Probability evaluation method and device for realizing carbon peak reaching and climate target, and storage medium

Abstract

The invention relates to the technical field of energy, in particular to a maximum likelihood estimation and Monte Carlo simulation-based probability evaluation method, device and equipment for realizing carbon peak reaching and climate targets, and a computer storage medium. The invention provides a maximum likelihood estimation and Monte Carlo simulation-based probability evaluation method for realizing carbon peak reaching and climate targets, and the method comprises the steps: constructing the uncertainty of the total energy consumption and non-fossil energy consumption in the future through maximum likelihood estimation and Monte Carlo simulation, and carrying out the quantitative calculation of the probability of realizing different climate targets. And evaluation standards are provided for various types of policy measures, a theoretical basis is provided for formulating effective and feasible policies, and the method has the advantages of quantification, multiple coverage uncertainty factors, flexible target object evaluation and the like.

Description

Technical Field

[0001] The present invention relates to the field of energy technology, and in particular to a probability assessment method, device, equipment and computer storage medium for achieving carbon peak and climate goals based on maximum likelihood estimation and Monte Carlo simulation. Background Art

[0002] Climate change caused by anthropogenic carbon dioxide emissions has become one of the key issues of concern to countries around the world in recent years.

[0003] China faces two major uncertainties in the process of completing its carbon peak mission: total energy consumption and non-fossil energy consumption. The two are contradictory and there is huge uncertainty.

[0004] The first is the total energy consumption. On the one hand, there is the rapid growth trend of the current total energy consumption, and on the other hand, there is the goal of dual control of energy consumption and the task of reaching carbon peak.

[0005] The second is the consumption of non-fossil energy. The uncertainty of wind power and photovoltaic installed capacity will directly affect the consumption of non-fossil energy.

[0006] To sum up, under the combined influence of the uncertainty of total energy consumption and non-fossil energy consumption, it is necessary to characterize the two major uncertainties and assess the probability of my country achieving carbon peak and other climate goals in the future, and analyze whether and how my country will achieve carbon peak. Summary of the invention

[0007] To this end, the technical problem to be solved by the present invention is to overcome the problem of inaccurate prediction of the probability of achieving carbon peak and other climate goals in the prior art.

[0008] In order to solve the above technical problems, the present invention provides a probability assessment method for achieving carbon peak and climate goals, including:

[0009] The maximum likelihood estimation method is used to construct the first probability density distribution function of the target region's GDP in the target year and the second probability density distribution function of the policy completion of renewable-related policies in the target region;

[0010] Establish scenarios for energy intensity reduction and different renewable energy capacity targets;

[0011] Using a Monte Carlo simulation combined with a random sampling method to sample the first probability density distribution function and the second probability density distribution function and combining the energy intensity reduction scenario with the renewable energy installed capacity target scenario to obtain a sub-scenario;

[0012] Calculate the carbon emissions and climate target indicator data for the target year for all sub-scenarios;

[0013] The frequency ratio of achieving carbon peak in all sub-scenarios is taken as the probability of achieving carbon peak, and the frequency ratio of achieving a certain climate target in all sub-scenarios is taken as the probability of achieving that climate target.

[0014] Preferably, constructing the first probability density distribution function of the target region's gross domestic product in the target year by using the maximum likelihood estimation method includes:

[0015] The distribution of GDP of the target region in the preset target year follows a normal distribution, and n GDP forecast values ​​of the target region in the target year from various pre-specified research institutions and economic organizations are selected as the sample set obtained by n samplings of the distribution;

[0016] Based on the probability density distribution function of the normal distribution, a joint probability distribution function is established according to the n GDP forecast values ​​as a first likelihood function;

[0017] The maximum value of the first likelihood function is solved, and the mean and standard deviation estimates of the normal distribution of the gross domestic product are determined according to the parameter values ​​to obtain a first probability density distribution function.

[0018] Preferably, the second probability density distribution function of the policy completion degree of the target area's renewable energy-related policies constructed by the maximum likelihood estimation method includes:

[0019] The distribution of policy completion of renewable energy-related policies in the preset target area follows a normal distribution, and the policy completion of m renewable energy-related policies calculated from m types of policy target data released by the government within a pre-specified time period and the actual completion values ​​is selected as the sample set obtained by m samplings of the distribution;

[0020] Based on the probability density distribution function of the normal distribution, a joint probability distribution function is established as a second likelihood function according to the policy completion of the m renewable energy-related policies;

[0021] The second likelihood function is solved for its maximum value, and the mean and standard deviation estimates of the normal distribution of the policy completion degree of renewable-related policies are determined according to parameter values ​​to obtain a second probability density distribution function.

[0022] Preferably, the establishment of energy intensity reduction scenarios and different renewable energy installed capacity target scenarios includes:

[0023] Set the average annual decline rate of the future energy intensity reduction path;

[0024] Set annual new installed capacity targets for future photovoltaic and onshore wind power under different policy scenarios;

[0025] Set the proportion of oil and gas in total energy consumption;

[0026] Set annual operating hours for nuclear power, hydropower, onshore wind power, offshore wind power and photovoltaic power;

[0027] Setting emission factors for coal, oil, and natural gas;

[0028] Set the proportion of coal used for power generation in total coal consumption in the future.

[0029] Preferably, the method of using Monte Carlo simulation combined with random sampling to sample from the first probability density distribution function and the second probability density distribution function and combining the energy intensity reduction scenario with the renewable energy installed capacity target scenario to obtain a sub-scenario includes:

[0030] Based on Monte Carlo simulation and random sampling methods, samples were taken from the first probability density distribution function and combined with the energy intensity reduction scenario to obtain p future energy consumption growth paths under a single energy consumption reduction path;

[0031] Based on Monte Carlo simulation and random sampling methods, sampling is performed from the second probability density distribution function and combined with a certain renewable energy installed capacity target scenario to obtain q future non-fossil energy consumption growth paths under a renewable energy policy scenario.

[0032] According to the growth path of energy consumption and the growth path of future non-fossil energy consumption, p*q future sub-scenarios are constructed.

[0033] Preferably, the calculation of the carbon emissions and climate target indicator data for the target year for all sub-scenarios includes:

[0034] Calculate the total energy consumption in the target year based on the GDP and energy intensity in the target year;

[0035] Among non-fossil energy sources, nuclear power, hydropower and offshore wind power are set as low uncertainty factors, and their future installed capacity is set by setting fixed parameters;

[0036] Photovoltaic power generation and onshore wind power in non-fossil energy are set as high uncertainty factors, and the total installed capacity in the target year is calculated based on the target installed capacity in the target year and the target completion rate of the target installed capacity in the target year;

[0037] The non-fossil energy consumption in the target year is obtained by multiplying the installed capacity of each type of non-fossil energy in the target year by its operating hours in the target year;

[0038] The oil and gas consumption in the target year of each sub-scenario is obtained by multiplying the total energy consumption in the target year by the proportion of oil and gas in the total energy consumption in each sub-scenario.

[0039] The coal consumption in the target year is obtained by deducting the non-fossil energy consumption in the target year and the oil and natural gas consumption in the target year from the total energy consumption in the target year.

[0040] Preferably, the calculation of the carbon emissions and climate target indicator data for the target year for all sub-scenarios also includes:

[0041] The total electricity consumption in the target year is calculated based on the non-fossil energy consumption in the target year and the power coal consumption in the target year.

[0042] The present invention also provides a probability assessment device for achieving carbon peak and climate goals, comprising:

[0043] A probability density distribution construction module, used to construct a first probability density distribution function of the gross domestic product of the target region in the target year and a second probability density distribution function of the policy completion of renewable-related policies in the target region by using a maximum likelihood estimation method;

[0044] Scenario building module, used to build energy intensity reduction scenarios and different renewable energy installation target scenarios;

[0045] A sub-scenario construction module, used to sample from the first probability density distribution function and the second probability density distribution function by using a Monte Carlo simulation combined with a random sampling method and to obtain a sub-scenario by combining the energy intensity reduction scenario with the renewable energy installed capacity target scenario;

[0046] The completion calculation module is used to calculate the carbon emissions and climate target indicator data for all sub-scenarios in the target year;

[0047] The probability assessment module is used to take the frequency ratio of achieving carbon peak in all sub-scenarios as the probability of achieving carbon peak, and the frequency ratio of achieving a certain climate target in all sub-scenarios as the probability of achieving the climate target.

[0048] The present invention also provides a probability assessment device for achieving carbon peak and climate goals, including:

[0049] Memory for storing computer programs;

[0050] A processor is used to implement the above-mentioned probability assessment method steps for achieving carbon peak and climate goals when executing the computer program.

[0051] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned probability assessment method for achieving carbon peak and climate goals are implemented.

[0052] The above technical solution of the present invention has the following advantages compared with the prior art:

[0053] The present invention proposes a probability assessment method for achieving carbon peak and climate goals based on maximum likelihood estimation and Monte Carlo simulation. The uncertainty of future total energy consumption and non-fossil energy consumption is constructed through maximum likelihood estimation and Monte Carlo simulation, and then the probability of achieving different climate goals is quantitatively calculated. Evaluation criteria are provided for various types of policy measures, and a theoretical basis is provided for formulating effective and feasible policies. The method has the advantages of quantification, coverage of multiple uncertainty factors, and flexible evaluation target objects. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein:

[0055] Figure 1 It is a flow chart of a probability assessment method for achieving carbon peak and climate goals provided by the present invention;

[0056] Figure 2 The uncertainty range of the total future energy consumption of the embodiment of this application;

[0057] Figure 3 The uncertainty range of the total amount of future non-fossil energy consumption under different policy scenarios of the embodiments of this application;

[0058] Figure 4 The uncertainty range of the total future electricity consumption under different policy scenarios of the embodiment of this application;

[0059] Figure 5 The uncertainty range of the total amount of future energy-related carbon emissions under different policy scenarios of the embodiments of this application;

[0060] Figure 6 This is a graph of the probability results of reaching the carbon peak target under different policy scenarios of an embodiment of this application. DETAILED DESCRIPTION

[0061] The core of the present invention is to provide a probability assessment method, device, equipment and computer storage medium for achieving carbon peak and climate goals, which effectively improves the accuracy of the probability assessment of achieving carbon peak and climate goals.

[0062] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0063] Please refer to Figure 1 , Figure 1 This is a flowchart of a probability assessment method for achieving carbon peak and climate goals provided by the present invention; the specific operation steps are as follows:

[0064] S101: constructing a first probability density distribution function of the gross domestic product of the target region in the target year and a second probability density distribution function of the policy completion of renewable energy-related policies in the target region using the maximum likelihood estimation method;

[0065] S102: Establish energy intensity reduction scenarios and different renewable energy capacity target scenarios;

[0066] S103: using a Monte Carlo simulation combined with a random sampling method to sample from the first probability density distribution function and the second probability density distribution function and combining the energy intensity reduction scenario with the renewable energy installed capacity target scenario to obtain a sub-scenario;

[0067] S104: Calculate the carbon emissions and climate target indicator data for the target year for all sub-scenarios;

[0068] S105: The frequency ratio of achieving carbon peak in all sub-scenarios is taken as the probability of achieving carbon peak, and the frequency ratio of achieving a certain climate target in all sub-scenarios is taken as the probability of achieving the climate target.

[0069] Based on the above embodiments, this embodiment describes step S101 in detail:

[0070] The distribution of GDP of the target region in the preset target year follows a normal distribution. The n GDP forecast values ​​of the target region in the target year from the pre-specified research institutions and economic organizations are selected as the sample set obtained by n samplings of the distribution:

[0071] A={x 1 ,x 2, …,x n}

[0072] Based on the probability density distribution function of the normal distribution, a joint probability distribution function is established according to the n GDP forecast values ​​as the first likelihood function:

[0073]

[0074] Solve the maximum value of the first likelihood function and determine the mean and standard deviation estimates of the normal distribution of GDP according to the values ​​of parameters μ and σ A first probability density distribution function is obtained.

[0075] In a specific embodiment, the probability density distribution function of China's gross domestic product (GDP) in 2030 and the probability density distribution function of the policy completion of China's renewable energy-related policies are constructed using maximum likelihood estimation. Assuming that the distribution of China's GDP value in 2030 follows a normal distribution, the economic forecast values ​​of China in 2030 from various research institutions and economic organizations are selected as the sample set obtained by sampling the distribution n times; the n GDP forecast values ​​are brought into the probability density distribution function of the normal distribution, and a joint probability distribution function is established as the likelihood function. The maximum value of this likelihood function is obtained, and the values ​​of the parameters μ and σ at this time are the estimated values ​​of the mean and standard deviation of the normal distribution of the GDP value in 2030.

[0076] The distribution of policy completion of renewable-related policies in the preset target area follows a normal distribution. The policy completion of m renewable-related policies calculated from m types of policy target data released by the government within a pre-specified time period and the actual completion values ​​is selected as the sample set obtained by m sampling distributions:

[0077] A={x 1 ,x 2, …,x m}

[0078] Based on the probability density distribution function of the normal distribution, a joint probability distribution function is established as the second likelihood function according to the policy completion degree of the m renewable energy-related policies:

[0079]

[0080] The maximum value of the second likelihood function is solved, and the mean and standard deviation estimation parameters μ and σ of the normal distribution of the policy completion degree of renewable related policies are determined according to the parameter values ​​to obtain the second probability density distribution function.

[0081] In a specific embodiment, it is assumed that the completion of China's renewable energy-related goals follows a normal distribution. The various types of policy target data and actual completion values ​​released by the government from 2016 to 2023 are selected. In this embodiment, the annual newly added installed capacity of wind and solar power, the proportion of wind and solar power generation in the total social electricity consumption, and the proportion of non-fossil energy consumption in total energy consumption are selected. The actual value ratio to the target value is used as the sample set obtained by sampling m times for the completion distribution; the m policy completions are substituted into the probability density distribution function of the normal distribution, and the joint probability distribution function is established as the likelihood function. The maximum value of this likelihood function is obtained. At this time, the values ​​of the parameters μ and σ are the mean and standard deviation estimates of the normal distribution of the required policy target completion.

[0082] Based on the above embodiments, this embodiment describes step S102 in detail:

[0083] Set the average annual decline rate of the future energy intensity reduction path;

[0084] Set annual new installed capacity targets for future photovoltaic and onshore wind power under different policy scenarios;

[0085] Set the proportion of oil and gas in total energy consumption;

[0086] Set annual operating hours for nuclear power, hydropower, onshore wind power, offshore wind power and photovoltaic power;

[0087] Setting emission factors for coal, oil, and natural gas;

[0088] Set the proportion of coal used for power generation in total coal consumption in the future.

[0089] The above parameters can be obtained by referring to reports from authoritative organizations or related research.

[0090] Based on the above embodiments, this embodiment describes step S103 in detail:

[0091] Based on Monte Carlo simulation and random sampling methods, samples were taken from the first probability density distribution function and combined with the energy intensity reduction scenario to obtain p future energy consumption growth paths under a single energy consumption reduction path;

[0092] Based on Monte Carlo simulation and random sampling methods, sampling is performed from the second probability density distribution function and combined with a certain renewable energy installed capacity target scenario to obtain q future non-fossil energy consumption growth paths under a renewable energy policy scenario.

[0093] According to the growth path of energy consumption and the growth path of future non-fossil energy consumption, p*q future sub-scenarios are constructed.

[0094] In a specific embodiment, GDP values ​​and policy completion are randomly extracted from the obtained distribution by Monte Carlo simulation combined with random sampling to characterize its uncertainty. In this embodiment, the average annual GDP growth rate a from 2021 to 2025 can be calculated by the equation, including:

[0095] GDP 2021 ×(1+a) 5 ×(1+a-0.005) 5 =GDP 2030

[0096] For each 2030 GDP value sampled from the distribution, the annual GDP value from 2024 to 2035 can be calculated and combined with the average annual decline rate of the energy intensity decline path to derive the annual total energy consumption from 2024 to 2030. The GDP value in 2035 is calculated using the following formula, assuming that the average annual GDP growth rate decreases by 0.5% every five years: GDP 2035 =GDP 2021 ×(1+a) 5 ×(1+a-0.005) 5 ×(1+a-0.01) 5

[0097] The uncertainty range of the total future energy consumption is as follows: Figure 2 As shown in the figure, the uncertainty range of future non-fossil energy consumption under the four renewable scenarios is as follows: Figure 3 shown.

[0098] In a specific embodiment, the GDP value in 2030 is sampled 1,000 times and combined with the energy consumption intensity to obtain 1,000 growth paths for China's energy consumption under a single energy consumption reduction path. The annual photovoltaic and onshore wind power installed capacity target completion rate is sampled 1,000 times in the future to obtain 1,000 future non-fossil energy consumption growth paths for China under a renewable energy policy scenario, constituting 1,000,000 future sub-scenarios, covering a wide range of uncertainty.

[0099] Based on the above embodiments, this embodiment describes step S104 in detail:

[0100] The total energy consumption in the target year is calculated based on the GDP in the target year and the energy consumption intensity in the target year; nuclear power, hydropower and offshore wind power in non-fossil energy are set as low uncertainty factors, and their future installed capacity is set by setting fixed parameters;

[0101] Photovoltaic power generation and onshore wind power in non-fossil energy are set as high uncertainty factors, and the total installed capacity in the target year is calculated based on the target installed capacity in the target year and the target completion rate of the target installed capacity in the target year.

[0102] Among them, i represents the high uncertainty factor (photovoltaic power generation, onshore wind power), IC i,t is the total installed capacity of technology i in year t, S i,j is the annual new installed capacity target of technology i in year t, α i,j The degree of completion of the policy target for the newly added installed capacity of technology i in year t.

[0103] The non-fossil energy consumption in the target year E is obtained by multiplying the installed capacity of each type of non-fossil energy in the target year by its operating hours in the target year. t =GDP t ×EI t

[0104] Among them, E t is the total energy consumption in year t, GDP t is the gross domestic product in year t, EI t is the energy consumption intensity in year t, and the GDP value in the target year can be obtained by sampling from the constructed distribution.

[0105] The oil and gas consumption in the target year of each sub-scenario is obtained by multiplying the total energy consumption in the target year by the proportion of oil and gas in the total energy consumption in each sub-scenario (with a maximum upper limit value set);

[0106] The coal consumption in the target year is obtained by deducting the non-fossil energy consumption in the target year and the oil and natural gas consumption in the target year from the total energy consumption in the target year.

[0107] The total electricity consumption in the target year P is calculated based on the non-fossil energy consumption in the target year and the coal consumption in the target year. t =E non-fossil,t +E coal,t ×K t ×c

[0108] Among them, P t is the total electricity consumption in year t, K t is the proportion of coal used for power generation in coal consumption, c is the power-coal conversion coefficient, and 300 grams of standard coal per kilowatt-hour is used here. The uncertainty range of future power consumption under the four renewable scenarios in this embodiment is as follows: Figure 4 shown.

[0109] Based on the above embodiments, this embodiment describes step S105 in detail:

[0110] The probability value can be approximated by the frequency ratio of achieving the corresponding target scenario in all sub-scenarios.

[0111] In one embodiment, by sampling the GDP in 2030 1,000 times, 1,000 GDP growth paths can be obtained. After combining with the set energy consumption intensity, 1,000 total energy consumption growth paths can be obtained. For the policy target completion, sampling 1,000 times, combined with the installed capacity target under the set specific policy scenario, 1,000 non-fossil energy installed capacity paths can be obtained. After combining with the total energy consumption, 1,000,000 seed scenarios can be obtained. For each sub-scenario, it can be calculated whether its specific policy target is achieved, such as achieving carbon peak before 2030, carbon emission intensity per unit GDP in 2030 is reduced by 65% ​​compared with the 2005 level, and non-fossil energy consumption in 2030 accounts for more than 25% of total energy consumption. The proportion of scenarios in which the target is achieved is approximated as the probability of achieving the target. In this embodiment, the policy target displayed is the probability of achieving carbon peak. The uncertainty range of China's energy-related carbon emissions and the probability of achieving carbon peak under four renewable policy scenarios are shown in the figure below. Figure 5 and Figure 6 shown.

[0112] The embodiment of the present invention also provides a probability assessment device for achieving carbon peak and climate goals; the specific device may include:

[0113] A probability density distribution construction module, used to construct a first probability density distribution function of the gross domestic product of the target region in the target year and a second probability density distribution function of the policy completion of renewable-related policies in the target region by using a maximum likelihood estimation method;

[0114] Scenario building module, used to build energy intensity reduction scenarios and different renewable energy installation target scenarios;

[0115] A sub-scenario construction module, used to sample from the first probability density distribution function and the second probability density distribution function by using a Monte Carlo simulation combined with a random sampling method and to obtain a sub-scenario by combining the energy intensity reduction scenario with the renewable energy installed capacity target scenario;

[0116] The completion calculation module is used to calculate the carbon emissions and climate target indicator data for all sub-scenarios in the target year;

[0117] The probability assessment module is used to take the frequency ratio of achieving carbon peak in all sub-scenarios as the probability of achieving carbon peak, and the frequency ratio of achieving a certain climate target in all sub-scenarios as the probability of achieving the climate target.

[0118] The probability assessment device for achieving carbon peak and climate targets in this embodiment is used to implement the aforementioned probability assessment method for achieving carbon peak and climate targets. Therefore, the specific implementation method of the probability assessment device for achieving carbon peak and climate targets can be seen in the embodiment part of the probability assessment method for achieving carbon peak and climate targets in the previous text, for example, the probability density distribution construction module, the scenario construction module, the sub-scenario construction module, the completion status calculation module, and the probability assessment module are respectively used to implement steps S101, S102, S103, S104 and S105 in the above-mentioned probability assessment method for achieving carbon peak and climate targets. Therefore, its specific implementation method can refer to the description of the corresponding each part of the embodiment, which will not be repeated here.

[0119] A specific embodiment of the present invention also provides a probability assessment device for achieving carbon peak and climate targets, including: a memory for storing computer programs; and a processor for implementing the steps of the above-mentioned probability assessment method for achieving carbon peak and climate targets when executing the computer program.

[0120] A specific embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned probability assessment method for achieving carbon peak and climate goals are implemented.

[0121] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0122] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0123] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0125] Obviously, the above embodiments are merely examples for the purpose of clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the present invention.

Claims

1. A probability assessment method for achieving carbon peak and climate goals, characterized in that: include: The maximum likelihood estimation method is used to construct the first probability density distribution function of the target region's GDP in the target year and the second probability density distribution function of the policy completion of the target region's renewable-related policies; Establish scenarios for energy intensity reduction and different renewable energy capacity targets; Using a Monte Carlo simulation combined with a random sampling method to sample the first probability density distribution function and the second probability density distribution function and combining the energy intensity reduction scenario with the renewable energy installed capacity target scenario to obtain a sub-scenario; Calculate the carbon emissions and climate target indicator data for the target year for all sub-scenarios; The frequency ratio of achieving carbon peak in all sub-scenarios is taken as the probability of achieving carbon peak, and the frequency ratio of achieving a certain climate target in all sub-scenarios is taken as the probability of achieving that climate target.

2. The probability assessment method for achieving carbon peak and climate goals according to claim 1 is characterized in that: The first probability density distribution function of the target region's gross domestic product for the target year is constructed using the maximum likelihood estimation method, including: The distribution of GDP of the target region in the preset target year follows a normal distribution, and n GDP forecast values ​​of the target region in the target year from various pre-specified research institutions and economic organizations are selected as the sample set obtained by n samplings of the distribution; Based on the probability density distribution function of the normal distribution, a joint probability distribution function is established according to the n GDP forecast values ​​as a first likelihood function; The maximum value of the first likelihood function is solved, and the mean and standard deviation estimates of the normal distribution of the gross domestic product are determined according to the parameter values ​​to obtain a first probability density distribution function.

3. The probability assessment method for achieving carbon peak and climate goals according to claim 1 is characterized in that: The second probability density distribution function of the policy completion of renewable energy-related policies in the target area is constructed using the maximum likelihood estimation method, including: The distribution of policy completion of renewable energy-related policies in the preset target area follows a normal distribution, and the policy completion of m renewable energy-related policies calculated from m types of policy target data released by the government within a pre-specified time period and the actual completion values ​​is selected as the sample set obtained by m samplings of the distribution; Based on the probability density distribution function of the normal distribution, a joint probability distribution function is established as a second likelihood function according to the policy completion of the m renewable energy-related policies; The second likelihood function is solved for its maximum value, and the mean and standard deviation estimates of the normal distribution of the policy completion degree of renewable-related policies are determined according to parameter values ​​to obtain a second probability density distribution function.

4. The probability assessment method for achieving carbon peak and climate goals according to claim 1 is characterized in that: The scenarios for establishing energy intensity reduction and different renewable energy installation capacity target scenarios include: Set the average annual decline rate of the future energy intensity reduction path; Set annual new installed capacity targets for future photovoltaic and onshore wind power under different policy scenarios; Set the proportion of oil and gas in total energy consumption; Set annual operating hours for nuclear power, hydropower, onshore wind power, offshore wind power and photovoltaic power; Setting emission factors for coal, oil, and natural gas; Set the proportion of coal used for power generation in total coal consumption in the future.

5. The probability assessment method for achieving carbon peak and climate goals according to claim 4 is characterized in that: The method of using Monte Carlo simulation combined with random sampling to sample from the first probability density distribution function and the second probability density distribution function and combining the energy intensity reduction scenario with the renewable energy installed capacity target scenario to obtain a sub-scenario includes: Based on Monte Carlo simulation and random sampling methods, samples were taken from the first probability density distribution function and combined with the energy intensity reduction scenario to obtain p future energy consumption growth paths under a single energy consumption reduction path; Based on Monte Carlo simulation and random sampling methods, sampling is performed from the second probability density distribution function and combined with a certain renewable energy installed capacity target scenario to obtain q future non-fossil energy consumption growth paths under a renewable energy policy scenario. According to the growth path of energy consumption and the growth path of future non-fossil energy consumption, p*q future sub-scenarios are constructed.

6. The probability assessment method for achieving carbon peak and climate goals according to claim 5 is characterized in that: The carbon emissions and climate target indicator data for the target year calculated for all sub-scenarios include: Calculate the total energy consumption in the target year based on the GDP and energy intensity in the target year; Among non-fossil energy sources, nuclear power, hydropower and offshore wind power are set as low uncertainty factors, and their future installed capacity is set by setting fixed parameters; Photovoltaic power generation and onshore wind power in non-fossil energy are set as high uncertainty factors, and the total installed capacity in the target year is calculated based on the target installed capacity in the target year and the target completion rate of the target installed capacity in the target year; The non-fossil energy consumption in the target year is obtained by multiplying the installed capacity of each type of non-fossil energy in the target year by its operating hours in the target year; The oil and gas consumption in the target year of each sub-scenario is obtained by multiplying the total energy consumption in the target year by the proportion of oil and gas in the total energy consumption in each sub-scenario. The coal consumption in the target year is obtained by deducting the non-fossil energy consumption in the target year and the oil and natural gas consumption in the target year from the total energy consumption in the target year.

7. The probability assessment method for achieving carbon peak and climate goals according to claim 6 is characterized in that: The calculation of carbon emissions and climate target indicator data for the target year for all sub-scenarios also includes: The total electricity consumption in the target year is calculated based on the non-fossil energy consumption in the target year and the power coal consumption in the target year.

8. A probability assessment device for achieving carbon peak and climate goals, characterized in that: include: A probability density distribution construction module, used to construct a first probability density distribution function of the gross domestic product of the target region in the target year and a second probability density distribution function of the policy completion of renewable-related policies in the target region by using a maximum likelihood estimation method; Scenario building module, used to build energy intensity reduction scenarios and different renewable energy installation target scenarios; A sub-scenario construction module, used to sample from the first probability density distribution function and the second probability density distribution function by using a Monte Carlo simulation combined with a random sampling method and to obtain a sub-scenario by combining the energy intensity reduction scenario with the renewable energy installed capacity target scenario; The completion calculation module is used to calculate the carbon emissions and climate target indicator data for all sub-scenarios in the target year; The probability assessment module is used to take the frequency ratio of achieving carbon peak in all sub-scenarios as the probability of achieving carbon peak, and the frequency ratio of achieving a certain climate target in all sub-scenarios as the probability of achieving the climate target.

9. A probability assessment device for achieving carbon peak and climate goals, characterized in that: include: Memory for storing computer programs; A processor, used to implement the steps of a probabilistic assessment method for achieving carbon peak and climate goals as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a probabilistic assessment method for achieving carbon peak and climate targets as described in any one of claims 1 to 7.

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