Urban agglomeration emission reduction method and system based on carbon emission space spillover under traffic influence

Abstract

The application discloses a kind of urban agglomeration emission reduction methods and systems based on carbon emission space overflow under traffic influence, the method is: collection target urban agglomeration carbon emission data and social, economic, traffic intensity and other multiple index data are pretreated;Target urban agglomeration is divided into multiple traffic types based on traffic intensity data index;Carbon emission space correlation network between each city in target urban agglomeration is constructed, and the overflow of carbon emission of target urban agglomeration is analyzed, then the carbon emission overflow diagnosis result is output in depth analysis;Based on the diagnosis result and the constructed city emission reduction strategy library, intelligent optimization algorithm is used, and individual carbon reduction scheme design is carried out for high-carbon city from the angle of carbon emission overflow.The application analyzes carbon emission space overflow under the influence of traffic transportation, proposes urban agglomeration collaborative carbon reduction method and optimization system, has the advantages of accurate control, considers comprehensively, strong universality, policy support, and can be applied to each urban agglomeration.

Description

Technical Field

[0001] This invention belongs to the field of carbon emission reduction technology for urban agglomerations, specifically relating to an emission reduction method and system for urban agglomerations based on the spatial spillover of carbon emissions under the influence of transportation. Background Technology

[0002] Carbon emissions are the primary cause of global warming, leading to severe environmental problems such as frequent extreme weather events, rising sea levels, and ecosystem collapse. Cities, as crucial units for carbon emission reduction, account for 75% of global carbon emissions from energy consumption. Influenced by human activities such as transportation and industrial relocation, urban clusters have formed, differing in function, spatial structure, and population size.

[0003] Carbon emissions from these urban clusters are not confined to the physical boundaries of the cities but spread between them and to neighboring areas through natural factors. With the acceleration of urbanization, the agglomeration and connections between cities are constantly strengthening, making urban clusters the most concentrated areas of carbon emissions and the core of future carbon reduction efforts. This highlights the importance of coordinated carbon reduction at the urban cluster level.

[0004] Within a single city, methods to reduce urban carbon emissions can be explored through urban planning, taking into account factors such as urban form, scale, spatial structure, compactness, and functional zoning. However, considering the spatial differentiation and clustering of various economic and social factors within a city, carbon reduction actions taken in one city may lead to an increase in carbon emissions in another region. Therefore, it is necessary to broaden the perspective to urban clusters to better implement carbon reduction measures.

[0005] Transportation, by its very nature, possesses a degree of mobility, which can strengthen inter-regional connections and accelerate the dissemination of green technologies, thereby achieving carbon reduction benefits. Therefore, many scholars have conducted extensive research on carbon emissions generated during transportation, including carbon emission peak prediction, emission reduction potential analysis, and investigation of influencing factors. However, transportation can also transfer carbon emissions from one region to other regions, creating carbon emission linkages between cities and leading to spatial accumulation and spillover of carbon emissions. Furthermore, there are differences in the development stages and levels among individual cities.

[0006] Therefore, research on carbon reduction measures should break through urban boundaries and take a systematic approach to consider both the overall urban cluster and individual cities in detail. It should explore regional collaborative carbon emission reduction methods and optimization systems to optimize urban transportation development and mitigate global warming. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides a method and system for carbon emission reduction in urban agglomerations based on spatial spillover of carbon emissions under the influence of traffic. By analyzing the spatial spillover of carbon emissions from traffic within the urban agglomeration and defining the functional roles of cities with different traffic types in the carbon emission spillover network, optimization schemes can be proposed for different types of cities to achieve the goal of reducing regional carbon emissions.

[0008] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0009] A method for carbon emission reduction in urban agglomerations based on spatial spillover of carbon emissions due to traffic impacts includes:

[0010] Step 1) Collect and calculate carbon emission data of all cities in the target urban agglomeration over a period of time, as well as various indicators including social, economic, and traffic intensity, using the nighttime light data inversion method;

[0011] Step 2) The z-score normalization method is used to preprocess the calculated carbon emission data and the collected data on various indicators including social, economic and traffic intensity.

[0012] Step 3) Based on the preprocessed traffic intensity data indicators, all cities within the target urban agglomeration are divided into multiple traffic types;

[0013] Step 4) Using the universal gravitation model, construct a spatial correlation network of carbon emissions among cities in the target urban agglomeration; the spatial correlation network of carbon emissions is the aggregation of spatial carbon gravity between every two cities, consisting of nodes representing cities and edges representing spatial carbon emission gravity between cities;

[0014] Step 5) Based on the constructed carbon emission spatial correlation network, the social network analysis method is used to analyze the carbon emission spillover of the target urban agglomeration and obtain carbon emission spatial correlation network analysis data. The carbon emission spatial correlation network analysis data includes the carbon emission transfer and spillover between cities in the target urban agglomeration under the influence of transportation.

[0015] Step 6) Using image recognition and machine learning algorithms, perform in-depth analysis on the obtained carbon emission spatial correlation network analysis data, identify the city types and specific cities with significant carbon emission spillover under the influence of transportation in the target urban agglomeration, and output detailed carbon emission spillover diagnosis results through data visualization.

[0016] Step 7) Based on the diagnostic results and the established urban emission reduction strategy library, use an intelligent optimization algorithm to design personalized carbon reduction solutions for high-carbon cities from the perspective of carbon emission spillover.

[0017] Furthermore, in step 1, the social data, economic data, and traffic intensity data are directly collected, while the carbon emission data is calculated using a nighttime light data inversion method. The specific steps of the nighttime light data inversion method are as follows:

[0018] First, calculate the carbon dioxide emissions from urban energy consumption based on the IPCC’s 2006 Greenhouse Gas Emission Inventory.

[0019] Then, a correlation was established between the total amount of nighttime lights in the city and the carbon dioxide emissions calculated based on energy consumption statistics to construct a carbon emission inversion model;

[0020] Next, the estimated provincial carbon emissions were calculated to verify the accuracy and applicability of the carbon emission inversion model.

[0021] Finally, the carbon dioxide emissions of each city were estimated using the total nighttime light volume of each city and the carbon emission inversion model.

[0022] The calculation formula for the nighttime light data inversion method is as follows:

[0023] (1);

[0024] In equation (1), C represents carbon dioxide emissions, in tens of thousands of tons;

[0025] K i Let be the number of carbon emission systems for the i-th energy source;

[0026] E i Let be the consumption of the i-th energy source, in ten thousand tons;

[0027] C it Let be the carbon dioxide emissions of province i in year t, in tons;

[0028] DN it It is the sum of the gray values ​​of all grid cells in province i in year t;

[0029] k t Let be the coefficient for year t.

[0030] Furthermore, in step 2, the calculation formula for the z-score normalization method is as follows:

[0031] Z = (X-μ) / σ (2);

[0032] In equation (2), Z is the standardized value;

[0033] X is the original value;

[0034] μ is the mean of the original values;

[0035] σ is the standard value of the original data.

[0036] Furthermore, in step 3, based on the traffic intensity data indicators of all cities in the target urban agglomeration, the K-means algorithm is used to perform cluster analysis on the cities, and all cities in the urban agglomeration are quantitatively classified, thereby dividing the complex cities in the target urban agglomeration into four types: Type I central cities, Type II transportation hub cities, Type III secondary cities, and Type IV peripheral cities.

[0037] The classification method for transportation types in urban agglomerations is as follows:

[0038] (3);

[0039] In equation (3), W represents the sum of distances from the samples to the cluster centers;

[0040] k represents the number of urban transportation types;

[0041] x jt For the t-th sample of class j;

[0042] m j Let be the cluster center of the j-th class;

[0043] n j Let t be the t-th sample of class j.

[0044] Furthermore, in step 4, the gravitational model, derived from the law of universal gravitation, can quantify the intensity of carbon emission interaction between cities based on socio-economic factors and spatial distance. When constructing the spatial correlation network of carbon emissions, not only economic and geographical factors are considered, but the influence of traffic intensity in the gravitational model is also examined. Traffic intensity values ​​are added to the formula, and the gravitational model is reconstructed to assess the impact of traffic on the carbon emission correlation between cities within the target urban agglomeration. A higher gravitational intensity value indicates a stronger correlation between carbon emissions generated by traffic between the two cities.

[0045] The calculation formula for constructing the spatial correlation network of carbon emissions is as follows;

[0046] (4);

[0047] In equation (4), y ij Let i be the carbon emission gravitational intensity pointing to region j;

[0048] M ij Let i be the spatial distance between regions i and j.

[0049] k ij The contribution rate of region i to the carbon emission linkage between region i and region j;

[0050] P represents the total population at the end of the year;

[0051] C represents the region's carbon emissions;

[0052] U represents the regional traffic intensity;

[0053] Q represents the region's gross domestic product.

[0054] Furthermore, in step 5, the specific method for analyzing the carbon emission spillover from the target urban agglomeration is as follows:

[0055] After constructing the spatial correlation network of carbon emissions, the social network analysis method is used to quantify the overall network and individual city networks of the target urban agglomeration from a system perspective. The characteristics of the overall urban agglomeration network and the characteristics of individual city networks are obtained respectively. The functional roles of cities with different transportation types in the spatial correlation network of carbon emissions are defined, thereby obtaining carbon emission spatial correlation network analysis data that includes the carbon emission transfer and spillover between cities in the target urban agglomeration under the influence of transportation.

[0056] The overall network characteristics of the urban agglomeration are represented by indicators including network connectivity, density, hierarchical structure, and efficiency. Among them, network connectivity (NC) reflects the robustness of the network and determines whether the carbon emission-related network includes all cities; network density (ND) represents the compactness of the network, and the higher the value, the tighter the connection of the carbon emission network and the more obvious the spatial spillover of the urban agglomeration; network hierarchy (NH) shows the dominance of nodes in the network, and the larger the value, the more complex the network structure and the more layers between cities; network efficiency (NE) measures the effectiveness of carbon emission linkages between cities, and the higher the value, the greater the possibility of carbon emission linkages between cities.

[0057] The formula for calculating the overall network characteristics of the urban agglomeration is as follows:

[0058] (5);

[0059] In equation (5), NC represents the network correlation degree, which reflects the overall network connectivity strength.

[0060] ND is the network density, representing the tightness of the connections between nodes in the network;

[0061] NH represents the network hierarchy, indicating the degree of dominance of a node in the network;

[0062] NE stands for network efficiency, which represents the efficiency with which carbon emissions are correlated between cities.

[0063] V is the number of unreachable node pairs in the network;

[0064] δ is the logarithm of a symmetric reachable province;

[0065] θ represents a redundant line;

[0066] The characteristics of the individual city networks are described by indicators including degree centrality, inter-degree centrality, and proximity centrality. Degree centrality (DC) measures a city's central position in the carbon emission network; a higher DC value indicates a greater influence on other cities and a higher likelihood of carbon emission spillover. Inter-degree centrality (BC) reflects a city's mediating role in the network; a higher BC value indicates a stronger ability to control carbon emissions and a higher likelihood of carbon emission spillover. Proximity centrality (CC) represents the degree to which a city is unaffected by other cities; a higher CC value indicates a stronger independence of the city in the carbon emission network and a lower likelihood of spatial carbon emission spillover with other cities.

[0067] The calculation formula for the urban individual network characteristics is as follows:

[0068] (6);

[0069] In equation (6), DC represents degree centrality, indicating the central position of a city in the carbon emission network;

[0070] BC stands for intermediate centrality, which indicates a city's intermediate position in the carbon emission network.

[0071] CC stands for proximity centrality, which indicates the degree to which a city is not controlled by other cities.

[0072] g jk (i) represents the number of shortest association paths between nodes j and k that pass through node i;

[0073] d ij Let be the shortest distance between node i and node j, i.e., the shortcut.

[0074] Furthermore, in step 7, the carbon emission reduction strategies for urban clusters and individual cities in the urban emission reduction strategy library are all manually input in advance.

[0075] At the city cluster level, the city cluster carbon emission reduction strategy focuses on coordinated carbon reduction, strengthens transportation links between cities, promotes the flow and sharing of carbon emission-influencing factors, including energy and technology, promotes the construction of regional integrated transportation networks, optimizes logistics routes to reduce transportation distance and emissions, and improves the correlation of carbon emissions.

[0076] At the city-level, the city-level carbon emission reduction strategy includes improving transportation infrastructure, promoting low-carbon transportation tools, and implementing traffic congestion management and green travel incentive policies.

[0077] Meanwhile, based on the latest research findings, policy releases, and successful cases, the carbon emission reduction strategies for urban clusters and individual cities in the urban emission reduction strategy database will be updated in a timely manner to ensure the timeliness of the database.

[0078] Furthermore, in step 7, the intelligent optimization algorithm includes, but is not limited to, using genetic algorithms and particle swarm optimization. The intelligent optimization algorithm comprehensively considers factors including economic cost, technical difficulty, and social acceptance to determine whether to strengthen its association with low-carbon emission type cities and to carry out optimization methods including industrial transfer, resource flow, and talent migration to find the optimal emission reduction path. In addition, based on annually updated traffic data, social data, economic data, etc., two action plans are formulated, one short-term and one long-term, to ensure timely optimization of the refined emission reduction collaborative optimization plan for the city cluster.

[0079] An optimization system for urban agglomeration carbon reduction using the aforementioned method based on spatial spillover of carbon emissions under traffic impact includes at least a data collection and preprocessing module, an urban clustering analysis module, a carbon emission spatial correlation network construction module, a carbon emission spatial correlation network analysis module, and an urban collaborative emission reduction scheme generation module; wherein,

[0080] The data collection and preprocessing module is responsible for calculating the carbon emission data of all cities in the target urban agglomeration over a period of time, and collecting various indicator data, including social, economic, and traffic intensity, of all cities in the target urban agglomeration over a period of time; at the same time, it is responsible for preprocessing the calculated carbon emission data and the collected various indicator data, including social, economic, and traffic intensity.

[0081] The city clustering analysis module is responsible for performing clustering analysis on cities based on traffic intensity data indicators of all cities in the target city cluster, using the K-means algorithm, quantitatively classifying all cities in the city cluster, and dividing the complex cities in the target city cluster into multiple types.

[0082] The carbon emission spatial correlation network construction module is responsible for constructing a carbon emission spatial correlation network among cities within the target urban agglomeration using the universal gravitation model. This network is an aggregation of the spatial carbon gravity of every two cities, consisting of nodes representing cities and edges representing the spatial carbon emission gravity between cities. When constructing the carbon emission spatial correlation network, the module considers both economic and geographical factors and the impact of traffic intensity in the universal gravitation model, incorporating traffic intensity values ​​into the formula and reconstructing the universal gravitation model to assess the impact of traffic on the carbon emission correlation among cities within the target urban agglomeration.

[0083] The carbon emission spatial correlation network analysis module is responsible for quantifying the overall network and individual city networks of the target urban agglomeration from a system perspective after constructing the carbon emission spatial correlation network, using social network analysis. This yields the characteristics of the overall urban agglomeration network and the individual city networks, and defines the functional roles of cities with different transportation types in the carbon emission spatial correlation network. As a result, carbon emission spatial correlation network analysis data is obtained, including the carbon emission transfer and spillover between cities in the target urban agglomeration under the influence of transportation.

[0084] The urban collaborative emission reduction scheme generation module consists of a carbon emission diagnosis interface under traffic impact, an urban emission reduction strategy library, and an intelligent optimization algorithm; wherein...

[0085] The carbon emission diagnosis interface under the influence of transportation is responsible for receiving the carbon emission spatial correlation network analysis data output by the carbon emission spatial correlation network analysis module, and using image recognition and machine learning algorithms to perform in-depth analysis on the obtained carbon emission spatial correlation network analysis data, identify the city types and specific cities with significant carbon emission spillover under the influence of transportation in the target urban agglomeration, and output detailed carbon emission spillover diagnosis results through data visualization.

[0086] The city emission reduction strategy database contains several carbon emission reduction strategies for city clusters and individual cities. Both the city cluster carbon emission reduction strategies and the individual city carbon emission reduction strategies are manually entered in advance. At the same time, based on the latest research results, policy releases, and successful cases, the city cluster carbon emission reduction strategies and individual city carbon emission reduction strategies in the city emission reduction strategy database will be updated in a timely manner to ensure the timeliness of the city emission reduction strategy database.

[0087] The intelligent optimization algorithm helps the city collaborative emission reduction scheme generation module design personalized carbon reduction schemes for high-carbon cities from the perspective of carbon emission spillover, based on the diagnostic results output by the carbon emission diagnostic interface under traffic impact and the constructed city emission reduction strategy library. The intelligent optimization algorithm comprehensively considers factors including economic cost, technical difficulty, and social acceptance to determine whether to strengthen its association with low-carbon emission type cities and find the optimal emission reduction path.

[0088] A computer device includes a processor, a memory, a communication interface, and a communication bus. The processor, the memory, and the communication interface communicate with each other via the communication bus. The memory stores at least one executable instruction, which causes the processor to perform the operation corresponding to the above-mentioned urban agglomeration carbon reduction method based on carbon emission spatial spillover under traffic impact.

[0089] A computer-readable storage medium storing at least one executable instruction that causes a processor to perform operations corresponding to the above-described urban agglomeration carbon reduction method based on spatial spillover of carbon emissions under traffic impact.

[0090] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0091] 1. Precise regulation: By quantitatively classifying cities in urban agglomerations into different types based on indicators, we can explore the spatiotemporal distribution characteristics of their carbon emissions and the spatial spillover caused by transportation, and propose targeted optimization schemes to achieve precise regulation.

[0092] 2. Comprehensive Consideration: The study examines the inter-regional carbon emission linkages from both the perspectives of the overall urban cluster and individual cities, and formulates differentiated carbon emission reduction and control measures under the premise of overall coordination.

[0093] 3. High applicability: The method and system of this invention are applicable to different urban clusters at home and abroad, and the analysis and optimization scheme can be flexibly adjusted according to specific circumstances.

[0094] 4. Policy support: Provides scientific decision-making basis for the government and relevant departments, which helps to achieve the goals of coordinated carbon reduction and dual-carbon development.

[0095] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the invention and to implement it according to the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Specific embodiments of the present invention are given in detail below with reference to the accompanying drawings. Attached Figure Description

[0096] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0097] Figure 1 This is a logic diagram of the refined carbon emission reduction method and optimization system for urban agglomerations based on the spatial overflow of carbon emissions under the influence of traffic, as presented in this invention.

[0098] Figure 2 This is a diagram showing the specific classification results of cities within an urban agglomeration in an embodiment of the present invention.

[0099] Figure 3 This is a diagram illustrating the distribution pattern of carbon emissions in urban agglomerations in an embodiment of the present invention.

[0100] Figure 4 This is a spatial spillover network diagram of carbon emissions in 2007, 2012, 2017, and 2020 analyzed in this embodiment of the invention.

[0101] Figure 5 The above are the overall network feature maps of urban agglomerations in 2007, 2012, 2017 and 2020 analyzed in this embodiment of the invention. Detailed Implementation

[0102] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings to provide a clearer understanding of the invention's purpose, features, and advantages. It should be understood that the embodiments shown in the drawings are not intended to limit the scope of the invention, but are merely illustrative of the essential spirit of the invention's technical solution.

[0103] In the following description, certain specific details are set forth for the purpose of illustrating various disclosed embodiments in order to provide a thorough understanding of the various disclosed embodiments. However, those skilled in the art will recognize that embodiments may be practiced without one or more of these specific details. In other instances, well-known apparatuses, structures, and techniques associated with this application may not have been shown or described in detail to avoid unnecessarily obscuring the description of the embodiments.

[0104] Unless the context requires otherwise, throughout the specification and claims, the word “comprising” and its variations, such as “including” and “having”, shall be understood to have an open, inclusive meaning, that is, to be interpreted as “including, but not limited to”.

[0105] Throughout this specification, references to "an embodiment" or "an embodiment" indicate that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Therefore, the appearance of "in an embodiment" or "an embodiment" in various places throughout the specification does not necessarily refer to the same embodiment. Furthermore, a particular feature, structure, or characteristic may be combined in any manner in one or more embodiments.

[0106] The singular forms “a” and “the” used in this specification and the appended claims include plural references unless otherwise expressly stated herein. It should be noted that the term “or” is generally used to mean “and / or” unless otherwise expressly stated herein.

[0107] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0108] See Figure 1 As shown, this invention also proposes a refined carbon emission reduction method and optimization system for urban agglomerations based on the spatial spillover of carbon emissions under the influence of traffic.

[0109] The architecture of the optimization system mainly includes a data collection and preprocessing module, a city clustering analysis module, a carbon emission spatial correlation network construction module, a carbon emission spatial correlation network analysis module, and a city collaborative emission reduction scheme generation module.

[0110] The data collection and preprocessing module is responsible for calculating the carbon emission data of all cities in the target urban agglomeration over a period of time, and for collecting various indicator data, including social, economic, and traffic intensity, of all cities in the target urban agglomeration over a period of time. It is also responsible for preprocessing the calculated carbon emission data and the collected data, including social, economic, and traffic intensity data.

[0111] The data collection and preprocessing module first collects and calculates carbon emission data for all cities within the target urban cluster over a past period, as well as various indicator data including social, economic, and traffic intensity data. The social, economic, and traffic intensity data are collected directly, while the carbon emission data is calculated using a nighttime light data inversion method.

[0112] The specific steps of the nighttime light data inversion method are as follows:

[0113] First, calculate the carbon dioxide emissions from urban energy consumption based on the IPCC’s 2006 Greenhouse Gas Emission Inventory.

[0114] Then, a correlation was established between the total amount of nighttime lights in the city and the carbon dioxide emissions calculated based on energy consumption statistics to construct a carbon emission inversion model;

[0115] Next, the estimated provincial carbon emissions were calculated to verify the accuracy and applicability of the carbon emission inversion model.

[0116] Finally, the carbon dioxide emissions of each city were estimated using the total nighttime light volume of each city and the carbon emission inversion model.

[0117] The calculation formula for the nighttime light data inversion method is as follows:

[0118] (1);

[0119] In equation (1), C represents carbon dioxide emissions, in tens of thousands of tons;

[0120] K i Let be the number of carbon emission systems for the i-th energy source;

[0121] E i Let be the consumption of the i-th energy source, in ten thousand tons;

[0122] C itLet be the carbon dioxide emissions of province i in year t, in tons;

[0123] DN it It is the sum of the gray values ​​of all grid cells in province i in year t;

[0124] k t Let be the coefficient for year t.

[0125] The data collection and preprocessing module then uses the z-score standardization method to preprocess the calculated carbon emission data and the collected data including various indicators such as social, economic, and traffic intensity.

[0126] The calculation formula for the z-score normalization method is as follows:

[0127] Z = (X-μ) / σ (2);

[0128] In equation (2), Z is the standardized value;

[0129] X is the original value;

[0130] μ is the mean of the original values;

[0131] σ is the standard value of the original data.

[0132] The city clustering analysis module is responsible for performing clustering analysis on cities (using the K-means algorithm) based on the traffic intensity index data of all cities in the target city cluster, quantitatively classifying all cities in the city cluster, and dividing the complex cities in the target city cluster into multiple types.

[0133] For example, cities within a target urban agglomeration can be divided into four types: Category I central cities, Category II transportation hub cities, Category III secondary cities, and Category IV peripheral cities.

[0134] The classification method for transportation types in urban agglomerations is as follows:

[0135] (3);

[0136] In equation (3), W represents the sum of distances from the samples to the cluster centers;

[0137] k represents the number of urban transportation types;

[0138] x jt For the t-th sample of class j;

[0139] m j Let be the cluster center of the j-th class;

[0140] n j Let t be the t-th sample of class j.

[0141] The carbon emission spatial correlation network construction module is responsible for using the universal gravitation model to construct a carbon emission spatial correlation network among cities within the target urban agglomeration.

[0142] The aforementioned gravitational model, derived from the law of universal gravitation, can quantify the intensity of carbon emission interactions between cities based on their socioeconomic factors and spatial distance. The spatial correlation network of carbon emissions is an aggregation of the spatial carbon gravitational forces between every two cities, consisting of nodes representing cities and edges representing the spatial gravitational forces of carbon emissions between cities.

[0143] In constructing the aforementioned spatial correlation network of carbon emissions, not only economic and geographical factors were considered, but the influence of traffic intensity in the gravitational model was also examined. The traffic intensity value was added to the formula, and the gravitational model was reconstructed to evaluate the impact of traffic on the carbon emission correlation between cities within the target urban agglomeration. The larger the gravitational intensity value, the stronger the correlation between carbon emissions generated by traffic between the two cities.

[0144] The calculation formula for constructing the spatial correlation network of carbon emissions is as follows;

[0145] (4);

[0146] In equation (4), y ij Let i be the carbon emission gravitational intensity pointing to region j;

[0147] M ij Let i be the spatial distance between regions i and j.

[0148] k ij The contribution rate of region i to the carbon emission linkage between region i and region j;

[0149] P represents the total population at the end of the year;

[0150] C represents the region's carbon emissions;

[0151] U represents the regional traffic intensity;

[0152] Q represents the region's gross domestic product.

[0153] The carbon emission spatial correlation network analysis module is responsible for constructing the carbon emission spatial correlation network and then using social network analysis to quantify both the overall network of the target urban agglomeration and the individual city networks from a systemic perspective. This yields the characteristics of both the overall urban agglomeration network and the individual city networks, and defines the functional roles of cities with different transportation types within the carbon emission spatial correlation network, thus obtaining carbon emission spatial correlation network analysis data. This data reflects the carbon emission transfer and spillover among cities within the target urban agglomeration under the influence of transportation.

[0154] The overall network characteristics of the urban agglomeration are represented by indicators including network connectivity, density, hierarchical structure, and efficiency. Among them, network connectivity (NC) reflects the robustness of the network and determines whether the carbon emission-related network includes all cities; network density (ND) represents the compactness of the network, and the higher the value, the tighter the connection of the carbon emission network and the more obvious the spatial spillover of the urban agglomeration; network hierarchy (NH) shows the dominance of nodes in the network, and the larger the value, the more complex the network structure and the more layers between cities; network efficiency (NE) measures the effectiveness of carbon emission linkages between cities, and the higher the value, the greater the possibility of carbon emission linkages between cities.

[0155] The formula for calculating the overall network characteristics of the urban agglomeration is as follows:

[0156] (5);

[0157] In equation (5), NC represents the network correlation degree, which reflects the overall network connectivity strength.

[0158] ND is the network density, representing the tightness of the connections between nodes in the network;

[0159] NH represents the network hierarchy, indicating the degree of dominance of a node in the network;

[0160] NE stands for network efficiency, which represents the efficiency with which carbon emissions are correlated between cities.

[0161] V is the number of unreachable node pairs in the network;

[0162] δ is the logarithm of a symmetric reachable province;

[0163] θ represents a redundant line.

[0164] The characteristics of the individual city network are described by indicators including degree centrality, inter-degree centrality, and proximity centrality. Degree centrality (DC) measures a city's central position in the carbon emission network; the higher the value, the greater its influence on other cities and the greater the possibility of carbon emission spillover. Inter-degree centrality (BC) reflects a city's mediating role in the network; the higher the value, the stronger its control over carbon emissions and the greater the possibility of carbon emission spillover. Proximity centrality (CC) represents the degree to which a city is not affected by other cities; the higher the value, the stronger the city's independence in the carbon emission network and the lower the possibility of spatial carbon emission spillover with other cities.

[0165] The calculation formula for the urban individual network characteristics is as follows:

[0166] (6);

[0167] In equation (6), DC represents degree centrality, indicating the central position of a city in the carbon emission network;

[0168] BC stands for intermediate centrality, which indicates a city's intermediate position in the carbon emission network.

[0169] CC stands for proximity centrality, which indicates the degree to which a city is not controlled by other cities.

[0170] g jk (i) represents the number of shortest association paths between nodes j and k that pass through node i;

[0171] d ij Let be the shortest distance between node i and node j, i.e., the shortcut.

[0172] The urban collaborative emission reduction scheme generation module consists of a carbon emission diagnosis interface under traffic impact, an urban emission reduction strategy library, and an intelligent optimization algorithm. Among them,

[0173] First, the carbon emission diagnosis interface under the influence of transportation is responsible for receiving the carbon emission spatial correlation network analysis data output by the carbon emission spatial correlation network analysis module. The carbon emission spatial correlation network analysis data records the carbon emission transfer and spillover between cities in the target urban agglomeration under the influence of transportation. Then, using image recognition and machine learning algorithms, the obtained carbon emission spatial correlation network analysis data is analyzed in depth to identify the city types (such as central cities, transportation hub cities, etc.) and specific cities in the target urban agglomeration that have significant carbon emission spillover under the influence of transportation. Detailed carbon emission spillover diagnosis results are output through data visualization, providing a theoretical basis for subsequent strategy formulation.

[0174] Secondly, carbon emission reduction strategies for urban clusters and individual cities are manually entered into the urban emission reduction strategy database in advance.

[0175] At the city cluster level, the carbon emission reduction strategy focuses on coordinated carbon reduction, strengthening inter-city transportation links, promoting the flow and sharing of energy, technology and other factors affecting carbon emissions, promoting the construction of regional integrated transportation networks, optimizing logistics routes to reduce transportation distance and emissions, and improving the correlation of carbon emissions.

[0176] At the city-level, the city-level carbon reduction strategy includes improving transportation infrastructure (such as building more public transportation facilities, bicycle lanes, and pedestrian paths), promoting low-carbon transportation tools (such as new energy vehicles), and implementing traffic congestion management and green travel incentive policies.

[0177] Meanwhile, based on the latest research findings, policy releases, and successful cases, the carbon emission reduction strategies for urban clusters and individual cities in the urban emission reduction strategy database will be updated in a timely manner to ensure the timeliness of the database.

[0178] Finally, based on the diagnostic results and the established urban emission reduction strategy library, intelligent optimization algorithms such as genetic algorithms and particle swarm optimization are used to design personalized carbon reduction plans for high-carbon cities from the perspective of carbon emission spillover. These intelligent optimization algorithms comprehensively consider factors including economic cost, technical difficulty, and social acceptance to determine whether to strengthen the connection with low-carbon emission cities and to explore optimization methods including industrial transfer, resource flow, and talent migration to find the optimal emission reduction path.

[0179] In addition, based on annually updated traffic, social, and economic data, both short-term and long-term action plans are formulated to ensure timely and optimal solutions for refined emission reduction and coordinated optimization within the urban cluster.

[0180] The following specific embodiment illustrates the carbon emission reduction method for urban agglomerations based on carbon emission spatial spillover under traffic impact according to the present invention, but the implementation of the present invention is not limited to this.

[0181] This case study selects the Yangtze River Delta urban agglomeration, a region with close transportation links and a certain degree of networked spatial pattern. Shanghai plays a leading role, while regional central cities exert a radiating and driving effect. It has evolved from a core urban agglomeration to a more balanced one, forming a multi-level and multi-category transportation network. Based on the actual conditions of its 26 cities in 2007, 2012, 2017, and 2020, the implementation plan was decided upon, and the specific implementation steps are as follows:

[0182] 1. See Figure 2 As shown, the Yangtze River Delta urban agglomeration was clustered based on transportation indicators using the K-means method, and was divided into four categories: Category I central cities, Category II transportation hub cities, Category III secondary cities, and Category IV peripheral cities.

[0183] 2. First, carbon dioxide emissions from energy consumption are calculated based on the IPCC's 2006 Greenhouse Gas Emissions Inventory. Second, a correlation is established between total nighttime light emissions and carbon dioxide emissions calculated based on energy consumption statistics to construct a carbon emission inversion model. Provincial carbon emission estimates are then calculated to verify the model's accuracy and applicability. Finally, carbon dioxide emissions for each city are estimated using the total nighttime light emissions and the carbon emission inversion model.

[0184] See Figure 3As shown, carbon emissions in the Yangtze River Delta region within the study area are on the rise, with an average annual growth rate of 2.7%. Carbon emissions from Class I central cities exceed 60 million tons, from Class II transportation hub cities exceed 35 million tons, from Class III secondary cities are around 15 million tons, and from Class IV peripheral cities are generally below 10 million tons.

[0185] 3. Utilize social network analysis to explore the spillover of carbon emissions from urban agglomerations. See [link / reference] Figure 4 and Figure 5 As shown, from the perspective of the entire urban agglomeration, the spatial spillover network of carbon emissions exhibits a pattern of being denser in the east and sparser in the west. From the perspective of individual cities, different types of cities play different roles in the carbon spillover network. Category I central cities occupy the central position in the carbon spillover network. Category II transportation hub cities act as "bridges" in the carbon spillover network, while Category III secondary cities and Category IV peripheral cities are less affected by carbon emission spillovers from other cities and occupy peripheral positions in the carbon spillover network.

[0186] 4. Based on the above analysis, the optimal design scheme is selected as follows: For Category I central cities, while steadily reducing their own carbon emissions, they should leverage their radiating and driving role. In terms of transportation, the first priority should be to optimize transportation infrastructure, reduce energy consumption, and promote the flow of low-carbon transportation technologies, funds, and human resources to peripheral cities. For Category II transportation hub cities, cooperation and resource sharing with core cities should be strengthened, while leveraging their hub role to enhance transportation with surrounding areas and coordinate carbon emission reduction with peripheral cities. For Category III secondary cities, direct high-speed rail and subway lines to central cities can be vigorously developed to shorten travel time, thereby strengthening flow and exchange while reducing energy consumption and carbon emissions. For Category IV peripheral cities, intercity railways and high-speed rail lines to transportation hub cities and secondary cities can be vigorously developed to enhance their ability to rely on external resources and strengthen carbon connections beyond regional boundaries.

[0187] The present invention also provides a computer device, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus, and the memory is used to store at least one executable instruction, wherein the executable instruction causes the processor to perform the operation corresponding to the above-mentioned urban agglomeration carbon emission reduction method based on the spatial overflow of carbon emissions under traffic impact.

[0188] The present invention also provides a computer-readable storage medium storing at least one executable instruction that causes a processor to perform the operations corresponding to the above-described urban agglomeration carbon reduction method based on spatial overflow of carbon emissions under traffic impact.

[0189] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for carbon emission reduction in urban agglomerations based on spatial spillover of carbon emissions due to traffic impacts, characterized in that, include: Step 1) Collect and calculate carbon emission data of all cities in the target urban agglomeration over a period of time, as well as various indicators including social, economic, and traffic intensity, using the nighttime light data inversion method; Step 2) The z-score normalization method is used to preprocess the calculated carbon emission data and the collected data on various indicators including social, economic and traffic intensity. Step 3) Based on the preprocessed traffic intensity data indicators, all cities within the target urban agglomeration are divided into multiple traffic types; Step 4) Using the universal gravitation model, construct a spatial correlation network of carbon emissions among cities in the target urban agglomeration; the spatial correlation network of carbon emissions is the aggregation of spatial carbon gravity between every two cities, consisting of nodes representing cities and edges representing spatial carbon emission gravity between cities; The aforementioned gravitational model, derived from the law of universal gravitation, can quantify the intensity of carbon emission interaction between cities based on their socio-economic factors and spatial distance. When constructing the spatial correlation network of carbon emissions, not only economic and geographical factors are considered, but the impact of traffic intensity in the gravitational model is also examined. Traffic intensity values ​​are incorporated into the formula to reconstruct the gravitational model and assess the impact of traffic on the carbon emission correlation between cities within the target urban agglomeration. A higher gravitational intensity value indicates a stronger correlation between carbon emissions generated by traffic between two cities. The calculation formula for constructing the spatial correlation network of carbon emissions is as follows: （4）； In equation (4), y ij Let i be the carbon emission gravitational intensity pointing to region j; M ij Let i be the spatial distance between regions i and j. k ij The contribution rate of region i to the carbon emission linkage between region i and region j; P represents the total population at the end of the year; C represents the region's carbon emissions; U represents the regional traffic intensity; Q represents the region's gross domestic product; Step 5) Based on the constructed carbon emission spatial correlation network, the social network analysis method is used to analyze the carbon emission spillover of the target urban agglomeration and obtain carbon emission spatial correlation network analysis data. The carbon emission spatial correlation network analysis data includes the carbon emission transfer and spillover between cities in the target urban agglomeration under the influence of transportation. The specific method for analyzing the carbon emission spillover from the target urban agglomeration is as follows: After constructing the spatial correlation network of carbon emissions, the social network analysis method is used to quantify the overall network and individual city networks of the target urban agglomeration from a system perspective. The characteristics of the overall urban agglomeration network and the characteristics of individual city networks are obtained respectively. The functional roles of cities with different transportation types in the spatial correlation network of carbon emissions are defined, thereby obtaining carbon emission spatial correlation network analysis data that includes the carbon emission transfer and spillover between cities in the target urban agglomeration under the influence of transportation. The overall network characteristics of the urban agglomeration are represented by indicators including network connectivity, density, hierarchical structure, and efficiency. Among them, network connectivity reflects the robustness of the network and determines whether the carbon emission-related network includes all cities; network density indicates the compactness of the network, and the higher the value, the tighter the connection of the carbon emission network and the more obvious the spatial spillover of the urban agglomeration; network hierarchy shows the dominance of nodes in the network, and the larger the value, the more complex the network structure and the more layers between cities; network efficiency measures the effectiveness of carbon emission linkages between cities, and the higher the value, the greater the possibility of carbon emission linkages between cities. The characteristics of the individual city network are described by indicators including degree centrality, between-degree centrality, and proximity centrality. Degree centrality measures a city's central position in the carbon emission network; a higher value indicates a greater influence on other cities and a higher probability of carbon emission spillover. Between-degree centrality reflects a city's mediating role in the network; a higher value indicates a stronger ability to control carbon emissions and a higher probability of carbon emission spillover. Proximity centrality indicates the degree to which a city is not affected by other cities; a higher value indicates a stronger independence of the city in the carbon emission network and a lower probability of spatial carbon emission spillover with other cities. Step 6) Using image recognition and machine learning algorithms, perform in-depth analysis on the obtained carbon emission spatial correlation network analysis data, identify the city types and specific cities with significant carbon emission spillover under the influence of transportation in the target urban agglomeration, and output detailed carbon emission spillover diagnosis results through data visualization. Step 7) Based on the diagnostic results and the established urban emission reduction strategy library, use intelligent optimization algorithms to design personalized carbon reduction solutions for high-carbon cities from the perspective of carbon emission spillover. The intelligent optimization algorithm includes, but is not limited to, genetic algorithms and particle swarm optimization. It comprehensively considers factors such as economic cost, technical difficulty, and social acceptance to determine whether to strengthen its connection with low-carbon emission cities and to carry out optimization methods including industrial transfer, resource flow, and talent migration to find the optimal emission reduction path. In addition, based on annually updated traffic data, social data, and economic data, it formulates both short-term and long-term action plans to ensure timely optimization of refined emission reduction and collaborative optimization schemes for urban clusters.

2. The urban agglomeration carbon emission reduction method based on spatial spillover of carbon emissions under traffic impact as described in claim 1, characterized in that, In step 1, the social data, economic data, and traffic intensity data are directly collected, while the carbon emission data is calculated using a nighttime light data inversion method. The specific steps of the nighttime light data inversion method are as follows: First, calculate the carbon dioxide emissions from urban energy consumption based on the IPCC’s 2006 Greenhouse Gas Emission Inventory. Then, a correlation was established between the total amount of nighttime lights in the city and the carbon dioxide emissions calculated based on energy consumption statistics to construct a carbon emission inversion model; Next, the estimated provincial carbon emissions were calculated to verify the accuracy and applicability of the carbon emission inversion model. Finally, the carbon dioxide emissions of each city were estimated using the total nighttime light volume of each city and the carbon emission inversion model. The calculation formula for the nighttime light data inversion method is as follows: （1）； In equation (1), C represents carbon dioxide emissions, in tens of thousands of tons; K i Let be the number of carbon emission systems for the i-th energy source; E i Let be the consumption of the i-th energy source, in ten thousand tons; C it Let be the carbon dioxide emissions of province i in year t, in tons; DN it It is the sum of the gray values ​​of all grid cells in province i in year t; k t Let be the coefficient for year t.

3. The urban agglomeration carbon reduction method based on spatial spillover of carbon emissions under traffic impact as described in claim 1, characterized in that, In step 3, based on the traffic intensity data indicators of all cities in the target urban agglomeration, the K-means algorithm is used to perform cluster analysis on the cities and quantitatively classify all cities in the urban agglomeration, thereby dividing the complex cities in the target urban agglomeration into four types: Category I central cities, Category II transportation hub cities, Category III secondary cities, and Category IV peripheral cities. The classification method for transportation types in urban agglomerations is as follows: （3）； In equation (3), W represents the sum of distances from the samples to the cluster centers; k represents the number of urban transportation types; x jt For the t-th sample of class j; m j Let j be the cluster center of the j-th class; n j Let t be the t-th sample of class j.

4. The urban agglomeration carbon reduction method based on spatial spillover of carbon emissions under traffic impact as described in claim 1, characterized in that, In step 5, The formula for calculating the overall network characteristics of the urban agglomeration is as follows: （5）； In equation (5), NC represents the network correlation degree, which reflects the overall network connectivity strength. ND is the network density, representing the tightness of the connections between nodes in the network; NH represents the network hierarchy, indicating the degree of dominance of a node in the network; NE stands for network efficiency, which represents the efficiency with which carbon emissions are correlated between cities. V is the number of unreachable node pairs in the network; δ is the logarithm of a symmetric reachable province; θ represents a redundant line; The calculation formula for the urban individual network characteristics is as follows: （6）； In equation (6), DC represents degree centrality, indicating the central position of a city in the carbon emission network; BC stands for intermediate centrality, which indicates a city's intermediate position in the carbon emission network. CC stands for proximity centrality, which indicates the degree to which a city is not controlled by other cities. g jk (i) represents the number of shortest association paths between nodes j and k that pass through node i; d ij Let be the shortest distance between node i and node j, i.e., the shortcut.

5. The urban agglomeration carbon reduction method based on spatial spillover of carbon emissions under traffic impact as described in claim 1, characterized in that, In step 7, the carbon emission reduction strategies for urban clusters and individual cities in the urban emission reduction strategy library are all manually input in advance. At the city cluster level, the city cluster carbon emission reduction strategy focuses on coordinated carbon reduction, strengthening inter-city transportation links, promoting the flow and sharing of carbon emission-influencing factors, including energy and technology, promoting the construction of regional integrated transportation networks, optimizing logistics routes to reduce transportation distances and emissions, and improving the correlation of carbon emissions. At the city-level, the city-level carbon emission reduction strategy includes improving transportation infrastructure, promoting low-carbon transportation tools, and implementing traffic congestion management and green travel incentive policies. Meanwhile, based on the latest research findings, policy releases, and successful cases, the carbon emission reduction strategies for urban clusters and individual cities in the urban emission reduction strategy database will be updated in a timely manner to ensure the timeliness of the database.

6. An optimization system for urban agglomeration carbon reduction based on spatial spillover of carbon emissions under traffic impact as described in any one of claims 1-5, characterized in that, It should include at least a data collection and preprocessing module, a city clustering analysis module, a carbon emission spatial correlation network construction module, a carbon emission spatial correlation network analysis module, and a city-wide collaborative emission reduction scheme generation module; among which, The data collection and preprocessing module is responsible for calculating the carbon emission data of all cities in the target urban agglomeration over a period of time, and collecting various indicator data, including social, economic, and traffic intensity, of all cities in the target urban agglomeration over a period of time; at the same time, it is responsible for preprocessing the calculated carbon emission data and the collected various indicator data, including social, economic, and traffic intensity. The city clustering analysis module is responsible for performing clustering analysis on cities based on traffic intensity data indicators of all cities in the target city cluster, using the K-means algorithm, quantitatively classifying all cities in the city cluster, and dividing the complex cities in the target city cluster into multiple types. The carbon emission spatial correlation network construction module is responsible for constructing a carbon emission spatial correlation network among cities within the target urban agglomeration using the universal gravitation model. This network is an aggregation of the spatial carbon gravity of every two cities, consisting of nodes representing cities and edges representing the spatial carbon emission gravity between cities. When constructing the carbon emission spatial correlation network, the module considers both economic and geographical factors and the impact of traffic intensity in the universal gravitation model, incorporating traffic intensity values ​​into the formula and reconstructing the universal gravitation model to assess the impact of traffic on the carbon emission correlation among cities within the target urban agglomeration. The carbon emission spatial correlation network analysis module is responsible for quantifying the overall network and individual city networks of the target urban agglomeration from a system perspective after constructing the carbon emission spatial correlation network, using social network analysis. This yields the characteristics of the overall urban agglomeration network and the individual city networks, and defines the functional roles of cities with different transportation types in the carbon emission spatial correlation network. As a result, carbon emission spatial correlation network analysis data is obtained, including the carbon emission transfer and spillover between cities in the target urban agglomeration under the influence of transportation. The urban collaborative emission reduction scheme generation module consists of a carbon emission diagnosis interface under traffic impact, an urban emission reduction strategy library, and an intelligent optimization algorithm; wherein... The carbon emission diagnosis interface under the influence of transportation is responsible for receiving the carbon emission spatial correlation network analysis data output by the carbon emission spatial correlation network analysis module, and using image recognition and machine learning algorithms to perform in-depth analysis on the obtained carbon emission spatial correlation network analysis data, identify the city types and specific cities with significant carbon emission spillover under the influence of transportation in the target urban agglomeration, and output detailed carbon emission spillover diagnosis results through data visualization. The city emission reduction strategy database contains several carbon emission reduction strategies for city clusters and individual cities. Both the city cluster carbon emission reduction strategies and the individual city carbon emission reduction strategies are manually entered in advance. At the same time, based on the latest research results, policy releases, and successful cases, the city cluster carbon emission reduction strategies and individual city carbon emission reduction strategies in the city emission reduction strategy database will be updated in a timely manner to ensure the timeliness of the city emission reduction strategy database. The intelligent optimization algorithm helps the city collaborative emission reduction scheme generation module design personalized carbon reduction schemes for high-carbon cities from the perspective of carbon emission spillover, based on the diagnostic results output by the carbon emission diagnostic interface under traffic impact and the constructed city emission reduction strategy library. The intelligent optimization algorithm comprehensively considers factors including economic cost, technical difficulty, and social acceptance to determine whether to strengthen its association with low-carbon emission type cities and find the optimal emission reduction path.

7. A computer device, characterized in that, include: The system includes a processor, a memory, a communication interface, and a communication bus. The processor, the memory, and the communication interface communicate with each other via the communication bus. The memory stores at least one executable instruction, which causes the processor to perform the operation corresponding to the urban agglomeration carbon reduction method based on carbon emission spatial spillover under traffic impact as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one executable instruction that causes a processor to perform operations corresponding to the urban agglomeration carbon reduction method based on carbon emission spatial spillover under traffic impact as described in any one of claims 1-5.

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