the sub-indicators of the park scale index and the total carbon emissions, and between the sub-indicators of the socio-economic index and the total carbon emissions respectively by using the linear regression algorithm.

[0218] An importance analysis unit, which is used to calculate the importance degrees of the sub-indicators of the night light index on the total carbon emissions, the sub-indicators of the park scale index on the total carbon emissions, and the sub-indicators of the socio-economic index on the total carbon emissions respectively by using the random forest algorithm.

[0219] In some alternative embodiments, the model construction module 804 includes:

[0220] A model construction unit, which is used to construct a stepwise regression model.

[0221] An index input unit, which is used to add the sub-indicators of the night light index, the park scale index, and the socio-economic index that are correlated with the total carbon emissions to the stepwise regression model in descending order of the importance degrees of the sub-indicators on the total carbon emissions.

[0222] A correlation judgment unit, which is used to calculate the correlation coefficient between the currently added sub-indicator combination and the stepwise regression model after each current sub-indicator is added.

[0223] The sub - index screening unit is used to delete the current sub - index if the correlation coefficient is less than the preset threshold; if the correlation coefficient is greater than or equal to the preset threshold, continue to add the next sub - index until all sub - indices of the night - time light index, sub - indices of the park scale index, and sub - indices of the socio - economic index are traversed.

[0224] The sub - index combination selection unit is used to select the step - by - step regression model corresponding to the sub - index combination with the largest correlation coefficient as the carbon emission assessment model.

[0225] In some alternative embodiments, it further includes: a carbon emission assessment module, which is used to obtain the index values of each sub - index in the sub - index combination corresponding to the current industrial park; input the index values into the carbon emission assessment model to obtain the total carbon emissions of the current industrial park.

[0226] The further functional descriptions of the above - mentioned various modules and units are the same as those in the corresponding above - mentioned embodiments, and will not be elaborated here.

[0227] The construction device of the industrial park carbon emission assessment model in this embodiment is presented in the form of functional units. Here, the unit refers to an FPGA (Field Programmable Gate Array) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above - mentioned functions.

[0228] The embodiment of the present invention also provides a computer device having the above - mentioned Figure 8 construction device of the industrial park carbon emission assessment model shown.

[0229] Please refer to Figure 9 , Figure 9 which is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As shown in Figure 9 , the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high - speed interface and a low - speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (such as a server array, a set of blade servers, or a multi - processor system). Figure 9 In

[0230] The processor 10 may be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 may further include a hardware chip. The above-mentioned hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device may be a complex programmable logic device, a field-programmable gate array, a generic array logic, or any combination thereof.

[0231] Among them, the memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiments.

[0232] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely set relative to the processor 10, and these remote memories may be connected to the computer device through a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0233] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 may also include a combination of the above types of memories.

[0234] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0235] Embodiments of the present invention also provide a computer-readable storage medium. The method according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored as such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0236] <Specific example>

[0237] In a specific example of the present invention, around the scientific issue of the coupling relationship between land intensive use and carbon emissions, land intensive use and carbon emissions are placed in the context of "carbon neutrality and carbon peak", and the impact mechanism of land intensive use change on net carbon emissions is explored. On this basis, taking the core starting area as the basic monitoring unit, the degree of land intensive use in the core starting area is evaluated based on the "evaluation index system of evaluating enterprises by per mu output", and combined with the CO2 emission data calculated from the questionnaire, remote sensing data, and land intensive use evaluation indicators, an optimal inversion model of carbon emissions and carbon sinks in the industrial functional areas of Chengdu is established, and finally the net carbon emissions of the industrial functional areas are obtained, and the current situation of net carbon emissions in the industrial functional areas and its correlation with the degree of land intensive use are analyzed. Finally, the main factors affecting the net carbon emissions of industrial functional areas under land intensive use are clarified.

[0238] On the basis of clarifying the connotation of intensive and low-carbon land use in industrial functional areas, comprehensively using data such as the third national land survey, basic information data of industrial functional areas in Chengdu, and questionnaires, the land intensive use efficiency of the core starting area of typical industrial-dominated industrial functional areas in Chengdu is evaluated. According to the carbon emission characteristics of industrial land in the core starting area, the inventory statistical analysis method is used to estimate the total carbon emissions of typical industrial functional areas, and further the multiple regression modeling is used to estimate the carbon emission efficiency per unit area of land. The evaluation results of carbon emission efficiency and land intensive use efficiency are correlated and analyzed to reveal the relationship between the intensive use efficiency of industrial land and carbon emission efficiency in the core starting area. Guided by the goal of intensive and low-carbon, policy suggestions for optimizing the allocation of land resources to promote intensive and low-carbon land use in industrial functional areas are put forward, and the land resource allocation system with "double guidance" of emission reduction and output is improved and perfected.

[0239] The system analyzes the policies and regulations on the land resource allocation of industrial functional areas in Chengdu, and systematically analyzes the current situation and problems of the land resource allocation of industrial-dominated industrial functional areas in Chengdu from multiple aspects such as land planning, allocation, transfer, and supervision. At the same time, it analyzes the requirements of cutting-edge policies such as green and low-carbon development and sustainable development, analyzes the carbon emission types in the core starting areas of each industrial functional area, and finds the correlation between the optimized allocation strategy of land use in industrial functional areas and relevant policies. It analyzes the possible problems and risks of existing land intensive and economical use evaluation methods under the background of "carbon peak and carbon neutrality". By comparing the differences in carbon emission channels in the core starting areas of different industrial-dominated industrial functional areas, it clarifies the connotation and requirements of intensive and low-carbon land use in industrial-dominated industrial functional areas.

[0240] Referring to the "Regulations on the Evaluation of Land Intensive Use in Development Zones in Chengdu" (Trial in 2014), comprehensively using data such as the third national land survey, basic information data of industrial functional areas in Chengdu, and questionnaires, several core starting areas of industrial-dominated industrial functional areas are selected targeted within the scope of Chengdu to carry out the evaluation of the land intensive use efficiency of the core starting areas of typical industrial-dominated industrial functional areas. According to the classification of industrial land of Class I, Class II, and Class III, it reveals the land use intensification level under different industrial types, providing a data basis for the land resource allocation with "double guidance" of emission reduction and output.

[0241] Combined with the inventory analysis method of IPCC greenhouse gases, the empirical coefficient method, and the latest research results at home and abroad, a method for calculating land use carbon emissions and carbon sinks for industrial functional areas in Chengdu is constructed to explore the current situation distribution law and reasons of carbon emissions in industrial-dominated industrial functional areas in Chengdu, providing a method basis and technical support for systematically carrying out research on intensive and low-carbon land at the industrial functional area level.

[0242] Combined with the evaluation results of the land intensive use of industrial-dominated industrial functional areas, explore the corresponding relationship between the existing land intensive use level of industrial functional areas and the land use carbon emission efficiency indicators, quantitatively calculate and analyze the land carbon emission efficiency under different intensive use levels in Chengdu, discuss the influence mechanism of land intensive use level on carbon emission efficiency, reveal the difference law and influencing factors of land carbon emission efficiency in industrial-dominated industrial functional areas in Chengdu, and analyze the impact of the existing land resource allocation in industrial functional areas in Chengdu on carbon emissions, providing theoretical support for the formulation of land regulation measures for regional low-carbon emissions.

[0243] Based on the evaluation of the intensive land use and the carbon emission efficiency of land use in the industrial-dominated industrial functional areas of Chengdu, combined with the main problems faced by the intensive and economical land use in the industrial-dominated industrial functional areas of Chengdu under the background of practicing the new concept of green and low-carbon development, policy suggestions and implementation paths for the optimal allocation of land elements in the core starting areas of the industrial-dominated industrial functional areas of Chengdu are put forward, providing a reference basis for realizing the efficient and green comprehensive utilization of land in the industrial functional areas and improving the modernization level of the national land space governance system and governance capacity.

[0244] Establishing a method for estimating carbon emissions and carbon sinks in industrial functional areas is the basis for intensive and low-carbon land allocation.

[0245] Taking the industrial functional areas as the object, by means of combining questionnaire surveys and satellite remote sensing, on the basis of the data of the world's first scientific satellite dedicated to serving the United Nations 2030 Agenda for Sustainable Development (Sustainable Development Science Satellite-1), the most suitable carbon emission inversion model for the industrial functional areas of Chengdu is explored. And the CASA model is used to estimate the carbon sink amount in the industrial functional areas of Chengdu, and the net carbon emission is obtained by the difference between the carbon emission amount and the carbon sink amount, and the spatial status quo of the net carbon emission in the industrial functional areas is analyzed, providing data support for further exploring the influencing factors of the net carbon emission in the industrial functional areas under intensive land use.

[0246] Revealing the coupling relationship between intensive land use and net carbon emissions is an important basis for low-carbon land resource allocation.

[0247] On the basis of evaluating the intensive land use of the industrial functional areas, scientifically accounting the carbon emission amount and the carbon sink amount, focusing on exploring the influencing relationship between intensive land use and net carbon emission and applying it to the field of carbon emission reduction policies will help to link the existing "evaluation index system of'making every mu of land count'" with net carbon emission, bridge the evaluation results of the intensive land use efficiency of the industrial functional areas and the estimated results of the net carbon emission in the core starting areas, and establish a "one-to-one" conceptual relationship framework. Overall, it can break through the two research hotspots of intensive land use and carbon emission, and enrich the relevant research perspectives and research ideas.

[0248] Proposing a policy path for low-carbon land use is an important entry point for practicing the concept of green and low-carbon development.

[0249] According to the relevant relationship between the intensive land use and carbon emission of the industrial functional areas, combined with the main problems faced by the intensive and economical land use in the industrial functional areas of Chengdu under the background of "practicing green and low-carbon development", aiming at the needs of improving the spatial carbon reduction benefit, linking the increase and decrease of land use indicators with the emission reduction scale, and the land resource allocation with "double guidance" of emission reduction and output, policy suggestions and implementation paths for the optimal allocation of land elements in the core starting areas of the industrial functional areas of Chengdu are put forward, providing a reference basis for realizing the efficient and green comprehensive utilization of land in the industrial functional areas and improving the modernization level of the national land space governance system and governance capacity.

[0250] The research technical route is as Figure 10 shown.

[0251] Among them, the data sources comprehensively utilized the existing land survey results, combined with means such as satellite remote sensing, and collected and processed various data of the industrial functional areas in Chengdu, including questionnaire survey data, the third national land survey data, basic data of industrial functional areas, SDGSAT-1 data, Landsat satellite data, Modis satellite data, and meteorological data. The detailed data situation is shown in the following table:

[0252] Details of the data

[0253]

[0254] A total of 66 questionnaires were distributed to the management committees of each industrial functional area, and 20 were recovered. After excluding the available data, 10 were selected for the construction of the carbon emission estimation model. The questionnaire indicators involve 6 indicators of natural gas (t), liquefied petroleum gas (t), electricity (kW·h), heat (GJ), annual water consumption (t / m3), and wastewater treatment volume (t) in each industrial functional area.

[0255] Using the third national land survey data, classified and summarized by major categories, the land types include cultivated land, garden land, forest land, grassland, commercial service land, industrial and mining land, residential land, public management and public service land, special land, transportation land, water area and water conservancy facilities land, and other land (as Figure 11 shown). Taking the core starting areas of industrial functional areas as the statistical scope, the land use types and areas of each core starting area were statistically calculated.

[0256] The land intensive use level of each industrial functional area was calculated using the industrial functional area data in the basic database of Chengdu's industrial functional areas. The basic database of industrial functional areas contains data such as the number of registered market entities, the number of "four types of scale" enterprises, the annual average number of employees, the annual total tax revenue, the area of land requisitioned but not supplied, the area of unreported requisition, the area of land supplied, the operating income of large-scale service enterprises, the land approved but not used, the conditional construction area, the permitted construction area, and the area of land requisitioned in each industrial functional area of Chengdu.

[0257] The research collected Landsat satellite remote sensing data with a resolution of 30m and Modis satellite remote sensing data with a resolution of 250m covering Chengdu. According to the accuracy requirements, after merging the data of the two satellites, radiometric calibration, atmospheric correction, and index calculation were carried out. Through processes such as image mosaicking and cropping, the monthly NDVI calculation results of Chengdu in 2020 were obtained. In addition, in order to establish a remote sensing estimation model for carbon emissions, this research also collected satellite data of the Sustainable Development Science Satellite-1 ( Figure 12 , the night light imaging of the SDGSAT-1 satellite in Chengdu).

[0258] The climate data used are the daily temperature data and rainfall data of 20 meteorological observation stations in and around Chengdu in 2020. To ensure the quality and reliability of the data, the following data processing was carried out: ① Data integrity and accuracy processing. Since the data of some stations were incomplete or abnormal, the temperature mean data of the same period in the previous and next three months were used for filling and calibration correction to ensure the integrity and accuracy of the data; ② Data spatialization processing. The data of 20 meteorological stations were spatially processed through Kriging interpolation.

[0259] Method

[0260] The carbon emissions of seven indicators are calculated through the following formula, and finally the total carbon emissions of each industrial functional area are obtained by summing up.

[0261] The parameters are based on the "Sichuan Province Net Carbon Emission Characterization Indicators and Accounting Methods (Trial)" (Sichuan Environmental Letter

[2019] No. 774). The carbon emissions of fossil fuels, electricity, and heat consumption are respectively accounted for, and the carbon reduction effect of clean energy is considered. The carbon emissions of clean energy are deducted from the total amount to obtain the total amount and carbon emissions per unit area.

[0262] ① Fossil fuels

[0263] The calculation formula is as follows:

[0264] ECO2_fuel = Σi(ADi × CCi × OFi × 44 / 12)(1)

[0265] In the formula: ECO2_fuel—the main fossil fuel CO2 emissions of the industrial functional area, unit: ton;

[0266] i—the type of fossil fuel;

[0267] ADi—the consumption of the i-th fuel, unit: ton for solid and liquid, 10,000 Nm for gas 3 ;

[0268] CCi—the carbon content of the i-th fuel, unit: ton carbon / ton fuel for solid, ton carbon / 10,000 Nm for gas 3 ;

[0269] OFi—the carbon oxidation rate of fuel i, value range: 0-1;

[0270] ② Electricity

[0271] The calculation formula is as follows:

[0272] ECO2_net_electricity = AD_electricity × EI (2)

[0273] ECO2_net_heat = AD_heat × EI (3)

[0274] Where: ECO2_net_electricity is the CO2 emissions embodied in the net purchased electricity of the industrial functional area, with the unit of ton CO2;

[0275] ECO2_net_heat is the CO2 emissions embodied in the net purchased heat of the industrial functional area, with the unit of ton CO2;

[0276] AD_electricity is the electricity consumption of the net purchased electricity of the enterprise, with the unit of MWh;

[0277] AD_heat is the heat consumption of the net purchased heat of the enterprise, with the unit of GJ;

[0278] EI is the CO2 emission factor of electricity supply, taking 0.1031, with the unit of ton CO2 / MWh;

[0279] E is the CO2 emission factor of heat supply, taking 0.11, with the unit of ton CO2 / GJ;

[0280] AD_hot_water = Maw × (Tw - 20) × 4.1868 × 10^(-3) (4)

[0281] AD_steam = Mast × (Enst - 83.74) × 10^(-3) (5)

[0282] Where: AD_hot_water is the heat of hot water, with the unit of GJ;

[0283] AD_steam is the heat of steam, with the unit of GJ;

[0284] Maw is the mass of hot water, with the unit of ton of hot water;

[0285] Mast is the mass of steam, with the unit of ton of steam;

[0286] Tw is the specific heat of water at normal temperature and pressure, with the unit of kJ / (kg·℃);

[0287] Enst is the heat enthalpy per kilogram of steam at the corresponding temperature and pressure of steam, with the unit of kJ / kg;

[0288] Regarding waste treatment emissions

[0289] Refer to the greenhouse gas emission accounting methods and reporting guidelines issued by the General Office of the National Development and Reform Commission to account for the methane emissions caused by the anaerobic treatment of industrial wastewater, and comprehensively obtain the total amount and per unit area carbon emission indicators of the industrial functional area.

[0290] ① Emissions from anaerobic treatment of wastewater

[0291] EGHG_wastewater = ECH4_wastewater × GWPCH4 × 10^(-3) (6)

[0292] In the formula: EGHG_wastewater—the carbon dioxide emission equivalent generated during the anaerobic treatment of wastewater, with the unit of ton carbon dioxide equivalent (tCO2e);

[0293] GWPCH4—the global warming potential (GWP) value of methane. According to the "Compilation Guide for Provincial Greenhouse Gas Inventories (Trial)", it is taken as 21.

[0294] ECH4_wastewater = (TOW - S) × EF - R (7)

[0295] In the formula: ECH4_wastewater—the heat of hot water, with the unit of GJ;

[0296] TOW—the total amount of organic matter removed during the anaerobic treatment of wastewater (kilogram COD);

[0297] S—the total amount of organic matter removed in the form of sludge (kilogram COD);

[0298] EF—the methane emission factor (kilogram methane / kilogram COD), taken as 0.25;

[0299] R—the methane recovery amount (kilogram methane), taken as 0.5;

[0300] Based on the above method, the total carbon emission values of Chengdu New Materials Industry Functional Zone, Chengdu Tianfu International Airport New Area, Chengdu International Railway Port, European Industry City in Qingbaijiang District of Chengdu, Green Food Industry Functional Zone in Qionglai City, Southwest Airport Economic Development Zone, Tianfu Modern Seed Industry Park in Qionglai City, Tianfu New Area Semiconductor Material Industry Functional Zone in Qionglai City, Tianfu Traditional Chinese Medicine Administrative Committee in Pengzhou City, and Chengdu Xindu Modern Transportation Industry Functional Zone in 2020 were calculated.

[0301] Calculation method of carbon emissions in industrial functional areas based on light remote sensing

[0302] The SDGSAT-1 satellite data is stored in RGB three bands. For the subsequent construction of the carbon emission inversion model, the Gamma correction algorithm (Equation 8) is used here to correct the RGB bands of the satellite to obtain the regional night light intensity.

[0303] Gray = 0.1140 * B + 0.5870 * G + 0.2989 * R (8)

[0304] In this study, the total light intensity (I), normalized value (N), and average light intensity (K) of the night light data in 2020 were selected to explore the correlation between the night light data and the energy carbon emissions. Among them, the total light intensity represents the sum of all DN values in the region, the normalized value represents the sum of the ratios of the DN values of individual pixels in the region to the maximum pixel DN value in the region, and the average light intensity represents the ratio of the sum of all DN values in the region to the area of the region. The formulas are as follows:

[0305]

[0306] In the formula, DN i is the pixel value in the i-th area; n is the number of pixels in the area; DN max is the maximum pixel value in the i-th area; s is the total area of the i-th area.

[0307] Taking the night light data in 2020 as an example, this paper illustrates the correlation between the three indicators of night light data and the carbon emissions of industrial functional areas in Chengdu. Many studies have shown that there are many land use indicators related to carbon emissions. In this section, many indicators are selected, including the comprehensive floor area ratio, the floor area ratio of each land use type, each land use type, the industrial floor area, fixed asset investment, population density, GDP per unit land area, etc., to find the indicators most relevant to carbon emissions and establish an estimation model for carbon emissions in industrial functional areas. In this study, the method of canonical correlation analysis is used for indicator selection, and indicators with high and significant correlation coefficients (p < 0.05) are selected. Canonical correlation analysis is an extension of the combination of multiple regression and correlation analysis. It is a multivariate statistical analysis method for studying the relationship between two sets of indicators, which can reveal the degree of correlation between two sets of variables. Its essence is to study the linear combination between two sets of variables, and considering the correlation between each indicator within the two variable sets, then select several representative linear combinations, and use the correlation relationship of these indicators to represent the correlation relationship of the original two sets of variables. The requirement of canonical correlation analysis for data is that both sets of variables are continuous variables, and their data must follow a multivariate normal distribution. The advantage of this method is that it can simply and clearly screen out the driving factors with relatively close associations. The disadvantage is that it cannot reflect the causal relationship and quantify the influence of different factors on the dependent variable, and in-depth analysis of other models is required subsequently. In this study, through canonical correlation analysis, factors with greater influence on carbon emissions can be screened out from many influencing factors, so as to establish a remote sensing inversion model for carbon emissions.

[0308] Calculation method of carbon sinks in industrial functional areas based on the CASA model

[0309] Most of the estimation methods of carbon sinks are based on biomass calculation. The biomass inventory method is the most fundamental method, that is, relevant data are obtained through on-site measurement to calculate the carbon storage, and then the carbon sink is estimated through the change amount of carbon storage. With the continuous improvement and in-depth of field survey data, the biomass conversion factor method, the empirical coefficient method, etc. have gradually developed. Due to their convenient and fast application and low data requirements, they have been more widely used. The biomass inventory method is relatively accurate in estimation, but requires a large amount of on-site measurement data such as biomass and carbon content rate, and is generally suitable for small-scale calculations; based on the empirical coefficient method, on the basis of previous studies, carbon sink data of long time series can be estimated to make up for the problem that historical data are difficult to obtain and cannot be re-measured, which is suitable for large-scale carbon sink estimation. However, since most of the empirical coefficients come from the summary of large-scale estimation results, when applied to the urban scale, the accuracy is not high. The NPP method calculates the carbon sink based on the change amount of biomass, and is quite widely used due to the small difficulty in obtaining data. To solve the problem of missing historical data and improve the estimation accuracy, combined with the characteristics of the urban scale and data collectability, this paper constructs a CASA ecological land carbon sink estimation model based on GIS and RS. The principle of this model is to obtain the carbon sink of vegetation based on the calculation of NPP, which mainly includes three parts: data input module, algorithm module, and result output module. The core content includes the CASA model algorithm and NDVI calculation, etc.

[0310] According to the IPCC National Greenhouse Gas Inventory Guidelines 2006, the main carbon pools in terrestrial ecosystems usually include biomass, dead wood, and soil organic matter carbon pools. Since the vegetation biomass in terrestrial ecosystems is the main carbon pool, this paper focuses on estimating the carbon sink of vegetation, and the key to estimating the vegetation carbon sink is to estimate the plant biomass. The growth trend and intensity of NPP are important factors driving the carbon sink of ecosystems. Determining the size and change trend of ecosystem NPP is the key to simulating the carbon sink of ecosystems.

[0311] The CASA model is the model with the most application cases and the most accurate estimation of NPP for natural and artificial vegetation at present, which is jointly estimated through the absorbed photosynthetically active radiation (APAR) and actual light use efficiency (ε) of vegetation (Liu Zhenzhen et al., 2017, Shen Beibei, 2019, Wang Yulong, 2020, Zhang Qiang et al., 2018). The spatio-temporal resolution of vegetation indices directly determines the accuracy of NPP inversion by the CASA model. The main factors affecting the NPP measurement results include land type, absorbed photosynthetically active radiation, and maximum light use efficiency, etc. Absorbed photosynthetically active radiation is the driving force for plant photosynthesis and directly affects the change of biomass. The absorbed photosynthetically active radiation by vegetation depends on the total solar radiation and the characteristics of the plant itself: the maximum light use efficiency is mainly affected by air temperature, precipitation, and vegetation type (Monteith, 1997). Therefore, the data of the model mainly include net solar radiation, remote sensing images, vegetation cover data, meteorological data, etc. The meteorological data are spatially processed by Kriging interpolation. The NPP inversion method process is as followsFigure 13 as shown

[0312] The NPP estimation principle of the CASA model is as shown in Equation 12:

[0313] NPP(x, t) = APAR(x, t) × ε(x, t) (12)

[0314] In the formula, NPP(x, t) is the NPP at pixel x in month t; APAR(x, t) represents the photosynthetically active radiation absorbed by pixel x in month t (gC·m -2 ·month -1 ); ε(x, t) represents the actual light use efficiency of the pixel in month t (gC·MJ -1 ).

[0315] Regarding the estimation of APAR

[0316] The photosynthetically active radiation (APAR) absorbed by vegetation is the part of the photosynthetically active radiation (PAR) absorbed by plant leaves. The estimation of APAR can be achieved by extracting the reflection information of vegetation in the infrared and near-infrared bands, which is mainly affected by the total solar radiation and the growth of plants themselves. The calculation formula is as shown in Equation 13:

[0317] APAR(x, t) = SOL(x, t) × FPAR(x, t) × 0.5 (13)

[0318] In the formula, SOL(x, t) represents the total solar radiation at pixel x in month t (MJ·m -2 ·month -1 , which is obtained by using the ERA5 meteorological reanalysis data released by the European Space Agency. FPAR(x, t) is the absorption ratio of incident photosynthetically active radiation for different planting attribute types, and the constant 0.5 represents the ratio relationship between FPAR(x, t) and SOL(x, t).

[0319] Numerous studies have shown that there is a significant linear relationship between FPAR(x, t) and the normalized difference vegetation index (NDVI) and the simple ratio vegetation index (SR). FPAR(x, t) can be determined based on the maximum value of NDVI or SR of a certain planting attribute and the corresponding maximum and minimum values of FPAR(x, t). However, the results of FPAR(x, t) calculated using the NDVI maximum and minimum values are usually higher than the true value, and the results of FPAR(x, t) calculated using the SR maximum and minimum values are usually lower than the true value. In this study, a comprehensive method of the two methods is adopted, and the mean value is used to represent the final FPAR(x, t). The calculation formula is as shown in Equations (14 - 17):

[0320]

[0321] FPAR (x，t) =(FPARNDVI(x，t) +FPAR SR(x，t) / 2 (17)

[0322] In the formula, NDVI i，max 、NDVI i，min and SR i，max 、SR i，min correspond to the 95% and 5% lower percentile values of NDVI and SR for the i-th planting attribute type respectively. The values of FPAR max and FPAR min are independent of the planting attribute type and are 0.001 and 0.95 respectively.

[0323] Based on the calculation of NDVI in this study, combined with the vegetation characteristics of Chengdu, referring to the research of Zhu Wenquan et al. (2007). The vegetation in Chengdu belongs to the subtropical region, and the carbon (C) content rate is 0.5 (Pettersen, 1984). According to the NPP results, the carbon sink (CO2) can be calculated as shown in the following table. The parameters in the NPP calculation model are determined as shown in the following table.

[0324] Table of NPP Estimation Parameters for Chengdu

[0325]

[0326] Calculation method of net carbon emissions in industrial functional areas

[0327] Using the SDGSAT-1 satellite data, an estimation model for carbon emissions in the industrial functional areas of Chengdu was established, the total carbon emissions in the industrial functional areas of Chengdu were calculated, and the NPP of Chengdu was calculated using the CASA model. The carbon sink in the industrial functional areas of Chengdu was calculated through empirical thresholds. The net carbon emissions in the industrial functional areas of Chengdu in this study is the difference between the total carbon emissions and the carbon sink.

[0328] Calculation method of intensive land use in industrial functional areas

[0329] Investigate the basic information, land use status, land use efficiency, management performance, land supply status, and typical enterprise conditions of the industrial functional areas. Make full use of existing achievements and collect various materials, mainly including statistical yearbooks or reports, economic and social censuses, land use surveys, and the achievements of national economic and social development plans, land use master plans, and urban and rural plans. During data collection, relevant departments and enterprises should be organized to fill in the work forms according to the specifications. Finally, on the basis of data sorting and verification, carry out summary analysis work and database construction.

[0330] The evaluation of the intensive land use of industrial functional areas in the whole city in 2020 was based on the "Technical Scheme for the Evaluation of Industrial Parks in Terms of 'Output per Mu' in 2022". The evaluation time point was uniformly January 1, 2020. There were 66 industrial functional areas in the whole city, including 45 industrial-dominated industrial functional areas. According to the analysis indicators in Table 4-3 and the index definitions in the "Technical Scheme for the Evaluation of Industrial Parks in Terms of 'Output per Mu' in 2022", the current values of the intensive land use indicators of industrial-dominated industrial functional areas in Chengdu were calculated, and the overall situation of intensive land use and the differences among different levels and types were analyzed from aspects such as land use status, land use efficiency, and management performance.

[0331] The evaluation index system includes three levels: objectives, sub-objectives, and indicators. The acquisition of indicator data is divided into two ways. The first way is based on the questionnaire data of the "Statistical Table of Land Use Carbon Emissions in Industrial Functional Areas of Chengdu" issued by the Chengdu Land Planning and Cadastral Affairs Center to the management committees of each industrial functional area to evaluate the intensive land use of each industrial functional area. The second way is based on the authoritative statistical data of government functional departments such as the basic database of industrial functional areas in Chengdu and the third national land survey to evaluate the intensive land use of industrial functional areas in Chengdu.

[0332] Evaluation Index System for the Intensive Degree of Industrial-Dominated Industrial Functional Areas

[0333]

[0334] Relationship between intensive land use and net carbon emissions in industrial functional areas

[0335] The analysis of the impact of land use on net carbon emissions is the focus and core of this study. In the analysis process, correlation analysis and a univariate regression model are mainly used. Correlation analysis is to analyze the degree of correlation between two variables pairwise. There are two calculation methods for correlation analysis used in this study, namely the Pearson correlation coefficient (suitable for quantitative data and the data satisfies the normal distribution) and the Spearman correlation coefficient (used when the data does not satisfy the normal distribution). The explanatory power of the model is characterized by R 2 (Rsquared, R square). The larger R 2 is, the stronger the explanatory power of the model; the authenticity of the model is characterized by the p value. p represents the difference between the predicted value and the true value of the model. The smaller the p value is, the closer the simulated value is to the real situation.

[0336] Linear regression is a statistical analysis method that uses regression analysis in mathematical statistics to determine the quantitative relationship of interdependence between two or more variables. In linear regression analysis, only one independent variable and one dependent variable are included, and the relationship between the two can be approximately represented by a straight line. This kind of regression analysis is called univariate linear regression analysis. If the independent variable and a dependent variable cannot be approximately represented by a straight line but are approximately represented by a curve, it is called univariate linear non-linear regression analysis.

[0337] Chengdu Industrial Functional Zones

[0338] Based on the background of Chengdu's "5 + 5 + 1" modern industries, 45 industrial-dominated industrial functional zones among the adjusted 66 industrial functional zones were selected as the research area, focusing on the relationship between the intensive land use and net carbon emissions in the core starting areas of industrial-dominated industrial functional zones. The research utilized the core starting area layer in the basic database of industrial functional zones, focusing on the relationship between the intensive land use and carbon emissions in the core starting areas. From the perspective of the evaluation scope, the total area of the core starting areas of industrial-dominated industrial functional zones is 284.64 square kilometers, and the average area of each industrial functional zone is approximately 6.33 square kilometers( Figure 14 ). Among them, the total number of annual employees is 528,675, the total annual tax revenue is 350,432,18,100 yuan, the total fixed asset investment is 4,859,530,000 yuan, the total area of land acquired but not supplied is 35.37 square kilometers, the total area of land not reported for acquisition is 103.75 square kilometers, the land supply area is 201.71 square kilometers, the area of land approved but not used is 56.2 square kilometers, the conditional construction area is 57.90 square kilometers, the permitted construction area is 215.37 square kilometers, the land acquisition area is 192.48 square kilometers, the average value of land development rate is 9.2%, the highest is the Chengdu East Suburb Memory Art Zone at 0.52%, and the lowest is the Xiling Snow Mountain Sports and Healthcare Industrial Functional Zone at 0.02%.

[0339] The research was based on the evaluation types of the development zones in the report on the evaluation of "evaluating heroes by per mu output" of the development zones in Chengdu in 2021. According to the location and dominant industries of the industrial functional zones, referring to the "Chengdu Territorial Spatial Master Plan", the industrial functional zones in Chengdu were divided into 21 industrial-city integration types and 45 industrial-dominated types. According to the parcel data within the industrial-dominated types, based on the actual uses of the parcel plots, the industrial functional zones with type I industrial land were divided into type I industrial land-dominated types, and the industrial functional zones with type II and III industrial land were divided into type II and III industrial land-dominated types.

[0340] The impact of industrial land on carbon emissions refers to the impact of the changes in industrial land itself and the social and economic activities it carries on carbon emissions. Industrial land has an indirect impact on urban carbon emissions through the social and economic activities it carries. Compared with natural ecosystems, the impact of social and economic activities on carbon emissions in urban areas is more significant. For example, changes in activities such as industrial production, urban transportation, and human life will bring about adjustments in energy demand and structure, thus affecting the carbon emission effect.

[0341] According to the internal plot data of the industrial-dominated type and the actual use of the plot, the first type of industrial land refers to industrial land that has basically no interference, pollution and safety hazards to the residential and public environment, and has no special control requirements for its layout; the second type refers to industrial land that has certain interference, pollution and safety hazards to the residential and public environment, and cannot be arranged in residential areas and areas with concentrated public facilities; the third type refers to industrial land that has serious interference, pollution and safety hazards to the residential and public environment, and has protection and isolation requirements for its layout.

[0342] According to the data from the third national land utilization survey, the top ten areas of industrial and warehousing land use are Qingbaijiang Advanced Materials Industry Functional Zone, Chengdu Electronic Information Industry Functional Zone, Longquanyi Automobile Industry Functional Zone, Chengdu Longtan New Economic Industry Functional Zone, Tianfu Chinese Medicine City, Chengdu Intelligent Application Industry Functional Zone, Chengdu Modern Industrial Port (provincial level), Chengdu Medical City, Xindu Modern Transportation Industry Functional Zone, and Bailuwan New Economic Headquarters Functional Zone (the proportions are 27.89%, 27.39%, 18.92%, 13.80%, 13.53%, 13.47%, 11.72%, 11.42%, 10.93%, and 10.74% respectively).

[0343] According to the division of plots in the core starting areas of industrial functional zones, the top ten types of Class A industrial land are Qingbaijiang European Industrial City, Qingbaijiang Advanced Materials Industrial Functional Zone, Chengdu New Materials Industrial Functional Zone, Chengdu Modern Industrial Port, Tianfu New Area New Energy and New Materials Industrial Functional Zone, Chengdu Electronic Information Industrial Functional Zone, Chengdu Sichuan Cuisine Industrial Park, Chengdu Longtan New Economic Industrial Functional Zone, Dayi Cultural and Sports Intelligent Equipment Industrial Functional Zone, and Xindu Modern Transportation Industrial Functional Zone (the proportions are 99.29%, 91.36%, 90.23%, 87.07%, 77.08%, 76.59%, 76.52%, 75.86%, 74.92%, and 70.52% respectively). There are 9 industrial functional zones with Class II industrial land, namely Chengdu Aerospace Industrial Functional Zone, Tianfu Modern Seed Industry Park, Chengdu Medical City, Qionglai Green Food Industrial Functional Zone, Chengdu Sino-French Ecological Park, Tianfu Intelligent Manufacturing Industrial Park, Longquanyi Automobile Industrial Functional Zone, Qingbaijiang Advanced Materials Industrial Functional Zone, Chengdu International Trade City Functional Zone (the proportions are: 84.54%, 67.42%, 37.80%, 13.96%, 13.57%, 10.98%, 8.30%, 7.75%, 0.70%). There are 2 industrial functional zones with Class III industrial land, namely Tianfu Traditional Chinese Medicine City (4.87%) and Jinniu Technology Service Industrial Functional Zone (0.98%).

[0344] Theoretical basis:

[0345] Carbon Peak

[0346] When the annual carbon dioxide emissions in a certain region or industry reach a historical high and then enter a plateau period followed by a continuous decline, it marks the historical turning point of the transition from increasing to decreasing carbon dioxide emissions, indicating the decoupling of carbon emissions from economic development. The peak target includes the peak year and the peak value.

[0347] Carbon neutrality

[0348] The total amount of carbon dioxide or greenhouse gas emissions directly or indirectly generated by a country, enterprise, product, activity or individual within a certain period of time. Through forms such as afforestation and energy conservation and emission reduction, it offsets its own carbon dioxide or greenhouse gas emissions, achieving a net zero balance and reaching relative "zero emissions".

[0349] Carbon sink

[0350] A carbon sink refers to the process, activity or mechanism that absorbs carbon dioxide in the atmosphere through measures such as afforestation and vegetation restoration, thereby reducing the concentration of greenhouse gases in the atmosphere.

[0351] Carbon source

[0352] A carbon source refers to the process, activity or mechanism that releases carbon into the atmosphere. In nature, the main carbon sources are the ocean, soil, rocks and organisms. In addition, industrial production, daily life, etc. will also produce greenhouse gases such as carbon dioxide, which are also the main carbon emission sources.

[0353] Carbon accounting

[0354] Measures to measure the direct and indirect emissions of carbon dioxide and its equivalent gases from industrial activities into the Earth's biosphere. It refers to a series of activities in which emission control enterprises collect, count and record data on carbon emission-related parameters according to the monitoring plan, and calculate and accumulate all emission-related data.

[0355] Carbon emission efficiency

[0356] Used to reflect the carbon emission performance, that is, the level of productivity in the industry under a given carbon emission level.

[0357] Intensive land use

[0358] The origin of intensive land use did not take shape overnight. It can be traced back to the earliest use of agriculture. The essence of intensive land use is the issue of land input and output, that is, on the basis of limited land, obtaining higher output and benefits with relatively low input. It mainly has the following meanings:

[0359] (1) Intensive use of plots. It refers to the intensive use on a certain fixed area of plots.

[0360] (2) Intensive utilization of the same type of land. For example, the research on the intensive utilization of industrial land, the intensive utilization of agricultural land, and the intensive utilization of commercial land, etc.

[0361] (3) Intensive utilization of land in a certain area. Various types of land are mixed together, and through reasonable utilization and allocation, the economic output benefit within the area is maximized, so as to achieve the optimal value of the intensive utilization of the land in this area.

[0362] Intensive land use

[0363] An industrial factor agglomeration platform formed by the combination of a specific industry and urban spatial planning, which includes the functional zoning of the comprehensive construction of the agglomeration of industrial factors, the supporting of living services, etc., promotes the integrated and chain-like development of the industrial chain, improves the supporting service capacity of the city for this industry, promotes the comprehensive layout of talent construction and industrial construction, promotes the integrated development of industry and city, and further promotes the high-quality development of the regional economy.

[0364] Optimal allocation of land resources

[0365] In order to achieve certain social, ecological and economic goals, for the land within the region, based on the characteristics of the land, some scientific and technological means and advanced management methods are adopted to optimize in terms of quantity structure, utilization mode, etc., and finally layout in space to improve the intensive degree and utilization efficiency of the land, and then realize the sustainable utilization of the land.

[0366] Estimation of carbon emissions and carbon sinks in industrial functional areas of Chengdu

[0367] The estimation and analysis of net carbon emissions is the basis for the study of the relationship between intensive land use and carbon emissions. This chapter mainly includes three parts: the estimation of carbon sink volume, the estimation of carbon emissions, and the estimation and analysis of net carbon emissions. Through the estimation of carbon emissions, the carbon sink volume and total carbon emissions of the city are obtained, and the net carbon emissions are calculated by the difference between the total carbon emissions and the carbon sink volume. According to the current situation of carbon sink volume, total carbon emissions, and net carbon emissions, analyze according to different types of industrial-dominated industrial functional areas to find out the internal laws.

[0368] Results of carbon emission estimation model

[0369] The results show that there is a high correlation between carbon emissions and building base area (correlation coefficient is 0.944 (0.000***)), land supply area (correlation coefficient is 0.951 (0.000***)), unused approved land area (correlation coefficient is 0.82 (0.013**)), permitted construction area (correlation coefficient is 0.949 (0.000***)), land acquisition area (correlation coefficient is 0.918 (0.001***)), actual floor area ratio (correlation coefficient is 0.758 (0.029**)), actual building density (correlation coefficient is 0.611 (0.108**)), total light intensity (I) (correlation coefficient is 0.656 (0.078*)), normalized value (N) (correlation coefficient is 0.676 (0.066*)), and average light intensity (k) (correlation coefficient is 0.527 (0.764**)), and the significance test is passed (* indicates the significance level, *: p < 0.05; **: p < 0.01; ***: p < 0.001).

[0370] The above indicators were selected for multiple regression analysis with carbon emissions. According to the analysis results, aiming to explore the most suitable carbon emission inversion model for Chengdu, univariate correlation analysis was conducted on the total carbon emissions of the industrial functional areas in Chengdu with the total light intensity, normalized value, average light intensity combined with the building base area, land supply area, unused approved land area, permitted construction area, land acquisition area, actual floor area ratio, and actual building density. According to the correlation between each indicator and carbon emissions, the most suitable carbon emission inversion indicators for Chengdu were explored. For the model passing the significance test (p < 0.05), according to the principle of the largest R2 (Rsquared, coefficient of determination) and the smallest RMSE (root mean squared error), model 5 was finally selected for the total carbon emission accounting of this study.

[0371] Comparison of carbon emission inversion models

[0372]

[0373] Estimation results of the CASA model

[0374] NPP is the basis for estimating the carbon sink. The NPP of Chengdu in 2020 is as Figure 15 shown. The total NPP of Chengdu is 165,391,661 gC, and the average value is 381.34 gC / m2. NPP is relatively high in the western and eastern regions and relatively low in the central and northern regions.

[0375] Estimation results of total carbon emissions

[0376] Overall situation of industrial functional areas

[0377] In 2020, the total carbon emissions of Chengdu's industrial functional zones were 2,536,581,6381 tons, and the average carbon emissions of industrial functional zones were 378,594,274 tons. The industrial functional zones that exceeded the average carbon emissions of Chengdu include Qingyang Headquarters Economic Zone, Western E-commerce Logistics Industrial Functional Zone, Shuangliu Aviation Economic Zone, Chengdu Electronic Information Industrial Functional Zone, Longquanyi Automobile Industrial Functional Zone, Chengdu Core Valley, Jinniu, High-tech Industrial Functional Zone, Chengdu Longtan New Economic Industrial Functional Zone, Chengdu Sino-French Ecological Park, Wuhou E-commerce Industrial Functional Zone, Tianfu Water City, Jinniu Technology Service Industrial Functional Zone, Chengdu International Trade City Functional Zone, Shaocheng International Cultural and Creative Valley, Bailuwan New Economic Headquarters Functional Zone, Tianfu Headquarters Business District, Chengdu New Economic Vitality Zone, Jiaozi Park Financial Business District, West China Health Industry Functional Zone, Three Kingdoms Creative Park, Chengdu East Suburb Memory Art District, Jinjiang Emerging Media Functional Zone, Chengdu Qingyang Cultural and Financial Business District, Chunxi Road Fashion Vitality Zone ( Figure 16 , statistics of total carbon emissions in Chengdu’s industrial functional zones (left) and total carbon emissions per unit area (right).

[0378] In 2020, the average carbon emissions per unit area of ​​Chengdu's industrial functional zones was 115.24 tons. The industrial functional zones that exceeded the average carbon emissions per unit area of ​​Chengdu include Qingbaijiang European Industrial City, Western E-commerce Logistics Industrial Functional Zone, Wuhou E-commerce Industrial Functional Zone, Tianfu Water City, Jinniu High-tech Industrial Functional Zone, Chengdu Core Valley, Egret Bay New Economy Headquarters Functional Zone, Chunxi Road Fashion Vitality Zone, Chengdu East Suburb Memory Art Zone, High-tech Aviation Economic Zone, Three Kingdoms Creative Park, West China Health Industry Functional Zone, Chengdu Qingyang Cultural and Financial Business District, Jinjiang Emerging Media Functional Zone ( Figure 16 ).

[0379] The spatial distribution of carbon emissions in Chengdu’s industrial functional areas is shown in Figure 2. Figure 17 and Figure 18 The carbon emissions of industrial functional zones in the central part are generally high, such as Chunxi Road Fashion Vitality Zone, Jiaozi Park Financial Business Zone, Huaxi Health Industry Functional Zone, Chengdu New Economic Vitality Zone, and Chengdu East Suburb Memory Art Zone. The carbon emissions per unit area of ​​industrial functional zones in the central region are also high. In addition, the carbon emissions per unit area of ​​the high-tech aviation economic zone in the east are also high.

[0380] The total carbon emissions of the industrial-dominated industrial functional areas were 1,492,958,7695 tons. The total carbon emissions of the Chengdu East Suburb Memory Art District were the highest, at 1,041,804,842 tons, while the total carbon emissions of the Xiling Snow Mountain Sports and Health Care Industrial Functional Area were the lowest, at 2,809,549 tons ( Figure 19 In terms of carbon emissions per unit area, Chengdu East Suburb Memory Art District has the highest carbon emissions per unit area, which is 286.43 tons per square meter, while Xiling Snow Mountain Sports and Health Care Industry Functional Zone has the lowest carbon emissions per unit area, which is 0.42 tons per square meter ( Figure 19, statistics of total carbon emissions in Chengdu’s industrial-dominated industrial functional zones (left) and total carbon emissions per unit area (right).

[0381] Industry-led

[0382] The total carbon sink of the first-class industrial-dominated industrial functional areas is 40,332,142.5 tons. The total carbon sink of Sanlang International Tourism Resort is the highest, which is 5,210,686 tons, while the total carbon sink of Qingbaijiang European Industrial City is the lowest, which is 235,294.5 tons ( Figure 20 In terms of carbon sink per unit area, the Xiling Snow Mountain Sports and Health Care Industrial Functional Zone has the highest carbon sink per unit area, which is 0.42 tons per square meter, while the Qingbaijiang European Industrial City has the lowest carbon sink per unit area, which is 0.09 tons per square meter ( Figure 20 , the total carbon sink (left) and carbon sink per unit area (right) of the first-class industrial-dominated industrial functional zones in Chengdu.

[0383] The net carbon emissions of industrial-dominated industrial functional areas were 14876031313 tons, the net carbon emissions of Chengdu East Suburb Memory Art District were the highest, at 1041349261 tons, and the net carbon emissions of Xiling Snow Mountain Sports and Health Care Industrial Functional Area were the lowest, at 6051.60 tons ( Figure 20 In terms of carbon emissions per unit area, Chengdu East Suburb Memory Art District has the highest net carbon emissions per unit area, which is 286.31 tons per square meter, while Xiling Snow Mountain Sports and Health Care Industrial Functional Zone has the lowest net carbon emissions per unit area, which is 0.01 tons per square meter ( Figure 21 , statistics of net carbon emissions in Chengdu’s industrial-dominated industrial functional zones (left) and net carbon emissions per unit area (right).

[0384] The net carbon emissions of the first-class industrial-dominated industrial functional areas were 1,138,901,1533 tons. The net carbon emissions of the Chengdu East Suburb Memory Art District were the highest, at 1,041,349,261 tons, while the net carbon emissions of the Xiling Snow Mountain Sports and Health Care Industrial Functional Area were the lowest, at 6,051.60 tons ( Figure 22 , Statistics of net carbon emissions of first-class industrial-dominated industrial functional zones in Chengdu (left) and statistics of net carbon emissions per unit area (right). In terms of carbon emissions per unit area, Chengdu Dongjiao Memory Art District has the highest net carbon emissions per unit area, which is 286.31 tons per square meter, while Xiling Snow Mountain Sports and Health Care Industrial Functional Zone has the lowest net carbon emissions per unit area, which is 0.01 tons per square meter ( Figure 22 ).

[0385] The net carbon emissions of the second and third type industrial-dominated industrial functional areas were 3487019780 tons. The net carbon emissions of the Chengdu International Trade City functional area were the highest, at 657263804 tons, while the net carbon emissions of the Chengdu Medical City were the lowest, at 95208938.3 tons ( Figure 23, Statistics of net carbon emissions of the second and third category industrial-dominated industrial functional zones in Chengdu (left) and statistics of net carbon emissions per unit area (right). In terms of carbon emissions per unit area, Tianfu Traditional Chinese Medicine City has the highest net carbon emissions per unit area, which is 108.78 tons per square meter, and Shuangliu Aviation Economic Zone has the lowest net carbon emissions per unit area, which is 13.21 tons per square meter ( Figure 23 ).

[0386] The carbon sinks and carbon emissions of industrial functional zones dominated by industry, first-class industrial dominant, and second- and third-class industrial dominant were analyzed from the three aspects of total carbon emissions, carbon sinks, and net carbon emissions. The results showed that the net carbon emissions of industrial functional zones dominated by industry were 1.48×10 10 Tons, among the industrial-dominated types, the net carbon emissions of Chengdu East Suburb Memory Art District are the highest, while the net carbon emissions of Xiling Snow Mountain Sports and Health Care Industry Functional Zone are the lowest. Among the first type of industrial-dominated types, the net carbon emissions of the East Suburb Memory Art District are the highest, while the net carbon emissions of the Xiling Snow Mountain Sports and Health Care Industry Functional Zone are the lowest. The high building density, comprehensive per capita tax revenue, and population density of the East Suburb Memory Art District are the reason for the highest net carbon emissions. There is no industrial land distribution in the Xiling Snow Mountain Sports and Health Care Industry Functional Zone, and the proportion of forest land and residential land is relatively large, which is the reason for the lowest net carbon emissions. Among the second and third types of industrial-dominated types, the net carbon emissions of the Chengdu International Trade City Functional Zone are the highest, while the net carbon emissions of the Chengdu Medical City are the lowest. The Chengdu International Trade City Functional Zone has the largest proportion of industrial land, and the fixed asset investment intensity and per capita tax revenue of industrial land are relatively high, which is the reason for the highest net carbon emissions. The low proportion of industrial land in the Chengdu Medical City, and the high proportion of cultivated land and forest land are the reasons for the lowest net carbon emissions.

[0387] Intensive land use in industrial functional areas of Chengdu

[0388] The land development rate is obtained by dividing the land area that has met the land supply conditions within the approved scope of the industrial functional zone by the land area other than the non-construction land. The average land development rate of each industrial functional zone is about 0.24%. Among them, Chengdu Qingyang Cultural and Financial Business District and Wenjiang Urban Modern Agricultural High-tech Industrial Park have the highest land development rate, reaching 0.71%, which is about 37 times that of the Xiling Snow Mountain Sports and Health Care Industrial Functional Zone (0.02%) with the lowest land development rate. Figure 24 ).

[0389] The land supply rate is the area of ​​state-owned construction land that has been supplied divided by the area of ​​land that has met the land supply conditions. The average land supply rate of each industrial functional zone is around 32.58%. Among them, the land supply rate of the West China Health Industry Functional Zone is the highest, reaching 79.73%; the land supply rate of Tianfu Cultural and Creative City is the lowest, only 0.78% ( Figure 25 ).

[0390] The land completion rate is the area of ​​completed urban construction land divided by the area of ​​supplied state-owned construction land. The average land completion rate of each industrial functional zone is about 47.28%. Among them, the land completion rate of Chengdu International Trade City Functional Zone is the highest, reaching 89.40%, which is about 121 times that of Chengdu Aerospace Industry Functional Zone (0.74%) with the lowest land completion rate. Figure 26 ).

[0391] The industrial land rate is obtained by dividing the area of ​​industrial and mining storage land by the area of ​​built urban construction land. The average industrial land rate of each industrial functional zone is about 40.68%. Among them, the industrial land rate of Chengdu International Trade City Functional Zone is the highest, reaching 99.10%; the industrial land rate of Tianfu Headquarters Business District, China Tianfu Agricultural Expo Park, Chunxi Road Fashion and Vitality Zone, Three Kingdoms Creative Park, West China Health Industry Functional Zone, Chengdu Qingyang Cultural and Financial Business District and Jinjiang Emerging Media Functional Zone is 0 ( Figure 27 ).

[0392] The comprehensive plot ratio is the total building area of ​​the completed urban construction land divided by the completed urban construction land area. This indicator has a great impact on the intensive evaluation score of the industrial functional zone. The average comprehensive plot ratio of each industrial functional zone is around 3.63. Among them, the Jinjiang Emerging Media Functional Zone has the highest comprehensive plot ratio, 9.47; the Sanlang International Tourism Resort has the lowest comprehensive plot ratio, 0.10 ( Figure 28 ).

[0393] The building density is the total building base area within the completed urban construction land divided by the completed urban construction land area. This indicator has a great impact on the intensive evaluation score of the industrial functional zone. The average building density of each industrial functional zone is about 52.36%. Among them, the Tianfu Headquarters Business District has the largest building density, reaching 98.00%; the Pujiang Modern Agricultural Industrial Park, Xiling Snow Mountain Sports and Health Industry Functional Zone, Tianfu Cultural and Creative City, Longmenshan Qianjiang River Valley Ecological Tourism Zone, Huaizhou New City, Tianfu Modern Seed Industry Park, Dayi Sports and Intelligent Equipment Industrial Functional Zone and Dujiangyan Jinghua Irrigation District Health Industry Functional Zone have a building density of 0( Figure 29 ).

[0394] The comprehensive plot ratio of industrial land is the total building area of ​​industrial and mining storage land within the scope of built-up urban construction land divided by the area of ​​industrial and mining storage land. This indicator has a great impact on the intensive evaluation score of industrial functional zones. The comprehensive plot ratio of industrial land in each industrial functional zone is about 1.98. Among them, the comprehensive plot ratio of industrial land in Jinjiang Emerging Media Functional Zone is the largest, reaching 6.29, which is about 70 times that of Chengdu Modern Industrial Port (0.09), which has the smallest comprehensive plot ratio of industrial land. Figure 30 ).

[0395] The building coefficient of industrial land is the total area of building structures, open storage yards, and open operation sites on industrial and mining storage land within the built-up urban construction land area, divided by the area of industrial and mining storage land. This indicator has a significant impact on the intensive evaluation score of industrial functional areas. The building coefficient of industrial land in each industrial functional area is approximately 0.47. Among them, the building coefficient of industrial land in Chengdu Longtan New Economy Industrial Functional Area is the largest, at 1; the building coefficient of industrial land in Chengdu Panda International Tourism Resort Area is the smallest, at 0.06( Figure 31 ).

[0396] The fixed asset investment intensity of industrial land is obtained by dividing the fixed asset investment of industrial (logistics) enterprises by the area of industrial and mining storage land. This indicator has a significant impact on the intensive evaluation score of industrial functional areas. The fixed asset investment intensity of industrial land in each industrial functional area is around 51.92. Among them, the fixed asset investment intensity of industrial land in the High-tech Aviation Economic Zone is the highest, at 1213.13; the fixed asset investment intensity of industrial land in Chengdu Qingyang Cultural and Financial Business District, Chunxi Road Fashion and Vitality Area, Huaxi Big Health Industrial Functional Area, Jinjiang Emerging Media Functional Area, Three Kingdoms Creative Park, and China Tianfu Agricultural Expo Park is 0( Figure 32 ).

[0397] Impact of intensive land use and carbon emissions in industrial functional areas of Chengdu

[0398] Land is the spatial carrier of natural ecological systems and social economic systems, and natural ecological systems and social economic systems are the fundamental sources of carbon emissions. During the rapid urbanization process, the changes in natural ecological systems and social economic systems are reflected through land use changes, thus having a profound impact on carbon emissions. This chapter focuses on analyzing the characteristics of land use and net carbon emissions during the rapid urbanization process. Based on the analysis of the impact mechanism of land use change on net carbon emissions, a land-carbon relationship analysis framework is constructed to provide a comprehensive analysis idea for solving the quantitative analysis of land-carbon and the formulation of emission reduction policies.

[0399] Impact of sub-indicators of intensive land use in industrial-dominated industrial functional areas on net carbon emissions

[0400] (I) Analysis of the impact of land use degree on net carbon emissions

[0401] Within the industrial-dominated industrial functional area, regression analysis was conducted on the land use degree and its sub-indicators and net carbon emissions in sequence, in order to clarify the impact of the land use degree in the industrial-dominated industrial functional area on carbon emissions. Figure 33(a)-(d) are the regression results of sub-indicators such as land development rate, land to-be-developed rate, land supply rate, and land completion rate on net carbon emissions. It can be seen from the figure that: (1) The growth rate of net carbon emissions is basically the same as that of the land development rate. In this stage, the impact of the land development rate on carbon emissions shows a synchronous growth relationship. The net carbon emissions increase linearly with the growth of the land development rate. In addition, it can also be seen from the figure that the correlation between the land development rate and carbon emissions is relatively strong and has a greater impact; (2) The net carbon emissions and the land to-be-developed rate show an obvious reverse trend. As the land completion rate decreases, the net carbon emissions also decrease; (3) The net carbon emissions and the land supply rate show a positive correlation. As the land supply increases, the net carbon emissions also show an increasing trend; (4) There is a positive correlation between the net carbon emissions and the land completion rate. The net carbon emissions increase significantly with the increase of the land completion rate. In addition, according to the quadratic coefficient of the regression equation, it can be known that the factor with the greatest impact on carbon emissions is the land development rate, followed by the land supply rate and the land completion rate, and the factor with the smallest impact is the land to-be-developed rate.

[0402] From the regression results of the degree of land use and net carbon emissions ( Figure 34 ), the p-value is less than 0.05, and R 2 is greater than 0.5. The two show a significant positive correlation, and the regression results are reliable. The carbon emissions show a certain fluctuating growth characteristic with the growth of the degree of land use, and it is obtained by derivation that the carbon emissions show a relatively obvious growth trend after the degree of land use reaches 0.5. This is because with the rapid economic development of Chengdu, the scale of construction land has expanded rapidly, and the use of fossil fuels such as coal, oil, and natural gas has also increased rapidly. Therefore, the growth rate of carbon emissions has gradually accelerated. Generally speaking, the degree of land use has a positive impact on carbon emissions. As the degree of land use increases, the relevant supporting industries and facilities are gradually improved, forming a "siphon effect" in industrial functional areas. The development of industries has created more labor-intensive employment opportunities; the aggregation of the population has provided the demand for people to serve each other; the concentrated investment in infrastructure and public service facilities is more likely to generate economies of scale, and the degree of carbon emissions increases exponentially.

[0403] Analysis of the impact of land use structure on net carbon emissions

[0404] In the industrial-dominated industrial functional area, a regression analysis was carried out on the land use structure and net carbon emissions to clarify the impact of the land use structure in the industrial-dominated industrial functional area on carbon emissions. From the regression results of the industrial land rate and carbon emissions in the industrial-dominated industrial functional area, p is less than 0.05, and R 2 is greater than 0.5. The industrial land rate and net carbon emissions show a significant positive correlation, and the fitting effect is good. The industrial land rate shows a gradually increasing growth trend, and the carbon emissions show a certain exponential growth characteristic ( Figure 35)。It can be seen from the derivative of the regression equation that the industrial land use rate shows a relatively obvious growth trend after 0.4. The land use structure refers to the industrial land use rate. The increase in the supply scale of industrial land will lead to the expansion of the industrial scale, resulting in a significant increase in industrial energy carbon emissions. At the same time, through the industrial land supply structure, it affects the carbon emissions per unit output and per capita industrial energy carbon emissions, mainly because it supplies industries with high carbon emission intensities in energy production and heavy industries.

[0405] (2) Analysis of the impact of land use intensity on net carbon emissions

[0406] In the industrial-dominated industrial functional area, regression analyses were successively conducted on land use intensity and its sub-indicators and net carbon emissions, aiming to clarify the impact of land use intensity in the industrial-dominated industrial functional area on carbon emissions. Figure 36 It reflects the regression results of sub-indicators such as the comprehensive plot ratio, building density, comprehensive plot ratio of industrial land, and building coefficient of industrial land with carbon emissions. It can be seen from the figure that: (1) The p-values of the regression results of each sub-indicator are all less than 0.1, and R 2 is greater than 0.5, indicating that there is a positive correlation between each sub-indicator of land use intensity and carbon emissions, and the results are credible. (2) The growth rates of carbon emissions and the comprehensive plot ratio are basically the same. At this stage, the impact of the comprehensive plot ratio on carbon emissions shows a synchronous growth relationship. Carbon emissions increase linearly with the growth of the comprehensive plot ratio. (3) There is an obvious positive correlation between carbon emissions and building density, comprehensive plot ratio of industrial land, and building coefficient of industrial land, and the trends are roughly the same. In addition, according to the quadratic term coefficient of the regression equation, it can be known that the building coefficient of industrial land is the factor with the greatest impact on carbon emissions, followed by building density and comprehensive plot ratio of industrial land, and the comprehensive plot ratio is the factor with the smallest impact.

[0407] From the overall trend of land use intensity and carbon emissions ( Figure 37 ), there is a positive correlation between land use intensity and carbon emissions, and carbon emissions increase linearly with the growth of land use intensity. Land use intensity is a comprehensive reflection of the comprehensive plot ratio, building density, comprehensive plot ratio of industrial land, and building coefficient of industrial land in the industrial functional area. With the increase in building density and plot ratio, the development of related industries gradually improves, and carbon emissions increase accordingly.

[0408] (3) Analysis of the impact of land use efficiency on net carbon emissions

[0409] In the industrial-dominated industrial functional area, regression analyses were successively conducted on land use efficiency and its sub-indicators and net carbon emissions, aiming to clarify the impact of land use efficiency in the industrial-dominated industrial functional area on carbon emissions. Figure 38Reflects the regression results of sub - indicators such as the fixed - asset investment intensity of industrial land and the land - average tax revenue of industry on carbon emissions. It can be seen from this: (1) The p - values of the regression results are all less than 0.1, and R 2 is all greater than 0.5. Each sub - indicator has a positive correlation with carbon emissions, and the fitting effect is good. (2) The growth rate of carbon emissions is basically the same as that of the land - average tax revenue of industrial land. In this stage, the impact of the land - average tax revenue of industrial land on carbon emissions shows a synchronous growth relationship. Carbon emissions increase linearly with the growth of the land - average tax revenue of industrial land. (3) There is an obvious positive correlation between carbon emissions and the fixed - asset investment intensity of industrial land, and the trend is generally the same as the overall trend.

[0410] From the overall trend of land - use efficiency and carbon emissions ( Figure 39 ), the p - value is less than 0.1, and R 2 is greater than 0.5. There is a positive correlation between land - use efficiency and carbon emissions, and carbon emissions show an exponential growth characteristic with the growth of land - use efficiency. This may be due to the acceleration of the urbanization process and the continuous increase in industrial land investment, which prompts other land - use types to be converted into land mainly for economic construction purposes, and a large number of ecological lands with carbon - sink functions disappear in a concentrated manner. In addition, from the quadratic - term coefficient of the regression equation, it can be seen that the impact of the land - average tax revenue of industrial land on carbon emissions is the largest, followed by the fixed - asset investment intensity of industrial land.

[0411] (4) Analysis of the impact of land - use supervision performance on carbon emissions

[0412] In the industrial - dominated industrial functional area, a regression analysis was carried out on the land - use supervision performance and carbon emissions to clarify the impact of the land - use supervision performance in the industrial - dominated industrial functional area on carbon emissions. From the regression results of the land - idle rate and carbon emissions in the industrial - dominated industrial functional area ( Figure 40 ), p < 0.1, R 2 > 0.5. The regression fitting degree is good. There is a negative correlation between land - use supervision performance and carbon emissions. As the land - idle rate increases, carbon emissions show a gradually decreasing trend, but reach the minimum value of carbon emissions near a land - idle rate of 0.8.

[0413] Type - I industrial - dominated

[0414] (1) Analysis of the impact of land - use degree on net carbon emissions

[0415] In the type - I industrial - dominated industrial functional area, a regression analysis was successively carried out on the land - use degree and its sub - indicators and net carbon emissions to further explore the impact of the land - use degree in the type - I industrial - dominated industrial functional area on carbon emissions. Figure 41(a)-(d) reflect the regression results of land development rate, land to-be-developed rate, land supply rate, and land built-up rate on carbon emissions. It can be seen from this that: (1) The growth rate of net carbon emissions is basically the same as that of the land development rate. At this stage, the impact of the land development rate on carbon emissions shows a synchronous growth relationship. The carbon emissions increase linearly with the growth of the land development rate; (2) The net carbon emissions and the land to-be-developed rate show an obvious reverse trend. As the land built-up rate decreases, the carbon emissions also decrease; (3) The net carbon emissions and the land supply rate show a positive correlation. As the land supply increases, the carbon emissions also show an increasing trend; (4) There is a positive correlation between the net carbon emissions and the land built-up rate. The carbon emissions increase significantly with the increase of the land built-up rate. In addition, from the quadratic term coefficient of the regression equation, the influence of each index on net carbon emissions can be obtained: The land development rate is the most influential factor, followed by the land supply rate, the land to-be-developed rate, and the land built-up rate is the least influential factor.

[0416] From the overall regression results of land use degree and net carbon emissions ( Figure 42 ), p < 0.05, R 2 > 0.5. There is a significant positive correlation between land use degree and net carbon emissions, and the regression fitting effect is good. The net carbon emissions increase exponentially with the growth of land use degree. This is because urbanization leads to more investment supply and consumption demand through economic development, putting forward higher requirements for various public service facilities including transportation, housing, medical and health care, groundwater facilities, and urban greening. The construction, maintenance, and operation of facilities and the enhancement of consumption demand all consume more fossil energy to a certain extent, resulting in a rapid increase in carbon emissions.

[0417] (2) Analysis of the impact of land use structure on net carbon emissions

[0418] In the industrial-dominated industrial functional area of type I, a regression analysis was carried out on the land use structure and net carbon emissions to further clarify the impact of the land use structure of the industrial-dominated industrial functional area of type I on carbon emissions. From the regression results of the industrial land rate and net carbon emissions in the industrial-dominated industrial functional area of type I ( Figure 43 ), p < 0.05, R 2 > 0.5. There is a significant positive correlation between the land use structure and net carbon emissions, and the model fitting degree is good. The industrial land rate shows a gradually increasing trend, and the net carbon emissions show an exponential growth characteristic. After the industrial land rate reaches 0.4, there is a relatively obvious growth trend. This may be because the development of various energy sources and the construction of infrastructure in the industrial functional area have changed the original land use structure to a certain extent, damaged the ecological environment, caused environmental pollution, led to a decline in the terrestrial carbon sink capacity, and caused an increase in carbon emissions.

[0419] (3) Analysis of the Impact of Land Use Intensity on Net Carbon Emissions

[0420] In the industrial-dominated industrial functional area of Type I, regression analysis was conducted on land use intensity, its sub-indicators, and net carbon emissions in sequence to further clarify the impact of land use intensity in industrial functional areas under different industrial categories on carbon emissions. Figure 44 It reflects the regression results of sub-indicators such as comprehensive plot ratio, building density, comprehensive plot ratio of industrial land, and building coefficient of industrial land on net carbon emissions. It can be seen from the regression results that: (1) The p-values are all less than 0.1, and R 2 is all greater than 0.5, indicating that there is a significant positive correlation between each sub-indicator and net carbon emissions, and the regression fitting degree is good; (2) The growth rate of net carbon emissions is basically the same as that of the comprehensive plot ratio of industrial land. At this stage, the impact of the comprehensive plot ratio on carbon emissions shows a synchronous growth relationship. Carbon emissions increase linearly with the increase of the comprehensive plot ratio; (3) There is an obvious positive correlation between net carbon emissions and building density and the building coefficient of industrial land, and the trends are generally the same. In addition, based on the regression equation, the comprehensive plot ratio of industrial land has the greatest impact on carbon emissions, followed by the building coefficient of industrial land and building density, and the factor with the smallest impact is the comprehensive plot ratio.

[0421] From the overall trend of land use intensity and net carbon emissions ( Figure 45 ), p < 0.1, R 2 > 0.5, there is a significant positive correlation between land use intensity and net carbon emissions, and the model fitting degree is good. The land use intensity shows a gradually increasing trend, and the net carbon emissions increase linearly with the increase of land use intensity. This is because Chengdu is in a period of rapid social and economic development, the urbanization rate is increasing rapidly, while the population is gathering in the city, a large amount of non-urban land is transformed into urban land, and a large number of industries have also moved to the periphery of the city. Therefore, it will inevitably drive the rapid growth of carbon emissions.

[0422] (4) Analysis of the Impact of Land Use Efficiency on Net Carbon Emissions

[0423] In the industrial-dominated industrial functional area of Type I, regression analysis was conducted on land use efficiency, its sub-indicators, and net carbon emissions in sequence to clarify the impact of land use efficiency in different industrial-dominated industrial functional areas on carbon emissions. Figure 46It reflects the regression results of sub-indicators such as per capita tax revenue for industrial land and investment intensity of fixed assets for industrial land and net carbon emissions. It can be seen from the figure that net carbon emissions are significantly positively correlated with per capita tax revenue for industrial land and investment intensity of fixed assets for industrial land. During this period, the impact of per capita tax revenue for industrial land and investment intensity of fixed assets for industrial land on net carbon emissions is synchronously increasing, and net carbon emissions increase linearly with the growth of the two. This reflects that the industrial development process in Chengdu has gradually changed from extensive utilization of land resources to intensive utilization, and the increase in population has led to an increase in energy consumption, which has caused a gradual increase in carbon emissions.

[0424] From the overall regression results of land use efficiency and net carbon emissions ( Figure 47 ), p is less than 0.05, R 2 When the land use efficiency is greater than 0.5, there is a significant positive correlation between land use efficiency and net carbon emissions, and the regression fitting effect is good. The land use efficiency shows a gradual upward trend, and the net carbon emissions increase linearly with the growth of land use efficiency. This shows that the economic growth of Chengdu has accelerated the development of high-carbon industries to a certain extent, causing an increase in demand for energy, transportation, etc., and carbon emissions have increased accordingly. In addition, according to the slope of the regression equation, it can be seen that the comprehensive per capita tax has the greatest impact on carbon emissions, followed by the fixed asset investment intensity of industrial land.

[0425] 5. Analysis on the impact of land use regulation performance on net carbon emissions

[0426] In a type of industrial-dominated industrial functional area, a regression analysis was conducted on land use supervision performance and net carbon emissions, and the impact of land use supervision performance on carbon emissions in a type of industrial-dominated industrial functional area was further analyzed. From the regression effect of land idle rate and net carbon emissions in a type of industrial-dominated industrial functional area ( Figure 48 ), p<0.05, R 2 >0.5, the idle land rate and net carbon emissions show a significant negative correlation, and the regression effect is good. As the idle land rate increases, net carbon emissions show a gradual downward trend, but the minimum carbon emissions are reached when the idle land rate is around 0.6-0.8.

[0427] 7.1.2 Type II and III Industry-Dominated Types

[0428] 1. Analysis of the impact of land use on net carbon emissions

[0429] In the second and third types of industrial-dominated industrial functional zones, regression analysis was conducted on land use intensity and its sub-indicators and net carbon emissions in turn to further explore the impact of land use intensity on carbon emissions in the second and third types of industrial-dominated industrial functional zones. Figure 49(a)-(d) reflect the regression results of sub - indicators such as land development rate, land to - be - developed rate, land supply rate, and land completion rate on net carbon emissions. From this, we can obtain: (1) The growth rate of net carbon emissions is basically the same as that of the land development rate. At this stage, the impact of the land development rate on carbon emissions shows a synchronous growth relationship. Net carbon emissions increase linearly with the growth of the land development rate. In addition, it can also be seen from the figure that the correlation between the land development rate and net carbon emissions is relatively strong and has a greater impact; (2) Net carbon emissions show an obvious reverse trend with the land to - be - developed rate. As the land completion rate decreases, net carbon emissions also decrease, but there is an obvious moderation trend near 0.7; (3) Net carbon emissions show a positive correlation with the land supply rate. As the land supply increases, net carbon emissions also show an increasing trend; (4) There is a synchronous growth relationship between net carbon emissions and the land completion rate. Net carbon emissions increase linearly with the increase of the land completion rate.

[0430] From the overall results of land use degree and net carbon emissions ( Figure 50 ), p < 0.05, R 2 > 0.5. There is a significant positive correlation between land use degree and net carbon emissions, and the regression fitting effect is good. The land use degree shows a gradually increasing trend, while net carbon emissions show an obvious linear growth characteristic. Based on the slope and coefficient of the regression equation, we know that the indicator with the greatest impact on net carbon emissions is the land development rate, followed by the land to - be - developed rate and land supply rate, and the indicator with the least impact is the land completion rate.

[0431] (II) Analysis of the impact of land use structure on net carbon emissions

[0432] In the industrial - dominated industrial function areas of the second and third categories, a regression analysis was conducted on the land use structure and net carbon emissions to further clarify the impact of the land use structure of the industrial - dominated industrial function areas of the second and third categories on carbon emissions. From the regression results of the industrial land rate and net carbon emissions in the industrial - dominated industrial function areas ( Figure 51 ), p < 0.05, R 2 > 0.5. There is a significant positive correlation between the land use structure and net carbon emissions, and the regression fitting effect is good. The industrial land rate shows a gradually increasing trend, while net carbon emissions show a certain exponential growth characteristic.

[0433] (III) Analysis of the impact of land use intensity on net carbon emissions

[0434] In the industrial - dominated industrial function areas of the second and third categories, a regression analysis was successively conducted on the land use intensity and its sub - indicators and net carbon emissions to further clarify the impact of the land use intensity of the industrial function areas under different industrial categories on carbon emissions. Figure 52(a)-(d) reflect the regression results of sub - indicators such as the comprehensive plot ratio, building density, comprehensive plot ratio of industrial land, and building coefficient of industrial land on net carbon emissions. It can be seen from the figure that: (1) The growth rate of net carbon emissions is basically the same as that of building density. At this stage, the impact of the comprehensive plot ratio on carbon emissions shows a synchronous growth relationship. The net carbon emissions increase linearly with the growth of the comprehensive plot ratio. In addition, it can also be seen from the figure that the correlation between building density and net carbon emissions is strong, and it has the greatest impact on net carbon emissions; (2) The relationship between net carbon emissions and the comprehensive plot ratio shows an inverted "U" - shaped trend. The net carbon emissions reach the peak when the comprehensive plot ratio is 4, and then gradually decrease; (3) There is an obvious positive correlation between net carbon emissions and the comprehensive plot ratio of industrial land and the building coefficient of industrial land, and the trends are generally the same.

[0435] From the overall regression results of land use intensity and net carbon emissions ( Figure 53 ), p < 0.1, R 2 < 0.5. There is a certain correlation between land use intensity and net carbon emissions, but the fitting effect is average. The net carbon emissions show a trend of first rising and then falling with the growth of land use intensity, and the inflection point is around 1.6. In addition, based on the slope and coefficient of the regression equation, the influence of each indicator on carbon emissions can be obtained: Building density is the indicator that has the greatest impact on net carbon emissions, followed by the building coefficient of industrial land and the comprehensive plot ratio of industrial land. The indicator that has the least impact on net carbon emissions is the comprehensive plot ratio.

[0436] (4) Analysis of the impact of land use efficiency on net carbon emissions

[0437] In the industrial - dominated industrial functional areas of the second and third categories, regression analyses were conducted on land use efficiency and its sub - indicators and net carbon emissions in turn to further clarify the impact of land use efficiency in industrial - dominated industrial functional areas on carbon emissions. Figure 54 It reflects the regression results of sub - indicators such as the fixed - asset investment intensity of industrial land and the land - average tax revenue of industrial land on net carbon emissions. It can be obtained from the figure that: The p - values are all less than 0.1, and R 2 are all greater than 0.5. There is a positive correlation between each sub - indicator and net carbon emissions, and the fitting effect is good.

[0438] From the overall results of land use efficiency and carbon emissions ( Figure 55 ), the p - value is less than 0.1, and R 2 is greater than 0.5. The regression fitting effect is good. There is a positive correlation between carbon emissions and land use efficiency, and the carbon emissions show an exponential upward trend with the growth of land use efficiency. In addition, based on the slope and coefficient of the regression equation, the influence of each indicator on carbon emissions can be further obtained: The land - average tax revenue of industrial land is the indicator that has the greatest impact on carbon emissions, followed by the fixed - asset investment intensity of industry.

[0439] (5) Analysis of the Impact of Land Use Supervision Performance on Net Carbon Emissions

[0440] In the industrial-dominated industrial functional areas of the second and third categories, a regression analysis was conducted on the land use supervision performance and net carbon emissions to further analyze the impact of the land use supervision performance in the industrial-dominated industrial functional areas of the second and third categories on carbon emissions. From the regression results of the land vacancy rate and net carbon emissions in the industrial-dominated industrial functional areas of the second and third categories ( Figure 56 ), p < 0.1, R 2 > 0.5, the regression fitting effect is good, and there is an obvious negative correlation between the land vacancy rate and net carbon emissions. As the land vacancy rate increases, the net carbon emissions show a gradually decreasing trend.

[0441] (6) Coupling Relationship between the Intensive Land Use Level and Net Carbon Emissions in Industrial-Dominated Industrial Functional Areas

[0442] In the industrial-dominated industrial functional areas, a coupling regression analysis was conducted on the intensive land use level and net carbon emissions to clarify the relationship between the intensive land use level and carbon emissions in the industrial-dominated industrial functional areas. From the coupling results of the intensive land use level and net carbon emissions in the industrial-dominated industrial functional areas ( Figure 57 ), P < 0.05, R 2 > 0.5, there is an obvious positive correlation between the intensive land use level and net carbon emissions. The net carbon emissions show a gradually increasing trend as the intensive land use level rises. This reflects that compared with the past, the industrial-dominated industrial functional areas pay more attention to the reasonable and efficient use of land resources and the research and selection of industrial sub-sectors. Each functional area accurately and differentially locates the leading industrial sub-sector directions based on its own industrial foundation, unique resource endowments, major urban functional facilities, and scientific forward-looking judgments on the future industrial development direction, and realizes dislocation and coordinated development. Therefore, the intensive land use level of the industrial-dominated type has a positive impact on carbon emissions.

[0443] Industrial-dominated of the first category

[0444] In the industrial-dominated industrial functional areas of the first category, a coupling regression analysis was conducted on the intensive land use level and net carbon emissions to further explore the relationship between the intensive land use level and carbon emissions in the industrial-dominated industrial functional areas of the first category. From the analysis results of the intensive land use level and net carbon emissions in the industrial-dominated industrial functional areas of the first category ( Figure 58 ), P < 0.05, R 2> 0.5, there is a significant positive correlation between the level of land intensive use and carbon emissions, and the net carbon emissions show a gradually increasing trend as the level of land intensive use rises. In recent years, thanks to the strong policy support in Chengdu, the industrial functional areas dominated by type I industries mainly focus on light industries, the proportion of the GDP of the tertiary industry has increased, the industrial structure has become more optimized, and high-clean energy enterprises have been vigorously developed. Therefore, the level of land intensive use in the industrial functional areas dominated by type I industries has a positive impact on net carbon emissions.

[0445] Type II and III industrial-dominated

[0446] In the industrial functional areas dominated by type II and III industries, a coupled regression analysis was conducted on the level of land intensive use and net carbon emissions to further explore the relationship between the level of land intensive use and carbon emissions in the industrial functional areas dominated by type II and III industries. From the analysis results of the level of land intensive use and net carbon emissions in the industrial functional areas dominated by type II and III industries ( Figure 59 ), p < 0.1, R 2 > 0.5, there is an obvious positive correlation between the level of land intensive use and net carbon emissions, and the net carbon emissions show a gradually increasing trend as the level of land intensive use rises. The industrial functional areas dominated by type II and III industries are led by the industrial basic capabilities, promoting the integration and agglomeration of the supply of production factors, the matching of industrial chains, the coordination of human resources, and the support of R & D and innovation. It has shifted from the traditional industrial functional areas that only focus on a single dimension of the industrial chain to the cultivation of an industrial ecosystem with the multi-dimensional aggregation of high-end factors such as talents, technologies, funds, and professional services, promoting the effective integration and penetration of the industrial chain, value chain, supply chain, and innovation chain. Therefore, the intensive level of land use in the industrial functional areas dominated by type II and III industries has a positive impact on carbon emissions.

[0447] Through the above analysis, the main conclusions are drawn: (1) When analyzing the impact of the degree of land use on carbon emissions, the correlation between the land development rate and carbon emissions is relatively strong and has the greatest impact; (2) When analyzing the impact of the land use structure on carbon emissions, there is a significant positive correlation between the land use structure and carbon emissions, and the model fitting degree is good. The industrial land rate shows a gradually increasing trend, while the carbon emissions show an exponential growth characteristic. The industrial land rate shows a relatively obvious growth trend after 0.4; (3) When analyzing the impact of the intensity of land use on carbon emissions, for the industrial functional areas dominated by industries, the comprehensive plot ratio of industrial land has the greatest impact on carbon emissions; (4) When analyzing the impact of land use efficiency on carbon emissions, among the industrial functional areas dominated by industries, the factor that has the greatest impact on carbon emissions is the land tax per unit area; (5) When analyzing the impact of the performance of land use supervision on carbon emissions, there is a significant negative correlation between the land vacancy rate and carbon emissions. As the land vacancy rate increases, the carbon emissions show a gradually decreasing trend, but the minimum value of carbon emissions is reached near the land vacancy rate of 0.6 - 0.8.

[0448] For the industrial-dominated type I, through the above analysis, the main conclusions are as follows: (1) When analyzing the impact of land use degree on carbon emissions, the correlation between land development rate and carbon emissions is relatively strong, with the greatest impact; (2) When analyzing the impact of land use structure on carbon emissions, there is a significant positive correlation between land use structure and carbon emissions, and the model fitting degree is good. The industrial land rate shows a gradually increasing trend, while the carbon emissions show an exponential growth characteristic. After the industrial land rate reaches 0.4, a relatively obvious growth trend appears; (3) When analyzing the impact of land use intensity on carbon emissions in the industrial-dominated type I industrial functional area, the comprehensive plot ratio of industrial land has the greatest impact on carbon emissions; (4) When analyzing the impact of land use efficiency on carbon emissions, the factor with the greatest impact on carbon emissions is the land tax per unit area; (5) When analyzing the impact of land use supervision performance on carbon emissions, there is a significant negative correlation between land vacancy rate and carbon emissions. As the land vacancy rate increases, the carbon emissions show a gradually decreasing trend, but the minimum value of carbon emissions is reached near the land vacancy rate of 0.6 - 0.8.

[0449] For the industrial-dominated areas of types II and III, through the above analysis, the main conclusions are as follows: (1) When analyzing the impact of land use intensity on carbon emissions, the correlation between the land development rate and carbon emissions is relatively strong, and the impact is the greatest. This may be because since 2017, a series of policy plans and work plans have been proposed in Chengdu around industrial functional areas. The overall planning and spatial structure of industrial functional areas are clear and definite, and the leading industries are accurately positioned, greatly promoting the development efficiency of the land in industrial functional areas; (2) When analyzing the impact of land use structure on carbon emissions, there is a significant positive correlation between the land use structure and carbon emissions, and the model fitting degree is good. The industrial land rate shows a gradually increasing trend, while the carbon emissions show an exponential growth characteristic. After the industrial land rate exceeds 0.4, a relatively obvious growth trend appears. For the industrial functional areas in Chengdu, an industrial land rate less than 0.4 means a series of prominent problems such as the lack of prominent leading industries and insufficient clustering and chain formation in industrial functional areas. When the industrial land rate is greater than 0.4, it indicates that the industrial functional areas have achieved efficient allocation of factors and the development of industrial agglomeration and chain formation through transformation and upgrading; (3) When analyzing the impact of land use intensity on carbon emissions in industrial-dominated industrial functional areas of types II and III, the building density has the greatest impact on carbon emissions. The main reason for this difference is that Chengdu has planned and constructed 66 industrial functional areas led by industrial ecosystems, that is, the 66 industrial functional areas are planned, organized, and spatially arranged in accordance with the concept of industrial ecosystems. Each industrial functional area has a more specific leading industry, rather than staying at a relatively broad industrial category as in the past, which results in different land use intensities for different types of industrial functional areas; (4) When analyzing the impact of land use efficiency on carbon emissions, the factor that has the greatest impact on carbon emissions is the land tax per unit area. The main reason is that the jurisdiction area of Chengdu is small, land resources are scarce, the land development intensity is high, the increase in construction land is restricted, the intensive use degree is relatively high, and land has become a scarce element in social and economic life, having a significant impact on social and economic life and thus affecting carbon emissions. At the same time, the quality of industrial development in Chengdu is relatively high, and the economic and social intensity carried by the unit land area is large, also having a great impact on carbon emissions; (5) When analyzing the impact of land use supervision performance on carbon emissions, there is a significant negative correlation between the land vacancy rate and carbon emissions. As the land vacancy rate increases, the carbon emissions show a gradually decreasing trend, but reach the minimum value of carbon emissions near the land vacancy rate of 0.6 - 0.8. This shows that attention should be paid to the performance evaluation and supervision management work of industrial functional areas.

[0450] In addition, it should be noted that unless otherwise specified, the terms "first", "second", "third", etc. in the specification are only used to distinguish each component, element, step, etc. in the specification, rather than to represent the logical relationship or sequential relationship, etc. between each component, element, step.

[0451] It will be understood that although the present invention has been disclosed above in preferred embodiments, the above embodiments are not intended to limit the present invention. For any person skilled in the art, without departing from the scope of the technical solution of the present invention, many possible variations and modifications can be made to the technical solution of the present invention by using the technical content disclosed above, or it can be modified into equivalent embodiments with equivalent changes. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for selecting an index combination for constructing an industrial park carbon emission assessment model, characterized in that include: Obtain the nighttime light index of the industrial park, wherein the sub-indicators of the nighttime light index include total light intensity, average light intensity, normalized light intensity and light intensity per unit area; the obtaining of the nighttime light index of the industrial park includes: obtaining satellite remote sensing data containing the area corresponding to the industrial park; correcting the RGB band of the satellite remote sensing data, and obtaining pixel information of pixels in the area corresponding to the industrial park, wherein the pixel information includes: pixel value and number of pixels; calculating the sum of the pixel values ​​of all pixels in the area corresponding to the industrial park to obtain the total light intensity; calculating the ratio of the sum of the pixel values ​​of all pixels in the area corresponding to the industrial park to the number of pixels to obtain the average light intensity; calculating the sum of the ratios of the pixel value of a single pixel in the area corresponding to the industrial park to the pixel value of the maximum pixel to obtain the normalized light intensity; calculating the ratio of the sum of the pixel values ​​of all pixels in the area corresponding to the industrial park to the area of ​​the region to obtain the light intensity per unit area; Obtaining park scale indicators; the sub-indicators of the park scale indicators of the industrial park include: total park area, logistics and warehousing land area, commercial service land area and industrial land area; Obtaining socio-economic indicators; the sub-indicators of the socio-economic indicators of the industrial park include: number of enterprises, annual average employment, annual total tax revenue and annual total fixed asset investment; Calculating the total carbon emissions of the industrial park, including: calculating the product of the industrial park's consumption of fossil fuels, the carbon content of the fossil fuels and the carbon oxidation rate, to obtain the fossil fuel carbon emissions of the industrial park; calculating the product of the industrial park's consumption of electricity and the carbon dioxide emission factor of electricity supply, to obtain the electricity carbon emissions of the industrial park; calculating the product of the industrial park's consumption of heat and the carbon dioxide emission factor of heat supply, to obtain the heat carbon emissions of the industrial park; calculating the product of the hot water heat in the industrial park and the global warming potential of methane, to obtain the waste treatment carbon emissions of the industrial park; based on the sum of the fossil fuel carbon emissions, the electricity carbon emissions, the heat carbon emissions and the waste treatment carbon emissions, the total carbon emissions of the industrial park are obtained; The linear regression algorithm is used to calculate the correlation coefficients between the sub-indicators of the night light index and the total carbon emissions, between the sub-indicators of the park scale index and the total carbon emissions, and between the sub-indicators of the socio-economic index and the total carbon emissions; the random forest algorithm is used to calculate the importance of the sub-indicators of the night light index to the total carbon emissions, the sub-indicators of the park scale index to the total carbon emissions, and the sub-indicators of the socio-economic index to the total carbon emissions; According to the correlation analysis results, through the stepwise regression analysis method, from the night light index, the park scale index and the socio-economic index, an indicator combination is selected, in which the sub-indicators determined to be irrelevant to the total carbon emissions based on the correlation analysis results are deleted, as the industrial park carbon emission assessment indicator combination, including: Construct stepwise regression models; Add the nighttime light index, the park scale index, and the sub-indicators of the socioeconomic index that are correlated with the total carbon emissions into the stepwise regression model in descending order of importance of the sub-indicators to the total carbon emissions; After each current sub-indicator is added, the correlation coefficient between the currently added sub-indicator combination and the stepwise regression model is calculated; If the correlation coefficient is less than a preset threshold, the current sub-indicator is deleted; If the correlation coefficient is greater than or equal to the preset threshold, continue to add the next sub-indicator until the sub-indicators of the night light indicator, the sub-indicators of the park scale indicator, and the sub-indicators of the socio-economic indicator are all traversed; Select the sub-indicator combination with the largest correlation coefficient.

2. The method for selecting an indicator combination for constructing an industrial park carbon emission assessment model according to claim 1, characterized in that: The formulas for calculating total light intensity (I), average light intensity (M), normalized light intensity (N), and light intensity per unit area (K) are as follows: Among them, DN i is the pixel value of each pixel in the area corresponding to the i-th industrial park, n is the number of pixels in the area corresponding to the i-th industrial park, DN max is the maximum pixel value in the area corresponding to the ith industrial park, and s is the total area of ​​the area corresponding to the ith industrial park.

3. The method for selecting an index combination for constructing an industrial park carbon emission assessment model according to claim 1 or 2, characterized in that: The calculation formula for fossil fuel carbon emissions of industrial parks is as follows: ECO 2-燃料 =∑AD j ×CC j ×OF j ×44÷12 Among them, ECO 2-燃料 is the CO2 emission of the main fossil fuels in the industrial park, in tons; j is the type of fossil fuel; AD j is the consumption of the jth fuel, in tons for solid and liquid and 10,000 Nm for gas 3 ;CC j is the carbon content of the jth fuel, with the solid unit being tons of carbon / ton of fuel and the gas unit being tons of carbon / 10,000 Nm 3 ;OF j is the carbon oxidation rate of fuel j, ranging from 0 to 1.

4. The method for selecting an indicator combination for constructing an industrial park carbon emission assessment model according to claim 1 or 2, characterized in that: The calculation formula for the industrial park’s electricity carbon emissions is as follows: ECO 2-净电 =AD 电力 ×I 电 Among them, ECO 2-净电 is the CO2 emissions implied by the net purchase of electricity in the industrial park, in tons of CO2; AD 电力 The net electricity consumption purchased by the enterprise, in MWh; EI 电 is the CO2 emission factor for electricity supply, which is 0.1031 and the unit is ton CO2 / MWh.

5. The method for selecting a combination of indicators for constructing an industrial park carbon emission assessment model according to claim 1 or 2, characterized in that: The calculation formula for thermal carbon emissions in industrial parks is as follows: ECO 2-净热 =AD 热力 ×I 热 Among them, ECO 2-净热 is the CO2 emissions implied by the net purchase of heat in the industrial park, in tons of CO2; AD 热力 The net purchased heat consumption of the enterprise, including hot water heat and steam heat, in GJ; EI 热 is the CO2 emission factor for heat supply, which is 0.11 and is expressed in tons CO2 / GJ; WILL 热水 =Maw×(Tw-20)×4.1868×10 -3 AD 蒸汽 =Mast×(Enst-83.74)×10 -3 Among them, AD 热水 is the heat of hot water, in GJ; AD 蒸汽 is the heat of steam, in GJ; Maw is the mass of hot water, in tons of hot water; Mast is the mass of steam, in tons of steam; Tw is the specific heat of water at room temperature and pressure, in kJ / (kg·℃); Enst is the thermal enthalpy of each kilogram of steam at the temperature and pressure corresponding to the steam, in kJ / kg.

6. The method for selecting a combination of indicators for constructing an industrial park carbon emission assessment model according to claim 1 or 2, characterized in that: The calculation formula for the carbon emissions of industrial parks is as follows: EGHG 废水 =I 4-废水 ×GWPCH4×10 -3 Among them, EGHG 废水 is the carbon dioxide emission equivalent generated by the anaerobic wastewater treatment process, in tons of carbon dioxide equivalent (tCO2); GWPCH4 is the global warming potential (GWP) value of methane, which is 21; I 4-废水 =(TOW-S)×EF-R Among them: ECH 4-废水 is the heat of hot water, in GJ; TOW is the total amount of organic matter removed by anaerobic wastewater treatment; S is the total amount of organic matter removed in the form of sludge (kg COD); EF is the methane emission factor; and R is the amount of methane recovered.

7. The method for selecting an index combination for constructing an industrial park carbon emission assessment model according to claim 1 or 2, characterized in that: The number of pixels n in the area corresponding to the industrial park and the pixel value DN of each pixel, i.e. the gray value Gray, are calculated as follows: Gray=0.1140*B+0.5870*G+0.2989*R Among them, R is the brightness of the red band in the RGB three-band, G is the brightness of the green band in the RGB three-band, and B is the brightness of the blue band in the RGB three-band.

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