Traffic carbon quota allocation method and system based on spatial SEE model

Abstract

The invention belongs to the technical field of carbon quota allocation, discloses a traffic carbon quota allocation method and system based on a spatial SEE model, and aims to allocate carbon emission quotas according to the average population proportion of each province in a reference period and tilt the quotas to provinces and cities with more population in jurisdictions. The distribution mode not only reflects the actual population demand of each province and city, but also ensures that the carbon emission right can be distributed nationwide more fairly, the uneven resource distribution caused by the population scale difference is avoided, the investment-oriented ZSG-DEA model is adopted to carry out efficiency evaluation on the traffic carbon emission of each province in China, and the efficiency of the traffic carbon emission of each province in China is improved. And redistributing the carbon dioxide emission of the invalid DMU so as to realize the optimization of the carbon emission efficiency of each province. According to the invention, through scientific, reasonable and overall carbon quota distribution, the emission reduction task can be effectively decomposed, the realization of a dual-carbon target is promoted, and local governments are guided to clarify respective emission reduction targets and development positioning so as to formulate corresponding policies.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon quota allocation, and in particular relates to a method and system for allocating traffic carbon quota based on a spatial SEE model. Background Art

[0002] In the context of global climate change, the issue of carbon emissions in the transportation industry has received increasing attention. As an important sector of energy consumption and greenhouse gas emissions, the transportation industry has a huge impact on the total global carbon emissions. Therefore, how to effectively control carbon emissions in the transportation industry and allocate carbon emission quotas within a reasonable framework has become a key issue in achieving carbon emission reduction goals. The transportation industry has complex systemic characteristics and involves multiple stakeholders, including government departments, enterprises, and the public. Therefore, to formulate a scientific and reasonable carbon quota allocation plan, it is necessary not only to consider economic and environmental benefits, but also to comprehensively consider social equity and the long-term stability of the industry. Based on this background, this paper proposes a transportation carbon quota allocation method and system based on the spatial SEE model (Stability-Equity-Efficiency) to provide a theoretical basis and technical support for carbon emission reduction in the transportation industry.

[0003] (1) Global climate change and the urgency of carbon emission reduction in the transportation industry

[0004] Climate change is one of the most serious challenges facing the world today. The massive emission of greenhouse gases (mainly carbon dioxide) leads to global warming, which in turn causes a series of ecological and environmental problems such as frequent extreme weather, melting glaciers, and rising sea levels. In response to this global challenge, the Paris Agreement sets emission reduction targets for each country, namely, to control the global average temperature rise within 2°C by the end of this century, and strive to limit the temperature rise to within 1.5°C. Under this framework, governments have successively proposed "dual carbon" goals, aiming to achieve sustainable development by reducing greenhouse gas emissions.

[0005] The transportation industry is one of the main sources of global energy consumption and carbon emissions. According to the International Energy Agency (IEA), the transportation industry accounts for about a quarter of global carbon dioxide emissions, of which road transportation accounts for the vast majority. With the rapid development of the global economy and the advancement of urbanization, transportation demand has increased rapidly, resulting in a continuous increase in carbon emissions in the transportation sector. Especially in developing countries, with the increase in car ownership and the rise in logistics demand, the problem of carbon emissions in transportation is becoming increasingly serious. Therefore, as one of the key areas for emission reduction, the urgency of carbon emission reduction in the transportation industry cannot be ignored.

[0006] (2) Theoretical basis and challenges of carbon quota allocation

[0007] As a market-based means of controlling greenhouse gas emissions, carbon quota allocation has been widely used in many fields. Its core idea is to set emission limits for each emission source, forcing it to emit within the limit, and then guide the innovation and implementation of emission reduction technologies through market mechanisms. Under this framework, how to reasonably, fairly and efficiently allocate carbon quotas is directly related to the emission reduction effect and the sustainable development of the economy and society.

[0008] At present, the mainstream carbon quota allocation methods are roughly divided into three categories: historical emission method, benchmark method and auction method. The historical emission method allocates quotas based on the historical emissions of each entity, emphasizing the recognition of historical contributions; the benchmark method encourages efficient and low-carbon enterprises based on the average emission level of the industry; and the auction method makes the allocation of carbon quotas more flexible through market bidding. However, these methods have some problems in actual operation. For example, the historical emission method is prone to "solidified unfairness", that is, enterprises with large historical emissions may obtain more quotas, thereby weakening their enthusiasm for emission reduction; although the benchmark method has advantages in encouraging low-carbon technologies, how to determine a reasonable benchmark is still a challenge; although the auction method has a high degree of marketization, the quota price fluctuates greatly, which may increase the operating risks of enterprises.

[0009] Through the above analysis, the problems and defects of the prior art are as follows:

[0010] At present, the mainstream carbon quota allocation methods have some problems in actual operation. For example, the historical emission method is prone to "fixed unfairness", that is, enterprises with large historical emissions may obtain more quotas, thereby weakening their enthusiasm for emission reduction; although the benchmark method has advantages in encouraging low-carbon technologies, how to determine a reasonable benchmark is still a challenge; although the auction method has a high degree of marketization, the quota price fluctuates greatly, which may increase the operating risks of enterprises.

[0011] Based on the above analysis, ① Most existing studies have formulated carbon emission allocation plans at the national and provincial levels, but have failed to focus on specific industries, especially the transportation industry, and therefore cannot provide a sound and strong support for the formulation of differentiated emission reduction policies for the industry. ② Based on the perspective of fairness and efficiency, the phased emission reduction plan is designed, but due to the lack of scientific evaluation standards, there is a lack of verification of the effectiveness of the emission reduction plan. ③ The allocation plan under the comprehensive principle is increasingly recognized, but the weight of the allocation principle needs to be scientifically discussed. Summary of the invention

[0012] In view of the problems existing in the prior art, the present invention provides a method and system for allocating transportation carbon quota based on a spatial SEE model.

[0013] The present invention is implemented as follows: a method for allocating transportation carbon quotas based on a spatial SEE model includes:

[0014] Step 1: Transportation carbon emissions accounting;

[0015] Adopt the "top-down" approach recommended by the Intergovernmental Panel on Climate Change; this approach builds a carbon footprint calculation model for the transportation industry based on the energy consumption of the transportation industry and the carbon emission factors of related energy;

[0016] Step 2, transportation carbon emission prediction;

[0017] Including model framework and variable selection;

[0018] Step 3, traffic assignment model;

[0019] Including the stability principle allocation model, the fairness principle allocation model, and the efficiency principle allocation model;

[0020] Step 4, spatial SEE model.

[0021] Including space benefit value calculation;

[0022] Furthermore, the transportation carbon emissions accounting:

[0023] The specific calculation formula is as follows:

[0024]

[0025] Where Em is the physical consumption of the mth type of transportation energy, Km is the reference coefficient of the mth type of energy converted into standard coal; Fm is the carbon emission coefficient of the mth type of energy.

[0026] Furthermore, the model framework:

[0027] The STIRPAT model is used to analyze the factors affecting China's transportation carbon emissions. The STIRPAT model is an improvement of the IPAT model. The logarithm of the left and right ends of the model can be expressed as:

[0028] i i =lna+blnP i +clnA i +dlnT i +lne(2)

[0029] Where I, P, A, and T represent the variables of environmental impact, population factors, economic factors, and technological factors, respectively; b, c, and d represent the elasticity coefficients of the variables corresponding to the three major factors of population, economy, and technology, respectively, to reflect the individual effects of these factors on the environment; a is the model coefficient; and e is the model error.

[0030] Further, the variable selection is:

[0031] 1) Economic factors: GDP per capita, as an important indicator of economic development, directly affects residents’ consumption capacity and living standards, promotes the growth of the transportation industry, and thus affects transportation demand and carbon emissions;

[0032] 2) Population factors: As the population increases, the demand for transportation also increases, which directly leads to an increase in carbon emissions from the transportation sector;

[0033] 3) Transportation factors: Passenger turnover and freight turnover are indicators of the intensity of transportation activities, which are directly related to energy consumption and carbon emission levels;

[0034] 4) Technical factors: Energy consumption per unit turnover and the proportion of clean energy reflect the energy utilization efficiency and technological progress level of the transportation industry.

[0035] Furthermore, the traffic assignment model:

[0036] 1) Stability principle allocation model

[0037] The formula is:

[0038]

[0039] In the formula, CQ1i represents the transportation carbon quota of the i-th province in 2030 under the stability principle, AEij represents the actual transportation carbon emissions of the i-th province in the j-th year, LC2030 is the predicted value of my country's transportation carbon emissions under the low-carbon scenario in 2030; J represents J years of historical data, and I represents I regions to be allocated.

[0040] 2) Fairness Principle Allocation Model

[0041] The formula is:

[0042]

[0043] In the formula, CQ2i represents the transportation carbon quota of the i-th province in 2030 under the principle of fairness, and Popij represents the actual population of the i-th province in the j-th year;

[0044] 3) Efficiency principle allocation model

[0045] The initial carbon quota allocation is selected as the input variable, and the population (Pop), freight turnover (FTK), and unit turnover energy consumption (ET) that have the most significant impact on transportation carbon emissions are selected as output variables; the specific formula is, and the final carbon quota allocation plan CQ3i is obtained;

[0046]

[0047] In the formula, φi is the relative carbon emission efficiency of the transportation industry in the i-th province, λi is the combination ratio of other decision-making units in the secondary construction decision-making unit for the i-th province, yi0 is the value of each output factor in the i-th province, yit is the t-th output factor in the i-th province; xi0 is the initial value of the carbon quota of the transportation industry in the i-th province, and xi are the adjusted values ​​of the carbon quota of the transportation industry in the i-th province.

[0048] Furthermore, the spatial SEE model:

[0049] Firstly, based on the three dimensions of "stability-fairness-efficiency", a SEE spatial model of the crisis scenario of carbon quota allocation in the transportation industry is constructed, and coordinates are used to measure the performance of the allocation scheme in terms of stability, fairness and efficiency. Among them, the x-axis measures stability, which is represented by the ratio of carbon quota deviation from the estimated carbon emissions; the y-axis measures fairness, which is represented by the Gini coefficient of per capita carbon emission rights; the z-axis measures efficiency, which is the initial allocation efficiency score calculated by ZSG-DEA.

[0050] Secondly, the spatial benefit value model Tp is constructed according to the principle of SEE spatial model, as shown in the following formula:

[0051]

[0052] Where, d AP and d AO Respectively represent the length from point A to point P and point O;

[0053] Among them, point A T A =1, that is, the spatial benefit value of this point is 1, that is, the spatial benefit value of this point is 1, and 100% of the carbon quota spatial effect is achieved; point O, that is, the spatial benefit value of this point is 0;

[0054] For the SEE space model coordinate point Q(x1, y1, z1), the optimization objective function is:

[0055] T Q =maxT P

[0056]

[0057] In the formula, α, β, and γ are the allocation weights of the three principles of stability, fairness, and efficiency;

[0058] Q i =α·Q 1i +β·Q 2i +γ·Q 3i

[0059]

[0060] In the formula, Q irepresents the target transportation carbon quota for 2030; Q 1i ,Q 2i ,Q 3i They represent the optimal transportation carbon quota under the principles of stability, fairness and efficiency respectively; n represents the number of provinces, which is 30; Gini i =1-Σ[(X i+1 -X i )(Y i+1 +Y i )], Xi is the cumulative population percentage of the ith province or city; Yi is the cumulative carbon dioxide emissions percentage of the ith province or city; Represents the initial efficiency under target carbon emission.

[0061] Another object of the present invention is to provide a transportation carbon quota allocation system based on a spatial SEE model, comprising:

[0062] Emission accounting module, used for transportation carbon emissions accounting; adopts the "top-down" approach recommended by the Intergovernmental Panel on Climate Change; this approach builds a carbon footprint calculation model for the transportation industry based on the energy consumption of the transportation industry and the carbon emission factors of related energy;

[0063] Emission prediction module, used for traffic carbon emission prediction; including model framework and variable selection;

[0064] Allocation model module, used for traffic allocation model; including stability principle allocation model, fairness principle allocation model, and efficiency principle allocation model;

[0065] SEE model module, used for spatial SEE model.

[0066] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the transportation carbon quota allocation method based on the spatial SEE model.

[0067] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the method for allocating transportation carbon quotas based on the spatial SEE model.

[0068] Another object of the present invention is to provide an information data processing terminal, which is used to implement the transportation carbon quota allocation system based on the spatial SEE model.

[0069] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0070] First, the carbon emission quota of the present invention is allocated according to the average population ratio of each province in the benchmark period, and the quota is tilted towards provinces and cities with larger populations within the jurisdiction. This allocation model not only reflects the actual population needs of each province and city, but also ensures that the carbon emission rights can be more fairly distributed across the country, avoiding the uneven distribution of resources caused by differences in population size. The present invention will use the input-oriented ZSG-DEA model to evaluate the efficiency of transportation carbon emissions in various provinces in China, and redistribute the carbon dioxide emissions of invalid DMUs accordingly to achieve the optimization of carbon emission efficiency in various provinces. Through scientific, reasonable and comprehensive carbon quota allocation, the present invention can effectively decompose emission reduction tasks and promote the realization of the "dual carbon" goals. At the same time, it guides local governments to clarify their respective emission reduction goals and development positioning, so as to formulate corresponding policies, optimize the structure of the transportation industry, promote green and low-carbon development, and maximize the value of carbon quotas. Therefore, the present invention first defines the crisis transformation of transportation carbon quotas as a state, aiming to seek the best allocation plan to avoid the "triple trap" crisis and achieve coordination between stability, fairness and efficiency.

[0071] Through the above-mentioned transportation carbon quota allocation method based on the spatial SEE model, accurate accounting and prediction of transportation carbon emissions are achieved, carbon quotas are reasonably allocated, traffic signal control strategies are optimized, carbon emissions are reduced, and the green and sustainable development of the transportation system is promoted. At the same time, the automation and real-time data collection and processing improve the efficiency and accuracy of carbon emission management, and the feedback and adjustment mechanism ensures the dynamic optimization and adaptability of the system, which significantly improves the scientificity and practicality of transportation carbon emission management, and has good social and economic benefits and environmental protection value.

[0072] Second, as auxiliary evidence of the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

[0073] (1) The expected benefits and commercial value of the technical solution of the present invention after transformation are:

[0074] The carbon emission rights trading mechanism is considered to be the most promising market-based emission reduction tool. Since 2011, China has launched carbon emission rights trading pilot projects and carbon emission rights trading management measures in Beijing, Tianjin and other provinces and cities, and has achieved remarkable results. As a cost-effective tool, carbon emission rights trading can achieve a win-win strategy of emission reduction and cost savings. In the context of the current global carbon market about to start, the transportation industry, as an important source of carbon emissions in China, has not yet had a set of scientific and reasonable allocation methods. This invention will make up for this problem.

[0075] (2) The technical solution of the present invention fills the technical gap in the industry at home and abroad:

[0076] The patent of this invention provides a carbon emission quota allocation model based on spatial SEE, which fully considers stability, equity and efficiency, and provides a scientific method for the allocation of transportation carbon quotas; secondly, the current carbon quota allocation results lack comparability, therefore, the spatial SEE model is proposed to meet the horizontal comparison of carbon emission quota allocation and fill the current gap.

[0077] (3) The technical solution of the present invention solves the technical problems that people have been eager to solve but have never been able to solve successfully:

[0078] This plan solves the problem of carbon emission accounting in transportation, and realizes the allocation of carbon quotas to various regions under the background of dual carbon, providing a reference basis for the development of low-carbon industries in local transportation industries.

[0079] This scheme realizes the horizontal comparability of transportation carbon quotas for the first time, demonstrates the advantages and disadvantages of various allocation results through spatial benefit values, and provides a technical method for comparing carbon quota results.

[0080] This technology is a general technology that mainly distributes carbon emissions based on the three principles of stability, equity and efficiency. It is universal to all industries and calculates the spatial benefit value on this basis to quantify the distribution performance.

[0081] Third, the technical solution of the present invention provides a method for allocating transportation carbon quotas based on a spatial SEE model, which effectively solves the problems of insufficient scientificity and fairness, uneven resource allocation, and difficulty in dynamic regulation in the existing technology in the allocation of transportation carbon emissions. Traditional carbon quota methods often use a single indicator (such as historical emissions, population, or economic level) to allocate transportation carbon emission quotas. This method often ignores the actual demand differences and development imbalances in different regions, resulting in a lack of adaptability and fairness in the allocation results, making it difficult to fully motivate local areas to achieve carbon emission reduction targets in the transportation field.

[0082] This invention uses the spatial SEE model to systematically optimize the allocation process of transportation carbon quotas based on the three principles of stability, fairness, and efficiency. It not only establishes a carbon quota allocation method that conforms to regional differences at the model level, but also realizes the prediction of future development trends through quantitative analysis. It can scientifically allocate and adjust carbon quotas based on multi-dimensional factors such as transportation carbon emission history, population distribution, economic growth, and energy efficiency in different regions.

[0083] In industrial applications, the technical solution of the present invention can significantly improve the rationality and fairness of carbon quotas, and is particularly suitable for carbon emission reduction and resource optimization in the fields of public transportation, logistics transportation, urban transportation, etc. It calculates the demand weights of different regions in terms of stability, fairness and efficiency through the SEE model, so that the carbon quota of each region is both consistent with historical data and fair, while encouraging energy use and carbon emission control technology application in efficient regions.

[0084] Specific technological progress is reflected in the following aspects:

[0085] 1. Improved scientific allocation: The introduction of the spatial SEE model enables carbon quota allocation to comprehensively consider multiple influencing factors and allocate carbon resources based on actual needs and development potential, avoiding the imbalance in allocation caused by a single indicator in traditional methods and significantly enhancing the scientific nature of allocation.

[0086] 2. Flexible dynamic regulation: Through real-time collection of data in transportation for dynamic analysis, this solution can still adapt to changing needs after carbon quota allocation and make necessary redistribution or adjustments, thereby improving the system's responsiveness and adaptability, and helping to achieve long-term and stable carbon emission control targets in the region.

[0087] 3. Combination of fairness and incentive mechanism: The scheme incorporates the principle of fairness into the allocation model, so that the quota allocation process takes into account social benefits, the actual conditions of different regions can be reasonably reflected, and underdeveloped regions are especially encouraged to actively participate in carbon emission reduction. At the same time, efficient regions can obtain more carbon quotas to encourage them to further improve energy efficiency.

[0088] 4. Enhanced operability of decision-making: With the support of the SEE model, different regions can obtain intuitive carbon emission quota effect evaluation. At the same time, through accurate quota calculation and risk warning, local managers can formulate carbon emission reduction policies in line with actual conditions, significantly reducing uncertainty in the decision-making process.

[0089] The technical solution of the present invention has broad promotion value in multiple application fields and is of great significance for promoting low-carbon development in the transportation industry, achieving regional coordinated emission reduction and social sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 It is a flow chart of a method for allocating transportation carbon quota based on a spatial SEE model provided in an embodiment of the present invention.

[0091] Figure 2 It is a structural block diagram of a transportation carbon quota allocation system based on a spatial SEE model provided in an embodiment of the present invention.

[0092] Figure 3It is a SEE space model diagram for constructing a crisis scenario of carbon quota allocation in the transportation industry based on the three dimensions of "stability-fairness-efficiency" provided by an embodiment of the present invention.

[0093] Figure 4 It is an overall flow chart of SEE provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0094] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0095] ###Example 1: Allocation of Carbon Quota for Urban Public Transport

[0096] In the field of public transportation in large cities, the transportation carbon quota allocation method based on the spatial SEE model can significantly optimize the control of public transportation carbon emissions. Assuming that the transportation carbon emissions of a megacity are high, due to its large population base and growing economic activities, the demand for public transportation is very high.

[0097] 1. Transportation carbon emissions accounting: A "top-down" approach is used to calculate the city's total carbon emissions from public transportation. Based on energy consumption data and carbon emission factors, a transportation carbon footprint model is constructed to derive the current carbon emission base.

[0098] 2. Transportation carbon emissions forecast: Using the STIRPAT model, we can predict the growth trend of carbon emissions from public transportation in the future, especially the target carbon emissions in 2030, based on factors such as the city’s population growth, economic development, and technological progress.

[0099] 3. Transportation allocation model: The allocation calculation is based on the three principles of stability, fairness and efficiency. The 2030 target carbon quota is allocated through the stability model, so that the historical carbon emission data of the city's public transportation can be smoothly transitioned; the fairness principle allocates carbon quotas according to the actual population of each administrative district, and the efficiency model will focus on energy efficiency and resource distribution.

[0100] 4. Spatial SEE model analysis: Use the coordinate system of the spatial SEE model to evaluate the allocation plan. Analyze the three dimensions of stability, fairness, and efficiency to optimize the balance of quota allocation.

[0101] 5. Scheme optimization and implementation: Apply the optimization scheme derived from the SEE model to public transportation policies, formulate a public transportation carbon quota plan based on regional differences, and monitor it regularly. Based on the carbon quota, promote technological innovation and increase the proportion of clean energy use to achieve the 2030 target carbon emission reduction.

[0102] Through the SEE model's allocation of transportation carbon quotas, the public transportation system achieves more efficient and reasonable carbon emission control, ensuring that transportation can meet the needs of urban growth while gradually moving towards low-carbon goals and reducing the city's environmental load.

[0103] Example 2: Carbon quota allocation for national highway transportation industry

[0104] In long-distance highway transportation, the carbon emissions of highways distributed in different regions vary significantly. The allocation of transportation carbon quotas through the SEE model will help to achieve more scientific emission reduction measures, especially in high-carbon emission areas such as heavy industrial intensive areas and coastal logistics hubs.

[0105] 1. Transportation carbon emissions accounting: Using a "top-down" accounting method, the total transportation carbon emissions for each region are obtained based on the fuel consumption of national highway transportation and the energy carbon emission factors of different regions.

[0106] 2. Transportation carbon emission forecast: Using the STIRPAT model, we predict future carbon emissions through variables such as per capita GDP, cargo turnover, and unit turnover energy consumption of each province to determine the target carbon emission quota by 2030.

[0107] 3. Transportation allocation model: Three allocation models are used for calculation. The stability model allocates steadily according to historical emissions; the fairness model allocates fairly based on the actual population and cargo turnover of different provinces; and the efficiency model allocates carbon quotas for transportation based on energy efficiency and the proportion of clean energy.

[0108] 4. Spatial SEE model analysis: On the coordinate axis of the SEE model, calculate the stability, fairness and efficiency indicators of each province and optimize the weight combination. According to the actual situation of each province, give appropriate carbon quota support.

[0109] 5. Implementation and monitoring of the plan: Through the optimized allocation plan, each province will link carbon quotas with transportation volume and fuel consumption and implement allocation policies. Carbon reduction measures will be implemented in areas with high carbon emission intensity, while introducing technological transformation and the use of clean fuels to gradually improve the emission reduction effect of highway transportation.

[0110] In high-emission logistics hub areas, through reasonable quota allocation, policy support and technological optimization, we can gradually achieve a balance in carbon emissions among regions, achieve the overall low-carbon development goal of the highway transportation industry, and effectively contribute to the national "dual carbon" process.

[0111] like Figure 1 As shown, a method for allocating transportation carbon quotas based on a spatial SEE model provided by an embodiment of the present invention includes the following steps:

[0112] S101, Transportation Carbon Emission Accounting;

[0113] Adopt the "top-down" approach recommended by the Intergovernmental Panel on Climate Change; this approach builds a carbon footprint calculation model for the transportation industry based on the energy consumption of the transportation industry and the carbon emission factors of related energy;

[0114] S102, transportation carbon emission prediction;

[0115] Including model framework and variable selection;

[0116] S103, traffic assignment model;

[0117] Including the stability principle allocation model, the fairness principle allocation model, and the efficiency principle allocation model;

[0118] S104, spatial SEE model.

[0119] In the method for allocating traffic carbon quota based on the spatial SEE model of the present invention, the signal data processing process includes the following steps:

[0120] 1. Data collection and preprocessing: First, the sensor network installed at major transportation hubs and along roads collects multi-source signal data such as traffic flow, vehicle speed, vehicle type, and traffic signal status in real time. Subsequently, the collected data is cleaned and filtered to remove noise and outliers to ensure the accuracy and integrity of the data.

[0121] 2. Data synchronization and integration: Synchronize data from different sensors in time and space, and use geographic information system (GIS) technology to combine traffic signal data with geographic location information to form a unified spatiotemporal data set, providing a basis for subsequent analysis.

[0122] 3. Feature extraction and selection: Apply statistical analysis and machine learning algorithms to extract key features from the synchronized data set, such as traffic flow fluctuation patterns, traffic congestion frequency and duration, etc. Through feature selection technology, key variables closely related to carbon emissions are screened out to improve the prediction accuracy and computational efficiency of the model.

[0123] 4. Real-time data analysis and modeling: Use the spatial SEE model to analyze the processed signal data in real time to identify the dynamic relationship between traffic flow and carbon emissions. Combined with historical data, build and optimize the carbon emission prediction model to achieve accurate prediction of future traffic carbon emissions.

[0124] 5. Carbon quota allocation optimization: Based on the prediction results, a multi-objective optimization algorithm is used to reasonably allocate traffic carbon quotas in accordance with the principles of stability, fairness and efficiency. The system automatically adjusts the timing strategy of traffic lights, optimizes traffic flow, reduces carbon emissions, and achieves green and sustainable development of the transportation system.

[0125] 6. Feedback and adjustment mechanism: Establish a closed-loop feedback system to continuously monitor the difference between actual carbon emissions and predicted values. Based on the feedback results, dynamically adjust the signal data processing process and model parameters to ensure the continuous optimization and adaptability of the carbon quota allocation method.

[0126] Through the above-mentioned detailed signal data processing process, the transportation carbon quota allocation method based on the spatial SEE model of the present invention can achieve efficient and accurate carbon emission prediction and quota allocation. First, the real-time collection and processing of multi-source traffic signal data ensures the comprehensiveness and real-time nature of the data, greatly improving the accuracy of carbon emission accounting. Secondly, the spatial SEE model combined with the multi-objective optimization algorithm can achieve the best balance between stability, fairness and efficiency, reasonably allocate carbon quotas, and promote the green transformation of the transportation system. In addition, the introduction of the closed-loop feedback mechanism enables the system to have self-optimization capabilities, and can continuously adjust and optimize the quota allocation strategy according to the actual operating conditions, ensuring the long-term applicability and efficiency of the method. In general, the present invention significantly improves the scientificity and practicality of transportation carbon emission management, and has broad application prospects and significant social and economic benefits.

[0127] The transportation carbon emission accounting provided by the embodiment of the present invention is as follows:

[0128] The specific calculation formula is as follows:

[0129]

[0130] Where Em is the physical consumption of the mth type of transportation energy, Km is the reference coefficient of the mth type of energy converted into standard coal; Fm is the carbon emission coefficient of the mth type of energy.

[0131] The model framework provided by the embodiment of the present invention is:

[0132] The STIRPAT model is used to analyze the factors affecting China's transportation carbon emissions. The STIRPAT model is an improvement of the IPAT model. The logarithm of the left and right ends of the model can be expressed as:

[0133] i i =lna+blnP i +clnA i +dlnT i +lne(2)

[0134] Where I, P, A, and T represent the variables of environmental impact, population factors, economic factors, and technological factors, respectively; b, c, and d represent the elasticity coefficients of the variables corresponding to the three major factors of population, economy, and technology, respectively, to reflect the individual effects of these factors on the environment; a is the model coefficient; and e is the model error.

[0135] Variable selection provided by the embodiment of the present invention:

[0136] 1) Economic factors: GDP per capita, as an important indicator of economic development, directly affects residents’ consumption capacity and living standards, promotes the growth of the transportation industry, and thus affects transportation demand and carbon emissions;

[0137] 2) Population factors: As the population increases, the demand for transportation also increases, which directly leads to an increase in carbon emissions from the transportation sector;

[0138] 3) Transportation factors: Passenger turnover and freight turnover are indicators of the intensity of transportation activities, which are directly related to energy consumption and carbon emission levels;

[0139] 4) Technical factors: Energy consumption per unit turnover and the proportion of clean energy reflect the energy utilization efficiency and technological progress level of the transportation industry.

[0140] 5. The method for allocating transportation carbon quotas based on the spatial SEE model according to claim 1, characterized in that the transportation allocation model:

[0141] 1) Stability principle allocation model

[0142] The formula is:

[0143]

[0144] In the formula, CQ1i represents the transportation carbon quota of the i-th province in 2030 under the stability principle, AEij represents the actual transportation carbon emissions of the i-th province in the j-th year, LC2030 is the predicted value of my country's transportation carbon emissions under the low-carbon scenario in 2030; J represents J years of historical data, and I represents I regions to be allocated.

[0145] 2) Fairness Principle Allocation Model

[0146] The formula is:

[0147]

[0148] In the formula, CQ2i represents the transportation carbon quota of the i-th province in 2030 under the principle of fairness, and Popij represents the actual population of the i-th province in the j-th year;

[0149] 3) Efficiency principle allocation model

[0150] The initial carbon quota allocation is selected as the input variable, and the population (Pop), freight turnover (FTK), and unit turnover energy consumption (ET) that have the most significant impact on transportation carbon emissions are selected as output variables; the specific formula is, and the final carbon quota allocation plan CQ3i is obtained;

[0151]

[0152] In the formula, φi is the relative carbon emission efficiency of the transportation industry in the i-th province, λi is the combination ratio of other decision-making units in the secondary construction decision-making unit for the i-th province, yi0 is the value of each output factor in the i-th province, yit is the t-th output factor in the i-th province; xi0 is the initial value of the carbon quota of the transportation industry in the i-th province, and xi are the adjusted values ​​of the carbon quota of the transportation industry in the i-th province.

[0153] The spatial SEE model provided by the embodiment of the present invention:

[0154] Firstly, based on the three dimensions of "stability-fairness-efficiency", a SEE spatial model of the crisis scenario of carbon quota allocation in the transportation industry is constructed, and coordinates are used to measure the performance of the allocation scheme in terms of stability, fairness and efficiency. Among them, the x-axis measures stability, which is represented by the ratio of carbon quota deviation from the estimated carbon emissions; the y-axis measures fairness, which is represented by the Gini coefficient of per capita carbon emission rights; the z-axis measures efficiency, which is the initial allocation efficiency score calculated by ZSG-DEA.

[0155] Secondly, the spatial benefit value model Tp is constructed according to the principle of SEE spatial model, as shown in the following formula:

[0156]

[0157] Where, d AP and d AO Respectively represent the length from point A to point P and point O;

[0158] Among them, point A T A =1, that is, the spatial benefit value of this point is 1, achieving 100% carbon quota spatial effect; point O T O =0, that is, the space benefit value of this point is 0;

[0159] For the SEE space model coordinate point Q(x1, y1, z1), the optimization objective function is:

[0160] T Q =maxT P

[0161]

[0162] In the formula, α, β, and γ are the allocation weights of the three principles of stability, fairness, and efficiency;

[0163] Q i =α·Q 1i +β·Q 2i +γ·Q 3i

[0164]

[0165] In the formula, Q i represents the target transportation carbon quota for 2030; Q 1i ,Q 2i ,Q 3i They represent the optimal transportation carbon quota under the principles of stability, fairness and efficiency respectively; n represents the number of provinces, which is 30; Gini i =1-Σ[(X i+1 -X i )(Y i+1 +Y i )], Xi is the cumulative population percentage of the ith province or city; Yi is the cumulative carbon dioxide emissions percentage of the ith province or city; Represents the initial efficiency under target carbon emission.

[0166] like Figure 2 As shown, a transportation carbon quota allocation system based on a spatial SEE model provided by an embodiment of the present invention includes:

[0167] Emission accounting module, used for transportation carbon emissions accounting; adopts the "top-down" approach recommended by the Intergovernmental Panel on Climate Change; this approach builds a carbon footprint calculation model for the transportation industry based on the energy consumption of the transportation industry and the carbon emission factors of related energy;

[0168] Emission prediction module, used for traffic carbon emission prediction; including model framework and variable selection;

[0169] Allocation model module, used for traffic allocation model; including stability principle allocation model, fairness principle allocation model, and efficiency principle allocation model;

[0170] SEE model module, used for spatial SEE model.

[0171] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the transportation carbon quota allocation method based on the spatial SEE model.

[0172] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the method for allocating transportation carbon quotas based on the spatial SEE model.

[0173] Another object of the present invention is to provide an information data processing terminal, which is used to implement the transportation carbon quota allocation system based on the spatial SEE model.

[0174] The present invention is specifically implemented:

[0175] Model building

[0176] (1) Transportation carbon emissions accounting

[0177] In view of the complexity of the sources of carbon emissions in the transportation industry, in order to improve the accuracy of carbon footprint calculation, this study adopted the "top-down" method recommended by the Intergovernmental Panel on Climate Change. This method constructs a carbon footprint calculation model for the transportation industry based on the energy consumption of the transportation industry and the carbon emission factors of related energy. The specific calculation formula is as follows:

[0178]

[0179] Where Em is the physical consumption of the mth type of transportation energy, Km is the reference coefficient of the mth type of energy converted into standard coal; Fm is the carbon emission coefficient of the mth type of energy.

[0180] (2) Transportation carbon emissions prediction

[0181] 1) Model framework

[0182] The STIRPAT model is used to analyze the factors affecting China's transportation carbon emissions. The STIRPAT model is an improvement of the IPAT model. The logarithms of the left and right ends of the model can be expressed as:

[0183] i i =lna+blnP i +clnA i +dlnT i +lne (2)

[0184] Where I, P, A, and T represent the variables of environmental impact, population factors, economic factors, and technological factors, respectively; b, c, and d represent the elasticity coefficients of the variables corresponding to the three major factors of population, economy, and technology, respectively, to reflect the individual effects of these factors on the environment; a is the model coefficient; and e is the model error.

[0185] 2) Variable selection

[0186] Combined with the actual situation of carbon emissions from transportation in my country, the influencing factors are selected by considering the following four factors.

[0187] 1) Economic factors: As an important indicator of economic development, GDP per capita directly affects residents’ consumption capacity and living standards, promotes the growth of the transportation industry, and further affects transportation demand and carbon emissions. Studies have shown that the growth of GDP per capita is the biggest driver of CO2 emissions.

[0188] 2) Population factors: As the population increases, the demand for transportation also increases, which directly leads to an increase in carbon emissions from the transportation sector. Population factors have become an important driving factor for China's transportation carbon emissions by affecting transportation demand, economic growth, changes in population structure, and regional development imbalances.

[0189] 3) Transportation factors: Passenger and freight turnover are indicators of the intensity of transportation activities, which are directly related to energy consumption and carbon emissions. A large number of studies have shown that passenger and freight turnover have different degrees of impact on urban carbon emissions.

[0190] 4) Technical factors: Energy consumption per unit of turnover and the proportion of clean energy reflect the energy efficiency and technological progress of the transportation industry. Therefore, this study selected energy consumption per unit of turnover and clean energy as key indicators to measure the technological level.

[0191] Finally, the variables that affect China's transportation carbon emissions are selected as industrial added value, per capita GDP, total population, passenger turnover, cargo turnover, energy consumption per unit turnover, and clean energy ratio. The explanation of model variables is shown in Table 1.

[0192] Table 1 Variables of factors affecting traffic carbon emissions

[0193]

[0194] (3) Traffic Assignment Model

[0195] 1) Stability principle allocation model

[0196] The stability principle allocates carbon quotas based on the historical emission behaviors of each entity. From the perspective of industry development, it measures the stability of regional transportation carbon emission allocation, balances the relationship between regional transportation industry energy consumption growth and carbon emission control under the "dual carbon" goal, and embodies the stability principle of "more emissions, more reductions, less emissions, less reductions". Based on the stability principle of historical carbon emission level allocation, it allocates according to the average value of transportation carbon emissions in each province during the base period, and tries to ensure the continuity of transportation operation industry production. The formula is:

[0197]

[0198] In the formula, CQ1i represents the transportation carbon quota of the i-th province in 2030 under the stability principle, AEij represents the actual transportation carbon emissions of the i-th province in the j-th year, and LC2030 is the predicted value of my country's transportation carbon emissions under the low-carbon scenario in 2030.

[0199] 2) Fairness Principle Allocation Model

[0200] The principle of fairness allocates carbon quotas based on the population proportion of each subject, and from the perspective of interpersonal fairness, emphasizes the equality of development rights. Based on this principle, carbon emission quotas are allocated according to the average population proportion of each province in the baseline period, and the quotas are tilted towards provinces and cities with larger populations within their jurisdiction. This allocation model not only reflects the actual population needs of each province and city, but also ensures that carbon emission rights can be distributed more fairly across the country, avoiding uneven resource allocation due to differences in population size. The formula is:

[0201]

[0202] Where CQ2i represents the transportation carbon quota of the i-th province in 2030 under the principle of fairness, and Popij represents the actual population of the i-th province in the j-th year.

[0203] 3) Efficiency principle allocation model

[0204] The principle of high efficiency allocates carbon quotas based on the input-output efficiency of each subject. From the perspective of scientific and technological innovation, more carbon quotas are allocated to regions with higher input-output efficiency, forcing inefficient regions to actively change their industrial structure and achieve transformation and upgrading. This plan will use the input-oriented ZSG-DEA model to evaluate the efficiency of transportation carbon emissions in various provinces in China, and redistribute the carbon dioxide emissions of ineffective DMUs accordingly to achieve the optimization of carbon emission efficiency in various provinces. The initial carbon quota allocation is selected as the input variable, and the population (Pop), freight turnover (FTK), and unit turnover energy consumption (ET) that have the most significant impact on transportation carbon emissions are selected as output variables. The specific formula is, and the final carbon quota allocation plan CQ3i is obtained.

[0205]

[0206] In the formula, φi is the relative carbon emission efficiency of the transportation industry in the i-th province, λi is the combination ratio of other decision-making units in the secondary construction decision-making unit for the i-th province, yi0 is the value of each output factor in the i-th province, yit is the t-th output factor in the i-th province; xi0 is the initial value of the carbon quota of the transportation industry in the i-th province, and xi are the adjusted values ​​of the carbon quota of the transportation industry in the i-th province.

[0207] (4) Spatial SEE model

[0208] In the context of global climate change, the issue of carbon emissions in the transportation industry has received increasing attention. Due to the significant differences in the development level, economic environment and policy background of the transportation industry among provinces and cities, different allocation schemes of carbon quotas may lead to the "triple trap" crisis, that is, it is difficult to find a balance between stability, fairness and efficiency. Specifically, over-emphasizing the stability of the industry may cause developed regions to maintain a high carbon emission share for a long time, thereby hindering the development of backward regions; while if an equal allocation method is adopted, it may inhibit the production enthusiasm of developed regions and affect their ability to transform to green; although efficient allocation can promote the upgrading and transformation of inefficient regions, it may also aggravate the unbalanced development between regions. However, opportunities are also hidden in this crisis. Through scientific, reasonable and comprehensive allocation of carbon quotas, emission reduction tasks can be effectively decomposed, the realization of the "dual carbon" goals can be promoted, and local governments can be guided to clarify their respective emission reduction targets and development positioning, so as to formulate corresponding policies, optimize the structure of the transportation industry, promote green and low-carbon development, and maximize the value of carbon quotas.

[0209] Therefore, this study first defines the crisis transformation of transportation carbon quota as a state, aiming to seek the best allocation plan to avoid the "triple trap" crisis and achieve coordination between stability, fairness and efficiency. To this end, under the premise of total carbon emissions constraints, an evaluation model is constructed to improve the efficiency of carbon quota allocation.

[0210] Table 2 Quantitative standards for SEE spatial model indicators

[0211]

[0212] First, based on the three dimensions of “stability-equity-efficiency” (SEE), a spatial model of the carbon quota allocation crisis scenario in the transportation industry is constructed. Figure 3 As shown in the figure, the coordinates are used to measure the performance of the allocation scheme in terms of stability, fairness and efficiency. The x-axis measures stability, which is represented by the ratio of carbon quota deviation from the estimated carbon emissions; the y-axis measures fairness, which is represented by the Gini coefficient of per capita carbon emission rights; the z-axis measures efficiency, which is the initial allocation efficiency score calculated by ZSG-DEA, as shown in Table 2.

[0213] Figure 3 Point A(1,1,1) indicates that the benefits of the three evaluation criteria of stability, fairness, and efficiency all reach 1, which is the best case for the transformation of the transportation carbon quota allocation crisis. Point O(0,0,0) indicates that the benefit values ​​of stability, fairness, and efficiency are all 0, which is the worst case for the transformation of the transportation carbon quota allocation crisis.

[0214] Secondly, the spatial benefit value model Tp is constructed according to the principle of SEE spatial model, as shown in the following formula:

[0215]

[0216] Where, d AP and d AO They represent the length from point A to point P and point O respectively.

[0217] Among them, point A T A =1, that is, the spatial benefit value of this point is 1, that is, the spatial benefit value of this point is 1, and 100% of the carbon quota spatial effect is achieved; point O, that is, the spatial benefit value of this point is 0.

[0218] In order to more effectively achieve the optimal balance among stability, fairness and efficiency, an optimization model with the maximum spatial benefit value under the SEE model is established, so as to provide a more reasonable solution for the setting of carbon quotas in various regions. For the coordinate point Q (x1, y1, z1) of the SEE spatial model, the optimization objective function is:

[0219] T Q =maxT P

[0220]

[0221] In the formula, α, β, and γ are the allocation weights of the three principles of stability, fairness, and efficiency.

[0222] Q i =α·Q 1i +β·Q 2i +γ·Q 3i

[0223]

[0224] In the formula, Q i represents the target transportation carbon quota for 2030; Q 1i ,Q 2i ,Q 3i They represent the optimal transportation carbon quota under the principles of stability, fairness and efficiency respectively; n represents the number of provinces, which is 30. i =1-Σ[(X i+1 -X i )(Y i+1 +Y i )], Xi is the cumulative population percentage of the ith province or city; Yi is the cumulative carbon dioxide emissions percentage of the ith province or city. Represents the initial efficiency under target carbon emission.

[0225] See the overall flow chart of SEE Figure 4 .

[0226] 1. Specific application fields or related products of the present invention.

[0227] (1) Urban traffic management system: In urban traffic, by formulating a reasonable carbon quota allocation strategy, the total carbon emissions of vehicles in the city can be effectively regulated. The system can help urban traffic management departments allocate carbon emission quotas more scientifically in urban development planning, encourage the use of low-carbon transportation modes (such as public transportation, walking, bicycles, etc.), and reduce carbon emissions from cars.

[0228] (2) Policy formulation and government supervision: Government departments can analyze the carbon emission characteristics of different areas of the transportation industry based on the SEE model and formulate corresponding carbon emission quotas and reward mechanisms. The fairness principle of the model can ensure the reasonable allocation of carbon quotas, help formulate sustainable transportation policies, and provide policy support for carbon emission reduction targets in the transportation sector.

[0229] (3) Carbon trading market: In the carbon trading market, the SEE model can be applied to the formulation of carbon quota allocation standards for the transportation industry to ensure fair allocation of carbon quotas and market stability. Through a reasonable carbon quota and trading mechanism, companies can be encouraged to improve low-carbon technologies and carbon efficiency, thereby achieving carbon reduction targets.

[0230] 2. Relevant evidence of the technical effects obtained by the embodiments of the present invention.

[0231] Based on the transportation energy consumption data from 2010 to 2021, the "top-down" approach and the spatial SEE allocation model constructed in this study were used to calculate the transportation carbon quota situation in 30 provinces and cities in China, as shown in the figure.

[0232] Guangdong Province topped the list with 78.0432 million tons of carbon quota, which not only reflects its high energy consumption characteristics as a major economic province, but also highlights the agglomeration effect of the region in carbon-intensive industries and services. In contrast, Qinghai and Ningxia have relatively low carbon quotas of 4.8117 million tons and 5.0563 million tons, respectively, which may be related to the smaller economic scale and lighter industrial structure of these regions. In addition, Shandong and Henan, two populous provinces and industrial powerhouses, received higher carbon quotas of 57.6195 million tons and 49.7400 million tons, respectively, which reflects the response to the industrial development needs of these regions.

[0234] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0235] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for allocating transportation carbon quotas based on a spatial SEE model, characterized in that: The following steps are involved: Step 1: Transportation carbon emissions accounting, using the "top-down" approach recommended by the Intergovernmental Panel on Climate Change, to build a transportation carbon footprint calculation model based on the transportation industry's energy consumption and related energy carbon emission factors; Step 2: predicting transportation carbon emissions, establishing a model framework and selecting relevant variables; Step 3: Allocation of transportation carbon quotas, based on the principles of stability, fairness and efficiency; Step 4, spatial SEE model, includes: a. data collection and preprocessing, real-time collection of multi-source signal data such as traffic flow, vehicle speed, vehicle type and traffic signal status, and data cleaning and filtering; b. Data synchronization and integration: synchronize the data from different sensors in time and space, and integrate traffic signal data and geographic location information using geographic information system (GIS) technology; c. Feature extraction and selection: Apply statistical analysis and machine learning algorithms to extract key features from the data set and screen variables that are closely related to carbon emissions; d. Real-time data analysis and modeling: Use the spatial SEE model to perform real-time analysis on the processed signal data, and build and optimize the carbon emission prediction model; e. Carbon quota allocation optimization: Based on the prediction results, a multi-objective optimization algorithm is used to reasonably allocate traffic carbon quotas according to the principles of stability, fairness and efficiency, and automatically adjust the traffic light timing strategy; f. Feedback and adjustment mechanism: establish a closed-loop feedback system, monitor the difference between actual carbon emissions and predicted values, and dynamically adjust data processing procedures and model parameters.

2. The method for allocating transportation carbon quotas based on the spatial SEE model as claimed in claim 1, characterized in that: The transportation carbon emission accounting is as follows: The specific calculation formula is as follows: Where Em is the physical consumption of the mth type of transportation energy, Km is the reference coefficient of the mth type of energy converted into standard coal; Fm is the carbon emission coefficient of the mth type of energy.

3. The method for allocating transportation carbon quotas based on the spatial SEE model as claimed in claim 1, characterized in that: The model framework: The STIRPAT model is used to analyze the factors affecting China's transportation carbon emissions. The STIRPAT model is an improvement of the IPAT model. The logarithm of the left and right ends of the model can be expressed as: lnI i =lna+blnP i +clnA i +dlnT i +lne(2) Where I, P, A, and T represent the variables of environmental impact, population factors, economic factors, and technological factors, respectively; b, c, and d represent the elasticity coefficients of the variables corresponding to the three major factors of population, economy, and technology, respectively, to reflect the individual effects of these factors on the environment; a is the model coefficient; and e is the model error.

4. The method for allocating transportation carbon quotas based on the spatial SEE model as claimed in claim 1, characterized in that: The variable selection: 1) Economic factors: GDP per capita, as an important indicator of economic development, directly affects residents’ consumption capacity and living standards, promotes the growth of the transportation industry, and thus affects transportation demand and carbon emissions; 2) Population factors: As the population increases, the demand for transportation also increases, which directly leads to an increase in carbon emissions from the transportation sector; 3) Transportation factors: Passenger turnover and freight turnover are indicators of the intensity of transportation activities, which are directly related to energy consumption and carbon emission levels; 4) Technical factors: Energy consumption per unit turnover and the proportion of clean energy reflect the energy utilization efficiency and technological progress level of the transportation industry.

5. The method for allocating transportation carbon quotas based on the spatial SEE model as claimed in claim 1, characterized in that: The traffic assignment model: 1) Stability principle allocation model The formula is: In the formula, CQ1i represents the transportation carbon quota of the i-th province in 2030 under the stability principle, AEij represents the actual transportation carbon emissions of the i-th province in the j-th year, and LC2030 is the predicted value of my country's transportation carbon emissions under the low-carbon scenario in 2030; 2) Fairness Principle Allocation Model The formula is: In the formula, CQ2i represents the transportation carbon quota of the i-th province in 2030 under the principle of fairness, and Popij represents the actual population of the i-th province in the j-th year; 3) Efficiency principle allocation model The initial carbon quota allocation is selected as the input variable, and the population (Pop), freight turnover (FTK), and unit turnover energy consumption (ET) that have the most significant impact on transportation carbon emissions are selected as output variables; the specific formula is, and the final carbon quota allocation plan CQ3i is obtained; In the formula, φi is the relative carbon emission efficiency of the transportation industry in the i-th province, λi is the combination ratio of other decision-making units in the secondary construction decision-making unit for the i-th province, yi0 is the value of each output factor in the i-th province, yit is the t-th output factor in the i-th province; xi0 is the initial value of the carbon quota of the transportation industry in the i-th province, and xi are the adjusted values ​​of the carbon quota of the transportation industry in the i-th province.

6. The method for allocating transportation carbon quotas based on the spatial SEE model as claimed in claim 1, characterized in that: The spatial SEE model: Firstly, based on the three dimensions of "stability-fairness-efficiency", a SEE spatial model of the crisis scenario of carbon quota allocation in the transportation industry is constructed, and coordinates are used to measure the performance of the allocation scheme in terms of stability, fairness and efficiency. Among them, the x-axis measures stability, which is represented by the proportion of carbon quota deviation from the estimated carbon emissions; the y-axis measures fairness, which is represented by the Gini coefficient of per capita carbon emission rights; the z-axis measures efficiency, which is the initial allocation efficiency score calculated by ZSG-DEA. Secondly, the spatial benefit value model Tp is constructed according to the principle of SEE spatial model, as shown in the following formula: Where, d AP and d AO Respectively represent the length from point A to point P and point O; Among them, point A T A =1, that is, the spatial benefit value of this point is 1, that is, the spatial benefit value of this point is 1, and 100% of the carbon quota spatial effect is achieved; point O, that is, the spatial benefit value of this point is 0; For the SEE space model coordinate point Q(x1, y1, z1), the optimization objective function is: T Q =maxT P In the formula, α, β, and γ are the allocation weights of the three principles of stability, fairness, and efficiency; Q i =α·Q 1i +β·Q 2i +γ·Q 3i In the formula, Q i represents the target transportation carbon quota for 2030; Q 1i ,Q 2i ,Q 3i They represent the optimal transportation carbon quota under the principles of stability, fairness and efficiency respectively; n represents the number of provinces, which is 30; Gini i =1-Σ[(X i+1 -X i )(Y i+1 +Y i )], Xi is the cumulative population percentage of the ith province or city; Yi is the cumulative carbon dioxide emissions percentage of the ith province or city; Represents the initial efficiency under target carbon emission.

7. A transportation carbon quota allocation system based on a spatial SEE model that implements the transportation carbon quota allocation method based on a spatial SEE model as described in any one of claims 1 to 6, characterized in that: The transportation carbon quota allocation system based on the spatial SEE model includes: Emission accounting module, used for transportation carbon emissions accounting; adopts the "top-down" approach recommended by the Intergovernmental Panel on Climate Change; this approach builds a carbon footprint calculation model for the transportation industry based on the energy consumption of the transportation industry and the carbon emission factors of related energy; Emission prediction module, used for traffic carbon emission prediction; including model framework and variable selection; Allocation model module, used for traffic allocation model; including stability principle allocation model, fairness principle allocation model, and efficiency principle allocation model; SEE model module, used for spatial SEE model.

8. A computer device, characterized in that: The computer device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for allocating transportation carbon quotas based on a spatial SEE model as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the method for allocating transportation carbon quotas based on a spatial SEE model as described in any one of claims 1 to 6.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the transportation carbon quota allocation system based on the spatial SEE model as described in claim 7.

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