Carbon emission spatial distribution simulation method

Abstract

The invention relates to the technical field of carbon emission monitoring and simulation, and discloses a carbon emission spatial distribution simulation method, which is based on total regional carbon emission data, calculates sub-regional carbon emission and simulates spatial distribution of the sub-regional carbon emission, and utilizes a Markowitz model to calculate weights of industrial carbon emission influence factors so as to obtain the sub-regional carbon emission. The method is suitable for carbon emission monitoring, policy making and low-carbon development planning.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission monitoring and simulation, and specifically relates to a method for calculating the carbon emissions of sub-regions and simulating their spatial distribution based on the total regional carbon emission data, which is applicable to carbon emission monitoring, policy formulation, and low-carbon development planning. Background Art

[0002] Accurate calculation of carbon emissions is an important basis for achieving the goals of carbon peak and carbon neutrality. At present, carbon emission data is usually statistically calculated on a relatively large spatial scale, such as at the national, provincial, or municipal levels. However, at a finer sub-regional scale, due to the low availability of data, reasonable calculation and allocation methods often need to be adopted to estimate carbon emissions. Existing methods mainly use fixed proportion allocation or extrapolation based on historical data, without fully considering the differences in industries, energy consumption, land use, etc. of each sub-region, resulting in insufficient accuracy and affecting refined carbon emission management. Therefore, there is an urgent need for a method for simulating the spatial distribution of carbon emissions based on the total regional carbon emission data, reasonably calculating the carbon emissions of sub-regions, and further refining to the plot scale, so as to support refined carbon emission management. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for simulating the spatial distribution of carbon emissions, which is based on the total regional carbon emission data, reasonably calculates the carbon emissions of sub-regions, and further refines to the plot scale of the carbon emission spatial distribution simulation method to support refined carbon emission management.

[0004] A method for simulating the spatial distribution of carbon emissions, characterized by comprising: Obtaining total regional carbon emission data; Allocating carbon emissions of industries: Dividing the total regional carbon emissions into multiple industry categories according to the carbon emission contribution ratios of different industries; Selecting industry carbon emission influencing factors: For each industry category, selecting multiple key influencing factors to establish an industry carbon emission influencing factor system; Calculating weights of the Markowitz model: Using the Markowitz model to calculate the weights of the influencing factors; Calculating carbon emissions of sub-regions: Calculating the carbon emissions of sub-regions based on the industry carbon emission influencing factors and weights; Allocating the spatial carbon emissions of plots: Based on the industry carbon emissions of sub-regions and combining the industry spatial distribution of each plot, calculating the carbon emissions at the plot scale and generating the carbon emission spatial distribution results.

[0005] Optionally, the obtaining of the total regional carbon emission data includes obtaining activity data such as energy consumption, industrial production, transportation, and residential life within the total region.

[0006] Optionally, obtain energy consumption data, including the consumption of coal, oil, natural gas, and electricity; Obtain industrial production data, including industrial output value, raw material consumption, and carbon emissions per unit product; Obtain transportation data, including road traffic flow, fuel consumption, and public transportation operation data; Obtain residential living data, including residential electricity consumption, gas consumption, and heating and cooling demand.

[0007] Optionally, the dividing the total regional carbon emissions into multiple industry categories includes Calculate the total regional carbon emissions based on the total regional carbon emission data; Determine the carbon emission contribution ratio of each industry based on statistical data, industry carbon emission coefficients, and industrial energy consumption; Calculate the carbon emissions of each industry based on the total regional carbon emissions and the carbon emission contribution ratio of each industry.

[0008] Optionally, the multiple key influencing factors include building density, road traffic volume, industrial energy consumption intensity, energy consumption intensity, etc.

[0009] Optionally, the calculation of the Markowitz model weights includes the steps of: Define the expected return of each influencing factor; Calculate the covariance matrix between the influencing factors; Calculate the optimal weights of the influencing factors based on the matrix and the minimized risk of the portfolio.

[0010] Optionally, the calculation of the carbon emissions at the plot scale is obtained by linearly weighting the plot industry spatial distribution and the industry carbon emissions.

[0011] Optionally, the generation of the carbon emission spatial distribution result includes annotating the carbon emission distribution result with different colors.

[0012] An apparatus for implementing any of the above carbon emission spatial distribution simulation methods, characterized by including: Total regional carbon emission data acquisition module; Industry carbon emission allocation module: Divide the total regional carbon emissions into multiple industry categories according to the carbon emission contribution ratio of different industries; Industry carbon emission influencing factor selection module: For each industry category, select multiple key influencing factors to establish an industry carbon emission influencing factor system; Markowitz model weight calculation module: Calculate the weights of the influencing factors using the Markowitz model; Sub - region carbon emission calculation module: Calculate the carbon emissions of sub - regions based on industry carbon emission impact factors and weights; Plot carbon emission spatial allocation module: Calculate the carbon emissions at the plot scale according to the industry carbon emissions of sub - regions and the industry spatial distribution of each plot, and generate the results of carbon emission spatial distribution.

[0013] Compared with the existing technologies, the beneficial effects of the present invention are as follows: 1. Consider the correlation between indicators and reduce redundant information. The Markowitz model uses the covariance matrix to measure the correlation between various impact factors, thereby reducing the impact of redundant indicators and improving the rationality of calculating weights. If the correlation between two indicators is high, the model will automatically adjust the weights to prevent duplicate calculation of information and improve the stability of the calculation.

[0014] 2. Balance the benefits (influence) and risks (volatility). The traditional entropy weight method only focuses on information entropy and does not consider the volatility of indicators. The Markowitz model can weigh the "benefits" (i.e., the influence of indicators on carbon emissions) and "risks" (i.e., the degree of data volatility) of various impact factors, thereby optimizing the weight allocation and making the calculation results more economically meaningful.

[0015] 3. Strong dynamic adaptability and suitable for complex systems. The Markowitz model can adapt to different distributions of industry carbon emission data and automatically adjust the weights when the indicators change, making it more adaptable in complex system modeling. Description of the Drawings

[0016] Figure 1 Schematic flow chart of a method for simulating carbon emission spatial distribution in an embodiment of the present invention; Figure 2 Schematic diagram of carbon emission spatial distribution annotation in a certain area in an embodiment of the present invention; Figure 3 Schematic diagram of a device for implementing the method for simulating carbon emission spatial distribution in an embodiment of the present invention. Detailed Embodiment

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0018] Refer to the attached Figure 1 The described method for simulating carbon emission spatial distribution, the attached Figure 2 Shown rehabilitation training schematic diagram, the technical solution of the present invention, A method for simulating carbon emission spatial distribution, characterized by comprising: Obtaining total - region carbon emission data; Industry carbon emissions allocation: Divide the total regional carbon emissions into multiple industry categories according to the proportion of carbon emissions contributed by different industries; Selection of industry carbon emission impact factors: For each industry category, select multiple key impact factors to establish an industry carbon emission impact factor system; Calculation of Markowitz model weights: Use the Markowitz model to calculate the weights of the impact factors; Calculation of sub-region carbon emissions: Calculate the sub-region carbon emissions based on the industry carbon emission impact factors and weights; Spatial allocation of plot carbon emissions: Based on the sub-region industry carbon emissions and combined with the industry spatial distribution of each plot, calculate the carbon emissions at the plot scale and generate the carbon emission spatial distribution results.

[0019] Optionally, the acquisition of the total regional carbon emission data includes obtaining activity data such as energy consumption, industrial production, transportation, and residential life within the total region.

[0020] Optionally, obtaining energy consumption data includes the consumption of coal, oil, natural gas, and electricity; Obtaining industrial production data includes industrial output value, raw material consumption, and carbon emissions per unit product; Obtaining transportation data includes road traffic flow, fuel usage, and public transportation operation data; Obtaining residential life data includes residential electricity consumption, gas consumption, and heating and cooling demand.

[0021] Specifically, the acquisition of carbon emission data is the basis of the entire simulation method, covering four aspects: energy consumption, industrial production, transportation, and residential life. The following are the specific data contents and acquisition methods for each aspect.

[0022] 1. Energy consumption data Energy consumption is one of the main sources of carbon emissions and involves different types of energy.

[0023] Data content Coal consumption (unit: ton): mainly involves the coal usage in power plants, industrial boilers, residential heating, etc.

[0024] Oil consumption (unit: barrel or ton): mainly used for transportation (gasoline, diesel), industrial production (lubricating oil, etc.).

[0025] Natural gas consumption (unit: cubic meter): applicable to scenarios such as residential gas use, industrial production, and natural gas power generation.

[0026] Electricity consumption (unit: kilowatt-hour kWh): includes industrial electricity, residential electricity, commercial electricity, etc., and needs to be segmented by industry.

[0027] Data sources: The energy department (such as the National Energy Administration); Power grid companies (such as State Grid Corporation of China, China Southern Power Grid); Related enterprises (such as coal enterprises, oil companies); Remote monitoring data (such as smart meters, gas meters).

[0028] 2. Industrial production data Industrial production activities involve a large amount of carbon emissions. Obtaining relevant data can reflect the emission situation in the industrial field.

[0029] Data content: Gross industrial output value (unit: 100 million yuan): used to evaluate the intensity of production activities in the entire industrial sector; Raw material consumption (unit: ton): such as the usage of main raw materials in industries like steel, cement, and chemicals; Carbon emissions per unit of product (unit: ton CO2 / unit product): calculate the carbon emission coefficients for the production of certain key products (such as steel, cement).

[0030] Data sources: Industrial statistical data released by the National Bureau of Statistics; Industry associations (such as the China Iron and Steel Association); Enterprise production reports and carbon emission accounting systems.

[0031] 3. Transportation data Transportation is an important source of carbon emissions and involves data on multiple transportation modes.

[0032] Data content: Road traffic flow (unit: vehicles / hour): monitor the traffic flow of urban roads, national highways, and expressways; Fuel consumption (unit: ton or liter): count the fuel consumption of different transportation modes such as motor vehicles, ships, and aviation; Public transportation operation data: such as the passenger volume, driving mileage, and energy consumption of buses and subways.

[0033] Data sources: Transportation departments (such as the Ministry of Transport, Highway Administration); Urban intelligent transportation systems (camera, signal light data); On-vehicle sensors, gas stations, shipping company data.

[0034] 4. Residential life data The energy consumption in residents' daily life also brings carbon emissions, and relevant data need to be collected.

[0035] Data content: Residential electricity consumption (unit: kWh): Statistics of the electricity consumption of households and residential communities; Gas consumption (unit: cubic meters): including the use of household gas such as natural gas and liquefied petroleum gas (LPG); Heating and cooling demand (unit: GJ or kWh): Energy consumption for heating and air conditioning, especially in winter and summer seasons.

[0036] Data source: Power grid companies (such as State Grid and local power supply companies); Gas companies (such as the gas branches of PetroChina and Sinopec); Property management companies (heating and air conditioning management data).

[0037] The acquisition of the above four categories of data constitutes the basis for simulating the spatial distribution of carbon emissions. Through the data in the four aspects of energy, industry, transportation, and residential life, the total regional carbon emissions can be calculated, providing support for subsequent industry allocation and spatial simulation.

[0038] Optionally, the division of the total regional carbon emissions into multiple industry categories includes Calculating the total regional carbon emissions based on the total regional carbon emission data; Determining the carbon emission contribution ratio of each industry according to statistical data, industry carbon emission coefficients, and industrial energy consumption; Calculating the carbon emissions of each industry based on the total regional carbon emissions and the carbon emission contribution ratio of each industry.

[0039] Specifically, the core goal of industry carbon emission allocation is to subdivide the total regional carbon emissions according to different industry categories, so as to obtain the carbon emissions of each industry. The specific operations are as follows: 1. Calculate the total regional carbon emissions First, it is necessary to calculate the overall carbon emission total based on the collected total regional carbon emission data.

[0040] Calculation method ; Where: : Total regional carbon emissions (unit: tons of CO2); : Carbon emissions related to energy consumption; : Carbon emissions related to industrial production; : Carbon emissions related to transportation; : Carbon emissions related to residents' lives.

[0041] The calculation method for each part is as follows: Emissions from energy consumption (coal, oil, natural gas, electricity): ; Among them, is the type of energy 's carbon emission factor (kg CO2 / unit consumption), is the energy consumption (such as tons, cubic meters, kilowatt-hours, etc.).

[0042] Emissions from industrial production (based on industry production data): ; Among them, is the industry 's total industrial output value, is the carbon emission factor per unit output value (kg CO2 / 10,000 yuan).

[0043] Emissions from transportation (based on vehicle and fuel consumption data): ; Among them, is the transportation mode 's fuel usage, is the carbon emission factor per unit of fuel (kg CO2 / liter).

[0044] Emissions from residents' lives (based on electricity, gas, and heating data): ; Among them, (electricity); (gas); (heating).

[0045] 2. Determine the carbon emission contribution ratio of each industry In order to divide the carbon emissions of each industry, it is necessary to calculate the contribution ratio of each industry in the total carbon emissions.

[0046] Factors affecting the contribution ratio: Statistical data (industry energy consumption, output value, etc. officially counted); Industry carbon emission factors (carbon emissions per unit output value or energy consumption vary among different industries); Industrial energy consumption situation (coal, oil, electricity, and natural gas consumption structures of each industry).

[0047] 1. Variable Definitions Let: : The industry Total energy consumption (unit: tons, cubic meters, kilowatt-hours, etc.); : Energy Carbon emission coefficient (unit: tons CO2 / unit energy consumption); : The industry Carbon emission contribution ratio coefficient (unitless); : Total consumption of all industries for energy : Energy Weighted contribution factor.

[0048] Energy types Include: coal, oil, electricity, natural gas.

[0049] 2. Calculation Steps (1) Calculate the total consumption of each energy First, calculate the total consumption of each energy by all industries in the total area: ; (2) Calculate the weighted contribution factor of each energy The weighted contribution factor of energy is determined by the carbon emission coefficient of this energy: ; (3) Calculate the carbon emission contribution ratio coefficient of the industry Industry Carbon emission contribution ratio coefficient Is obtained by weighted summation of the proportions of this industry in the four energies: ; (4) Normalization To ensure that the sum of the contribution ratio coefficients of all industries is 1, normalization is required: ; Where: Is the industry Final carbon emission contribution ratio.

[0050] 3. Calculate the carbon emissions of each industry Finally, use the contribution ratio of each industry To calculate the carbon emissions of each industry, the calculation formula ; Wherein: : Carbon emissions (tons of CO2) of the industry ; Total regional carbon emissions (tons of CO2); : Proportion of carbon emission contribution of the industry : Industry 's carbon emission contribution ratio

[0051] Example calculation: Assume that the total regional carbon emissions are 50 million tons of CO2, and the contribution ratios of each industry are as follows: Thermal power generation (40%) → million tons; Iron and steel smelting (15%) → million tons; Cement manufacturing (10%) → million tons; Transportation (12%) → million tons; Residential building (8%) → million tons; Other industries (15%) → million tons.

[0052] Optionally, the multiple key impact factors include building density, road traffic volume, industrial energy consumption intensity, energy consumption intensity, etc.

[0053] Specifically, in the simulation of carbon emission spatial distribution, the carbon emission levels of different industries are affected by multiple factors. For each industry category, key impact factors need to be selected and an industry carbon emission impact factor system needs to be established for subsequent calculation of sub - regional carbon emissions.

[0054] The role of industry carbon emission impact factors, carbon emission impact factors are used to measure the relationship between the carbon emission level of a certain region and different environmental, economic, and social factors, helping to allocate carbon emissions more accurately. For example: The carbon emissions in industrial areas are mainly affected by energy consumption intensity and industrial energy consumption intensity; The transportation industry is affected by road traffic volume and fuel consumption; Residents' lives are affected by building density and residents' energy usage.

[0055] By selecting appropriate impact factors, a more accurate carbon emission spatial distribution model can be constructed.

[0056] Optionally, the steps of calculating the weights of the Markowitz model include: Define the expected return of each influencing factor; Calculate the covariance matrix between the influencing factors; Based on this matrix and the minimized risk of the portfolio, calculate the optimal weights of the influencing factors.

[0057] Specifically, the Markowitz model was first used for portfolio optimization, aiming to minimize risk under a certain return. In carbon emission simulation, we use it to calculate the optimal weights of the influencing factors of industry carbon emissions to ensure that the allocation of carbon emissions is more scientific.

[0058] Calculation steps: (1) Define the expected return of each influencing factor In portfolio optimization, the expected return is the rate of return of the asset; in carbon emission calculation, we regard the contribution degree of the influencing factor as "return".

[0059] The "return" of the influencing factor can be obtained through historical data statistics, for example: The contribution rate of building density to carbon emissions (%); The impact of road traffic volume on carbon emissions in the transportation industry (%); The impact of industrial energy consumption intensity on industrial carbon emissions (%).

[0060] Let: be the influencing factor be the value of (such as building density, road traffic volume, etc.); be the impact of on carbon emissions (contribution rate), which can be obtained through regression analysis or historical data fitting.

[0061] (2) Calculate the covariance matrix between the influencing factors The covariance matrix is used to measure the correlation between different influencing factors. The covariance formula is as follows: ; Where: is the observed value of the influencing factor at time ; is the mean value of the influencing factor ; is the covariance between the influencing factors and .

[0062] The covariance matrix is used to measure the relationship between different influencing factors: Positive covariance: The trends of two factors are consistent, such as building density and electricity consumption; Negative covariance: The trends of two factors are opposite, such as wind energy utilization rate and coal consumption.

[0063] (3) Calculate the optimal weights (minimize risk) The goal is to minimize the uncertainty of carbon emission calculation, that is, to minimize the variance of the portfolio: ; Where: is the weight vector (to be solved).

[0064] is the covariance matrix.

[0065] Constraint conditions: (the sum of the weights of all influencing factors is 1).

[0066] The optimal weights can be obtained using Quadratic Programming (QP).

[0067] Optionally, the carbon emissions at the plot scale are calculated by linearly weighting the spatial distribution of industries in the plot and the industry carbon emissions.

[0068] After determining the weights of each influencing factor, we can calculate the carbon emissions at the plot scale, that is, allocate carbon emissions at a finer spatial scale.

[0069] Calculation steps: (1) Calculate the plot allocation of industry carbon emissions The carbon emissions of a certain plot are determined by the industrial structure of the plot and the contribution of industry carbon emissions. The calculation formula is as follows: ; Where: is the industry carbon emission influencing factor weight (calculated by the Markowitz model); is the industry total carbon emissions in this area; is the spatial proportion of industry in this plot.

[0070] (2) Linearly weight to calculate plot carbon emissions Assume the industry distribution in plot is: ; Wherein: is the proportion of the industry in the plot (e.g., construction accounts for 40%, industry accounts for 30%, and transportation accounts for 30%); is the carbon emission of the industry amount.

[0071] Then: .

[0072] Optionally, the generating of the carbon emission spatial distribution result includes annotating the carbon emission distribution result with different colors.

[0073] Specifically, referring to the Figure 2 schematic diagram of the carbon emission spatial distribution annotation of a certain area shown in the appendix, the plot carbon emission spatial allocation is the final step of carbon emission simulation, that is, further refining the carbon emission amount at the sub-region level to specific plots (such as building blocks, industrial land, transportation road networks, etc.), so as to obtain a high-precision carbon emission spatial distribution map. This process needs to combine the industry carbon emission data of the sub-region and the industry distribution of each plot, and finally generate a visual carbon emission distribution map, and mark the spatial differences of carbon emissions through colors.

[0074] Use GIS (Geographic Information System) or Python visualization tools (Matplotlib, GeoPandas) to draw the carbon emission distribution map, and use different colors to represent the carbon emission intensity of different regions. For example: Green: Low-carbon emission area; Yellow: Medium-carbon emission area; Red: High-carbon emission area.

[0075] An apparatus for implementing any one of the above carbon emission spatial distribution simulation methods, characterized by including: Total regional carbon emission data acquisition module; Industry carbon emission allocation module: divide the total regional carbon emission amount into multiple industry categories according to the carbon emission contribution ratio of different industries; Industry carbon emission impact factor selection module: select multiple key impact factors for each industry category to establish an industry carbon emission impact factor system; Markowitz model weight calculation module: calculate the weights of the impact factors using the Markowitz model; Sub-region carbon emission calculation module: calculate the sub-region carbon emission amount based on the industry carbon emission impact factors and weights; Plot carbon emission space allocation module: Based on the carbon emissions of industries in sub-regions and combined with the industrial spatial distribution of each plot, calculate the carbon emissions at the plot scale and generate the results of the carbon emission space distribution.

[0076] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

[0077] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. A method for simulating the spatial distribution of carbon emissions, characterized in that Including: Obtaining total regional carbon emission data; Allocating industry carbon emissions: Dividing the total regional carbon emissions into multiple industry categories according to the carbon emission contribution ratios of different industries; Selecting industry carbon emission impact factors: For each industry category, selecting multiple key impact factors to establish an industry carbon emission impact factor system; Calculating weights using the Markowitz model: Calculating the weights of industry carbon emission impact factors using the Markowitz model; Calculating sub-regional carbon emissions: Calculating sub-regional carbon emissions based on industry carbon emission impact factors and weights; Allocating carbon emissions in plot space: Calculating carbon emissions at the plot scale based on sub-regional industry carbon emissions and combining with the industry spatial distribution of each plot, and generating a carbon emission spatial distribution result.

2. The simulation method according to claim 1, characterized in that The obtaining of total regional carbon emission data includes obtaining activity data such as energy consumption, industrial production, transportation, and residential life within the total region.

3. The simulation method according to claim 2, characterized in that Obtaining energy consumption data includes the consumption amounts of coal, oil, natural gas, and electricity; Obtaining industrial production data includes industrial output value, raw material consumption, and carbon emissions per unit product; Obtaining transportation data includes road traffic flow, fuel usage, and public transportation operation data; Obtaining residential life data includes residential electricity consumption, gas consumption, and heating and cooling demands.

4. The simulation method according to claim 1, characterized in that The dividing of the total regional carbon emissions into multiple industry categories includes Calculating the total regional carbon emissions based on the total regional carbon emission data; Determining the carbon emission contribution ratios of each industry based on statistical data, industry carbon emission coefficients, and industrial energy consumption situations; Calculating the carbon emissions of each industry based on the total regional carbon emissions and the carbon emission contribution ratios of each industry.

5. The simulation method according to claim 1, characterized in that The multiple key impact factors include Building density, road traffic volume, industrial energy consumption intensity, energy consumption intensity, etc.

6. The simulation method according to claim 1, characterized in that The steps of the calculating weights using the Markowitz model include: Defining the expected return of each impact factor; Calculating the covariance matrix between each impact factor; Calculating the optimal weights of each impact factor based on this matrix and minimizing the risk of the portfolio.

7. The simulation method according to claim 6, characterized in that The calculating of the carbon emissions at the plot scale is obtained by linearly weighting the plot industry spatial distribution and industry carbon emissions.

8. The simulation method according to claim 1, characterized in that The generating of the carbon emission spatial distribution result includes annotating the carbon emission distribution result with different colors.

9. An apparatus for implementing the carbon emission spatial distribution simulation method according to any one of claims 1-8, characterized in that, Including: Total regional carbon emission data acquisition module; Industry carbon emission allocation module: Dividing the total regional carbon emissions into multiple industry categories according to the carbon emission contribution ratios of different industries; Industry carbon emission impact factor selection module: For each industry category, selecting multiple key impact factors to establish an industry carbon emission impact factor system; Markowitz Model Weight Calculation Module: Calculate the weights of influencing factors using the Markowitz model; Sub-region Carbon Emission Calculation Module: Calculate the carbon emissions of sub-regions based on industry carbon emission influencing factors and weights; Plot Carbon Emission Spatial Allocation Module: Calculate the carbon emissions at the plot scale according to the industry carbon emissions of sub-regions and the industry spatial distribution of each plot, and generate the results of carbon emission spatial distribution.

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