Carbon emission index acquisition method and device, equipment and storage medium

Abstract

The invention provides a carbon emission index obtaining method and device, equipment and a storage medium, and the method comprises the steps: obtaining the carbon emission data of a to-be-detected region, the carbon emission data comprises a region classification label, a first index parameter related to carbon emission core control, a second index parameter related to an energy structure, a third index parameter related to supporting capacity, a weight parameter and a reward and punishment parameter; the carbon emission index of the to-be-detected area is determined based on the carbon emission data of the to-be-detected area and a carbon emission index determination model, the carbon emission index determination model comprises a target function which takes the determined carbon emission index of the to-be-detected area as the target function, and the target function is constructed based on the carbon emission data. By collecting multi-dimensional carbon emission data covering regional characteristics, core control, energy structure, supporting capacity and dynamic reward and punishment, systematic quantitative evaluation of regional carbon emission conditions is realized, and the accuracy of the obtained carbon emission index is higher.

Description

Technical Field

[0001] This application relates to the field of carbon emission control, and particularly to a method, device, equipment and storage medium for obtaining a carbon emission index. Background Art

[0002] Existing methods for obtaining carbon emission indices only focus on the total amount of carbon emissions or carbon emission intensity, with overly single indicators, resulting in relatively low accuracy of the obtained carbon emission indices. Summary of the Invention

[0003] Embodiments of this application provide a method, device, equipment and storage medium for obtaining a carbon emission index to solve the problem of relatively low accuracy of the obtained carbon emission index in the prior art.

[0004] To solve the above technical problem, this application is implemented as follows:

[0005] In a first aspect, embodiments of this application provide a method for obtaining a carbon emission index. The method includes:

[0006] Obtain carbon emission data of the area to be measured, where the carbon emission data includes area classification labels, first indicator parameters related to core carbon emission control, second indicator parameters related to energy structure, third indicator parameters related to support capabilities, weight parameters, and reward and punishment parameters;

[0007] Determine the carbon emission index of the area to be measured based on the carbon emission data of the area to be measured and a carbon emission index determination model, where the carbon emission index determination model includes an objective function for determining the carbon emission index of the area to be measured, and the objective function is constructed based on the carbon emission data.

[0008] Optionally, the first indicator parameters include the completion degree of the carbon emission intensity decline rate and the completion degree of the growth rate of the total carbon emissions; the second indicator parameters include the completion degree of the non-fossil energy consumption ratio, the completion degree of the energy consumption intensity decline rate, and the completion degree of the clean production coverage rate; the third indicator parameters include the completion degree of the resource utilization efficiency, the carbon sink increment, and the completion degree of the proportion of low-carbon technology R & D investment, and the completion degree is the ratio of the obtained actual value to the preset target value.

[0009] Optionally, the reward and punishment parameters include reward addition item data, reward factors, punishment deduction item data, and punishment factors.

[0010] Optionally, the objective function is:

[0011]

[0012] where X represents the carbon emission index, ω i represents the i-th indicator parameter; I iThe weight parameter representing the i-th indicator; α represents the reward factor; j represents the data of the reward bonus item; β represents the penalty factor; z represents the data of the penalty deduction item.

[0013] Optionally, the regional classification labels include: late industrialization cities, ecological function areas, resource-based cities, and emerging development areas, and the regional classification labels are used to adjust the weight parameters.

[0014] Optionally, after determining the carbon emission index of the area to be measured based on the carbon emission data and the carbon emission index determination model of the area to be measured, it further includes:

[0015] Optimizing the carbon quota allocation of the area to be measured according to the carbon emission index.

[0016] Optionally, the optimizing the carbon quota allocation of the area to be measured according to the carbon emission index includes:

[0017] In the case where the carbon emission index is greater than or equal to the first value, increasing the carbon quota of the area to be measured;

[0018] In the case where the carbon emission index is less than the first value and greater than or equal to the second value, maintaining the carbon quota of the area to be measured;

[0019] In the case where the carbon emission index is less than the second value, reducing the carbon quota of the area to be measured;

[0020] The first value is greater than the second value.

[0021] In a second aspect, an embodiment of the present application further provides a carbon emission index acquisition device. The carbon emission index acquisition device includes:

[0022] An acquisition module, configured to acquire carbon emission data of an area to be measured, where the carbon emission data includes a regional classification label, a first indicator parameter related to carbon emission core control, a second indicator parameter related to an energy structure, a third indicator parameter related to a support capacity, a weight parameter, and a reward and punishment parameter;

[0023] A determination module, configured to determine the carbon emission index of the area to be measured based on the carbon emission data of the area to be measured and a carbon emission index determination model, where the carbon emission index determination model includes an objective function for determining the carbon emission index of the area to be measured, and the objective function is constructed based on the carbon emission data.

[0024] Optionally, the first index parameter includes the completion degree of the carbon emission intensity decline rate and the completion degree of the growth rate of the total carbon emissions; the second index parameter includes the completion degree of the proportion of non-fossil energy consumption, the completion degree of the energy consumption intensity decline rate, and the completion degree of the clean production coverage rate; the third index parameter includes the completion degree of the resource utilization efficiency, the carbon sink increment, and the completion degree of the proportion of low-carbon technology R & D investment, and the completion degree is the ratio of the obtained actual value to the preset target value.

[0025] Optionally, the reward and punishment parameters include reward bonus item data, reward factors, punishment deduction item data, and punishment factors.

[0026] Optionally, the objective function is:

[0027]

[0028] where X represents the carbon emission index, ω i represents the i-th index parameter; I i represents the weight parameter of the i-th index; α represents the reward factor; j represents the reward bonus item data; β represents the punishment factor; z represents the punishment deduction item data.

[0029] Optionally, the regional classification labels include: late industrialization cities, ecological function areas, resource-based cities, and emerging development areas, and the regional classification labels are used to adjust the weight parameters.

[0030] Optionally, the device further includes:

[0031] An optimization module, configured to optimize the carbon quota allocation of the area to be measured according to the carbon emission index.

[0032] Optionally, the optimization module can also be used for:

[0033] In the case where the carbon emission index is greater than or equal to a first value, increasing the carbon quota of the area to be measured;

[0034] In the case where the carbon emission index is less than the first value and greater than or equal to a second value, maintaining the carbon quota of the area to be measured;

[0035] In the case where the carbon emission index is less than the second value, reducing the carbon quota of the area to be measured;

[0036] The first value is greater than the second value.

[0037] In a third aspect, an embodiment of the present application further provides an electronic device, including a processor, a memory, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, the steps of the above carbon emission index acquisition method are implemented.

[0038] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned carbon emission index acquisition method are implemented.

[0039] The carbon emission index acquisition method according to the embodiment of the present application includes obtaining carbon emission data of a region to be measured, where the carbon emission data includes a region classification label, a first index parameter related to core carbon emission control, a second index parameter related to energy structure, a third index parameter related to support capacity, a weight parameter, and a reward and punishment parameter; determining the carbon emission index of the region to be measured based on the carbon emission data of the region to be measured and a carbon emission index determination model, where the carbon emission index determination model includes an objective function for determining the carbon emission index of the region to be measured, and the objective function is constructed based on the carbon emission data.

[0040] This method collects multi-dimensional carbon emission data covering regional characteristics, core control, energy structure, support capacity, and dynamic rewards and punishments, constructs a carbon emission index determination model based on the objective function, realizes a systematic quantitative assessment of the regional carbon emission situation, solves problems such as single index, neglect of regional differences, and lack of dynamic supervision in the prior art, and makes the obtained carbon emission index more accurate. In addition, it can also provide a scientific basis for subsequent carbon quota allocation, promote the accurate implementation of the dual control objectives of "intensity-based and total amount-assisted", and improve the pertinence and effectiveness of local government emission reduction policies. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for description in the embodiments of the present application will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 is a flowchart of the carbon emission index acquisition method provided by the embodiment of the present application;

[0043] Figure 2 is a structural diagram of a carbon emission index acquisition device provided by an embodiment of the present application;

[0044] Figure 3 is a structural diagram of an electronic device provided by an embodiment of the present application;

[0045] Figure 4 is a flowchart for calculating the carbon emission dual control comprehensive index provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0047] An embodiment of the present application provides a method for obtaining a carbon emission index. Refer to Figure 1 , Figure 1 which is a flowchart of the method for obtaining a carbon emission index provided by the embodiment of the present application. As Figure 1 shown, it includes the following steps:

[0048] Step 101: Obtain the carbon emission data of the area to be measured. The carbon emission data includes a regional classification label, a first index parameter related to the core control of carbon emissions, a second index parameter related to the energy structure, a third index parameter related to the support ability, a weight parameter, and a reward and punishment parameter;

[0049] In this step, the area to be measured can be understood as a specific geographical area that needs to be evaluated for carbon emissions, which can be a city, a province, or other specifically demarcated areas. The carbon emission data can be understood as a collection of various data reflecting the carbon emission-related situations of the corresponding areas, and specifically can include the following categories:

[0050] The regional classification label can be understood as dividing different regions into different categories according to factors such as the resource endowment, development stage, and functional orientation of the region, and each category corresponds to a label. Exemplarily, the regions are divided into four categories: "post-industrialization cities", "ecological function areas", "resource-based cities", and "emerging development areas". Taking Beijing as an example, it is in the post-industrialization stage, with a service-based industrial structure and strong scientific and technological innovation capabilities, so Beijing can be classified as a "post-industrialization city"; while for areas like Changbai Mountain, with prominent ecological functions and a high forest coverage rate, which play a more obvious role in carbon sinks, it belongs to an "ecological function area";

[0051] The above-mentioned first index parameter related to the core control of carbon emissions can be understood as a parameter used to directly measure the achievement of the core goal of carbon emission control; the above-mentioned second index parameter related to the energy structure can be understood as a parameter reflecting the optimization degree of the regional energy structure; the above-mentioned third index parameter related to the support ability can be understood as a parameter reflecting the support ability of the region to achieve low-carbon development.

[0052] The above weight parameters are used to determine the relative importance of each indicator in the carbon emission assessment model. They can be set by the user or adjusted adaptively according to the environment, policies, etc. The specific setting method of the weight parameters is not specifically limited in the embodiments of the present application. Exemplarily, the weight ratio of the first indicator related to the core carbon emission control is set to 45%. If the first indicator related to the core carbon emission control further includes the carbon emission intensity decline rate indicator and the total carbon emission growth rate indicator, and their corresponding ratios are 30% and 15% respectively, then the weight parameter corresponding to the carbon emission intensity decline rate indicator is 30, and the weight corresponding to the total carbon emission growth rate indicator is 15.

[0053] The above reward and punishment parameters can be understood as key data for motivating and restricting the actions of regions in carbon emission control. They are closely related to the additional points for key areas and the deduction points for early warnings, and will be adjusted in a timely manner in combination with annual policies. Exemplarily, if the green substitution rate of the production capacity of high-energy-consuming industries in the industrial field of a certain city exceeds the target by 20%, this is the data for additional points for rewards and will be given points according to certain rules; if the completion rate of the carbon emission intensity decline rate in this city is less than 80%, this belongs to the data for deduction points for punishment and will be deducted points according to the corresponding rules.

[0054] Step 102: Determine the carbon emission index of the area to be measured based on the carbon emission data of the area to be measured and the carbon emission index determination model. The carbon emission index determination model includes a target function for determining the carbon emission index of the area to be measured, and the target function is constructed based on the carbon emission data.

[0055] In this step, the above carbon emission index determination model can be understood as a model that processes and calculates the input carbon emission data and outputs a value that can comprehensively reflect the carbon emission control level of the area to be measured, that is, the carbon emission index. This model aims to determine the carbon emission index of the area to be measured and realizes this goal through specific calculation rules and logics. The above carbon emission index can also be understood as the local carbon emission dual-control comprehensive index. The specific form of the above carbon emission index determination model is not specifically limited in the embodiments of the present application. It can be a mathematical model, or a hybrid model that combines formulas and machine learning. In some alternative embodiments, it can also be a neural network model. The above target function can be understood as the core part of the carbon emission index determination model and can be constructed according to different types of carbon emission data. Specifically, the indicator parameters can be combined with the corresponding weight parameters, and the influence of the reward and punishment parameters can be considered.

[0056] In the carbon emission index acquisition method of the embodiment of the present application, by collecting multi-dimensional carbon emission data covering regional characteristics, core control, energy structure, support capacity and dynamic rewards and punishments, a carbon emission index determination model based on the objective function is constructed, which realizes a systematic quantitative evaluation of regional carbon emissions, solves the problems of single indicators, neglect of regional differences, lack of dynamic supervision in the prior art, and makes the obtained carbon emission index more accurate. In addition, it can also provide a scientific basis for the subsequent allocation of carbon quotas, promote the precise implementation of the dual control targets of "intensity as the main and total amount as the auxiliary", and enhance the pertinence and effectiveness of local government emission reduction policies.

[0057] Optionally, the first indicator parameter includes the completion degree of the carbon emission intensity reduction rate and the completion degree of the total carbon emission growth rate; the second indicator parameter includes the completion degree of the proportion of non-fossil energy consumption, the completion degree of the energy consumption intensity reduction rate and the completion degree of the clean production coverage rate; the third indicator parameter includes the completion degree of resource utilization efficiency, the completion degree of carbon sink increment and the proportion of low-carbon technology R&D investment, and the completion degree is the ratio of the actual value obtained to the preset target value.

[0058] In the carbon emission index acquisition method of the embodiment of the present application, the above completion is the ratio of the actual value obtained to the preset target value. For example, for the above carbon emission intensity reduction rate, the preset target is a 10% reduction, and the actual reduction is 8%. The completion of the carbon emission intensity reduction rate is 8% / 10%=0.8. The above carbon emission total growth rate can be understood as controlling the growth of carbon emission scale, which is a key indicator of "total constraint". The proportion of non-fossil energy consumption in the above second indicator parameter can be understood as assessing the proportion of clean energy consumption such as wind power and photovoltaics, reflecting the progress of energy structure decarbonization; the completion of the above energy consumption intensity reduction rate can be understood as measuring the decline in energy consumption per unit of gross domestic product (Gross Domestic Product, GDP), which is highly correlated with carbon emission intensity and can reflect the effectiveness of energy efficiency improvement; the completion of the above clean production coverage rate can be understood as assessing the proportion of enterprises implementing clean production technologies in industries such as steel and chemical industry, promoting the coordinated reduction of pollution and carbon in high-energy-consuming industries, and building the underlying support of "source emission reduction" from the three dimensions of energy consumption, energy efficiency level, and industrial transformation, which can avoid short-term behavior that relies on end-of-pipe governance to a certain extent. The degree of completion of resource utilization efficiency in the above-mentioned third indicator parameter can be understood as the decline rate of unit GDP consumption covering water, land, minerals and other resources, reflecting the resource-saving development capacity; the above-mentioned carbon sink increment can be understood as the carbon fixation of ecosystems such as forests and wetlands, which is an important way to offset carbon emissions; the above-mentioned proportion of low-carbon technology R&D investment can be understood as the proportion of R&D investment in GDP, reflecting the driving force of technological innovation on emission reduction.

[0059] In this implementation, through the unified calculation of the completion degree of different indicators, indicators of different dimensions, such as percentage, ten thousand tons, proportion, etc., are converted into dimensionless values in the range of 0-1, which is convenient for weighted calculation; at the same time, it realizes the multi-dimensional coverage of "core constraints-structural optimization-capacity support" for carbon emission data acquisition, so that the obtained regional carbon emission index is more accurate.

[0060] Optionally, the reward and punishment parameters include reward point addition item data, reward factor, penalty point deduction item data and penalty factor.

[0061] In the carbon emission index acquisition method of the embodiment of the present application, the above-mentioned bonus point data can be understood as the achievement data reflecting the over-achievement of green transformation in key areas such as industry, construction, and transportation. As the "incentive input" in the model, it can include over-achievement in aspects such as the green replacement rate of production capacity in high-energy-consuming industries, the proportion of green buildings in newly built buildings, and the penetration rate of new energy vehicles. The above-mentioned reward factor can be understood as an elasticity coefficient adjusted according to the national annual policy focus, which is used to amplify or reduce the intensity of the reward. For example, if the penetration rate of new energy vehicles in a city exceeds the target by 40% in a certain year, and the policy focus of that year is transportation emission reduction (α=1.3), then the bonus for this indicator can be 5×1.3=6.5 points (original weight 5 points). The above-mentioned penalty deduction data can be understood as negative data recording the failure to meet the core control indicators, the occurrence of systemic risks or abnormal process supervision. As the "constraint input" in the model, the above-mentioned penalty factors can be understood as the penalty intensity coefficient dynamically adjusted according to the risk level; for example, if the intensity completion rate of a resource-based city is less than 80% for two consecutive quarters, the penalty factor β = 0.8, and the deduction for this indicator can be (80% - actual completion rate) × 0.8 × 30 points.

[0062] In this implementation, a two-way adjustment mechanism of "dynamic incentive-risk constraint" is constructed by limiting the reward and punishment parameters to reward plus data, reward factors, penalty deduction data and penalty factors: by combining reward plus data with reward factors, quantitative rewards are given to the green transformation results in key areas such as industry, construction, and transportation; by coordinating penalty deduction data with penalty factors, progressive deductions are implemented for core indicators that fail to meet standards or systemic risks, which can strengthen the constraints on weak links in emission reduction and solve the problems in existing technologies of disconnection between obtaining carbon emission index and policy implementation and failure of incentive constraints, thereby improving the accuracy of carbon emission index acquisition.

[0063] Optionally, the objective function is:

[0064]

[0065] Wherein, X represents the carbon emission index, ω i Represents the i-th index parameter; I iThe weight parameter representing the i-th indicator; α represents the reward factor; j represents the reward bonus item data; β represents the penalty factor; z represents the penalty deduction item data.

[0066] In the carbon emission index acquisition method of the embodiment of the present application, the above objective function is the core calculation model of the carbon emission index. By weighted comprehensive basic indicators and superimposing dynamic reward and penalty items, a quantitative evaluation of the regional carbon emission control level is realized. Exemplarily, in measuring the core control layer indicators of a certain region, the completion degree of the intensity decline rate I1 = 0.9, and the weight parameter is set to 30; the completion degree of the total amount growth rate I2 = 0.8 (80%), and the weight parameter is 15. Then, without including the reward and penalty items, the carbon emission index corresponding to the core control layer indicators can be 30×0.9 + 15×0.8 = 27 + 12 = 39.

[0067] For the above reward and penalty items, if the completion degree of the key area indicators > 100% (such as the green substitution rate of industrial production capacity in the industrial field exceeds the target by 20%, and the completion degree = 1.2), the reward factor for the annual key area (such as building energy conservation in a certain year) can be increased. Exemplarily, if the completion degree of the new energy penetration rate in the urban transportation field of a certain city = 1.3 (exceeding the target by 30%), the weight parameter is 5, and the reward factor α = 1.3, then the bonus item can be 5×(1.3 - 1)×1.3 = 1.95 points.

[0068] For the above penalty item, exemplarily, if the completion degree of the total amount growth rate of a resource-based city = 0.6 (exceeding the target by 4%), the weight parameter is 15, and it fails to meet the standard for the first time, the penalty factor β = 0., and at this time, the penalty deduction of the penalty item can be (1 - 0.6)×0.9×15 = 5.4 points.

[0069] In this implementation manner, a quantitative model of "weighted basic indicators + dynamic reward and penalty items" is constructed for multi-dimensional systematic evaluation to solve the problem of single indicators. It not only retains policy transparency through the setting of weight parameters but also endows the model with the flexibility to adapt to regional differences and policy changes through dynamically adjustable parameters, thereby improving the accuracy of obtaining the regional carbon emission index.

[0070] Optionally, the regional classification labels include: late industrialization cities, ecological function areas, resource-based cities, and emerging development areas, and the regional classification labels are used to adjust the weight parameters.

[0071] In the carbon emission index acquisition method of the embodiment of the present application, different regions are divided into four categories: "late industrialization cities", "ecological function areas", "resource-based cities", and "emerging development areas", and the weights are adjusted accordingly according to different categories. For example, the weight of "carbon sink increment" in the ecological function area is increased to 10%.

[0072] In some alternative embodiments, parameters such as per capita GDP and urbanization rate can be introduced to set an elastic buffer period for the carbon emission intensity target in underdeveloped regions.

[0073] In this embodiment, regional classification labels are converted into specific index weights using regional differentiation weights. For example, for ecological function areas and resource-based cities, the carbon sink weight of ecological function areas is increased to 10%, enabling the model to dynamically adjust the evaluation focus according to local resource endowments and development stages, so as to more accurately obtain the carbon emission index "in line with local conditions".

[0074] Optionally, after determining the carbon emission index of the area to be measured based on the carbon emission data and carbon emission index determination model of the area to be measured, the following steps are further included:

[0075] Optimizing the carbon quota allocation for the area to be measured according to the carbon emission index.

[0076] In the carbon emission index acquisition method of the embodiments of the present application, after calculating the carbon emission index of the area to be measured based on the carbon emission index determination model, the carbon quota allocation can be optimized according to this index. Essentially, it realizes the scientific allocation of quota resources through quantitative evaluation results. The above carbon emission index, as a quantitative indicator comprehensively reflecting the regional carbon emission performance, directly constitutes the "policy interface" for quota adjustment. For the specific carbon quota allocation optimization method, the embodiments of the present application do not make specific limitations. In some alternative embodiments, the grades can be divided according to the carbon emission index (such as grades A, B, C, and D), corresponding to different quota adjustment strategies; in some other alternative embodiments, the quota allocation can be linked to the regional function positioning by combining the regional differentiation weights in the objective function. Exemplarily, in ecological function areas, such as key forest areas and wetland protection areas, if the carbon sink increment index weight is high and the completion is good, that is, the carbon sink item contributes more to the carbon emission index, "ecological protection quotas" can be allocated additionally, allowing them to realize the conversion of ecological value through carbon sink trading. The reward factor and penalty factor in the objective function can also be converted into specific rules in the quota allocation. Exemplarily, if the region gets a reward score due to low-carbon technology R & D investment, carbon sink project construction, etc., such as j = 5, the bonus effect can be amplified at a ratio of α = 1.2, corresponding to an additional increase of α×j = 6 points in the quota adjustment base.

[0077] In some alternative embodiments, after determining the carbon emission index of the area to be measured based on the carbon emission data and carbon emission index determination model of the area to be measured, the following operations can be performed by a carbon emission assessment system with data processing and communication functions to optimize the carbon quota allocation for the area to be measured:

[0078] The evaluation system can conduct in-depth analysis on the calculated carbon emission index and compare it with a preset first value and a second value. These values can be comprehensively determined based on various factors such as national or local carbon emission control targets, industry average levels, and regional development plans, and are stored in the parameter database of the system. For example, in a certain set scenario, the first value is set to 90 and the second value is set to 80;

[0079] After the evaluation system generates a corresponding quota adjustment plan based on the analysis results, it can, through the internal data communication module, package the generated quota adjustment plan in a specific data format and send it to the server port of the relevant government department responsible for carbon quota management through a secure encrypted network channel. At the same time, the system will automatically record information such as the sending time and the receiving party's server IP address for subsequent query and traceability;

[0080] After the server of the government department receives the quota adjustment instruction, its internal data verification program can verify the integrity and accuracy of the instruction. If the verification passes, the adjustment instruction can be forwarded to the carbon quota allocation execution system, which updates the carbon quota information of the corresponding area to be measured according to the instruction and feeds back a confirmation message to the carbon emission evaluation system. After receiving the confirmation message, the carbon emission evaluation system stores it in the operation log database of the system to complete the closed-loop record of the entire carbon quota allocation optimization process.

[0081] In this implementation, the carbon emission index is linked to the carbon quota allocation, dynamically adjusted according to the index level, and precisely adapted by combining regional differential weights and dynamic reward and punishment parameters, breaking the "extensive" and "one-size-fits-all" drawbacks of traditional quota allocation, and building a two-way driving mechanism of "rewarding emission reduction and restricting high emissions". It not only provides a scientific basis for quota allocation through the quantification result of the index, but also stimulates the regional initiative to reduce emissions through market-based means, while connecting with the carbon market to promote the optimal allocation of resources, and finally forms a policy closed-loop of "evaluation - allocation - incentive - optimization", effectively supporting the systematic promotion from indicator assessment to implementation.

[0082] Optionally, optimizing the carbon quota allocation for the area to be measured according to the carbon emission index includes:

[0083] In the case where the carbon emission index is greater than or equal to the first value, increase the carbon quota of the area to be measured;

[0084] In the case where the carbon emission index is less than the first value and greater than or equal to the second value, maintain the carbon quota of the area to be measured;

[0085] In the case where the carbon emission index is less than the second value, reduce the carbon quota of the area to be measured;

[0086] The first value is greater than the second value.

[0087] In the carbon emission index acquisition method according to the embodiments of the present application, for example, if the first value is set to 90 and the second value is set to 80, the following grading can be performed according to the carbon emission index:

[0088] Grade A (excellent): Comprehensive index ≥ 90, and the carbon emission intensity decreases by more than 20% compared to the target;

[0089] Grade B (meeting the standard): 80 ≤ comprehensive index < 90;

[0090] Grade C (warning): Comprehensive index < 80, and the carbon quota tightening mechanism can be triggered in a timely manner.

[0091] In this implementation manner, the mechanism divides the carbon emission index into three intervals by setting double thresholds, corresponding to different carbon quota adjustment strategies, forming a three-level management system of "encouraging the advanced, maintaining compliance, and restricting the backward". A clear boundary of "excellent - good - warning" is formed to ensure the gradient correspondence between the evaluation results and the quota adjustment. This implementation manner can be a specific implementation of "optimizing carbon quota allocation", which transforms the abstract "optimization" into an operable rule through threshold division.

[0092] In some alternative implementation manners, the acquisition method of the carbon emission data can be from:

[0093] Government statistical departments: data such as energy consumption and GDP;

[0094] Ecological environment monitoring: enterprise carbon emission implementation monitoring data;

[0095] Remote sensing information data: data such as forest carbon sinks and land use changes;

[0096] Enterprise reported data: data such as green technology applications.

[0097] In some other alternative implementation manners, reference can be made to Figure 4 During the "15th Five-Year Plan" period, at the beginning of each year, each region combines the actual situation to set the target layer for obtaining the local carbon emission dual-control comprehensive index and the criterion layer including multiple indicators;

[0098] Evaluate the carbon emission control situation of each city quarterly to timely master the progress of target completion, so as to achieve quarterly dynamic monitoring of the area to be measured;

[0099] Score each city every six months. For those whose scores reach the C-level, give a timely warning notice and urge them to take relevant measures;

[0100] Score each city annually according to the determined carbon emission index and publicize the scoring results;

[0101] Apply the scoring results and link them to the policies of the corresponding regions, such as optimizing financial subsidies, project approvals, etc.

[0102] See Figure 2 , Figure 2 It is the structural diagram of the carbon emission index acquisition device provided by another embodiment of the present application.

[0103] Such as Figure 2 As shown, the carbon emission index acquisition device 200 includes:

[0104] An acquisition module 201 for acquiring carbon emission data of the area to be measured, where the carbon emission data includes area classification labels, first index parameters related to core carbon emission control, second index parameters related to energy structure, third index parameters related to support capabilities, weight parameters, and reward and punishment parameters;

[0105] A determination module 202 for determining the carbon emission index of the area to be measured based on the carbon emission data of the area to be measured and a carbon emission index determination model, where the carbon emission index determination model includes an objective function for determining the carbon emission index of the area to be measured, and the objective function is constructed based on the carbon emission data.

[0106] Optionally, the first index parameters include the completion degree of the carbon emission intensity decline rate and the completion degree of the growth rate of the total carbon emissions; the second index parameters include the completion degree of the non-fossil energy consumption ratio, the completion degree of the energy consumption intensity decline rate, and the completion degree of the clean production coverage rate; the third index parameters include the completion degree of the resource utilization efficiency, the carbon sink increment, and the completion degree of the low-carbon technology R & D investment ratio, and the completion degree is the ratio of the obtained actual value to the preset target value.

[0107] Optionally, the reward and punishment parameters include reward bonus item data, reward factors, punishment deduction item data, and punishment factors.

[0108] Optionally, the objective function is:

[0109]

[0110] Among them, X represents the carbon emission index, ω i represents the i-th index parameter; I i represents the weight parameter of the i-th item; α represents the reward factor; j represents the reward bonus item data; β represents the punishment factor; z represents the punishment deduction item data.

[0111] Optionally, the area classification labels include: late industrialization cities, ecological function areas, resource-based cities, and emerging development areas, and the area classification labels are used to adjust the weight parameters.

[0112] Optionally, the device further includes:

[0113] An optimization module, configured to optimize the carbon quota allocation of the area to be measured according to the carbon emission index.

[0114] Optionally, the optimization module may further be configured to:

[0115] Increase the carbon quota of the area to be measured when the carbon emission index is greater than or equal to a first value;

[0116] Maintain the carbon quota of the area to be measured when the carbon emission index is less than the first value and greater than or equal to a second value;

[0117] Reduce the carbon quota of the area to be measured when the carbon emission index is less than the second value;

[0118] The first value is greater than the second value.

[0119] Refer to Figure 3 , Figure 3 is a structural diagram of an electronic device provided in another embodiment of the present application. As Figure 3 shown, the electronic device includes: a processor 301, a communication interface 302, a communication bus 304, and a memory 303. Among them, the processor 301, the communication interface 302, and the memory 303 complete mutual interaction through the communication bus 304.

[0120] Among them, the memory 303 is used to store a computer program; the processor 301 is configured to obtain carbon emission data of the area to be measured, and the carbon emission data includes a regional classification label, a first index parameter related to carbon emission core control, a second index parameter related to energy structure, a third index parameter related to support capacity, a weight parameter, and a reward and punishment parameter; determine the carbon emission index of the area to be measured based on the carbon emission data of the area to be measured and a carbon emission index determination model, and the carbon emission index determination model includes an objective function for determining the carbon emission index of the area to be measured, and the objective function is constructed based on the carbon emission data.

[0121] Optionally, the first index parameter includes the completion degree of the carbon emission intensity decline rate and the completion degree of the carbon emission total amount growth rate; the second index parameter includes the completion degree of the non-fossil energy consumption ratio, the completion degree of the energy consumption intensity decline rate, and the completion degree of the clean production coverage rate; the third index parameter includes the completion degree of the resource utilization efficiency, the carbon sink increment, and the completion degree of the low-carbon technology R & D investment ratio, and the completion degree is the ratio of the obtained actual value to the preset target value.

[0122] Optionally, the reward and punishment parameter includes reward bonus item data, a reward factor, punishment deduction item data, and a punishment factor.

[0123] Optionally, the objective function is:

[0124]

[0125] where X represents the carbon emission index, ω i represents the i-th index parameter; I i represents the weight parameter of the i-th index; α represents the reward factor; j represents the reward bonus item data; β represents the penalty factor; z represents the penalty deduction item data.

[0126] Optionally, the regional classification labels include: late industrialization cities, ecological function areas, resource-based cities, and emerging development areas, and the regional classification labels are used to adjust the weight parameters.

[0127] Optionally, after determining the carbon emission index of the area to be measured based on the carbon emission data and carbon emission index determination model of the area to be measured, it further includes:

[0128] Optimizing the carbon quota allocation of the area to be measured according to the carbon emission index.

[0129] Optionally, the optimizing the carbon quota allocation of the area to be measured according to the carbon emission index includes:

[0130] In the case where the carbon emission index is greater than or equal to a first value, increasing the carbon quota of the area to be measured;

[0131] In the case where the carbon emission index is less than the first value and greater than or equal to a second value, maintaining the carbon quota of the area to be measured;

[0132] In the case where the carbon emission index is less than the second value, reducing the carbon quota of the area to be measured;

[0133] The first value is greater than the second value.

[0134] The communication bus 304 mentioned in the above electronic device may be an external device interconnect standard (Peripheral Component Interconnect, PCT) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, etc. This communication bus 304 can be divided into an address bus, a data bus, a control bus, etc. For the sake of easy identification, only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one data type.

[0135] The communication interface 302 is used for communication between the above terminal and other devices.

[0136] The memory 303 may include a Random Access Memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. Optionally, the memory 303 may also be at least one storage device located far from the aforementioned processor 301. The aforementioned processor 301 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0137] The embodiments of the present application also provide a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, it implements each process of the above-mentioned embodiment of the carbon emission index acquisition method and can achieve the same technical effect. To avoid repetition, it will not be elaborated here. Among them, the computer-readable storage medium, such as a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disc, etc.

[0138] It should be noted that in this article, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article, or device. Without more limitations, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article, or device including that element.

[0139] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0140] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific implementation manners. The above specific implementation manners are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them belong to the protection scope of the present application.

Claims

1. A method for obtaining a carbon emission index, characterized in that, The method includes: Obtaining carbon emission data of the area to be measured, where the carbon emission data includes a regional classification label, a first index parameter related to core carbon emission control, a second index parameter related to energy structure, a third index parameter related to support capacity, a weight parameter, and a reward and punishment parameter; Determining the carbon emission index of the area to be measured based on the carbon emission data of the area to be measured and a carbon emission index determination model, where the carbon emission index determination model includes an objective function for determining the carbon emission index of the area to be measured, and the objective function is constructed based on the carbon emission data.

2. The method according to claim 1, wherein The first index parameter includes the completion degree of the carbon emission intensity decline rate and the completion degree of the total carbon emission growth rate; the second index parameter includes the completion degree of the non-fossil energy consumption ratio, the completion degree of the energy consumption intensity decline rate, and the completion degree of the clean production coverage rate; the third index parameter includes the completion degree of the resource utilization efficiency, the carbon sink increment, and the completion degree of the low-carbon technology R & D investment ratio, and the completion degree is the ratio of the obtained actual value to the preset target value.

3. The method according to claim 1 or 2, characterized in that, The reward and punishment parameter includes reward bonus item data, a reward factor, punishment deduction item data, and a punishment factor.

4. The method according to claim 3, wherein The objective function is: Among them, X represents the carbon emission index, ω i represents the i-th index parameter; I i represents the weight parameter of the i-th index; α represents the reward factor; j represents the reward bonus item data; β represents the penalty factor; z represents the penalty deduction item data.

5. The method according to claim 1 or 2, characterized in that, The regional classification label includes: late industrialization cities, ecological function areas, resource-based cities, and emerging development areas, and the regional classification label is used to adjust the weight parameter.

6. The method according to claim 1 or 2, characterized in that, After determining the carbon emission index of the area to be measured based on the carbon emission data of the area to be measured and the carbon emission index determination model, it further includes: Optimizing the carbon quota allocation of the area to be measured according to the carbon emission index.

7. The method according to claim 6, wherein The optimizing the carbon quota allocation of the area to be measured according to the carbon emission index includes: Increasing the carbon quota of the area to be measured when the carbon emission index is greater than or equal to a first value; Maintaining the carbon quota of the area to be measured when the carbon emission index is less than the first value and greater than or equal to a second value; Reducing the carbon quota of the area to be measured when the carbon emission index is less than the second value; The first value is greater than the second value.

8. An apparatus for obtaining a carbon emission index, characterized in that, The device includes: An acquisition module for acquiring carbon emission data of the area to be measured, where the carbon emission data includes a regional classification label, a first index parameter related to core carbon emission control, a second index parameter related to energy structure, a third index parameter related to support capacity, a weight parameter, and a reward and punishment parameter; A determination module for determining the carbon emission index of the area to be measured based on the carbon emission data of the area to be measured and a carbon emission index determination model, where the carbon emission index determination model includes an objective function for determining the carbon emission index of the area to be measured, and the objective function is constructed based on the carbon emission data.

9. An electronic device apparatus, characterized in that, It includes a processor, a memory, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the carbon emission index acquisition method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, it implements the steps of the carbon emission index acquisition method according to any one of claims 1 to 6.

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