Carbon emission reduction evaluation method and device

Abstract

The invention discloses a carbon emission reduction evaluation method and device, and the method comprises the steps: carrying out the region division of an emission reduction region based on a preset region division rule, obtaining region setting parameters, and presetting a carbon assimilation inversion model based on the region setting parameters; acquiring satellite remote sensing data and ground observation data, and acquiring regional carbon flux data based on the satellite remote sensing data, the ground observation data and the carbon assimilation inversion model; setting an emission reduction target based on the target total emission reduction amount and the target emission reduction cost, and constructing a carbon emission reduction optimization model based on the emission reduction target; acquiring a carbon emission reduction cost and a carbon emission reduction strategy based on the carbon emission reduction optimization model and the regional carbon flux data; and obtaining an emission reduction index of the emission reduction area based on the carbon emission reduction strategy, and evaluating an emission reduction effect of the emission reduction area based on the emission reduction index.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission reduction, and in particular to a carbon emission reduction assessment method and device. Background Art

[0002] In response to the challenge of global climate change, a greenhouse gas emission accounting system has been established to effectively monitor and evaluate the achievement of these emission reduction targets. Carbon monitoring, as an important part of the system, obtains information on greenhouse gas emission intensity, environmental concentration, ecosystem carbon sinks, and carbon source and sink conditions and their changing trends through comprehensive observation, numerical simulation, statistical analysis and other means, serving climate change research and emission reduction assessment.

[0003] Existing carbon emission reduction assessment methods are mainly divided into three categories: "top-down" methods based on assimilation inversion of greenhouse gas concentration detection, "bottom-up" methods based on ecological process model simulation, and data-driven machine learning model methods. Carbon assimilation inversion technology is a comprehensive method that integrates atmospheric science, ecology, and data assimilation technology to estimate and monitor carbon sources and sinks at global and regional scales. This technology combines the observational data of CO2 concentration in the atmosphere with the carbon cycle model to simulate the transmission process of greenhouse gases under specific meteorological conditions, and then analyzes its spatiotemporal distribution characteristics. Existing carbon emission methods often fail to fully consider the cost-effectiveness of strategy implementation when formulating emission reduction strategies. At the same time, the current temporal and spatial resolution of emission inventories and ecological flux data is low, so carbon emission reduction strategies are not very practical, which leads to poor evaluation results when conducting emission reduction assessments based on carbon emission reduction strategies. Summary of the invention

[0004] The present invention provides a carbon emission reduction assessment method and device to improve the carbon emission reduction assessment effect and the effectiveness of emission reduction assessment.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a carbon emission reduction assessment method, comprising:

[0006] Dividing the emission reduction area into regions based on preset regional division rules, obtaining regional setting parameters, and presetting the carbon assimilation inversion model based on the regional setting parameters;

[0007] Acquire satellite remote sensing data and ground observation data, and acquire regional carbon flux data based on the satellite remote sensing data, the ground observation data and the carbon assimilation inversion model;

[0008] Setting emission reduction targets based on the target total emission reduction amount and the target emission reduction cost, and constructing a carbon emission reduction optimization model based on the emission reduction targets;

[0009] Obtaining carbon emission reduction costs and carbon emission reduction strategies based on the carbon emission reduction optimization model and the regional carbon flux data;

[0010] An emission reduction index of the emission reduction area is obtained based on the carbon emission reduction strategy, and an emission reduction effect of the emission reduction area is evaluated based on the emission reduction index.

[0011] The present invention divides the emission reduction area into regions based on preset regional division rules and sets up a carbon assimilation inversion model based on this, so that the carbon assimilation inversion model is more adapted to the emission reduction area, and comprehensively obtains carbon emission data through satellite remote sensing data and ground observation data, thereby improving the accuracy of regional carbon flux data generated by the carbon assimilation inversion model, and then being able to customize emission reduction strategies according to the actual conditions of specific regions, thereby improving the pertinence and effectiveness of the strategies; and determining emission reduction targets based on the target total emission reduction amount and the target emission reduction cost, quantifying the emission reduction targets, and thereby constructing a carbon emission reduction optimization model to obtain the carbon emission reduction cost and the carbon emission reduction strategy, thereby evaluating the emission reduction effect of the emission reduction area based on the carbon emission reduction strategy, thereby improving the effectiveness of the emission reduction evaluation.

[0012] Furthermore, the step of dividing the emission reduction area based on the preset area division rule and obtaining the area setting parameters includes:

[0013] Acquire the geographical boundary and functional division of the emission reduction area based on the area division rule, and divide the emission reduction area into a plurality of grids based on the geographical boundary and the functional division;

[0014] The horizontal resolution, the vertical resolution and the time resolution of the plurality of grids are respectively set, and the area setting parameters are acquired based on the horizontal resolution, the vertical resolution and the time resolution.

[0015] The present invention determines the geographical boundaries and functional zoning of the emission reduction area based on the regional division rules. Since different emission reduction areas have different characteristics, the emission reduction area is divided into different grids according to the geographical boundaries and functional zoning, so that different resolutions are set for different grids, the regional resolution is refined, and the characteristics of each area are ensured to be accurately reflected.

[0016] Furthermore, the obtaining of satellite remote sensing data and ground observation data, and obtaining regional carbon flux data based on the satellite remote sensing data, the ground observation data and the carbon assimilation inversion model, includes:

[0017] Acquire satellite remote sensing data and ground observation data, and input the satellite remote sensing data and the ground observation data into the carbon assimilation inversion model to acquire fossil fuel emission carbon sink data;

[0018] Regional carbon flux data are generated based on the fossil fuel emission carbon sink data.

[0019] The present invention collects carbon sink data from multiple aspects through satellite remote sensing data and ground observation data to improve the accuracy and resolution of carbon sink data, thereby obtaining fossil fuel emission carbon sink data, and generating regional carbon flux data based on the assimilation inversion model and fossil fuel emission carbon sink data, thereby providing near real-time carbon flux data.

[0020] Furthermore, the setting of emission reduction targets based on the target total emission reduction amount and the target emission reduction cost includes:

[0021] Construct carbon emission reduction cost model, excess emission cost model and emission reduction cap model based on target emission reduction total amount and target emission reduction cost;

[0022] An emission reduction target is set based on the carbon emission reduction cost model, the excess emission cost model and the emission reduction upper limit model, wherein the emission reduction target is that the total amount of carbon emission reduction reaches the target total amount of emission reduction and the carbon emission reduction cost is minimized.

[0023] The present invention constructs multiple emission reduction models through the total amount of carbon emission reduction and the target emission reduction cost, thereby performing multi-objective optimization through multiple emission reduction targets to ensure the scientific nature and feasibility of the emission reduction targets; and according to the emission reduction targets, ensures that the strategy achieves the target total amount of emission reduction while minimizing the carbon emission reduction cost.

[0024] Furthermore, the carbon emission reduction optimization model is constructed based on the emission reduction target, including:

[0025] Constructing an optimization target model and a regional carbon emission total model based on the emission reduction target;

[0026] The carbon emission reduction optimization model is constructed based on the optimization target model, the regional carbon emission total model and the HJB equation.

[0027] The present invention constructs a carbon emission reduction optimization model by utilizing the HJB (Hamilton-Jacobi-Bellman) equation, integrates the optimization target model and the regional carbon emission total model into the HJB equation, and finds the optimal emission reduction path through the HJB equation to ensure that resource allocation is optimized while achieving the emission reduction target.

[0028] In a second aspect, the present invention provides a carbon emission reduction assessment device, comprising: a region division module, a carbon sink calculation module, a target setting module, a strategy generation module and an assessment module;

[0029] The regional division module is used to divide the emission reduction area into regions based on preset regional division rules, obtain regional setting parameters, and preset the carbon assimilation inversion model based on the regional setting parameters;

[0030] The carbon sink calculation module is used to obtain satellite remote sensing data and ground observation data, and obtain regional carbon flux data based on the satellite remote sensing data, the ground observation data and the carbon assimilation inversion model;

[0031] The target setting module is used to set an emission reduction target based on the target total emission reduction amount and the target emission reduction cost, and to build a carbon emission reduction optimization model based on the emission reduction target;

[0032] The strategy generation module is used to obtain carbon emission reduction costs and carbon emission reduction strategies based on the carbon emission reduction optimization model and the regional carbon flux data;

[0033] The evaluation module is used to obtain the emission reduction index of the emission reduction area based on the carbon emission reduction strategy, and evaluate the emission reduction effect of the emission reduction area based on the emission reduction index.

[0034] Furthermore, the area division module is used to:

[0035] Acquire the geographical boundary and functional division of the emission reduction area based on the area division rule, and divide the emission reduction area into a plurality of grids based on the geographical boundary and the functional division;

[0036] The horizontal resolution, the vertical resolution and the time resolution of the plurality of grids are respectively set, and the area setting parameters are acquired based on the horizontal resolution, the vertical resolution and the time resolution.

[0037] Furthermore, the carbon sink calculation module is used to:

[0038] Acquire satellite remote sensing data and ground observation data, and input the satellite remote sensing data and the ground observation data into the carbon assimilation inversion model to acquire fossil fuel emission carbon sink data;

[0039] Regional carbon flux data are generated based on the fossil fuel emission carbon sink data.

[0040] Furthermore, the target setting module is used to:

[0041] Construct carbon emission reduction cost model, excess emission cost model and emission reduction cap model based on target emission reduction total amount and target emission reduction cost;

[0042] An emission reduction target is set based on the carbon emission reduction cost model, the excess emission cost model and the emission reduction upper limit model, wherein the emission reduction target is that the total amount of carbon emission reduction reaches the target total amount of emission reduction and the carbon emission reduction cost is minimized.

[0043] Furthermore, the target setting module is used to:

[0044] Constructing an optimization target model and a regional carbon emission total model based on the emission reduction target;

[0045] The carbon emission reduction optimization model is constructed based on the optimization target model, the regional carbon emission total model and the HJB equation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flowchart of a carbon emission reduction assessment method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0048] The terms "first" and "second" and the like in the specification, claims and drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0049] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0050] Example 1

[0051] See also Figure 1 , Figure 1 A flowchart of a carbon emission reduction assessment method provided by an embodiment of the present invention. The present invention provides a carbon emission reduction assessment method, including steps 101 to 105, which are as follows:

[0052] Step 101: Divide the emission reduction area into regions based on preset regional division rules, obtain regional setting parameters, and preset a carbon assimilation inversion model based on the regional setting parameters;

[0053] In this embodiment, the step of dividing the emission reduction area based on the preset area division rule and obtaining the area setting parameters includes:

[0054] Acquire the geographical boundary and functional division of the emission reduction area based on the area division rule, and divide the emission reduction area into a plurality of grids based on the geographical boundary and the functional division;

[0055] The horizontal resolution, the vertical resolution and the time resolution of the plurality of grids are respectively set, and the area setting parameters are acquired based on the horizontal resolution, the vertical resolution and the time resolution.

[0056] In this embodiment, according to the size and complexity of the emission reduction area in the geographical boundary and functional zoning study area, the emission reduction area is divided into several grids, and the horizontal resolution of different grids in the model is adjusted to capture more detailed terrain and meteorological features, for example, the grid spacing is adjusted from the default 10-20 kilometers to a more detailed 6 kilometers. And by increasing the number of layers in the vertical direction and adjusting the spacing of each layer, the atmospheric processes at different altitudes are better simulated, especially the detailed information of the near-ground layer, such as increasing from the default 30 layers to 35 layers.

[0057] In this embodiment, the geographic boundaries are boundaries that define regions based on geographic features, such as rivers, mountains, administrative boundaries, etc. These boundaries can be naturally formed or based on administrative divisions.

[0058] In this embodiment, the functional zoning is based on the functional properties of the region (such as industrial area, agricultural area, urban area, nature reserve, etc.) The carbon emission and absorption characteristics of each functional area may be different, so it is divided into different grids to set parameters.

[0059] In this embodiment, the resolution of the grid may be uniform (eg, a fixed spatial resolution, such as 6 km, 9 km, 27 km, etc.), or may be dynamically adjusted according to the needs of a specific area.

[0060] As a specific example of an embodiment of the present invention, the emission reduction area is divided into three nested layers, and the horizontal resolution is set to 27km, 9km, and 3km. The third layer (3km) core nested area covers the target area and its surrounding areas. The vertical resolution is 25 layers, and the distance from the surface to the highest point of measurement is 3km; the time resolution is daily.

[0061] In this embodiment, the carbon assimilation inversion model consists of two parts: the regional atmospheric transport model WRF-GHG and the EnKF-4Dvar data assimilation algorithm. WRF-GHG is an atmosphere-greenhouse gas model that is directly and dynamically coupled by the mesoscale weather research and forecasting model WRF and the vegetation photosynthesis and respiration model VPRM. It realizes multi-core parallel computing and can efficiently and high-resolution simulate regional carbon fluxes and the transmission, diffusion, chemical reactions and spatiotemporal evolution of CO2, CO, and 14CO2 concentrations. EnKF-4Dvar is a new efficient assimilation algorithm that couples EnKF and 4DVar. It achieves synchronous optimization of natural carbon flux and anthropogenic carbon emissions through the joint assimilation of satellite-ground multi-source observation data.

[0062] In this embodiment, the carbon assimilation inversion model is set based on the horizontal resolution, the vertical resolution and the time resolution.

[0063] In this embodiment, the horizontal resolution of each grid is set (such as 3km, 9km, 27km) to ensure the simulation accuracy of different regions. High-resolution grids (such as 3km) are suitable for core areas, and low-resolution grids (such as 27km) are suitable for peripheral areas. The vertical resolution of each grid is set (such as 25 layers, from the surface to 3km) to ensure accurate simulation of carbon flux changes at different heights. Vertical stratification helps capture detailed information about the near-ground and atmospheric boundary layers. Set the time resolution of the data (such as daily updates) to ensure the timeliness and dynamism of the simulation results. The setting of time resolution helps to capture short-term changes and trends. Subsequently, carbon assimilation inversion is performed based on the set carbon assimilation inversion model to obtain carbon flux data.

[0064] Step 102: Acquire satellite remote sensing data and ground observation data, and acquire regional carbon flux data based on the satellite remote sensing data, the ground observation data and the carbon assimilation inversion model;

[0065] In this embodiment, the obtaining of satellite remote sensing data and ground observation data, and obtaining regional carbon flux data based on the satellite remote sensing data, the ground observation data and the carbon assimilation inversion model, includes:

[0066] Acquire satellite remote sensing data and ground observation data, and input the satellite remote sensing data and the ground observation data into the carbon assimilation inversion model to acquire fossil fuel emission carbon sink data;

[0067] Regional carbon flux data are generated based on the fossil fuel emission carbon sink data.

[0068] In this embodiment, high-resolution remote sensing data from satellites are obtained, which include information such as vegetation coverage, land use type, atmospheric composition (such as CO2, CH4 concentration), etc. The advantages of satellite remote sensing data are wide coverage, high temporal and spatial resolution, and the ability to provide continuous monitoring over a large area.

[0069] In this embodiment, the carbon assimilation inversion model is an advanced carbon emission monitoring model, which estimates carbon sources and sinks at regional or global scales by combining satellite remote sensing data and ground observation data using an assimilation inversion model. Carbon assimilation inversion is performed using satellite remote sensing data. Since satellite remote sensing technology can provide carbon emission data with higher spatial resolution, it helps to more accurately identify and quantify carbon sources and sinks. It covers areas that ground observation stations cannot reach, providing continuous monitoring data at a global or regional scale. Satellite remote sensing data can be acquired in real time or near real time, which helps to quickly respond to climate change and carbon emission trends. It also reduces human interference in ground observations and improves the objectivity of monitoring results.

[0070] In this embodiment, the carbon emission data in the atmosphere is obtained based on the carbon assimilation inversion model. The atmospheric carbon emission data includes atmospheric CO2 (CO 2sim ), atmospheric background CO 2 (CO 2bg ), fossil fuel emissions CO 2 (CO 2ff ) and ecological flux CO 2 (CO 2eco ), and other sources of CO 2 (CO 2other ) are related to the mixture.

[0071] In this embodiment, according to the mass balance equation, it can be obtained:

[0072] CO 2sim =CO 2bg +CO 2eco +CO 2ff +CO 2other (1)

[0073] Among them, CO 2sim , CO 2bg , CO 2eco , CO 2ff and CO 2other They are all functions of spatial coordinates (x, y, z) and time t, and are the outputs of the carbon assimilation inversion model.

[0074] In this embodiment, for a given region (x, y)∈S and a time interval [t, t+△t], the total amount of fossil fuel carbon emissions within the region S and the time interval △t is I t(S) can be written as:

[0075]

[0076] Among them, z top is the highest point of vertical stratification, CO 2ff Carbon emissions from fossil fuels.

[0077] In this embodiment, only horizontal grid points (x i ,y j ), vertical layering z k , and time node t l Discrete value CO 2ff (x i ,y j , z k ;t l ) is used to calculate the regional carbon flux data.

[0078] In this example, the fossil fuel emission flux CO output by the carbon assimilation inversion model is 2ff Calculated regional carbon emissions I t (S) has the advantages of high spatial resolution and high temporal resolution. Based on the output of the carbon assimilation inversion system, the fossil fuel emission flux CO 2ff Calculated regional carbon emissions I t (S) can reach the horizontal grid scale (3km) and daily scale, not only can it provide near-real-time data, but also can predict future trends. Real-time monitoring of greenhouse gas emissions at a small scale (such as a certain park) and the proposal of optimized control measures will greatly improve the authenticity, accuracy and timeliness of greenhouse gas emission simulation analysis, monitoring and forecasting results.

[0079] Step 103: setting an emission reduction target based on the target emission reduction total amount and the target emission reduction cost, and constructing a carbon emission reduction optimization model based on the emission reduction target;

[0080] In this embodiment, the emission reduction target is set based on the target emission reduction total amount and the target emission reduction cost, including:

[0081] Construct carbon emission reduction cost model, excess emission cost model and emission reduction cap model based on target emission reduction total amount and target emission reduction cost;

[0082] An emission reduction target is set based on the carbon emission reduction cost model, the excess emission cost model and the emission reduction upper limit model, wherein the emission reduction target is that the total amount of carbon emission reduction reaches the target total amount of emission reduction and the carbon emission reduction cost is minimized.

[0083] In this embodiment, the total carbon emission reduction amount to be achieved is determined according to the target total emission reduction amount, which is set based on environmental goals and social needs to ensure the scientificity and feasibility of the emission reduction target. At the same time, according to the target emission reduction cost, on the premise of achieving the target total emission reduction amount, the carbon emission reduction cost is minimized. This is achieved by optimizing the parameters and solutions of the model to find the emission reduction path with the lowest cost.

[0084] In this embodiment, since the emission reduction plan stipulates that an administrative region or an industrial park (collectively referred to as a region hereinafter) needs to reduce the carbon emissions within the region to a certain limit within a given time period, otherwise a fine needs to be paid for the carbon emissions exceeding the limit at the expiration. Considering the cost of carbon emission reduction, an appropriate carbon emission reduction strategy needs to be selected to minimize the sum of the total cost during the emission reduction process and the fine generated due to excessive carbon emissions at the expiration.

[0085] In this embodiment, a carbon emission reduction cost model is constructed based on the target total emission reduction amount and the target emission reduction cost. The carbon emission reduction cost is the cost continuously generated during the control process due to reducing the growth rate of carbon emissions, and is generally described by the total cost function g 1 (C). g 1 (C) is related to the Marginal Abatement Cost Curve (MACC), and usually represents the relationship between the total carbon emission reduction amount and the marginal cost of emission reduction (i.e., the cost paid to achieve the last unit of emission reduction). After determining the marginal cost curve of carbon emission reduction f MACC (x), the carbon emission reduction cost model can be obtained.

[0086] In this embodiment, the carbon emission reduction cost model is specifically:

[0087]

[0088] Among them, the marginal cost curve is set as a quadratic function f MACC (x) = m 1 x 2 + m 2 x, and its coefficients m 1 and m 2 are obtained by fitting the Chinese marginal emission reduction cost curve. The unit of g 1 (C) is in billions of US dollars. From time to t + dt, the emission reduction cost is g 1 (C t )dt. Assuming that the risk-free interest rate is a constant r > 0, then from time t (<T) to the maturity date T, the present value of the total carbon emission reduction cost is:

[0089]

[0090] In this embodiment, an over-emission cost model is constructed based on the target emission reduction total amount and the target emission reduction cost. At the agreed due date T, a fine is imposed for the carbon emissions exceeding the limit. Assume that at the due date T, if the carbon emissions in this region exceed the limit agreed at t = 0 then a fine of a 0 per ton of excess carbon emissions needs to be paid. Therefore, the cost incurred at time T (due date) due to non-compliance of carbon emissions, that is, the present value of the fine caused by excess carbon emissions is:

[0091]

[0092] where the superscript + represents taking the maximum value between the variable and 0, that is, (x) + = max(x, 0)

[0093] In this embodiment, a total emission reduction cost model can be constructed based on the carbon emission reduction cost model and the over-emission cost model. At time t (<T), the total emission reduction cost model generated by carbon emissions and carbon emission reduction during the control process in this region is:

[0094]

[0095] where the conditional expectation E(Y|X = x) is the expected value of the random variable Y under the condition that the random variable X = x is given. For two random variables X and Y, assume that they have a density function f(x, y), and the conditional density function of Y under the condition that X = x is known as g(y|x). Under the condition that X = x, the conditional expectation of Y is defined as: E(Y|X = x) = ∫y * g(y|x)dy, where * represents the complex conjugate.

[0096] In this embodiment, an emission reduction upper limit model is constructed based on the target emission reduction total amount and the target emission reduction cost. During the implementation of emission reduction, due to technical or cost limitations, the carbon emission reduction capacity that can be achieved in a short period is generally limited. In the long run, this emission reduction capacity will gradually approach the upper limit that may be reached. Therefore, an upper bound t is added to C that is, the allowable control strategy C t satisfies for any t ≥ 0 where is a positive constant. The set of allowable control strategies is defined as C.

[0097] In this embodiment, constructing the carbon emission reduction optimization model based on the emission reduction target includes:

[0098] Constructing an optimization target model and a regional carbon emission total amount model based on the emission reduction target;

[0099] The carbon emission reduction optimization model is constructed based on the optimization target model, the regional carbon emission total model and the HJB equation.

[0100] In this embodiment, by selecting the optimal carbon reduction strategy Minimize the total cost J(I, t, C) and define the optimized minimum total cost model as:

[0101] Φ(I, t) = inf C∈C J(I, t, C) (7)

[0102] In this embodiment, a regional carbon emission total model is constructed based on the emission reduction target:

[0103]

[0104] Among them, I t is the total carbon emissions of the region in one year, and assuming that the initial total emissions I 0 >0. The coefficients a and b are assumed to be constants, representing the weights of population and GDP on regional carbon emission intensity. μ is a positive constant, representing the rate of return of GDP. δ 1 and δ 2 are normal numbers, representing the volatility of population and GDP, and They represent the standard Brownian motion of population and GDP fluctuations, respectively, and their expectations variance And are independent of each other, that is, their covariance f0(t) is the population migration function.

[0105] In this embodiment, f(t) is the deterministic part of the growth rate of total carbon emissions, indicating the trend of total carbon emissions without any random fluctuations. When the population is close to the population capacity, f(t) will gradually approach zero, reflecting the limiting effect of population growth on carbon emissions.

[0106] In this embodiment, It is the volatility in the changes in total carbon emissions, which indicates the random fluctuation intensity of total carbon emissions.

[0107] In this embodiment, W t is a standard Brownian motion, representing the random part of the change in total carbon emissions.

[0108] In this embodiment, the change in the total amount of regional carbon emissions satisfies the stochastic differential equation, which is a simplified STIRPAT model (Stochastic Impacts by Regression on Population, Affluence and Technology, an exponential growth model of population, economy, technology and environmental pollution) that describes the carbon emission process, which can be simplified as follows:

[0109]

[0110] Among them, P t and Y t are the population and GDP at time t, respectively, which are described by the population retardation growth model and the random GDP model.

[0111] In this embodiment, the total population P at time t t It can be expressed by the blocked growth model as:

[0112]

[0113] Among them, ρ is the inherent population growth rate, which represents the natural growth rate of the population without obstacles; P 0 is the population at the initial moment; P m is the population capacity, that is, the maximum population that can be accommodated by natural resources and environmental conditions. Population migration function f 0 (t) shows that with the total population P t Increase, the net population growth rate will decrease; when the total population P t Increase to the maximum population capacity P m When the net population growth rate approaches zero.

[0114] In this embodiment, the parameters of the retardation growth model are fitted using the domestic population data from 1964 to 2007, and ρ = 5.171%, P m =1.47743×10 9 For simplicity, we can assume

[0115] In this example, the total GDP Y t It can be expressed by the stochastic GDP model as:

[0116]

[0117] Among them, μ and δ 2 are normal numbers, and are the return rate and volatility of GDP respectively. Using the domestic GDP growth rate from 1953 to 2008, we can get μ = 11.93% and 6 2 =9.88%.

[0118] In this embodiment, based on the GDP model and the blocked growth model, the total regional carbon emissions f(t), and W t It can be written as:

[0119]

[0120] Among them, a and b represent the weights of population and GDP on carbon emission intensity respectively. μ is the rate of return of GDP, which indicates the GDP growth rate. ρ is the inherent population growth rate, which indicates the natural growth rate of population. 0 is the population at the initial moment. m is the population capacity, that is, the maximum number of people that the environment can support. -ρt It means that over time, population growth is gradually limited by environmental capacity. 1 and δ 2 Represent the volatility of population and GDP respectively. and are standard Brownian motions representing population and GDP fluctuations, respectively.

[0121] In this embodiment, when considering carbon emission reduction, without loss of generality, let δ 1 =0,δ 2 =σ, That is, we do not consider population fluctuations, but only the volatility of GDP. At the same time, the coefficients representing the weights of population and GDP on regional carbon emission intensity are written as a=a 2 , b=a 1 Therefore, the regional carbon emission total model is:

[0122]

[0123] Among them, μ, σ, a 1 , a 2 is a positive constant; W t is the standard Brownian motion; C t is the growth rate of reduced carbon emissions, which is a function of carbon emissions I t The random process is related to time t and satisfies other constraints of the random control theory on this control process. According to Assumption 4, the control strategy C t The set of is C, with a positive upper bound For any t≥0, It can also be simplified to an upper bound, that is This holds for all t≥0.

[0124] In this embodiment, the total cost model of emission reduction is used to represent the conditional expectation of the total cost of regional carbon emissions and carbon emission reduction in the control process. The minimum total cost model represents the selection of the optimal carbon emission reduction strategy to minimize the total cost.

[0125] In this embodiment, the carbon emission reduction optimization model is constructed based on the optimization target model, the regional carbon emission total model and the HJB equation. The total emission reduction cost model, the minimum total cost model and the regional carbon emission total model are integrated according to the HBJ equation, thereby converting the optimal control problem into a differential equation problem through the dynamic programming principle.

[0126] In this embodiment, the carbon emission reduction optimization model is specifically:

[0127]

[0128] in,

[0129] Step 104: Obtain carbon emission reduction costs and carbon emission reduction strategies based on the carbon emission reduction optimization model and the regional carbon flux data, obtain emission reduction indicators of the emission reduction area based on the carbon emission reduction strategy, and evaluate the emission reduction effect of the emission reduction area based on the emission reduction indicators.

[0130] In this embodiment, the regional carbon flux data is input into the carbon reduction optimization model to obtain the carbon reduction strategy

[0131] In this embodiment, if Φ is continuous, then Φ is the viscosity solution of the following Hamilton-Jacobi-Bellman (HJB) equation. The HJB equation is nonlinear and it is difficult to directly obtain its analytical solution. Perform a logarithmic transformation on it, let x = lnI, τ = Tt (still recorded as t), it can be converted into the corresponding semi-linear partial differential equation initial value problem, and the optimal control process is obtained by solving the partial differential equation

[0132] In this embodiment, a carbon reduction optimization model is established to solve the optimal carbon reduction strategy. The model takes into account factors such as carbon reduction costs and fines for excess carbon emissions, and transforms the optimal control problem into a differential equation problem through the principle of dynamic programming to solve the optimal carbon reduction strategy.

[0133] Step 105: Acquire the emission reduction index of the emission reduction area based on the carbon emission reduction strategy, and evaluate the emission reduction effect of the emission reduction area based on the emission reduction index.

[0134] In this embodiment, the carbon emission reduction strategy includes a total emission reduction index for the emission reduction area. The acquired regional carbon flux data is used to analyze the carbon emission sources and sinks of each region, determine the main sources of carbon emissions, such as industrial emissions, transportation emissions, agricultural activities, etc., and decompose the overall emission reduction index according to factors such as the carbon emission contribution, economic development level, and technological capabilities of each region. Specific carbon emission reduction quantitative indicators are set for each region, such as carbon emissions per unit GDP, per capita carbon emissions, etc., to facilitate monitoring and evaluation, so as to evaluate the emission reduction effect of each grid area separately according to the emission reduction index of each grid area, and then integrate the emission reduction effects of all grid areas to evaluate the evaluation effect of the emission reduction area.

[0135] In this embodiment, the number of grids in the emission reduction area whose carbon emissions meet the carbon emission reduction quantitative index is counted, and this is used to evaluate the evaluation effect of the emission reduction area.

[0136] In this embodiment, the present invention divides the emission reduction area into regions based on preset regional division rules and sets up a carbon assimilation inversion model based on this, so that the carbon assimilation inversion model is more adapted to the emission reduction area, and obtains carbon emission data in all aspects through satellite remote sensing data and ground observation data, thereby improving the accuracy of regional carbon flux data generated by the carbon assimilation inversion model, and thus being able to customize emission reduction strategies according to the actual conditions of specific regions, thereby improving the pertinence and effectiveness of the strategies; and determining emission reduction targets based on the target total emission reduction amount and the target emission reduction cost, quantifying the emission reduction targets, and constructing a carbon emission reduction optimization model based on this, so as to obtain carbon emission reduction costs and carbon emission reduction strategies, and then evaluating the emission reduction effects of emission reduction areas based on carbon emission reduction strategies, thereby improving the effectiveness of emission reduction evaluations.

[0137] The embodiment of the present invention further provides a carbon emission reduction assessment device, comprising: a region division module, a carbon sink calculation module, a target setting module, a strategy generation module and an assessment module;

[0138] The regional division module is used to divide the emission reduction area into regions based on preset regional division rules, obtain regional setting parameters, and preset the carbon assimilation inversion model based on the regional setting parameters;

[0139] The carbon sink calculation module is used to obtain satellite remote sensing data and ground observation data, and obtain regional carbon flux data based on the satellite remote sensing data, the ground observation data and the carbon assimilation inversion model;

[0140] The target setting module is used to set an emission reduction target based on the target total emission reduction amount and the target emission reduction cost, and to build a carbon emission reduction optimization model based on the emission reduction target;

[0141] The strategy generation module is used to obtain carbon emission reduction costs and carbon emission reduction strategies based on the carbon emission reduction optimization model and the regional carbon flux data;

[0142] The evaluation module is used to obtain the emission reduction index of the emission reduction area based on the carbon emission reduction strategy, and evaluate the emission reduction effect of the emission reduction area based on the emission reduction index.

[0143] In this embodiment, the area division module is used to:

[0144] Acquire the geographical boundary and functional division of the emission reduction area based on the area division rule, and divide the emission reduction area into a plurality of grids based on the geographical boundary and the functional division;

[0145] The horizontal resolution, the vertical resolution and the time resolution of the plurality of grids are respectively set, and the area setting parameters are acquired based on the horizontal resolution, the vertical resolution and the time resolution.

[0146] In this embodiment, the carbon sink calculation module is used to:

[0147] Acquire satellite remote sensing data and ground observation data, and input the satellite remote sensing data and the ground observation data into the carbon assimilation inversion model to acquire fossil fuel emission carbon sink data;

[0148] Regional carbon flux data are generated based on the fossil fuel emission carbon sink data.

[0149] In this embodiment, the target setting module is used to:

[0150] Construct carbon emission reduction cost model, excess emission cost model and emission reduction cap model based on target emission reduction total amount and target emission reduction cost;

[0151] An emission reduction target is set based on the carbon emission reduction cost model, the excess emission cost model and the emission reduction upper limit model, wherein the emission reduction target is that the total amount of carbon emission reduction reaches the target total amount of emission reduction and the carbon emission reduction cost is minimized.

[0152] In this embodiment, the target setting module is used to:

[0153] Constructing an optimization target model and a regional carbon emission total model based on the emission reduction target;

[0154] The carbon emission reduction optimization model is constructed based on the optimization target model, the regional carbon emission total model and the HJB equation.

[0155] In an embodiment of the present invention, a terminal device is also provided, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the above-mentioned carbon emission reduction assessment method when executing the computer program.

[0156] In an embodiment of the present invention, a computer-readable storage medium is further provided. The computer-readable storage medium includes a stored computer program. When the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned carbon emission reduction assessment method.

[0157] Exemplarily, the computer program may be divided into one or more modules, one or more modules are stored in a memory and executed by a processor to implement the present invention. One or more modules may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in a terminal device.

[0158] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The terminal device may include, but is not limited to, a processor, a memory, and a display. Those skilled in the art will appreciate that the above components are merely examples of the terminal device and do not constitute a limitation on the terminal device. The terminal device may include more or fewer components than the components, or may combine certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0159] The processor may be a central processing unit (CPU), 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. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0160] The memory can be used to store computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, a text conversion function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, text message data, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0161] Among them, if the module based on the carbon emission reduction assessment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0162] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A carbon emission reduction assessment method, characterized in that: include: Dividing the emission reduction area into regions based on preset regional division rules, obtaining regional setting parameters, and presetting the carbon assimilation inversion model based on the regional setting parameters; Acquire satellite remote sensing data and ground observation data, and acquire regional carbon flux data based on the satellite remote sensing data, the ground observation data and the carbon assimilation inversion model; Setting emission reduction targets based on the target total emission reduction amount and the target emission reduction cost, and constructing a carbon emission reduction optimization model based on the emission reduction targets; Obtaining carbon emission reduction costs and carbon emission reduction strategies based on the carbon emission reduction optimization model and the regional carbon flux data; An emission reduction index of the emission reduction area is obtained based on the carbon emission reduction strategy, and an emission reduction effect of the emission reduction area is evaluated based on the emission reduction index.

2. A carbon emission reduction assessment method according to claim 1, characterized in that: The step of dividing the emission reduction area based on the preset area division rules and obtaining area setting parameters includes: Acquire the geographical boundary and functional division of the emission reduction area based on the area division rule, and divide the emission reduction area into a plurality of grids based on the geographical boundary and the functional division; The horizontal resolution, the vertical resolution and the time resolution of the plurality of grids are respectively set, and the area setting parameters are acquired based on the horizontal resolution, the vertical resolution and the time resolution.

3. A carbon emission reduction assessment method according to claim 1, characterized in that: The obtaining of satellite remote sensing data and ground observation data, and obtaining regional carbon flux data based on the satellite remote sensing data, the ground observation data and the carbon assimilation inversion model, includes: Acquire satellite remote sensing data and ground observation data; Inputting the satellite remote sensing data and the ground observation data into the carbon assimilation inversion model to obtain fossil fuel emission carbon sink data; Regional carbon flux data are generated based on the fossil fuel emission carbon sink data.

4. A carbon emission reduction assessment method as claimed in claim 3, characterized in that: The emission reduction target is set based on the target emission reduction total amount and the target emission reduction cost, including: Construct carbon emission reduction cost model, excess emission cost model and emission reduction cap model based on target emission reduction total amount and target emission reduction cost; An emission reduction target is set based on the carbon emission reduction cost model, the excess emission cost model and the emission reduction upper limit model, wherein the emission reduction target is that the total amount of carbon emission reduction reaches the target total amount of emission reduction and the carbon emission reduction cost is minimized.

5. A carbon emission reduction assessment method as claimed in claim 4, characterized in that: The step of constructing a carbon emission reduction optimization model based on the emission reduction target includes: Constructing an optimization target model and a regional carbon emission total model based on the emission reduction target; The carbon emission reduction optimization model is constructed based on the optimization target model, the regional carbon emission total model and the HJB equation.

6. A carbon emission reduction assessment device, characterized in that: include: Regional division module, carbon sink calculation module, target setting module, strategy generation module and evaluation module; The regional division module is used to divide the emission reduction area into regions based on preset regional division rules, obtain regional setting parameters, and preset the carbon assimilation inversion model based on the regional setting parameters; The carbon sink calculation module is used to obtain satellite remote sensing data and ground observation data, and obtain regional carbon flux data based on the satellite remote sensing data, the ground observation data and the carbon assimilation inversion model; The target setting module is used to set an emission reduction target based on the target total emission reduction amount and the target emission reduction cost, and to build a carbon emission reduction optimization model based on the emission reduction target; The strategy generation module is used to obtain carbon emission reduction costs and carbon emission reduction strategies based on the carbon emission reduction optimization model and the regional carbon flux data; The evaluation module is used to obtain the emission reduction index of the emission reduction area based on the carbon emission reduction strategy, and evaluate the emission reduction effect of the emission reduction area based on the emission reduction index.

7. A carbon emission reduction assessment device as claimed in claim 6, characterized in that: The area division module is used for: Acquire the geographical boundary and functional division of the emission reduction area based on the area division rule, and divide the emission reduction area into a plurality of grids based on the geographical boundary and the functional division; The horizontal resolution, the vertical resolution and the time resolution of the plurality of grids are respectively set, and the area setting parameters are acquired based on the horizontal resolution, the vertical resolution and the time resolution.

8. A carbon emission reduction assessment device as claimed in claim 6, characterized in that: The carbon sink calculation module is used to: Acquire satellite remote sensing data and ground observation data, and input the satellite remote sensing data and the ground observation data into the carbon assimilation inversion model to acquire fossil fuel emission carbon sink data; Regional carbon flux data are generated based on the fossil fuel emission carbon sink data.

9. A carbon emission reduction assessment device as claimed in claim 8, characterized in that: The target setting module is used to: Construct carbon emission reduction cost model, excess emission cost model and emission reduction cap model based on target emission reduction total amount and target emission reduction cost; An emission reduction target is set based on the carbon emission reduction cost model, the excess emission cost model and the emission reduction upper limit model, wherein the emission reduction target is that the total amount of carbon emission reduction reaches the target total amount of emission reduction and the carbon emission reduction cost is minimized.

10. A carbon emission reduction assessment device as claimed in claim 9, characterized in that: The target setting module is used to: Constructing an optimization target model and a regional carbon emission total model based on the emission reduction target; The carbon emission reduction optimization model is constructed based on the optimization target model, the regional carbon emission total model and the HJB equation.

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