Carbon emission prediction method, computing device and storage medium

Abstract

The invention relates to a carbon emission prediction method, a computing device and a storage medium, and the method comprises the steps: predicting the population data of a to-be-predicted region according to a constructed population prediction model; according to the constructed economic prediction model, predicting economic data of the to-be-measured area; according to the population data predicted by the population prediction model and the economic data predicted by the economic prediction model, constructing an energy consumption prediction model; constructing a carbon emission prediction model according to the energy consumption predicted by the energy consumption prediction model; and based on the carbon emission prediction model, predicting the carbon emission of a to-be-detected area to obtain a prediction result. Therefore, the invention provides the method for predicting the future carbon emission of the specific area by comprehensively analyzing the population growth, the economic development trend and the energy consumption condition and constructing the corresponding carbon emission prediction model according to the population growth, the economic development trend and the energy consumption condition, and the accuracy and the reliability of carbon emission prediction are improved.

Description

Technical Field

[0001] The present application relates to the technical field of carbon emission prediction, and in particular to a carbon emission prediction method, computing device, and storage medium. Background Art

[0002] As global climate change becomes increasingly serious, reducing greenhouse gas emissions has become a common challenge facing the international community. As a major source of greenhouse gases, the effective management and control of carbon emissions is crucial for mitigating climate change. Therefore, accurately predicting carbon emission trends not only facilitates the formulation of scientifically sound emission reduction policies but also serves as an important tool for evaluating the effectiveness of these policies.

[0003] However, traditional carbon emission forecasting methods often focus on analyzing the impact of a single factor, such as linear extrapolation from historical data or considering only one factor (such as industrial production activity). This approach ignores the complex impact of multiple factors on carbon emissions, such as population growth, resulting in inaccurate forecasts that are difficult to meet the needs of practical applications. Summary of the Invention

[0004] In light of the aforementioned issues with the prior art, this application provides a carbon emissions prediction method, computing device, and storage medium. This invention provides a method for predicting future carbon emissions in a specific region by comprehensively analyzing population growth, economic development trends, and energy consumption, and constructing a corresponding carbon emissions prediction model based on this analysis. This method improves the accuracy and reliability of carbon emissions predictions.

[0005] To achieve the above objectives, the present application provides a first aspect of a carbon emissions prediction method, comprising:

[0006] According to the constructed population prediction model, the population data of the tested area is predicted;

[0007] According to the constructed economic forecast model, the economic data of the tested area is forecasted;

[0008] constructing an energy consumption prediction model based on the population data predicted by the population prediction model and the economic data predicted by the economic prediction model;

[0009] Constructing a carbon emission prediction model based on the energy consumption predicted by the energy consumption prediction model;

[0010] Based on the carbon emission prediction model, the carbon emission of the area to be measured is predicted to obtain a prediction result.

[0011] In this way, this application constructs an energy consumption forecasting model by integrating the data of the population forecasting model and the economic forecasting model, and then establishes a carbon emission forecasting model based on this to accurately predict the carbon emissions of the test area. This method can not only comprehensively consider the key factors affecting carbon emissions (such as population growth and economic development), but also identify the main sources of carbon emissions through detailed energy consumption analysis, thereby providing solid data support and decision-making basis for the formulation of scientific and effective emission reduction strategies. This method improves the accuracy of the forecast, helps policymakers take targeted measures, and promotes the transformation of the regional economy towards a low-carbon and sustainable direction. At the same time, it also enhances the public's awareness of the importance of energy conservation and emission reduction, and jointly promotes the realization of environmental protection and climate change goals.

[0012] As a possible implementation of the first aspect, constructing a population prediction model includes:

[0013] Get the population growth rate; its expression is: Among them, r p is the population growth rate, r p0 is the initial population growth rate, P is the current population, and P m is the maximum population;

[0014] According to the population growth rate, a differential equation is established; its expression is: in, is the rate of change of population over time, t0 is the initial time point, and p0 is the initial population size;

[0015] Based on the differential equation, the population prediction model is determined; its expression is:

[0016] Because population size directly affects a city's energy consumption patterns and total volume, a larger population means greater demand for housing, transportation, and industrial production, all of which increase energy consumption and, consequently, carbon emissions. Therefore, by building a population forecasting model, we can accurately predict future population trends, providing a reliable data foundation for subsequent energy consumption and carbon emissions forecasts.

[0017] As a possible implementation of the first aspect, constructing an economic forecasting model includes:

[0018] Get the economic growth rate; its expression is: Among them, r G is the economic growth rate, r G0 is the initial economic growth rate, G is the current economic value, G m is the maximum economic value;

[0019] According to the economic growth rate, a differential equation is established; its expression is: in, is the rate of change of the economy over time, t0 is the initial time point, and G0 is the initial economic value;

[0020] Based on the differential equation, the economic forecast model is determined; its expression is:

[0021] Economic growth is often accompanied by increased energy demand, as production, transportation, and daily life all require energy, leading to increased carbon emissions. Therefore, by building an economic forecasting model, we can accurately predict future economic trends, providing a reliable data foundation for subsequent energy consumption and carbon emissions forecasts.

[0022] As a possible implementation of the first aspect, constructing the energy consumption prediction model includes:

[0023] Get the energy growth rate; its expression is: r E =ε1+ε2P m +ε3G m ; Among them, r E is the energy growth rate, ε1 is the constant term coefficient, ε2 is the impact coefficient of population on energy consumption, ε3 is the impact coefficient of economy on energy consumption, P m is the maximum population, G m is the maximum economic value;

[0024] According to the economic growth rate, a differential equation is established; its expression is: in, is the rate of change of energy consumption over time, t0 is the initial time point, and E0 is the initial energy value;

[0025] Based on the differential equation, the energy consumption prediction model is determined; its expression is: Among them, r p is the population growth rate, r G is the economic growth rate, p0 is the initial population, G0 is the initial economic value, P m is the maximum population, G m is the maximum economic value, P is the population data predicted by the population forecasting model, and G is the economic data predicted by the economic forecasting model.

[0026] Thus, in this application, by combining the forecast data of population growth and economic development, analyzing the impact of these factors on energy consumption, an energy consumption forecast model closely related to population and economy is established, making the forecast results more accurate and close to reality.

[0027] As a possible implementation of the first aspect, constructing a carbon emission prediction model based on the energy consumption predicted by the energy consumption prediction model includes:

[0028] Identify the consumption sectors that affect carbon emissions, including energy consumption in the industrial sector, energy consumption in the construction sector, energy consumption in the transportation sector, energy consumption in the residential sector, energy consumption in the agriculture and forestry sector, and energy consumption in the energy supply sector;

[0029] Each consumer department makes a prediction based on the energy consumption prediction model;

[0030] A carbon emission prediction model is constructed according to the energy consumption predicted by each department based on the energy consumption prediction model.

[0031] In this way, different consumer sectors have different energy usage patterns and carbon emission characteristics. By predicting the energy consumption of different consumer sectors separately and building a carbon emission prediction model based on these prediction results, the accuracy of carbon emission prediction can be improved.

[0032] As a possible implementation of the first aspect, the step of constructing a carbon emission prediction model includes:

[0033] According to the relationship between carbon emissions and energy consumption, the carbon emission prediction model is determined; its expression is: Among them, C is carbon emissions, ω is energy efficiency, E i is the energy consumption predicted by each department based on the energy consumption prediction model, θ is the carbon emission factor, a i represents the proportion of non-fossil energy consumption in the i-th sector.

[0034] As a possible implementation of the first aspect, the energy utilization efficiency is expressed as follows: Among them, E23 is the energy supply amount of the energy supply department, and E is the total energy consumption.

[0035] As a possible implementation of the first aspect, the carbon emission factor is expressed as θ=-0.0115(t-2010)+2.5447; wherein t represents the year, and θ represents the comprehensive carbon emission factor.

[0036] This application comprehensively considers the energy consumption and efficiency of each sector, resulting in a more accurate forecast of total carbon emissions. This approach, by precisely analyzing the energy usage characteristics and emission reduction potential of different sectors, provides solid data support for developing a scientifically sound carbon peak pathway.

[0037] To achieve the above-mentioned object, the second aspect of the present application provides a computing device, including:

[0038] processor, and

[0039] A memory having program instructions stored thereon, wherein when the program instructions are executed by the processor, the processor executes the carbon emission prediction method described in the first aspect.

[0040] To achieve the above-mentioned purpose, the third aspect of the present application provides a computer-readable storage medium having program instructions stored thereon, which, when executed by a computer, enables the computer to implement the carbon emission prediction method described in the first aspect above. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of the main steps of a carbon emission prediction method provided by this application;

[0042] Figure 2 This is a population data fitting chart provided by this application;

[0043] Figure 3 It is a schematic diagram of a population data forecast map provided by this application;

[0044] Figure 4 This is a schematic diagram of an economic data fitting forecast chart provided by this application;

[0045] Figure 5 It is a schematic diagram of an energy consumption fitting diagram provided by this application;

[0046] Figure 6 It is a schematic diagram of an energy consumption forecast map provided by this application;

[0047] Figure 7 This is a schematic diagram of a scatter plot of carbon emissions and energy consumption provided by this application;

[0048] Figure 8 This is a schematic diagram of the fitting and prediction images of the fossil energy consumption in the agricultural and forestry consumption sectors provided by this application;

[0049] Figure 9 This is a schematic diagram of a fitting and forecast image of fossil energy consumption in an energy supply sector provided by this application;

[0050] Figure 10 This is a schematic diagram of the fitting and prediction image of fossil energy consumption in industrial consumption sectors provided by this application;

[0051] Figure 11 This is a schematic diagram of a fitting and prediction image of fossil energy consumption in the transportation consumption sector provided by this application;

[0052] Figure 12 This is a schematic diagram of a fitting and prediction image of fossil energy consumption in the building consumption sector provided by this application;

[0053] Figure 13 This is a schematic diagram of a fitting and prediction image of the fossil energy consumption of residents' daily life provided by this application;

[0054] Figure 14 This is a schematic diagram of the fitting and prediction image of non-fossil energy consumption in industrial consumption sectors provided by this application;

[0055] Figure 15 is a schematic diagram of a carbon emissions forecast and percentage error graph provided by this application;

[0056] Figure 16 is a schematic diagram of a revised carbon emissions forecast and percentage error graph provided by this application;

[0057] Figure 17 It is a structural diagram of a computing device provided by this application.

[0058] It should be understood that the sizes and shapes of the blocks in the above structural diagrams are for reference only and should not constitute an exclusive interpretation of the embodiments of the present invention. The relative positions and inclusion relationships between the blocks presented in the structural diagrams are merely schematic representations of the structural relationships between the blocks and do not limit the physical connection methods of the embodiments of the present invention. DETAILED DESCRIPTION

[0059] The technical solution provided by this application is further described below with reference to the accompanying drawings and examples. It should be understood that the system structure and business scenarios provided in the examples of this application are mainly for illustrating possible implementation methods of the technical solution of this application and should not be interpreted as the sole limitation of the technical solution of this application. It is known to those skilled in the art that with the evolution of the system structure and the emergence of new business scenarios, the technical solution provided by this application is also applicable to similar technical problems.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art in the art of this application. In the event of any inconsistency, the meaning described in this specification or the meaning derived from the contents recorded in this specification shall prevail. In addition, the terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit this application.

[0061] The present application embodiment provides a method for predicting carbon emissions, such as Figure 1 Shown, including:

[0062] S101. Predicting population data of the area to be tested based on the constructed population prediction model;

[0063] S102. Predicting economic data of the region to be tested based on the constructed economic forecasting model;

[0064] S103, constructing an energy consumption prediction model based on the population data predicted by the population prediction model and the economic data predicted by the economic prediction model;

[0065] S104: Constructing a carbon emission prediction model based on the energy consumption predicted by the energy consumption prediction model;

[0066] S105: Based on the carbon emission prediction model, predict the carbon emissions of the area to be measured to obtain a prediction result.

[0067] In this way, this application constructs an energy consumption forecasting model by integrating the data of the population forecasting model and the economic forecasting model, and then establishes a carbon emission forecasting model based on this to accurately predict the carbon emissions of the test area. This method can not only comprehensively consider the key factors affecting carbon emissions (such as population growth and economic development), but also identify the main sources of carbon emissions through detailed energy consumption analysis, thereby providing solid data support and decision-making basis for the formulation of scientific and effective emission reduction strategies. This method improves the accuracy of the forecast, helps policymakers take targeted measures, and promotes the transformation of the regional economy towards a low-carbon and sustainable direction. At the same time, it also enhances the public's awareness of the importance of energy conservation and emission reduction, and jointly promotes the realization of environmental protection and climate change goals.

[0068] In some embodiments, constructing a population prediction model includes:

[0069] Get the population growth rate; its expression is: Among them, r p is the population growth rate, r p0 is the initial population growth rate, P is the current population, and P m is the maximum population;

[0070] According to the population growth rate, a differential equation is established; its expression is: in, is the rate of change of population over time, t0 is the initial time point, and p0 is the initial population size;

[0071] Based on the differential equation, the population prediction model is determined; its expression is:

[0072] Because population size directly affects a city's energy consumption patterns and total volume, a larger population means greater demand for housing, transportation, and industrial production, all of which increase energy consumption and, consequently, carbon emissions. Therefore, by building a population forecasting model, we can accurately predict future population trends, providing a reliable data foundation for subsequent energy consumption and carbon emissions forecasts.

[0073] Furthermore, in some embodiments, the method further includes:

[0074] Acquire historical population data; divide the historical population data into training group data and test group data;

[0075] Using the training set data to train the population prediction model;

[0076] The trained population prediction model is verified using the test group data.

[0077] This will be described below through an example.

[0078] See Table 1 for a statistical table of population and economic data for a certain region.

[0079] Table 1

[0080]

[0081] To verify the accuracy of the constructed population forecast model, historical population data from 2010 to 2020 were used for analysis. Specifically, these 11 years of data were divided into two groups: the first eight years (2010-2017) served as the training group, used for model fitting and regression analysis; and the last three years (2018-2020) served as the test group, used to evaluate the model's forecast accuracy. For a comparison between the fitted curves constructed based on the training group data and the actual values ​​of the test group data, see [1]. Figure 2 shown.

[0082] Through fitting analysis, the coefficient of determination of the curve regression was 0.996, which shows that the population prediction model has a high fitting accuracy. Therefore, the population prediction model can be reliably applied to population prediction. In addition, according to the model prediction results, the maximum population size P m It is expected to reach 8549.8, with a population growth rate of r p It is 0.24%.

[0083] Based on the population data from 2010 to 2020, the population changes in a certain area from 2021 to 2060 are predicted. Figure 3The population trends shown show that from 2021 to 2025, the region's population will continue to grow at a moderate rate, but the rate of growth will gradually slow over time. From 2026 to 2030, the population will essentially stabilize, with minimal growth. From 2030 to 2060, the rate of population growth will gradually decrease, almost cease, and eventually stabilize. Overall, from 2021 to 2060, a trend of initial growth followed by stability will emerge.

[0084] In some embodiments, constructing an economic forecasting model includes:

[0085] Get the economic growth rate; its expression is: Among them, r G is the economic growth rate, r G0 is the initial economic growth rate, G is the current economic value, G m is the maximum economic value;

[0086] According to the economic growth rate, a differential equation is established; its expression is: in, is the rate of change of the economy over time, t0 is the initial time point, and G0 is the initial economic value;

[0087] Based on the differential equation, the economic forecast model is determined; its expression is:

[0088] Economic growth is often accompanied by increased energy demand, as production, transportation, and daily life all require energy, leading to increased carbon emissions. Therefore, by building an economic forecasting model, we can accurately predict future economic trends, providing a reliable data foundation for subsequent energy consumption and carbon emissions forecasts.

[0089] Furthermore, in some embodiments, the method further includes:

[0090] Acquire historical economic data; divide the historical economic data into training group data and test group data;

[0091] Using the training set data to train the economic forecasting model;

[0092] The trained economic forecasting model is verified using the test group data.

[0093] The following will be described with reference to an embodiment in conjunction with Table 1 above.

[0094] In order to verify the accuracy of the constructed economic forecast model, historical economic data from 2010 to 2020 were used for analysis. The specific method is to divide the data of these 11 years into two groups: the data of the first 8 years (2010-2017) is used as the training group for fitting the model and performing regression analysis; and the data of the last 3 years (2018-2020) is used as the test group for evaluating the forecast accuracy of the model. In addition, based on the long-term development goals, it is assumed that: by 2035, GDP will double compared to the base period of 2020; by 2060, GDP will quadruple compared to the base period of 2020. The fitting curve established based on the above training and hypothetical data is as follows Figure 4 shown.

[0095] Through fitting analysis, the coefficient of determination of the curve regression is 0.9941, which shows that the economic forecast model has a high fitting accuracy. Therefore, the economic forecast model can be reliably applied to economic forecasting. In addition, according to the model prediction results, the maximum economic value G m It is expected to reach 375540, with an economic growth rate of r G It is 0.0985.

[0096] Based on the economic data from 2010 to 2020, the economic changes of a certain region from 2021 to 2060 are predicted. Figure 4 The economic trends shown show that from 2021 to 2035, regional GDP will grow rapidly, showing strong growth momentum. From 2035 to 2060, the economic growth rate will begin to slow slightly, but will still maintain a high growth rate. After that, although the growth rate will gradually slow down, the economy will continue to grow significantly.

[0097] In some embodiments, constructing an energy consumption prediction model includes:

[0098] Get the energy growth rate; its expression is: r E =ε1+ε2P m +ε3G m ; Among them, r E is the energy growth rate, ε1 is the constant term coefficient, ε2 is the impact coefficient of population on energy consumption, ε3 is the impact coefficient of economy on energy consumption, P m is the maximum population, G m is the maximum economic value;

[0099] According to the economic growth rate, a differential equation is established; its expression is: in, is the rate of change of energy consumption over time, t0 is the initial time point, and E0 is the initial energy value;

[0100] Based on the differential equation, the energy consumption prediction model is determined; its expression is: Among them, r p is the population growth rate, r G is the economic growth rate, p0 is the initial population, G0 is the initial economic value, P m is the maximum population, G m is the maximum economic value, P is the population data predicted by the population forecasting model, and G is the economic data predicted by the economic forecasting model.

[0101] Thus, in this application, by combining the forecast data of population growth and economic development, analyzing the impact of these factors on energy consumption, an energy consumption forecast model closely related to population and economy is established, making the forecast results more accurate and close to reality.

[0102] Furthermore, in some embodiments, the method further includes:

[0103] Acquire historical energy data; divide the historical energy data into training group data and test group data;

[0104] Using the training set data to train the energy consumption prediction model;

[0105] The trained energy consumption prediction model is verified using the test group data.

[0106] This will be described below through an example.

[0107] See Table 2 for a statistical table of energy consumption in a certain region.

[0108] Table 2

[0109]

[0110] To verify the accuracy of the constructed energy consumption prediction model, the energy consumption data from 2010 to 2020 were divided into two groups: the first eight years of data (2010-2017) were used as the training group for fitting the function and performing regression analysis; the last three years of data (2018-2020) were used as the test group for verifying the accuracy of the model. The comparison between the fitting curve established based on the training group data and the actual values ​​of the test group data can be seen in Figure 2. Figure 5 shown.

[0111] Through fitting analysis, we can get ε1+ε2Pm+ε3G m =0.02339, ε2=-0.0023, ε3=0.4886, where ε2 is a negative value, indicating that population growth has a slight negative impact on energy consumption, and ε3 is a positive and large value, indicating that economic growth has a significant positive impact on energy consumption.

[0112] Based on the energy consumption from 2010 to 2020 as the basic data, the energy consumption of a certain region from 2021 to 2060 is predicted. Figure 6 The energy consumption trend shown in the figure shows that the overall energy consumption level has shown a significant growth trend over time, and the growth rate has gradually increased. This shows that the annual increase in energy demand has become larger and larger over time, showing a strong growth momentum.

[0113] In some embodiments, constructing a carbon emission prediction model based on the energy consumption predicted by the energy consumption prediction model includes:

[0114] Identify the consumption sectors that affect carbon emissions, including energy consumption in the industrial sector, energy consumption in the construction sector, energy consumption in the transportation sector, energy consumption in the residential sector, energy consumption in the agriculture and forestry sector, and energy consumption in the energy supply sector;

[0115] Each consumer department makes a prediction based on the energy consumption prediction model;

[0116] A carbon emission prediction model is constructed according to the energy consumption predicted by each department based on the energy consumption prediction model.

[0117] The following will explain in detail why this application identifies the above-mentioned consumer sectors.

[0118] Industrial consumption sector: As one of the important sources of energy consumption and carbon emissions, the industrial sector covers many sub-industries, such as steel, chemicals, etc., which often rely on large amounts of fossil fuels and are therefore key areas for emission reduction.

[0119] Building consumption sector: Buildings consume a lot of energy during construction and operation, especially in heating and cooling. With the acceleration of urbanization, their energy demand continues to grow, becoming a source of carbon emissions that cannot be ignored.

[0120] Transportation consumption sector: The transportation industry mainly relies on petroleum products and is one of the main contributors to greenhouse gas emissions, especially in areas such as road transportation and aviation, where there is huge potential for emission reduction.

[0121] Residential consumption: Energy use in daily household life (such as electricity and gas) is also an important component of carbon emissions. With the improvement of living standards, this part of emissions is also gradually increasing.

[0122] Agriculture and forestry consumption sectors: Although the direct energy consumption of agriculture and forestry is relatively small compared to other sectors, the methane and nitrous oxide emissions from agricultural production cannot be ignored. At the same time, forests are important carbon sinks, and research on them is equally important.

[0123] Energy supply sector: This sector is responsible for transporting energy from the production end to the consumption end, including activities such as power generation, transmission, and distribution. Its efficiency directly affects the energy utilization efficiency and carbon emission levels of end users.

[0124] It should be noted that in practical applications, in addition to the major consumer sectors mentioned above, other sectors with significant energy consumption or carbon emissions can also be considered, depending on the characteristics of the specific study area and data availability. For example, the waste disposal sector: Landfills and wastewater treatment facilities release greenhouse gases such as methane, which have a significant impact on global warming as a non-CO2 greenhouse gas. Therefore, carbon emissions from waste disposal are equally important.

[0125] In this way, different consumer sectors have different energy usage patterns and carbon emission characteristics. By predicting the energy consumption of different consumer sectors separately and building a carbon emission prediction model based on these prediction results, the accuracy of carbon emission prediction can be improved.

[0126] In some embodiments, constructing a carbon emission prediction model includes:

[0127] According to the relationship between carbon emissions and energy consumption, the carbon emission prediction model is determined; its expression is: Among them, C is carbon emissions, ω is energy efficiency, E i is the energy consumption predicted by each department based on the energy consumption prediction model, θ is the carbon emission factor, a i represents the proportion of non-fossil energy consumption in the i-th sector.

[0128] The expression of energy utilization efficiency is: Among them, E23 is the energy supply amount of the energy supply department, and E is the total energy consumption.

[0129] The expression of the carbon emission factor is θ=-0.0115(t-2010)+2.5447; wherein t represents the year, and θ represents the comprehensive carbon emission factor.

[0130] It is worth noting that Figure 7To analyze the relationship between total carbon emissions and total energy consumption, a scatter plot is plotted. This scatter plot provides a visual representation of the relationship between total carbon emissions and total energy consumption. A first-order linear regression analysis yields a goodness of fit of 0.9463, indicating a strong linear relationship between total carbon emissions and total energy consumption. Therefore, it can be concluded that total energy consumption is a key factor influencing total carbon emissions.

[0131] This application comprehensively considers the energy consumption and efficiency of each sector, resulting in a more accurate forecast of total carbon emissions. This approach, by precisely analyzing the energy usage characteristics and emission reduction potential of different sectors, provides solid data support for developing a scientifically sound carbon peak pathway.

[0132] In order to more clearly illustrate the above method, this application provides a specific embodiment.

[0133] The population data and economic data of the area to be tested are predicted by constructing a population prediction model and an economic prediction model; and an energy consumption prediction model is constructed based on the population data predicted by the population prediction model and the economic data predicted by the economic prediction model; and the energy consumption of each consumer department is determined based on the energy consumption prediction model.

[0134] The energy consumption of various consumption sectors includes: energy consumption of the industrial consumption sector, energy consumption of the construction consumption sector, energy consumption of the transportation consumption sector, energy consumption of residential consumption, energy consumption of the agricultural and forestry consumption sector, and energy consumption of the energy supply sector. Each consumption sector includes both fossil energy consumption and non-fossil energy consumption. It is worth noting that only fossil energy produces carbon emissions; non-fossil energy (such as solar energy and wind energy) does not.

[0135] Data Assumptions: Non-fossil energy use in the agriculture and forestry, transportation, construction, and residential sectors is zero. Based on this assumption, it is assumed that these sectors will continue to use non-fossil energy in the future, meaning these indicators are considered constant at zero.

[0136] Based on the above assumptions, the energy consumption prediction model is used to predict the above-mentioned consumption sectors. The specific fitting image is shown in the attached figure.

[0137] Specifically, the fossil energy consumption of the agricultural and forestry sectors (temporarily referred to as E11) is as follows: Figure 8 As shown;

[0138] The fossil energy consumption of the energy supply sector (temporarily referred to as E21) is as follows: Figure 9 As shown;

[0139] Fossil energy consumption in the industrial consumption sector (temporarily referred to as E31) is as follows: Figure 10 As shown;

[0140] The fossil energy consumption of the transportation sector (temporarily referred to as E41) is as follows: Figure 11 As shown;

[0141] The fossil energy consumption of the building consumption sector (temporarily referred to as E51) is as follows: Figure 12 As shown;

[0142] Fossil energy consumption for residents' daily life (temporarily referred to as E61) Figure 13 As shown;

[0143] The non-fossil energy consumption of the industrial consumption sector (temporarily referred to as E32) is as follows: Figure 14 As shown;

[0144] Based on the above-mentioned fitting and prediction images of energy consumption of each department, according to the carbon emission prediction model, using time as the independent variable output, the carbon emission prediction image and the percentage error scatter plot of the carbon emission prediction model are obtained, as shown in Figure 15 shown.

[0145] according to Figure 15 As can be seen, the forecast error for carbon emissions ranges from approximately 2% to 9%, with the largest error occurring in 2010. By analyzing the proportion of fossil energy consumption by each consumer sector, we can determine that the large error in that year was primarily due to the significant discrepancy between the actual fossil energy consumption of the energy supply sector and the estimated value from the forecast model. Therefore, to correct this error, we can use the actual fossil energy consumption at the 2010 data point instead of the original forecast value for regression analysis, resulting in a revised diagram, as shown in the figure below. Figure 16 This step helps to improve the accuracy and reliability of the overall model.

[0146] according to Figure 16 The revised model improves prediction accuracy and generates a carbon emissions forecast for a specific region over the next 40 years (i.e., the next 40 years from the current time point). In other words, by correcting the model's errors, it can more accurately predict the region's carbon emissions trends over the next 40 years, providing a scientific basis for developing appropriate emission reduction strategies and policies.

[0147] Figure 17 900 is a structural diagram of a computing device provided in an embodiment of the present application. The computing device executes the above method, such as Figure 17 As shown, the computing device 900 includes: a processor 910 , a memory 920 , and a communication interface 930 .

[0148] It should be understood that Figure 17The communication interface 930 in the computing device 900 shown may be used to communicate with other devices, and may specifically include one or more transceiver circuits or interface circuits.

[0149] The processor 910 may be connected to a memory 920. The memory 920 may be used to store the program code and data. Therefore, the memory 920 may be a storage unit within the processor 910, an external storage unit independent of the processor 910, or a component including both a storage unit within the processor 910 and an external storage unit independent of the processor 910.

[0150] Optionally, the computing device 900 may further include a bus. The memory 920 and the communication interface 930 may be connected to the processor 910 via the bus. The bus may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 17 A line without an arrow is used to represent the bus, but this does not mean that there is only one bus or one type of bus.

[0151] It should be understood that in the embodiment of the present application, the processor 910 can adopt a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. Alternatively, the processor 910 uses one or more integrated circuits to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0152] The memory 920 may include a read-only memory and a random access memory, and provides instructions and data to the processor 910. A portion of the processor 910 may also include a non-volatile random access memory. For example, the processor 910 may also store information about the device type.

[0153] When the computing device 900 is running, the processor 910 executes the computer-executable instructions in the memory 920 to perform any operation step of the above method and any optional embodiment thereof.

[0154] It should be understood that the computing device 900 according to the embodiment of the present application can correspond to the corresponding subject in executing the method according to each embodiment of the present application, and the above-mentioned and other operations and / or functions of each module in the computing device 900 are respectively for implementing the corresponding processes of each method of the present embodiment. For the sake of brevity, they will not be repeated here.

[0155] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0156] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0157] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0158] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0159] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0160] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0161] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the program is used to execute the above method, which includes at least one of the solutions described in the above embodiments.

[0162] The computer storage medium of the embodiment of the present application can adopt any combination of one or more computer-readable media.Computer-readable media can be computer-readable signal media or computer-readable storage media.Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or components, or any combination thereof.More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connection with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination thereof.In this document, computer-readable storage media can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0163] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0164] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0165] The computer program code for performing the operations of the present application can be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0166] In addition, the words "first, second, third, etc." or module A, module B, module C and other similar terms in the specification and claims are only used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that the specific order or sequence can be interchanged where permitted so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0167] In the above description, the numbers representing the steps, such as S110, S120, etc., do not necessarily mean that the steps must be executed in this manner. If permitted, the order of the steps can be interchanged or they can be executed simultaneously.

[0168] The term "comprising" as used in the specification and claims should not be construed as limiting to what is listed thereafter; it does not exclude other elements or steps. Thus, it should be interpreted as specifying the presence of the features, integers, steps, or components mentioned, but not excluding the presence or addition of one or more other features, integers, steps, or components, or groups thereof. Thus, the expression "a device comprising means A and B" should not be limited to a device consisting solely of components A and B.

[0169] References in this specification to "one embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Therefore, the phrases "in one embodiment" or "in an embodiment" appearing throughout this specification do not necessarily refer to the same embodiment, but may refer to the same embodiment. Furthermore, in one or more embodiments, the particular features, structures, or characteristics can be combined in any suitable manner, as would be apparent to one of ordinary skill in the art from this disclosure.

[0170] Note that the above are only preferred embodiments of the present application and the technical principles employed. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of protection of the present application, all of which fall within the scope of protection of the present application.

Claims

1. A method for predicting carbon emissions, characterized in that: include: According to the constructed population prediction model, the population data of the tested area is predicted; According to the constructed economic forecast model, the economic data of the tested area is forecasted; constructing an energy consumption prediction model based on the population data predicted by the population prediction model and the economic data predicted by the economic prediction model; Constructing a carbon emission prediction model based on the energy consumption predicted by the energy consumption prediction model; Based on the carbon emission prediction model, the carbon emission of the area to be measured is predicted to obtain a prediction result.

2. The prediction method according to claim 1, characterized in that The constructing of the population prediction model comprises: Get the population growth rate; its expression is: Among them, r p is the population growth rate, r p0 is the initial population growth rate, P is the current population, and P m is the maximum population; According to the population growth rate, a differential equation is established; its expression is: in, is the rate of change of population over time, t0 is the initial time point, and p0 is the initial population size; Based on the differential equation, the population prediction model is determined; its expression is:

3. The prediction method according to claim 1, wherein: The constructing of the economic forecasting model includes: Get the economic growth rate; its expression is: Among them, r G is the economic growth rate, r G0 is the initial economic growth rate, G is the current economic value, G m is the maximum economic value; According to the economic growth rate, a differential equation is established; its expression is: in, is the rate of change of the economy over time, t0 is the initial time point, and G0 is the initial economic value; Based on the differential equation, the economic forecast model is determined; its expression is:

4. The prediction method according to claim 1, wherein: The energy consumption prediction model is constructed, including: Get the energy growth rate; its expression is: r E =ε1+ε2P m +ε3G m ; Among them, r E is the energy growth rate, ε1 is the constant term coefficient, ε2 is the impact coefficient of population on energy consumption, ε3 is the impact coefficient of economy on energy consumption, P m is the maximum population, G m is the maximum economic value; According to the economic growth rate, a differential equation is established; its expression is: in, is the rate of change of energy consumption over time, t0 is the initial time point, and E0 is the initial energy value; Based on the differential equation, the energy consumption prediction model is determined; its expression is: Among them, r p is the population growth rate, r G is the economic growth rate, p0 is the initial population, G0 is the initial economic value, P m is the maximum population, G m is the maximum economic value, P is the population data predicted by the population forecasting model, and G is the economic data predicted by the economic forecasting model.

5. The prediction method according to claim 1, wherein: The step of constructing a carbon emission prediction model based on the energy consumption predicted by the energy consumption prediction model includes: Identify the consumption sectors that affect carbon emissions, including energy consumption in the industrial sector, energy consumption in the construction sector, energy consumption in the transportation sector, energy consumption in residential consumption, energy consumption in the agriculture and forestry sector, and energy consumption in the energy supply sector; Each consumer department makes a prediction based on the energy consumption prediction model; A carbon emission prediction model is constructed according to the energy consumption predicted by each department based on the energy consumption prediction model.

6. The prediction method according to claim 5, characterized in that The carbon emission prediction model is constructed, including: According to the relationship between carbon emissions and energy consumption, the carbon emission prediction model is determined; its expression is: Among them, C is carbon emissions, ω is energy efficiency, E i is the energy consumption predicted by each department based on the energy consumption prediction model, θ is the carbon emission factor, a i represents the proportion of non-fossil energy consumption in the i-th sector.

7. The prediction method according to claim 6, characterized in that The expression of energy utilization efficiency is: Among them, E23 is the energy supply amount of the energy supply department, and E is the total energy consumption.

8. The prediction method according to claim 5, characterized in that The expression of the carbon emission factor is θ=-0.0115(t-2010)+2.5447; wherein t represents the year, and θ represents the comprehensive carbon emission factor.

9. A computing device, characterized in that include: processor, and A memory having program instructions stored thereon, wherein when the program instructions are executed by the processor, the processor executes the carbon emission prediction method according to any one of claims 1 to 8.

10. A storage medium, characterized in that: Program instructions are stored thereon, and when the program instructions are executed by a computer, the computer is enabled to execute the carbon emission prediction method according to any one of claims 1 to 8.

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