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

Abstract

The invention discloses an urban agglomeration emission reduction method and system based on carbon emission space overflow under traffic influence. The method comprises the following steps: collecting and calculating carbon emission data of a target urban agglomeration and various index data of society, economy, traffic intensity and the like, and carrying out preprocessing; dividing the target urban agglomeration into a plurality of traffic types based on the traffic intensity data indexes; constructing a carbon emission space correlation network among the cities in the target urban group, analyzing the carbon emission overflow of the target urban group, and then performing deep analysis to output a carbon emission overflow diagnosis result; and on the basis of the diagnosis result and the constructed city emission reduction strategy library, an intelligent optimization algorithm is adopted, and personalized carbon reduction scheme design is carried out on the high-carbon city from the aspect of carbon emission overflow. The urban agglomeration collaborative carbon reduction method and the optimization system are provided by analyzing the carbon emission space overflow under the influence of traffic transportation, have the advantages of accurate regulation and control, comprehensive consideration, high universality and policy support, and can be suitable for each urban agglomeration.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission reduction in urban agglomerations, and specifically relates to an urban agglomeration emission reduction method and system based on carbon emission spatial spillover under the influence of traffic. Background Art

[0002] Carbon emissions are the main cause of global warming, which has led to serious environmental problems such as frequent extreme weather events, rising sea levels and ecosystem collapse. As an important unit for carbon emission reduction, cities account for 75% of global carbon emissions due to their energy consumption. Influenced by human activities such as transportation and industrial transfer, urban agglomerations with different functions, spatial structures and population sizes have been formed between cities.

[0003] The carbon emissions generated by these urban agglomerations are not confined to the physical boundaries of the cities, but spread between cities and spread to neighboring areas through natural factors. With the acceleration of urbanization, the agglomeration and connection between cities are constantly strengthening, which makes urban agglomerations the areas with the most concentrated carbon emissions and the core of future carbon reduction work. This highlights the importance of coordinated carbon reduction at the urban agglomeration level.

[0004] Within a single city, we can explore ways to reduce urban carbon emissions from urban planning methods based on urban form, scale, spatial structure, compactness, and functional division. However, considering that various economic and social elements of cities will be differentiated and agglomerated in space, taking carbon reduction actions in a certain city may lead to an increase in carbon emissions in another region. We need to expand our perspective to urban agglomerations in order to better carry out carbon reduction actions.

[0005] Among them, transportation itself has a certain degree of mobility, which can strengthen the connection between regions and accelerate the dissemination of green technology, thereby achieving carbon reduction benefits. Therefore, many scholars have done extensive research on carbon emissions generated during transportation, including carbon emission peak prediction, emission reduction potential analysis, and influencing factor investigation. However, during transportation, carbon emissions from one region will be transferred to other regions, which will lead to certain carbon emission connections between cities, resulting in spatial agglomeration and spillover of carbon emissions. At the same time, there are certain differences in the development stages and levels between individual cities.

[0006] Therefore, research on carbon reduction measures should go beyond urban boundaries and conduct detailed considerations on both the urban agglomeration as a whole and individual cities from a systematic perspective, explore regional coordinated carbon emission reduction methods and optimization systems, and thus optimize urban transportation development and alleviate global warming. Summary of the invention

[0007] In response to the problems existing in the prior art, the present invention provides a method and system for carbon emission reduction in urban agglomerations based on the spatial spillover of carbon emissions under the influence of traffic. By analyzing the spatial spillover of carbon emissions caused by traffic within the urban agglomeration and defining the functional role of cities with different traffic types in the carbon emission spillover network, optimization plans are proposed for different types of cities to achieve the goal of reducing regional carbon emissions.

[0008] In order to solve the above technical problems, the present invention is implemented through the following technical solutions:

[0009] A carbon emission reduction method for urban agglomerations based on carbon emission spatial spillover under the influence of traffic, including:

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

[0011] Step 2) Using the z-score standardization method, the calculated carbon emission data and the collected data on various indicators including social, economic, and traffic intensity are preprocessed;

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

[0013] Step 4) Using the universal gravitational model, a carbon emission spatial association network between cities in the target urban agglomeration is constructed; the carbon spatial association network is an aggregation of the spatial carbon gravity of every two cities, and is composed of nodes representing cities and edges representing the spatial gravity of carbon emissions between cities;

[0014] Step 5) Based on the constructed carbon space association network, a social network analysis method is used to analyze the spillover of carbon emissions in the target urban agglomeration to obtain carbon space association network analysis data, wherein the carbon space association 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) Use image recognition and machine learning algorithms to conduct in-depth analysis of the obtained carbon spatial association network analysis data, identify the city types and specific cities in the target urban agglomeration with significant carbon emission spillovers under the influence of transportation, and output detailed carbon emission spillover diagnosis results through data visualization;

[0016] Step 7) Based on the diagnosis results and the constructed urban emission reduction strategy library, an intelligent optimization algorithm is used to design personalized carbon reduction plans for high-carbon cities from the perspective of carbon emission spillover.

[0017] Furthermore, in step 1, the social data, the economic data and the traffic intensity data are directly collected, and the carbon emission data is calculated by the night light data inversion method. The specific steps of the night light data inversion method are:

[0018] First, the carbon dioxide emissions generated by urban energy consumption are calculated based on the 2006 Greenhouse Gas Emissions Inventory published by the IPCC;

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

[0020] Next, the estimated values ​​of provincial carbon emissions were back-calculated to test the accuracy and applicability of the carbon emission inversion model;

[0021] Finally, the carbon dioxide emissions of each city are estimated using the total amount of nighttime lights in each city and the carbon emission inversion model;

[0022] The calculation formula of the night light data inversion method is as follows:

[0023]

[0024] C it =k t ×DN it (1);

[0025] In formula (1), C is the carbon dioxide emissions, 10,000 tons;

[0026] K i is the carbon emission system number of the i-th energy source;

[0027] E i is the consumption of energy in the ith energy source, 10,000 tons;

[0028] C it is the carbon dioxide emissions of the i-th province in the t-th year, 10,000 tons;

[0029] DN it is the sum of the grayscale values ​​of all grids in the tth year of the ith province;

[0030] k t is the coefficient for year t.

[0031] Furthermore, in step 2, the calculation formula of the z-score standardization method is as follows:

[0032] Z = (X - μ) / σ (2);

[0033] In formula (2), Z is the standardized value;

[0034] X is the original value;

[0035] μ is the mean of the original values;

[0036] σ is the standard value of the original data.

[0037] 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, namely, type I central cities, type II transportation hub cities, type III secondary cities, and type IV marginal cities;

[0038] The traffic types of urban agglomerations are divided as follows:

[0039]

[0040] In formula (3), W is the distance from the sample to the cluster center;

[0041] k is the number of urban transportation types;

[0042] x jt is the tth sample of the jth class;

[0043] m j is the cluster center of the jth class;

[0044] n j is the tth sample of the jth class.

[0045] Furthermore, in step 4, the universal gravitational model is derived from the law of universal gravitation and can quantify the interaction intensity of carbon emissions between cities according to the socio-economic factors and spatial distances of cities; when constructing the carbon spatial association network, not only economic and geographical factors are considered, but also the influence of traffic intensity in the universal gravitational model is examined, and the traffic intensity value is added to the formula to reconstruct the universal gravitational model to evaluate the impact of traffic on the carbon emission correlation between cities in the target urban agglomeration; the larger the gravitational intensity value, the stronger the correlation between carbon emissions generated by traffic between two cities;

[0046] The calculation formula for constructing the carbon space association network is as follows;

[0047]

[0048] In formula (4), y ij is the gravitational force of carbon emissions from i to region j;

[0049] M ij is the spatial distance between region i and region j;

[0050] k ijis the contribution rate of region i to the linkage of carbon emissions between region i and region j;

[0051] P is the total population at the end of the year;

[0052] C is the regional carbon emissions;

[0053] U is the regional traffic intensity;

[0054] Q is the regional gross domestic product.

[0055] Furthermore, in step 5, the specific method for analyzing the spillover of carbon emissions of the target urban agglomeration is:

[0056] After constructing the carbon emission spatial correlation network, the social network analysis method is used to quantify the overall network of the target urban agglomeration and the individual network of cities from a systematic perspective, and the overall network characteristics of the urban agglomeration and the individual network characteristics of cities are obtained respectively. The functional role of cities with different transportation types in the carbon emission spatial correlation network is defined, thereby obtaining carbon spatial correlation network analysis data including the carbon emission transfer and spillover between cities in the target urban agglomeration under the influence of transportation;

[0057] The overall network characteristics of the urban agglomeration are represented by indicators including network connectivity, density, hierarchy 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) indicates the compactness of the network. The higher its 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 dominant position of the node in the network. The larger its value, the more complex the network structure and the more levels between cities. Network efficiency (NE) measures the effectiveness of carbon emission associations between cities. The larger its value, the greater the possibility of forming carbon emission associations between cities.

[0058] The calculation formula for the overall network characteristics of the urban agglomeration is as follows:

[0059]

[0060] In formula (5), NC is the network correlation, which reflects the connection strength of the overall network;

[0061] ND is the network density, which indicates the tightness of the connections between nodes in the network;

[0062] NH is the network level, which indicates the degree of dominance of the node in the network;

[0063] NE is the network efficiency, which indicates the efficiency of carbon emissions association between cities;

[0064] V is the number of unreachable point pairs in the network;

[0065] δ is the logarithm of symmetrically reachable provinces;

[0066] θ is the redundant line;

[0067] The individual network characteristics of the cities are described by indicators including degree centrality, inter-degree centrality and closeness centrality. Among them, degree centrality (DC) measures the central position of a city in the carbon emission network. The higher its value, the greater its influence on other cities and the greater the possibility of carbon emission spillover. Betweenness centrality (BC) reflects the intermediary role of a city in the network. The higher its value, the stronger its control over carbon emissions and the greater the possibility of carbon emission spillover. Closeness centrality (CC) indicates the degree to which a city is not affected by other cities. The higher its value, the stronger its independence in the carbon emission network and the smaller the possibility of spatial carbon emission spillover with other cities.

[0068] The calculation formula of the individual city network characteristics is as follows:

[0069]

[0070] In formula (6), DC is the degree centrality, which indicates the central position of the city in the carbon emission network;

[0071] BC is the betweenness centrality, which indicates the intermediary position of a city in the carbon emission network;

[0072] CC is closeness centrality, which indicates the degree to which a city is not controlled by other cities;

[0073] g jk (i) is the number of shortest associated paths between node j and node k passing through node i;

[0074] d ij is the shortest distance between node i and node j, i.e., shortcut.

[0075] Furthermore, in step 7, the carbon emission reduction strategies of urban agglomerations and single-city emission reduction strategies in the urban emission reduction strategy library are both manually input in advance;

[0076] At the city cluster level, the city cluster carbon emission reduction strategy focuses on coordinated carbon reduction, strengthening transportation links between cities, promoting the flow and sharing of factors affecting carbon emissions, including energy and technology, promoting the construction of regional integrated transportation networks, optimizing logistics routes to reduce transportation distances and emissions, and improving carbon emission correlation;

[0077] At the city level, the city emission reduction strategy includes improving transportation infrastructure, promoting low-carbon transportation, and implementing traffic congestion management and green travel incentive policies;

[0078] At the same time, according to the latest research results, policy releases and successful cases, the urban agglomeration carbon emission reduction strategies and single city emission reduction strategies in the urban emission reduction strategy library are updated in a timely manner to ensure the timeliness of the urban emission reduction strategy library.

[0079] Furthermore, in step 7, the intelligent optimization algorithm includes but is not limited to the use of genetic algorithms and particle swarm optimization. The intelligent optimization algorithm comprehensively considers aspects including economic costs, technical difficulties, and social recognition, and decides whether to strengthen its association with low-carbon emission type cities, and conducts 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 short-term and long-term action plans are formulated to ensure timely optimization of urban agglomeration refined emission reduction collaborative optimization plans.

[0080] An optimization system for urban agglomeration carbon emission reduction method based on carbon emission spatial spillover under traffic influence at least includes a data collection and preprocessing module, an urban cluster analysis module, a carbon emission correlation spatial network construction module, a carbon emission correlation spatial network analysis module and an urban collaborative emission reduction plan generation module; wherein,

[0081] The data collection and preprocessing module is responsible for calculating the carbon emission data of all cities in the target city cluster over a period of time in the past, and collecting various indicator data including social, economic, and traffic intensity of all cities in the target city cluster over a period of time in the past; and is also responsible for preprocessing the calculated carbon emission data and the collected various indicator data including social, economic, and traffic intensity;

[0082] The city cluster analysis module is responsible for clustering the cities using the K-means algorithm based on the traffic intensity data indicators 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;

[0083] The carbon emission correlation spatial network construction module is responsible for constructing a carbon emission spatial correlation network among cities in the target urban agglomeration using the universal gravitation model; the carbon spatial correlation network is an aggregation of the spatial carbon gravity of every two cities, and is composed of nodes representing cities and edges representing the spatial gravity of carbon emissions between cities; when constructing the carbon spatial correlation network, the carbon emission correlation spatial network construction module not only considers economic and geographical factors, but also examines the impact of traffic intensity in the universal gravitation model, adds the traffic intensity value to the formula, and reconstructs the universal gravitation model to evaluate the impact of traffic on the carbon emission correlation among cities in the target urban agglomeration;

[0084] The carbon emission correlation spatial network analysis module is responsible for quantifying the overall network of the target urban agglomeration and the individual city network from a system perspective by using the social network analysis method after constructing the carbon emission spatial correlation network, obtaining the overall network characteristics of the urban agglomeration and the individual city network characteristics respectively, and defining the functional role of cities with different transportation types in the carbon emission spatial correlation network, thereby obtaining carbon spatial correlation network analysis data including the carbon emission transfer and spillover between cities in the target urban agglomeration under the influence of transportation;

[0085] The city collaborative emission reduction plan generation module is composed of a carbon emission diagnosis interface under traffic influence, a city emission reduction strategy library and an intelligent optimization algorithm;

[0086] The carbon emission diagnosis interface under the influence of transportation is responsible for receiving the carbon space association network analysis data output by the carbon emission association space network analysis module, and using image recognition and machine learning algorithms to conduct in-depth analysis on the obtained carbon space association 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;

[0087] The urban emission reduction strategy library contains a number of urban agglomeration carbon emission reduction strategies and urban agglomeration carbon emission reduction strategies, which are manually input in advance; at the same time, according to the latest research results, policy releases and successful cases, the urban agglomeration carbon emission reduction strategies and urban single-unit emission reduction strategies in the urban emission reduction strategy library will be updated in a timely manner to ensure the timeliness of the urban emission reduction strategy library;

[0088] The intelligent optimization algorithm helps the city collaborative emission reduction plan generation module to design personalized carbon reduction plans for high-carbon cities from the perspective of carbon emission spillovers based on the diagnostic results output by the carbon emission diagnostic interface under the influence of traffic and the constructed city emission reduction strategy library; the intelligent optimization algorithm comprehensively considers aspects including economic cost, technical difficulty, and social recognition to decide whether to strengthen its connection with low-carbon emission type cities and find the optimal emission reduction path.

[0089] A computer device includes: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus, and the memory is used to store at least one executable instruction, wherein the executable instruction enables the processor to perform operations corresponding to the above-mentioned method for reducing carbon emissions in urban agglomerations based on spatial spillover of carbon emissions under the influence of traffic.

[0090] A computer-readable storage medium stores at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the above-mentioned urban agglomeration carbon emission reduction method based on carbon emission spatial overflow under the influence of traffic.

[0091] Compared with the prior art, the present invention has the following beneficial effects:

[0092] 1. Precision control: By quantifying the cities in the urban agglomeration, we can classify them into different types according to the indicators, explore the spatiotemporal distribution characteristics of their carbon emissions and the spatial spillovers caused by the influence of transportation, and propose targeted optimization plans to achieve precision control.

[0093] 2. Comprehensive consideration: Study the relationship between regional carbon emissions from the perspectives of both the overall urban agglomeration and individual cities, and specify differentiated carbon emission reduction controls under the premise of overall coordination

[0094] 3. Strong universality: The method and system of the present invention are applicable to different urban agglomerations at home and abroad, and the analysis and optimization scheme can be flexibly adjusted according to specific circumstances.

[0095] 4. Policy support: Providing scientific decision-making basis for the government and relevant departments, which will help achieve the development goal of coordinated carbon reduction.

[0096] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0098] 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 in the present invention.

[0099] Figure 2 This is a diagram showing the specific classification results of cities within an urban agglomeration in an embodiment of the present invention.

[0100] Figure 3 This is a distribution pattern diagram of carbon emissions of urban agglomerations in an embodiment of the present invention.

[0101] Figure 4 The spatial spillover network diagram of carbon emissions in 2007, 2012, 2017 and 2020 analyzed in the embodiment of the present invention;

[0102] Figure 5 This is the overall network characteristic diagram of the urban agglomeration in 2007, 2012, 2017 and 2020 analyzed in the embodiment of the present invention. DETAILED DESCRIPTION

[0103] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings so that the purpose, features and advantages of the invention can be more clearly understood. It should be understood that the embodiments shown in the accompanying drawings are not intended to limit the scope of the present invention, but are only intended to illustrate the essential spirit of the technical solution of the present invention.

[0104] In the following description, certain specific details are set forth for the purpose of illustrating various disclosed embodiments to provide a thorough understanding of the various disclosed embodiments. However, those skilled in the relevant art will recognize that the embodiments may be practiced without one or more of these specific details. In other cases, well-known devices, structures, and techniques associated with the present application may not be shown or described in detail to avoid unnecessarily obscuring the description of the embodiments.

[0105] Unless the context requires otherwise, throughout the specification and claims, the word "comprise" and variations such as "include" and "have" should be construed in an open, inclusive sense, ie, should be interpreted as "including, but not limited to."

[0106] References throughout the specification to "one 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. Thus, the appearances of "in one embodiment" or "in an embodiment" in various places throughout the specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any manner in one or more embodiments.

[0107] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should be noted that the term "or" is generally employed in its sense including "and / or" unless the context clearly dictates otherwise.

[0108] In addition, 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.

[0109] See also Figure 1 As shown, the present 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.

[0110] The architecture of the optimization system mainly includes data collection and preprocessing module, city clustering analysis module, carbon emission correlation spatial network construction module, carbon emission correlation spatial network analysis module and city collaborative emission reduction plan generation module.

[0111] 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 in the past, 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 in the past; it is also responsible for preprocessing the calculated carbon emission data and the collected various indicator data including social, economic, and traffic intensity.

[0112] The data collection and preprocessing module first collects and calculates the carbon emission data of all cities in the target urban agglomeration over a period of time in the past, as well as various indicator data including social, economic, and traffic intensity. Among them, the social data, economic data, and traffic intensity data are directly collected, and the carbon emission data is calculated by the night light data inversion method.

[0113] The specific steps of the night light data inversion method are:

[0114] First, the carbon dioxide emissions generated by urban energy consumption are calculated based on the 2006 Greenhouse Gas Emissions Inventory published by the IPCC;

[0115] Then, a carbon emission inversion model was constructed by establishing a correlation between the total amount of nighttime lights in the city and the carbon dioxide emissions calculated based on energy consumption statistics;

[0116] Next, the estimated values ​​of provincial carbon emissions were back-calculated to test the accuracy and applicability of the carbon emission inversion model;

[0117] Finally, the carbon dioxide emissions of each city are estimated using the total amount of nighttime lights in each city and the carbon emission inversion model;

[0118] The calculation formula of the night light data inversion method is as follows:

[0119]

[0120] C it =k t ×DN it (1);

[0121] In formula (1), C is the carbon dioxide emissions, 10,000 tons;

[0122] K i is the carbon emission system number of the i-th energy source;

[0123] E i is the consumption of energy in the ith energy source, 10,000 tons;

[0124] C itis the carbon dioxide emissions of the i-th province in the t-th year, 10,000 tons;

[0125] DN it is the sum of the grayscale values ​​of all grids in the tth year of the ith province;

[0126] k t is the coefficient for year t.

[0127] The data collection and preprocessing module then uses the z-score standardization method to preprocess the calculated carbon emission data and the collected various indicator data including social, economic, and traffic intensity.

[0128] The calculation formula of the z-score standardization method is as follows:

[0129] Z = (X - μ) / σ (2);

[0130] In formula (2), Z is the standardized value;

[0131] X is the original value;

[0132] μ is the mean of the original values;

[0133] σ is the standard value of the original data.

[0134] The city cluster analysis module is responsible for clustering the 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.

[0135] For example, the cities within the target urban agglomeration can be divided into four types, namely, Type I central cities, Type II transportation hub cities, Type III secondary cities, and Type IV marginal cities.

[0136] The traffic types of urban agglomerations are divided as follows:

[0137]

[0138] In formula (3), W is the distance from the sample to the cluster center;

[0139] k is the number of urban transportation types;

[0140] x jt is the tth sample of the jth class;

[0141] m j is the cluster center of the jth class;

[0142] n j is the tth sample of the jth class.

[0143] The carbon emission correlation spatial network construction module is responsible for constructing a carbon emission spatial correlation network among cities in the target urban agglomeration by using a universal gravitation model.

[0144] The universal gravitation model is derived from the law of universal gravitation and can quantify the interaction intensity of carbon emissions between cities based on the socio-economic factors and spatial distance of cities. The carbon spatial association network is the aggregation of the spatial carbon gravity of every two cities, and is composed of nodes representing cities and edges representing the spatial gravity of carbon emissions between cities.

[0145] When constructing the carbon spatial correlation network, not only economic and geographical factors were taken into consideration, but also the impact of traffic intensity in the gravity model was examined. The traffic intensity value was added to the formula, and the gravity model was reconstructed to evaluate the impact of traffic on the carbon emission correlation between cities in the target urban agglomeration. The larger the gravity intensity value, the stronger the carbon emission correlation between the two cities due to traffic.

[0146] The calculation formula for constructing the carbon space association network is as follows;

[0147]

[0148] In formula (4), y ij is the gravitational force of carbon emissions from i to region j;

[0149] M ij is the spatial distance between region i and region j;

[0150] k ij is the contribution rate of region i to the linkage of carbon emissions between region i and region j;

[0151] P is the total population at the end of the year;

[0152] C is the regional carbon emissions;

[0153] U is the regional traffic intensity;

[0154] Q is the regional gross domestic product.

[0155] The carbon emission correlation spatial network analysis module is responsible for quantifying the overall network of the target urban agglomeration and the individual city network from a system perspective after constructing the carbon emission spatial correlation network, using the social network analysis method, and obtaining the overall network characteristics of the urban agglomeration and the individual city network characteristics respectively, and defining the functional role of cities with different transportation types in the carbon emission spatial correlation network, thereby obtaining carbon spatial correlation network analysis data. The carbon spatial correlation network analysis data can reflect the carbon emission transfer and spillover between cities in the target urban agglomeration under the influence of transportation.

[0156] The overall network characteristics of the urban agglomeration are represented by indicators including network connectivity, density, hierarchy 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) indicates the compactness of the network. The higher its 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 dominant position of the node in the network. The larger its value, the more complex the network structure and the more levels between cities. Network efficiency (NE) measures the effectiveness of carbon emission associations between cities. The larger its value, the greater the possibility of forming carbon emission associations between cities.

[0157] The calculation formula for the overall network characteristics of the urban agglomeration is as follows:

[0158]

[0159] In formula (5), NC is the network correlation, which reflects the connection strength of the overall network;

[0160] ND is the network density, which indicates the tightness of the connections between nodes in the network;

[0161] NH is the network level, which indicates the degree of dominance of the node in the network;

[0162] NE is the network efficiency, which indicates the efficiency of carbon emissions association between cities;

[0163] V is the number of unreachable point pairs in the network;

[0164] δ is the logarithm of symmetrically reachable provinces;

[0165] θ is a redundant line.

[0166] The individual network characteristics of the cities are described by indicators including degree centrality, betweenness centrality and closeness centrality. Among them, degree centrality (DC) measures the central position of a city in the carbon emission network. The higher its value, the greater its influence on other cities and the greater the possibility of carbon emission spillover. Betweenness centrality (BC) reflects the intermediary role of a city in the network. The higher its value, the stronger its control over carbon emissions and the greater the possibility of carbon emission spillover. Closeness centrality (CC) indicates the degree to which a city is not affected by other cities. The higher its value, the stronger its independence in the carbon emission network and the smaller the possibility of spatial carbon emission spillover with other cities.

[0167] The calculation formula of the individual city network characteristics is as follows:

[0168]

[0169] In formula (6), DC is the degree centrality, which indicates the central position of the city in the carbon emission network;

[0170] BC is the betweenness centrality, which indicates the intermediary position of a city in the carbon emission network;

[0171] CC is closeness centrality, which indicates the degree to which a city is not controlled by other cities;

[0172] g jk (i) is the number of shortest associated paths between node j and node k passing through node i;

[0173] d ij is the shortest distance between node i and node j, i.e., shortcut.

[0174] The city collaborative emission reduction plan generation module is composed of a carbon emission diagnosis interface under traffic influence, a city emission reduction strategy library and an intelligent optimization algorithm.

[0175] First, the carbon emission diagnosis interface under the influence of transportation is responsible for receiving the carbon space association network analysis data output by the carbon emission association space network analysis module, and the carbon space association network analysis data records the carbon emission transfer and spillover between cities in the target urban agglomeration under the influence of transportation. Then, image recognition and machine learning algorithms are used to conduct in-depth analysis on the obtained carbon space association network analysis data to identify the city types (such as central cities, transportation hub cities, etc.) and specific cities with significant carbon emission spillovers under the influence of transportation in the target urban agglomeration, and output detailed carbon emission spillover diagnosis results through data visualization to provide a theoretical basis for subsequent strategy formulation.

[0176] Secondly, the carbon emission reduction strategies of urban agglomerations and urban agglomeration carbon reduction strategies are manually input into the urban emission reduction strategy database in advance.

[0177] At the city agglomeration level, the city agglomeration carbon emission reduction strategy focuses on coordinated carbon reduction, strengthening transportation links between cities, promoting the flow and sharing of factors such as energy and technology that affect carbon emissions, promoting the construction of regional integrated transportation networks, optimizing logistics routes to reduce transportation distances and emissions, and improving the correlation of carbon emissions.

[0178] At the city level, the city emission reduction strategy includes improving transportation infrastructure (such as building more public transportation facilities, bicycle lanes, and pedestrian paths), promoting low-carbon transportation (such as new energy vehicles), and implementing traffic congestion management and green travel incentive policies.

[0179] At the same time, based on the latest research results, policy releases and successful cases, the urban agglomeration carbon emission reduction strategies and urban single-unit emission reduction strategies in the urban emission reduction strategy library will be updated in a timely manner to ensure the timeliness of the urban emission reduction strategy library.

[0180] Finally, based on the diagnosis results and the constructed 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. The intelligent optimization algorithm comprehensively considers aspects including economic cost, technical difficulty, and social recognition, and decides whether to strengthen its connection with low-carbon emission type cities, and conducts optimization methods including industrial transfer, resource flow, and talent migration to find the optimal emission reduction path.

[0181] In addition, based on annually updated traffic data, social data, economic data, etc., short-term and long-term action plans are formulated to ensure timely identification of coordinated optimization plans for refined emission reduction in urban agglomerations.

[0182] The following is a specific example to illustrate the carbon emission reduction method for urban agglomerations based on carbon emission spatial overflow under the influence of traffic of the present invention, but the implementation mode of the present invention is not limited to this.

[0183] This implementation case selects the Yangtze River Delta city cluster, which has close traffic connections and a certain networked spatial pattern. Shanghai plays a central role as the leader, and the regional central cities play a radiating and driving role. It has evolved from a core city cluster to a balanced one, forming a multi-level and multi-category transportation network. The plan decision was made based on the actual situation of its 26 cities in 2007, 2012, 2017, and 2020. The specific implementation steps are as follows:

[0184] 1. See Figure 2 As shown in the figure, the Yangtze River Delta urban agglomeration is clustered according to transportation indicators using the K-means method and is divided into four categories, namely, Type I central cities, Type II transportation hub cities, Type III secondary cities, and Type IV marginal cities.

[0185] 2. First, the carbon dioxide emissions generated by energy consumption are calculated based on the "2006 Greenhouse Gas Emissions Inventory" published by the IPCC. Secondly, the correlation between the total amount of night lights and the carbon dioxide emissions calculated based on energy consumption statistics is established to build a carbon emission inversion model, and the provincial carbon emission estimates are calculated to test the accuracy and applicability of the model. Finally, the carbon dioxide emissions of each city are estimated using the total amount of night lights and the carbon emission inversion model.

[0186] See also Figure 3 As shown in the data, the carbon emissions in the Yangtze River Delta region within the research scope are on the rise, with an average annual growth rate of 2.7%. The carbon emissions of Type I central cities exceed 60 million tons, the carbon emissions of Type II transportation hub cities exceed 35 million tons, the carbon emissions of Type III secondary cities are around 15 million tons, and the carbon emissions of Type IV marginal cities are generally below 10 million tons.

[0187] 3. Use social network analysis to explore the spillover of carbon emissions from urban agglomerations. Figure 4 and Figure 5 As shown in the figure, from the perspective of the entire urban agglomeration, the spatial spillover network of carbon emissions presents a pattern of dense in the east and sparse in the west. From the perspective of a single city, different types of cities play different roles in the carbon spillover network. Type I central cities occupy a central position in the carbon spillover network. Type II transportation hub cities play the role of "bridge" in the carbon spillover network. Type III secondary cities and Type IV marginal cities are not easily affected by the carbon emission spillovers of other cities and are in a marginal position in the carbon spillover network.

[0188] 4. Select the optimal design scheme through the above analysis: For Class I central cities, they should play their radiating and driving role while steadily reducing their own carbon emissions. In terms of transportation, they should first optimize transportation infrastructure, reduce energy consumption, and promote the flow of low-carbon transportation technology, funds and human resources to marginal cities. For Class II transportation hub cities, they should strengthen cooperation and resource sharing with core cities, and at the same time play a good hub role, strengthen transportation with surrounding areas, and coordinate carbon emission reduction with marginal cities. For Class III secondary cities, they can vigorously develop direct high-speed railways and subways with central cities to shorten the travel time, thereby reducing energy consumption and carbon emissions in the process while strengthening mobility exchanges. For Class IV marginal cities, they can vigorously develop intercity railways and high-speed railways and other transportation facilities with transportation hub cities and secondary cities, enhance their ability to rely on external resources, and strengthen carbon connections outside the regional boundaries.

[0189] 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, and the executable instruction enables the processor to perform operations corresponding to the above-mentioned urban agglomeration carbon emission reduction method based on carbon emission spatial overflow under the influence of traffic.

[0190] The present invention also provides a computer-readable storage medium, in which at least one executable instruction is stored, and the executable instruction enables a processor to execute operations corresponding to the above-mentioned urban agglomeration carbon emission reduction method based on carbon emission spatial overflow under the influence of traffic.

[0191] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A carbon emission reduction method for urban agglomerations based on carbon emission spatial spillover under the influence of traffic, characterized in that: include: Step 1) Collect and calculate the carbon emission data of all cities in the target urban agglomeration over a period of time and various indicator data including social, economic, and traffic intensity through the night light data inversion method; Step 2) Using the z-score standardization method, the calculated carbon emission data and the collected data on various indicators including social, economic, and traffic intensity are preprocessed; Step 3) Based on the preprocessed traffic intensity data indicators, all cities in the target urban agglomeration are divided into multiple traffic types; Step 4) Using the universal gravitational model, a carbon emission spatial association network between cities in the target urban agglomeration is constructed; the carbon spatial association network is an aggregation of the spatial carbon gravity of every two cities, and is composed of nodes representing cities and edges representing the spatial gravity of carbon emissions between cities; Step 5) Based on the constructed carbon space association network, a social network analysis method is used to analyze the spillover of carbon emissions in the target urban agglomeration to obtain carbon space association network analysis data, wherein the carbon space association network analysis data includes the carbon emission transfer and spillover between cities in the target urban agglomeration under the influence of transportation; Step 6) Use image recognition and machine learning algorithms to conduct in-depth analysis of the obtained carbon spatial association network analysis data, identify the city types and specific cities in the target urban agglomeration with significant carbon emission spillovers under the influence of transportation, and output detailed carbon emission spillover diagnosis results through data visualization; Step 7) Based on the diagnosis results and the constructed urban emission reduction strategy library, an intelligent optimization algorithm is used to design personalized carbon reduction plans for high-carbon cities from the perspective of carbon emission spillover.

2. The carbon emission reduction method for urban agglomerations based on carbon emission spatial spillover under the influence of traffic according to claim 1 is characterized in that: In step 1, the social data, the economic data and the traffic intensity data are directly collected, and the carbon emission data is calculated by the night light data inversion method. The specific steps of the night light data inversion method are: First, the carbon dioxide emissions generated by urban energy consumption are calculated based on the 2006 Greenhouse Gas Emissions Inventory published by the IPCC; Then, a carbon emission inversion model was constructed by establishing a correlation between the total amount of nighttime lights in the city and the carbon dioxide emissions calculated based on energy consumption statistics; Next, the estimated values ​​of provincial carbon emissions were back-calculated to test the accuracy and applicability of the carbon emission inversion model; Finally, the carbon dioxide emissions of each city are estimated using the total amount of nighttime lights in each city and the carbon emission inversion model; The calculation formula of the night light data inversion method is as follows: C it =k t ×DN it (1); In formula (1), C is the carbon dioxide emissions, 10,000 tons; K i is the carbon emission system number of the i-th energy source; E i is the consumption of energy in the ith energy source, 10,000 tons; C it is the carbon dioxide emissions of the i-th province in the t-th year, 10,000 tons; DN it is the sum of the grayscale values ​​of all grids in the tth year of the ith province; k t is the coefficient for year t.

3. The carbon emission reduction method for urban agglomerations based on carbon emission spatial spillover under the influence of traffic according to claim 1 is 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 all cities in the urban agglomeration are quantitatively classified, thereby dividing the complex cities in the target urban agglomeration into four types, namely, type I central cities, type II transportation hub cities, type III secondary cities, and type IV marginal cities; The traffic types of urban agglomerations are divided as follows: In formula (3), W is the distance from the sample to the cluster center; k is the number of urban transportation types; x jt is the tth sample of the jth class; m j is the cluster center of the jth class; n j is the tth sample of the jth class.

4. The carbon emission reduction method for urban agglomerations based on carbon emission spatial spillover under the influence of traffic according to claim 1 is characterized in that: In step 4, the universal gravitation model is derived from the law of universal gravitation and can quantify the interaction intensity of carbon emissions between cities according to the socio-economic factors and spatial distances of cities. When constructing the carbon spatial association network, not only economic and geographical factors are considered, but also the influence of traffic intensity in the universal gravitation model is examined. The traffic intensity value is added to the formula, and the universal gravitation model is reconstructed to evaluate the impact of traffic on the carbon emission correlation between cities in the target urban agglomeration. The larger the gravitational intensity value, the stronger the correlation between carbon emissions caused by traffic between two cities. The calculation formula for constructing the carbon space association network is as follows; In formula (4), y ij is the gravitational force of carbon emissions from i to region j; M ij is the spatial distance between region i and region j; k ij is the contribution rate of region i to the linkage of carbon emissions between region i and region j; P is the total population at the end of the year; C is the regional carbon emissions; U is the regional traffic intensity; Q is the regional gross domestic product.

5. The carbon emission reduction method for urban agglomerations based on carbon emission spatial spillover under the influence of traffic according to claim 1 is characterized in that: In step 5, the specific method for analyzing the spillover of carbon emissions of the target urban agglomeration is: After constructing the carbon emission spatial correlation network, the social network analysis method is used to quantify the overall network of the target urban agglomeration and the individual network of cities from a systematic perspective, and the overall network characteristics of the urban agglomeration and the individual network characteristics of cities are obtained respectively. The functional role of cities with different transportation types in the carbon emission spatial correlation network is defined, thereby obtaining carbon spatial correlation network analysis data including 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, hierarchy 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. The higher its value, the tighter the connection of the carbon emission network and the more obvious the spatial spillover of the urban agglomeration. The network hierarchy shows the dominant position of the node in the network. The larger its value, the more complex the network structure and the more levels between cities. Network efficiency measures the effectiveness of carbon emission associations between cities. The larger its value, the greater the possibility of forming carbon emission associations between cities. The calculation formula for the overall network characteristics of the urban agglomeration is as follows: In formula (5), NC is the network correlation, which reflects the connection strength of the overall network; ND is the network density, which indicates the tightness of the connections between nodes in the network; NH is the network level, which indicates the degree of dominance of the node in the network; NE is the network efficiency, which indicates the efficiency of carbon emissions association between cities; V is the number of unreachable point pairs in the network; δ is the logarithm of symmetrically reachable provinces; θ is the redundant line; The individual network characteristics of the cities are described by indicators including degree centrality, inter-degree centrality and closeness centrality. Among them, degree centrality measures the central position of a city in the carbon emission network. The higher its value, the greater its influence on other cities and the greater the possibility of carbon emission spillover. Betweenness centrality reflects the intermediary role of a city in the network. The higher its value, the stronger its control over carbon emissions and the greater the possibility of carbon emission spillover. Closeness centrality indicates the degree to which a city is not affected by other cities. The higher its value, the stronger its independence in the carbon emission network and the smaller the possibility of spatial carbon emission spillover with other cities. The calculation formula of the individual city network characteristics is as follows: In formula (6), DC is the degree centrality, which indicates the central position of the city in the carbon emission network; BC is the betweenness centrality, which indicates the intermediary position of a city in the carbon emission network; CC is closeness centrality, which indicates the degree to which a city is not controlled by other cities; g jk (i) is the number of shortest associated paths between node j and node k passing through node i; d ij is the shortest distance between node i and node j, i.e., shortcut.

6. The carbon emission reduction method for urban agglomerations based on carbon emission spatial spillover under the influence of traffic according to claim 1 is characterized in that: In step 7, the carbon emission reduction strategies of urban agglomerations and single-city emission reduction strategies in the urban emission reduction strategy library are both manually input in advance; At the city cluster level, the city cluster carbon emission reduction strategy focuses on coordinated carbon reduction, strengthening transportation links between cities, promoting the flow and sharing of factors affecting carbon emissions, including energy and technology, promoting the construction of regional integrated transportation networks, optimizing logistics routes to reduce transportation distances and emissions, and improving carbon emission correlation; At the city level, the city emission reduction strategy includes improving transportation infrastructure, promoting low-carbon transportation, and implementing traffic congestion management and green travel incentive policies; At the same time, according to the latest research results, policy releases and successful cases, the urban agglomeration carbon emission reduction strategies and single city emission reduction strategies in the urban emission reduction strategy library are updated in a timely manner to ensure the timeliness of the urban emission reduction strategy library.

7. The carbon emission reduction method for urban agglomerations based on carbon emission spatial spillover under the influence of traffic according to claim 1 is characterized in that: In step 7, the intelligent optimization algorithm includes but is not limited to the use of genetic algorithms and particle swarm optimization. The intelligent optimization algorithm comprehensively considers aspects including economic costs, technical difficulties, and social recognition, and decides whether to strengthen its association with low-carbon emission type cities, and conducts 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 short-term and long-term action plans are formulated to ensure timely optimization of urban agglomeration refined emission reduction collaborative optimization plans.

8. An optimization system for an urban agglomeration carbon emission reduction method based on carbon emission spatial spillover under traffic influence as described in any one of claims 1 to 7, characterized in that: It at least includes a data collection and preprocessing module, a city clustering analysis module, a carbon emission related spatial network construction module, a carbon emission related spatial network analysis module and a city collaborative emission reduction plan generation module; among which, The data collection and preprocessing module is responsible for calculating the carbon emission data of all cities in the target city cluster over a period of time in the past, and collecting various indicator data including social, economic, and traffic intensity of all cities in the target city cluster over a period of time in the past; and is also responsible for preprocessing the calculated carbon emission data and the collected various indicator data including social, economic, and traffic intensity; The city cluster analysis module is responsible for clustering the cities using the K-means algorithm based on the traffic intensity data indicators 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; The carbon emission correlation spatial network construction module is responsible for constructing a carbon emission spatial correlation network among cities in the target urban agglomeration using the universal gravitation model; the carbon spatial correlation network is an aggregation of the spatial carbon gravity of every two cities, and is composed of nodes representing cities and edges representing the spatial gravity of carbon emissions between cities; when constructing the carbon spatial correlation network, the carbon emission correlation spatial network construction module not only considers economic and geographical factors, but also examines the impact of traffic intensity in the universal gravitation model, adds the traffic intensity value to the formula, and reconstructs the universal gravitation model to evaluate the impact of traffic on the carbon emission correlation among cities in the target urban agglomeration; The carbon emission correlation spatial network analysis module is responsible for quantifying the overall network of the target urban agglomeration and the individual city network from a system perspective by using the social network analysis method after constructing the carbon emission spatial correlation network, obtaining the overall network characteristics of the urban agglomeration and the individual city network characteristics respectively, and defining the functional role of cities with different transportation types in the carbon emission spatial correlation network, thereby obtaining carbon spatial correlation network analysis data including the carbon emission transfer and spillover between cities in the target urban agglomeration under the influence of transportation; The city collaborative emission reduction plan generation module is composed of a carbon emission diagnosis interface under traffic influence, a city emission reduction strategy library and an intelligent optimization algorithm; The carbon emission diagnosis interface under the influence of transportation is responsible for receiving the carbon space association network analysis data output by the carbon emission association space network analysis module, and using image recognition and machine learning algorithms to conduct in-depth analysis on the obtained carbon space association 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 urban emission reduction strategy library contains a number of urban agglomeration carbon emission reduction strategies and urban agglomeration carbon emission reduction strategies, which are manually input in advance; at the same time, according to the latest research results, policy releases and successful cases, the urban agglomeration carbon emission reduction strategies and urban single-unit emission reduction strategies in the urban emission reduction strategy library will be updated in a timely manner to ensure the timeliness of the urban emission reduction strategy library; The intelligent optimization algorithm helps the city collaborative emission reduction plan generation module to design personalized carbon reduction plans for high-carbon cities from the perspective of carbon emission spillovers based on the diagnostic results output by the carbon emission diagnostic interface under the influence of traffic and the constructed city emission reduction strategy library; the intelligent optimization algorithm comprehensively considers aspects including economic cost, technical difficulty, and social recognition to decide whether to strengthen its connection with low-carbon emission type cities and find the optimal emission reduction path.

9. A computer device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus, and the memory is used to store at least one executable instruction, wherein the executable instruction enables the processor to perform operations corresponding to the method for carbon emission reduction in urban agglomerations based on spatial spillover of carbon emissions under the influence of traffic as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer storage medium stores at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the urban agglomeration carbon emission reduction method based on carbon emission spatial spillover under traffic influence as described in any one of claims 1 to 7.

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