A system, medium and equipment for optimizing and determining carbon reduction paths in urban construction industries

Abstract

The present invention discloses a system, medium and equipment for optimizing and determining industrial carbon reduction paths for urban construction. The system establishes a model consisting of four modules: an industrial carbon reduction object evaluation index acquisition module, an industrial carbon reduction object classification module, an industrial carbon reduction technology path generation module and a carbon reduction technology path optimization module. The system uses a genetic algorithm to evaluate industries within the boundary range of urban construction, and the city constructs a comprehensive evaluation function based on its own development characteristics to obtain the optimal carbon reduction technology path for each industrial type, and finally forms a low-carbon urban construction industrial field carbon reduction technology path with the best comprehensive economic and carbon reduction performance. The path can fully reflect the city's low-carbon development advantages, characteristics and carbon reduction targets, facilitate the city to achieve its carbon reduction targets, effectively solve the problem of unclear industrial carbon reduction paths in the current urban construction process, and provide an urban construction industrial carbon reduction path that conforms to the city's development policy while ensuring urban economic development.

Description

Technical Field

[0001] The present invention relates to the field of urban construction industry carbon reduction, and in particular to a method for optimizing urban construction industry carbon reduction paths based on environmental and economic coupling benefits. Background Art

[0002] Cities, as centers of economic activities, produce 70% of CO2. As one of the world's industrial producers, my country's industrial production has driven the rapid development of the urban economy, generating a large amount of greenhouse gases and pollutants, making my country face severe CO2 emissions and environmental pollution problems. Building an industrial carbon reduction technology path is of great strategic significance to the construction of low-carbon cities.

[0003] The "National Low-Carbon City Pilot Project Progress Assessment Report" indicates that among the 81 pilot cities, less than 50% received an excellent rating. The average scores for "innovative measures" and "low-carbon development institutional mechanisms" across the pilot cities were slightly low. Most pilot cities are insufficiently committed to low-carbon development innovation, and generally have room for improvement in establishing low-carbon development institutions and mechanisms.

[0004] In the existing technology, the "A pollution reduction and carbon reduction path planning method, system, medium and equipment" disclosed in CN202410215629.1 constructs a scale-emission-cost pollution reduction and carbon reduction path planning model that changes dynamically under technological progress, and improves the accuracy of solving the optimal pollution reduction and carbon reduction path; the "A method and system for evaluating the synergistic effect of urban pollution reduction and carbon reduction" disclosed in CN202410665080.6 constructs a long-term series greenhouse gas and pollutant emission inventory for different industries in the city, analyzes the driving factors corresponding to greenhouse gases and pollutants, and evaluates the synergistic effect of urban pollution reduction and carbon reduction based on this. However, the above-mentioned technologies have the following limitations: First, the degree of concentration is insufficient. The above-mentioned similar patents analyze the impact of factors such as urban green technology on carbon emissions, and construct or evaluate urban pollution reduction and carbon reduction paths based on the degree of influence of each factor. However, the pollution reduction and carbon reduction paths fail to reflect the characteristics of industries and other sectors, resulting in the pollution reduction and carbon reduction paths being unable to represent the carbon reduction paths in the urban construction and industrial fields; Second, the content is incomplete. The above-mentioned and similar patents do not take into account the strategic positioning of urban low-carbon development and the basic laws and phased characteristics of low-carbon development in the region and their impact on urban carbon reduction paths. They cannot develop their own low-carbon development advantages and distinctive highlights and respond to the country's demand for low-carbon cities.

[0005] Therefore, in view of the urgent need for low-carbon city construction and the wide variety of industrial sectors and carbon reduction technologies applicable to each sector in each city and the complex characteristics of carbon reduction paths, technical personnel in this field urgently need to build a system for optimizing and determining industrial carbon reduction paths for urban construction. Summary of the Invention

[0006] In response to the problems existing in the existing technology, the purpose of the present invention is to provide a method for optimizing the carbon reduction path of urban construction industry based on the coupled benefits of environment and economy, and to provide the urban construction industry with an optimal industrial carbon reduction path with urban characteristics.

[0007] To achieve the above purpose, the present invention adopts the following technical solutions:

[0008] A system for optimizing and determining industrial carbon reduction paths for urban construction, including an industrial carbon reduction target evaluation index acquisition module, an industrial carbon reduction target classification module, an industrial carbon reduction technology path generation module, and a carbon reduction technology path optimization module;

[0009] The industrial carbon reduction target evaluation index acquisition module is used to obtain evaluation indicators that can reflect the future development benefits of urban industries and transmit them to the industrial carbon reduction target classification module;

[0010] The industrial carbon reduction target classification module is used to establish a classification model to classify urban construction industrial carbon reduction targets, match classification labels for each urban construction industrial carbon reduction target, and transmit them to the specific industrial carbon reduction technology path generation module;

[0011] The industrial carbon reduction technology path generation module is used to match carbon reduction strategies and carbon reduction technologies based on the classification labels of industrial carbon reduction targets for low-carbon city construction through a decision tree model, obtain a set of carbon reduction technology paths based on the classification results of industrial carbon reduction targets for low-carbon city construction, and transmit the result to the carbon reduction technology path selection and optimization module;

[0012] The carbon reduction technology path optimization module is used to combine the background characteristics of the carbon reduction path with the city's low-carbon development goals, so as to obtain the optimal carbon reduction technology path under each industrial type, and finally form the optimal urban construction industry carbon reduction technology path.

[0013] The industrial carbon reduction target evaluation index acquisition module uses CiteSpace software to perform keyword analysis on the literature related to current industrial development research, removes keywords with a frequency of less than q, and combines the context information of the remaining keywords to obtain the corresponding word vectors and evaluation indicators For keywords that cannot directly obtain evaluation indicators in the text, the missing evaluation indicators are supplemented through literature search Form the industrial development basic word set T1, perform similarity matching between the word set T1 and the three dimensions of industrial foundation, economic proportion and environmental impact, and evaluate the evaluation index of each keyword according to the matching results. Classify into corresponding sections and give labels P(A ij )={A 1j -Industrial Base-Indicator Name, A 2j -Economic proportion-Indicator name, A3j - environmental impact - indicator name}, forming the industrial evaluation indicator set T2, and transmitting it to the industrial carbon reduction object classification module. The calculation method is:

[0014]

[0015] Among them, R(x) is the similarity matching value, is the text embedding vector extracted after training for the i-th keyword in T1; B j The text embedding vectors extracted after training for industrial base, economic share, and environmental impact respectively;

[0016] In the industrial carbon reduction target classification module, combined with the urban land types classified in the "Classification of Urban and Rural Land Use and Planning and Construction Land Standards" GB50137, the urban construction industrial carbon reduction targets are locked into the manufacturing category. According to the corresponding evaluation indicators in the industrial evaluation indicator set T2, the entropy weight TOPSIS model is established to obtain the ranking of urban construction industrial categories. Based on the ranking, each category is divided into leading industries and non-leading industries in urban construction industrial development, and corresponding industrial type labels are assigned. According to the carbon benefit scores of each industrial category, the industry is further divided into encouraged, restricted and eliminated categories, forming an urban construction industrial category set T3 with industrial classification labels, and transmitted to the industrial carbon reduction technology path generation module.

[0017] The specific steps of establishing the entropy weight TOPSIS model to obtain the ranking of urban construction industrial categories and form the urban construction industrial category set T3 with industrial classification labels include:

[0018] 1) Build a decision matrix

[0019] A decision matrix is ​​constructed for m objects in the manufacturing industry and n evaluation indicators in the industrial development evaluation indicator set T2 of each city. The evaluation object set is A = (A1, A2, ..., A m ), the evaluation index set is T2=(T1,T2,…,T n ), A i The value of each indicator in T2 is X ij (i=1, 2, ..., m; j=1, 2, ..., n), forming a decision matrix:

[0020]

[0021] 2) Standardization of evaluation indicators

[0022] The evaluation index includes positive index and negative index. For negative index, take X mn The reciprocal of the negative indicator is converted into a positive indicator and substituted into the decision matrix to replace X mn, and obtain the standardized matrix.

[0023] 3) Entropy weight method to determine the weight of evaluation indicators

[0024] 1. Use the power coefficient method to perform dimensionless processing on the standardized matrix:

[0025]

[0026] 2. Calculate the entropy value based on the proportion of m evaluation objects under n evaluation indicators in the dimensionless data:

[0027]

[0028] Where: E j is the entropy value of evaluation index j, d ij is the proportion of the i-th evaluation object under the j-th evaluation index;

[0029] 3. Calculate the index weight W based on the dimensionless processing results and entropy value j :

[0030]

[0031] 4) Establish a TOPSIS model based on entropy weight

[0032] 4.1) Construct a weighting matrix based on the dimensionless data and entropy value. The weighting matrix Z is calculated as follows:

[0033] Z ij =W j ×D ij (6)

[0034] Z=[Z ij ] m×n (7)

[0035] 4.2) Determine the optimal solution Z in the weighted matrix for each evaluation index + and the worst solution Z - :

[0036]

[0037] Where: Z + is the optimal solution, is the maximum value of each column, Z - is the optimal solution, is the minimum value of each column, j = 1, 2, ..., n;

[0038] 4.3) Obtain the distance between each evaluation object and the optimal solution and the worst solution. The distance calculation formula between the evaluation object and the optimal solution and the worst solution is as follows:

[0039]

[0040] Where: is the distance between the evaluation object and the optimal solution, is the distance between the evaluation object and the worst solution;

[0041] 4.4) The relative closeness is calculated based on the distance between each evaluation object and the optimal solution and the worst solution. The closer each evaluation object is to the optimal solution, the higher the development potential of the industrial category. The closer each evaluation object is to the worst solution, the worse the development potential of the industrial category. The relative closeness K i The calculation formula is as follows:

[0042]

[0043] 5) Obtain the city construction industry development industry type

[0044] According to K i The different evaluation objects are ranked according to the size of the values, and the industrial categories in the first half of the ranking are composed of the leading industries in urban construction industrial development, and the industrial categories in the second half are composed of non-leading industries.

[0045] The carbon benefits of the leading and non-leading industries in urban construction industry development are calculated respectively. The carbon benefit score calculation formula is as follows:

[0046]

[0047] C i =E i ×EF j (14)

[0048]

[0049] Where: CE i is the carbon efficiency score of the i-th industrial sector, ICE i is the carbon benefit of the i-th industrial sector, W i is the operating income of the i-th industrial category, C i is the carbon emissions of the i-th industrial sector, E i is the comprehensive energy consumption value of the i-th industrial sector, EF j ICE is the carbon emission factor corresponding to the converted energy in the industry's comprehensive energy consumption value. i0 is the median carbon benefit of the i-th industrial sector in cities across China, ICE imax is the optimal carbon benefit of the i-th industrial category in cities across the country, when CE i When the calculation structure is less than zero, it is directly assigned to 0;

[0050] Industries with carbon efficiency scores below 40 are classified as eliminated, industries with carbon efficiency scores above 80 are classified as encouraged, and industries with carbon efficiency scores between 40 and 80 are classified as restricted. According to the structure of "industry dominance-industry category", each industry is given a label P(B ij )={B 11 -Leading Industry-Encouraged Category, B 12 -Leading Industry-Restricted Category, B 13 -Leading Industry-Eliminated Category, B 21 -Non-dominant industry-encouraged category, B 22 - Non-dominant industry - restricted category, B 23 - non-dominant industries-elimination categories}, forming an urban construction industrial category set T3 with industrial classification labels, which is transmitted to the industrial carbon reduction technology path generation module.

[0051] The industrial carbon reduction technology path generation module, by searching policy documents, sorts out six major industrial carbon reduction measures and their corresponding carbon reduction technology types, namely green product substitution production, industrial infrastructure construction carbon reduction, industrial production process carbon reduction path design, resource recycling, industry substitution and exit policy, to form an industrial carbon reduction measure set T4. Based on the industrial classification labels in set T3 and the carbon reduction measure descriptions in the industrial carbon reduction measure set T4, a decision tree model is constructed to match industrial carbon reduction measures, thereby forming a composition of various types of industrial carbon reduction measures for low-carbon city construction. Carbon reduction technologies suitable for each carbon reduction measure are selected from the carbon reduction technology library for traversal and combination, and the carbon reduction technology path of each industrial type is assigned a label P(B) based on the economic cost and carbon reduction effect of each carbon reduction technology path. ij C ijk )={B 11 -C ijk -Serial number-Carbon reduction cost-Carbon reduction amount, B 12 -C ijk -Serial number-Carbon reduction cost-Carbon reduction amount, B 13 -C ijk -Serial number-Carbon reduction cost-Carbon reduction amount, B 21 -C ijk -Serial number-Carbon reduction cost-Carbon reduction amount, B 22 -C ijk -Serial number-Carbon reduction cost-Carbon reduction amount, B 23 -C ijk -serial number-carbon reduction cost-carbon reduction amount}, forming a carbon reduction technology path set T5 of various types of industries with labels, and transmitting it to the carbon reduction technology path optimization module.

[0052] The carbon reduction technology path optimization module classifies the paths according to the labels of the paths in the carbon reduction technology path set T5 for each type of industry, and then optimizes the carbon reduction technology path for each type of industry. Based on the expected weight coefficients of carbon reduction cost and carbon reduction effect set by the decision maker, the comprehensive evaluation score M of the carbon reduction path for urban construction industry is obtained. ij The difference between economic efficiency and carbon reduction effect D ij , the comprehensive evaluation score calculation formula is:

[0053]

[0054] M ij =X EF ER′ ij +(1-X BF )C′ ij (18)

[0055] D ij =C′ iE -ER′ ij (19)

[0056] Where: M ij is the comprehensive evaluation score of j carbon reduction technology pathways for industry type i; D ij is the difference between the economic efficiency and carbon reduction effect of j carbon reduction technology paths for type i industry; ER′ ij C′ is the amount of carbon dioxide reduced by the j carbon reduction technology pathways of type i industry within the applicable range after normalization, with a value range of 0-1; ij ER is the normalized carbon reduction cost of the j carbon reduction technology paths for the i-type industry within the applicable range, with a value range of 0-1; jh C is the amount of carbon dioxide reduced by j carbon reduction technology paths of type i industry within the applicable range; ij is the carbon reduction cost of j carbon reduction technology paths within the applicable range for type i industry; X EF ER is the expected weight coefficient of carbon reduction determined by decision makers based on their own needs, with a value range of 0-1; i,max and ER i,min C is the maximum and minimum amount of carbon dioxide reduced by the carbon reduction technology path of type i industry within the applicable range; i,max and c i,min The maximum and minimum carbon reduction costs of the carbon reduction technology path for type i industry within the applicable range.

[0057] In view of the fact that cities hope to have lower economic efficiency and better carbon reduction effect, the comprehensive evaluation score maximization MAX{M ij} and minimize the difference between economic efficiency and carbon reduction effect MIN{Dij} is the optimization goal, and the result that appears most frequently in 100 calculation results of NSGA-II algorithm is taken as the optimal solution M with comprehensive evaluation score. best By calculating the carbon reduction technology path and optimal solution M for each industrial type best The relative closeness F ij , and its calculation formula is as follows:

[0058]

[0059] Where: F ij M is the relative closeness between the j carbon reduction technology paths of type i industry and the optimal solution; best is the optimal solution for the comprehensive evaluation score of type i industry; M ij is the comprehensive evaluation score of j carbon reduction technology pathways for industry type i.

[0060] According to the relative closeness F ij , sort the carbon reduction technology paths of each industrial type, output the industrial carbon reduction technology path that ranks first under each industrial type, and finally form the optimal industrial carbon reduction technology path for urban construction.

[0061] The present invention also includes a computer-readable storage medium and a computer device. The storage medium stores a computer program that, when executed by a processor, implements the functions of the system for optimizing carbon reduction paths for urban construction and industry. The computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the functions of the system for optimizing carbon reduction paths for urban construction and industry are implemented.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1. The present invention establishes a model consisting of four modules: an industrial carbon reduction object evaluation index acquisition module, an industrial carbon reduction object classification module, an industrial carbon reduction technology path generation module, and a carbon reduction technology path optimization module. The genetic algorithm is used for industries within the boundary range of urban construction. The city constructs a comprehensive evaluation function based on its own development characteristics to obtain the optimal carbon reduction technology path for each industrial type, and finally forms a low-carbon city construction industrial field carbon reduction technology path with the best comprehensive economic and carbon reduction performance. This path can fully reflect the city's low-carbon development advantages, characteristics and carbon reduction goals, and facilitate the city to achieve its carbon reduction goals.

[0064] 2. This invention can formulate industrial development directions based on local resource endowments: Based on current research, this method derives industrial development potential evaluation indicators and categorizes urban construction industries into dominant and non-dominant industries. This approach can address the issue of unclear industrial development goals during low-carbon city construction. Based on a city's carbon efficiency score, each industrial sector is further divided into encouraged, restricted, and eliminated categories. Differentiated industrial carbon reduction technology pathways are developed based on the characteristics of each industry, addressing the issue of unclear carbon reduction pathways in urban construction industries.

[0065] 3. The system must cover the following functions: First, identify key areas for industrial carbon reduction in urban construction, thereby obtaining evaluation indicators for industrial carbon reduction targets and ensuring their relevance; second, develop an industrial classification strategy that combines urban resource endowments and industrial development goals, assigning classification labels to urban construction industrial development targets; third, generate carbon reduction technology pathways corresponding to each industrial type based on the classification labels of urban construction industrial development targets; fourth, based on the economic efficiency and carbon reduction effects of the carbon reduction pathways and in combination with the city's low-carbon development goals, obtain the optimal carbon reduction technology pathways for each industrial type, and ultimately form the optimal urban construction industrial carbon reduction technology pathway. By developing an optimization method for urban construction industrial carbon reduction pathways based on environmental and economic coupling benefits that can cover the above functions, we can better provide strong technical support for low-carbon city construction, promote green and sustainable development in the urban industrial sector, and solve the problem of unclear development and carbon reduction pathways in the urban construction industrial sector.

[0066] 4. Each module in the urban construction industry carbon reduction path acquisition system of the present invention can be implemented in whole or in part by software, hardware or a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above modules. The computer readable storage medium stores a computer program, which can be used to execute Figure 1 Provides carbon reduction pathways and planning methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is an example diagram of optimizing the carbon reduction path for urban construction industry based on the environmental and economic coupling benefits provided in the embodiment.

[0068] Figure 2 This is a judgment logic diagram of the decision tree model in the industrial carbon reduction path generation module provided in the embodiment. DETAILED DESCRIPTION

[0069] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. The specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. The following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but rather merely represents selected embodiments of the present invention.

[0070] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0071] The present invention provides a system for optimizing and determining carbon reduction paths for urban construction industries, which is implemented by constructing the following modules:

[0072] 1) Industrial carbon reduction target evaluation index acquisition module: used to obtain evaluation indicators that can reflect the future development benefits of urban industries and transmit them to the industrial carbon reduction target classification module;

[0073] 2) Industrial carbon reduction target classification module: used to establish a classification model to classify urban construction industrial carbon reduction targets, match classification labels for each urban construction industrial carbon reduction target, and transmit them to the specific industrial carbon reduction technology path generation module;

[0074] 3) Industrial carbon reduction technology path generation module: used to match carbon reduction strategies and technologies based on the classification labels of industrial carbon reduction targets for low-carbon city construction through a decision tree model, obtain a set of carbon reduction technology paths based on the classification results of industrial carbon reduction targets for low-carbon city construction, and transmit it to the carbon reduction technology path selection and optimization module;

[0075] 4) Carbon reduction technology path optimization module: used to optimize specific carbon reduction technology paths based on the city’s carbon reduction goals and needs, and obtain the optimal carbon reduction path for the low-carbon city construction industry.

[0076] like Figure 1 As shown in the figure, each of the above modules is further explained.

[0077] In the 1) industrial carbon reduction target evaluation index acquisition module, in specific application implementation, the specific processing steps for acquiring the industrial carbon reduction target evaluation index include:

[0078] (1) Using the keyword “industrial development foundation”, the full text of the literature published in all academic fields was searched, and X relevant documents were selected. The keywords were analyzed using CiteSpace software, and the keywords with a frequency of less than q were eliminated. The keyword frequency can be set by the user according to his or her own requirements for the number of evaluation indicators.

[0079] (2) Extract and clean the context information covering the remaining keywords through structured data processing functions and text extraction functions to obtain the evaluation indicators corresponding to each keyword And context information, for those who do not obtain evaluation indicators Keywords are used as supplementary search terms to conduct literature search again and supplement the missing evaluation indicators. Form the basic vocabulary set T1 for industrial development.

[0080] (3) Combined with the contextual information corresponding to each keyword, the word vector of the keyword is obtained by using a pre-trained word vector model (such as Word2Vec, GloVe). In the specific application implementation, the Word2Vec word embedding model can be used for calculation. The contextual information corresponding to each keyword is used as a data set to train one's own Word2Vec model to ensure the model's ability to understand the keyword, and finally output the corresponding word vector.

[0081] (4) The word set T1 is matched with the three dimensions of industrial base, economic proportion and environmental impact for similarity. The calculation method is:

[0082]

[0083] Among them, R(x) is the similarity matching value, is the text embedding vector extracted after training for the i-th keyword in T1; B j The text embedding vectors extracted after training for industrial base, economic share, and environmental impact respectively;

[0084] (5) According to the matching results, the evaluation index of each keyword Classify into corresponding sections and give labels P(A ij )={A 1j -Industrial Base-Indicator Name, A 2j -Economic proportion-Indicator name, A 3j -environmental impact-indicator name}, forming the industrial evaluation indicator set T2 and transmitting it to the industrial carbon reduction object classification module.

[0085] To make the result presentation clearer, this embodiment briefly obtains the evaluation indicators based on the above steps, as shown in Table 1. It should be noted that this section is for reference only, and the user needs to refer to the above steps to obtain specific evaluation indicators.

[0086] Table 1 Industrial evaluation index set T2

[0087]

[0088] In the 2) industrial carbon reduction target classification module, combined with the urban land types classified in the "Classification of Urban and Rural Land Use and Planning and Construction Land Standards" GB50137, the urban construction industrial carbon reduction targets applicable to the present invention are locked into the manufacturing category, and the urban construction industrial category ranking is obtained by establishing an entropy weight TOPSIS model. The leading industries and non-leading industries of urban construction industrial development are formed based on the ranking, and corresponding industrial type labels are assigned. Based on the carbon benefit scores of industrial categories in each set, the industries are further divided into encouraged, restricted and eliminated categories to form an urban construction industrial category set T3 with industrial classification labels, and the labels are transmitted to the industrial carbon reduction technology path generation module.

[0089] The specific steps of establishing the entropy weight TOPSIS model to obtain the ranking of urban construction industrial categories and form the urban construction industrial category set T3 with industrial classification labels include:

[0090] 2.1) Constructing a decision matrix

[0091] A decision matrix is ​​constructed for m objects in the manufacturing industry and n evaluation indicators in the industrial development evaluation indicator set T3 of each city. The evaluation object set is A = (A1, A2, ..., A m ), the evaluation index set is T3=(T1,T2,…,T n ), A i The value of each indicator in T3 is X ij (i=1, 2, ..., m; j=1, 2, ..., n), forming a decision matrix:

[0092]

[0093] 2.2) Standardization of evaluation indicators

[0094] The evaluation index includes positive index and negative index. For negative index, take X mn The reciprocal of the negative indicator is converted into a positive indicator and substituted into the decision matrix to replace X mn , and obtain the standardized matrix.

[0095] 2.3) Determine the weight of evaluation indicators using entropy weight method

[0096] 2.3.1) Use the power coefficient method to make the normalized matrix dimensionless:

[0097]

[0098] 2.3.2) Calculate the entropy value based on the proportion of m evaluation objects under n evaluation indicators in the dimensionless data:

[0099]

[0100] Where: E j is the entropy value of evaluation index j, d ij is the proportion of the i-th evaluation object under the j-th evaluation index;

[0101] 2.4) Calculate the index weight W based on the dimensionless processing results and entropy value j :

[0102]

[0103] 2.4) Establishing a TOPSIS model based on entropy weight

[0104] 2.4.1) Construct a weighting matrix based on the dimensionless data and entropy value. The weighting matrix Z is calculated as follows:

[0105] Z ij =W j ×D ij (6)

[0106] Z=[Z ij ] m×n (7)

[0107] 2.4.2) Determine the optimal solution Z in the weighted matrix for each evaluation index + and the worst solution Z - :

[0108]

[0109] Where: Z + is the optimal solution, is the maximum value of each column, Z - is the optimal solution, is the minimum value of each column, j = 1, 2, ..., n;

[0110] 2.4.3) Obtain the distance between each evaluation object and the optimal solution and the worst solution. The distance calculation formula between the evaluation object and the optimal solution and the worst solution is as follows:

[0111]

[0112] Where: is the distance between the evaluation object and the optimal solution, is the distance between the evaluation object and the worst solution;

[0113] 2.4.4) The relative closeness is calculated based on the distance between each evaluation object and the optimal solution and the worst solution. The closer each evaluation object is to the optimal solution, the higher the development potential of the industrial category. The closer each evaluation object is to the worst solution, the worse the development potential of the industrial category. The relative closeness K i The calculation formula is as follows:

[0114]

[0115] 2.5) Obtain the city construction industry development industry type

[0116] According to K i The different evaluation objects are ranked according to the size of the values, and the industrial categories in the first half of the ranking are composed of the leading industries in urban construction industrial development, and the industrial categories in the second half are composed of non-leading industries.

[0117] The carbon benefits of the leading and non-leading industries in urban construction industry development are calculated respectively. The carbon benefit score calculation formula is as follows:

[0118]

[0119] C i =E i ×EF j (14)

[0120]

[0121] Where: CE i is the carbon efficiency score of the i-th industrial sector, ICE i is the carbon benefit of the i-th industrial sector, W i is the operating income of the i-th industrial category, C i is the carbon emissions of the i-th industrial sector, E i is the comprehensive energy consumption value of the i-th industrial sector, EF j ICE is the carbon emission factor corresponding to the converted energy in the industry's comprehensive energy consumption value. i0 is the median carbon benefit of the i-th industrial sector in cities across China, ICE imax is the optimal carbon benefit of the i-th industrial category in cities across the country, when CE i When the calculation structure is less than zero, it is directly assigned to 0;

[0122] Industries with carbon efficiency scores below 40 are classified as eliminated, industries with carbon efficiency scores above 80 are classified as encouraged, and industries with carbon efficiency scores between 40 and 80 are classified as restricted. According to the structure of "industry dominance-industry category", each industry is given a label P(B ij )={B 11 -Leading Industry-Encouraged Category, B 12 -Leading Industry-Restricted Category, B 13 -Leading Industry-Eliminated Category, B 21 -Non-dominant industry-encouraged category, B 22 - Non-dominant industry - restricted category, B 23 - non-dominant industries - elimination categories}, forming a collection of urban construction industry categories with industry classification labels T3, which is transmitted to the industrial carbon reduction technology path generation module. To provide a clearer description of the present invention, this example, combined with relevant literature and reports, has compiled a list of urban construction industry category classification results (see Table 2 for details) for illustration.

[0123] Table 2 Urban construction industry categories with industrial classification labels

[0124]

[0125] In the 3) industrial carbon reduction technology path generation module, six major industrial carbon reduction measures and their corresponding carbon reduction technology types, including green product substitution production, industrial infrastructure construction carbon reduction, industrial production process carbon reduction path design, resource recycling, and industrial substitution and exit policies, were sorted out by searching policy documents to form the industrial carbon reduction measures set T4.

[0126] Among them, a decision tree model is constructed based on the industrial classification labels in set T3 and the carbon reduction measures descriptions in the industrial carbon reduction measures set T4. The detailed process is as follows: Figure 2 As shown. Eliminated industries under the main industrial types will adopt green product replacement production. These green products will be selected from the same type of industries in the encouraged category of the national industrial adjustment catalogue for replacement production, and their production must still meet the following judgment criteria. Eliminated industries under non-main industrial types will cease production and implement exit policies within the city's prescribed exit time limit. All industries must implement carbon reduction measures for industrial infrastructure construction. Industries where carbon emissions from raw materials and fuel use during production account for ≥50% must design a carbon reduction path based on their industrial production processes. Industries where the comprehensive utilization rate of solid waste and the recycling rate of industrial water during production do not meet the E and F values ​​specified in the national carbon peak action plan will implement resource recycling transformation. When an industry falls into the restricted category, its carbon benefits will be calculated. Industries with carbon benefits below 60 will implement industrial substitution policies, where the replacement industries are selected from the encouraged category of the industrial adjustment catalogue. The final output is a carbon reduction path formed by matching carbon reduction measures with each industry.

[0127] Then, industrial carbon reduction measures are matched to form various types of industrial carbon reduction measures for low-carbon city construction. Carbon reduction technologies suitable for each carbon reduction measure are selected from the carbon reduction technology library for traversal and combination. The carbon reduction technology types of each carbon reduction measure are shown in Table 3.

[0128] Table 3 Composition of carbon reduction technology types under various carbon reduction measures

[0129]

[0130]

[0131] The carbon reduction technology paths of each industrial type are given labels P(B ij C ijk )={B 11 -C ijk -Serial number-Carbon reduction cost-Carbon reduction amount, B 12 -C ijk -Serial number-Carbon reduction cost-Carbon reduction amount, B 13 -C ijk -Serial number-Carbon reduction cost-Carbon reduction amount, B 21 -C ijk -Serial number-Carbon reduction cost-Carbon reduction amount, B 22 -C ijk -Serial number-Carbon reduction cost-Carbon reduction amount, B 23 -C ijk -serial number-carbon reduction cost-carbon reduction amount}, forming a carbon reduction technology path set T5 of various types of industries with labels, and transmitting it to the carbon reduction technology path optimization module, where the calculation formulas for carbon reduction amount and carbon reduction cost are:

[0132] ER i =ES i ×EF e ×EA

[0133] Where: ER i is the amount of carbon dioxide reduced by the i technology path within the applicable range; ES i Energy savings brought by the use of technology path i; EF e is the CO2 emission factor of the e-th energy source, t / GJ; EA is the total amount of units that can be constructed and used in the i-th technology path.

[0134]

[0135] Where: C i,y is the unit emission reduction cost of technology path i in year y, 10,000 yuan / t; IC i,y is the investment and maintenance cost of technology path i in year y, in ten thousand yuan; ERi is the CO2 emission reduction potential generated by using technology pathway i.

[0136] It should be made clear that the above-mentioned carbon emission reduction technology paths have their own calculation methods for carbon reduction amounts and carbon reduction costs, which are for reference only. When applying them, users should formulate a specific set of carbon reduction technology paths based on actual conditions.

[0137] In the carbon reduction technology path optimization module, the specific steps are as follows:

[0138] (1) According to the labels of each path in the carbon reduction technology path set T5 of each type of industry, they are classified according to the industrial type, and then the carbon reduction technology path optimization is performed for each industrial type.

[0139] (2) Based on the carbon reduction cost and expected weight coefficient of carbon reduction effect set by the decision maker, the comprehensive evaluation score M of the urban construction industry carbon reduction path is obtained. ij The difference between economic efficiency and carbon reduction effect D ij , the comprehensive evaluation score calculation formula is:

[0140]

[0141] M ij =X EF ER′ ij +(1-X EF )C′ ij (18)

[0142] D ij =C′ ij -ER′ ij (19)

[0143] Where: M ij is the comprehensive evaluation score of j carbon reduction technology pathways for industry type i; D ij is the difference between the economic efficiency and carbon reduction effect of j carbon reduction technology paths for type i industry; ER′ ij C′ is the amount of carbon dioxide reduced by the j carbon reduction technology pathways of type i industry within the applicable range after normalization, with a value range of 0-1; ij ER is the normalized carbon reduction cost of the j carbon reduction technology paths for the i-type industry within the applicable range, with a value range of 0-1; ij C is the amount of carbon dioxide reduced by j carbon reduction technology paths of type i industry within the applicable range; ij is the carbon reduction cost of j carbon reduction technology paths within the applicable range for type i industry; X EF ER is the expected weight coefficient of carbon reduction determined by decision makers based on their own needs, with a value range of 0-1; i,max and ERi,min C is the maximum and minimum amount of carbon dioxide reduced by the carbon reduction technology path of type i industry within the applicable range; i,max and C i,min The maximum and minimum carbon reduction costs of the carbon reduction technology path for type i industry within the applicable range.

[0144] (3) Select the comprehensive evaluation score that maximizes MAX{M ij} and minimize the difference between economic efficiency and carbon reduction effect MIN{D ij} is the optimization goal, and the NSGA-II algorithm is used to obtain the optimal solution M of the comprehensive evaluation score. best .

[0145] (4) By calculating the carbon reduction technology path and optimal solution M for each industrial type best The relative closeness F ij , and its calculation formula is as follows:

[0146]

[0147] Where: F ij M is the relative closeness between the j carbon reduction technology paths of type i industry and the optimal solution; best is the optimal solution for the comprehensive evaluation score of type i industry; M ij is the comprehensive evaluation score of j carbon reduction technology pathways for industry type i.

[0148] (5) According to the relative closeness F ij , sort the carbon reduction technology paths of each industrial type, output the industrial carbon reduction technology path that ranks first under each industrial type, and finally form the optimal industrial carbon reduction technology path for urban construction.

[0149] In order to make the description of this embodiment clearer, it is proposed to encourage the leading industry B 11 To optimize carbon reduction technology paths, 50 carbon reduction technology paths were randomly generated using a Python function. The expected carbon reduction weight coefficient for the city was set to 0.65. Some carbon reduction technology paths and their parameters are shown in Table 4:

[0150] Table 4 Carbon reduction technology paths and parameters

[0151]

[0152]

[0153] The population capacity of the NSGA-II algorithm is set to 100, the number of iterations is set to 150, and the mode of the 100 calculation results is selected as the optimal solution M for the carbon reduction technology path. best, because the algorithm maintains a diversified solution set in the solution process, the calculated value M best It is not unique. Users can reduce the fluctuation between output results by increasing the group capacity and the number of iterations. According to the parameters in Table 4, the carbon reduction technology path and parameters can be calculated as shown in Table 5. According to the relative closeness F ij , sort the carbon reduction technology paths of each industrial type (details are shown in Table 5 below, this example only shows the top 20 carbon reduction technology paths), and get the encouraged category B of the leading industry 11 The optimal carbon reduction technology path is carbon reduction technology path 33. The optimal carbon reduction technology path for each industrial type is output, and finally the optimal industrial carbon reduction technology path for urban construction is formed.

[0154] Table 5 Carbon reduction technology paths and parameters

[0155]

[0156]

[0157] In summary, decision makers should evaluate and determine the applicability of the preferred carbon reduction technology pathway based on their own energy mix, technological level, and economic situation. If the preferred pathway is not applicable, they should then review and determine the pathways one by one based on the ranking of the pathways, and then select an appropriate alternative pathway. Secondly, after determining the final carbon reduction technology pathway, it should be implemented within the corresponding manufacturing sector, with ongoing real-time monitoring and evaluation of the carbon reduction effects. Optimization and adjustments should be made based on the feedback. Simultaneously, each manufacturing sector should ensure the continuous improvement of carbon reduction measures through measures such as equipment modification, process optimization, and carbon emissions monitoring. Finally, each manufacturing sector should strengthen employee training, promote green production concepts, and enhance social responsibility and brand image through transparent carbon emissions reporting and green certification, thereby achieving sustainable development goals. This approach can flexibly respond to the actual needs of different manufacturing sectors and ensure the effectiveness and long-term sustainability of carbon reduction measures.

[0158] It will be understood by those skilled in the art that the above-described embodiment system is implemented by instructing the relevant hardware through a computer program, and the computer program may be stored in a non-volatile computer-readable storage medium, and the computer program includes the system of the above-described embodiment. Wherein, any reference to memory, storage, database or other media used may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. As an illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0159] This invention categorizes industrial sectors, excluding mining, based on the characteristics of urban industrial development. It then develops differentiated carbon reduction technology pathways for each industrial type, optimizing each pathway based on the economic benefits and carbon reduction effects of each pathway in conjunction with urban development goals. Ultimately, it identifies the optimal carbon reduction pathway for urban construction industries based on the coupled environmental and economic benefits. This invention, by integrating local industrial development characteristics with carbon reduction goals, effectively addresses the current issue of unclear industrial carbon reduction pathways during urban construction. While safeguarding urban economic development, it provides cities with industrial carbon reduction pathways for urban construction that align with urban development policies.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A system for optimizing and determining carbon reduction paths for urban construction industries, characterized by: It includes an industrial carbon reduction target evaluation index acquisition module, an industrial carbon reduction target classification module, an industrial carbon reduction technology path generation module, and a carbon reduction technology path optimization module; The optimization and determination system selects the comprehensive evaluation score to maximize MAX{M ij } and minimize the difference between economic efficiency and carbon reduction effect MIN{D ij } is the optimization goal, and the result that appears most frequently in 100 calculation results of NSGA-II algorithm is taken as the optimal solution M with comprehensive evaluation score. best By calculating the carbon reduction technology path and optimal solution M for each industrial type best The relative closeness F ij , and its calculation formula is: Where: F ij M is the relative closeness between the j carbon reduction technology paths of type i industry and the optimal solution; best is the optimal solution for the comprehensive evaluation score of type i industry; M ij is the comprehensive evaluation score of j carbon reduction technology pathways for industry type i; According to the relative closeness F ij , sort the carbon reduction technology paths of each industrial type, output the top-ranked industrial carbon reduction technology paths under each industrial type, and finally form the optimal industrial carbon reduction technology paths for urban construction; in, The industrial carbon reduction target evaluation index acquisition module is used to obtain evaluation indicators that can reflect the future development benefits of urban industries and transmit them to the industrial carbon reduction target classification module; The industrial carbon reduction target classification module is used to establish a classification model to classify urban construction industrial carbon reduction targets, match classification labels for each urban construction industrial carbon reduction target, and transmit them to the specific industrial carbon reduction technology path generation module; The industrial carbon reduction technology path generation module is used to match carbon reduction strategies and carbon reduction technologies based on the classification labels of industrial carbon reduction targets for low-carbon city construction through a decision tree model, obtain a set of carbon reduction technology paths based on the classification results of industrial carbon reduction targets for low-carbon city construction, and transmit the result to the carbon reduction technology path optimization module; The carbon reduction technology path optimization module is used to combine the background characteristics of the carbon reduction path with the city's low-carbon development goals, so as to obtain the optimal carbon reduction technology path under each industrial type, and finally form the optimal urban construction industry carbon reduction technology path.

2. The urban construction industry carbon reduction path optimization and determination system according to claim 1 is characterized by: The industrial carbon reduction target evaluation index acquisition module uses CiteSpace software to perform keyword analysis on the literature related to current industrial development research, removes keywords with a frequency of less than q, and combines the context information of the remaining keywords to obtain the corresponding word vectors and evaluation indicators For keywords that cannot directly obtain evaluation indicators in the text, the missing evaluation indicators are supplemented through literature search Form the industrial development basic word set T1, perform similarity matching between the word set T1 and the three dimensions of industrial foundation, economic proportion and environmental impact, and evaluate the evaluation index of each keyword according to the matching results. Classify into corresponding sections and give labels P(A ij )={A 1j -Industrial Base-Indicator Name, A 2j -Economic proportion-Indicator name, A 3j - environmental impact - indicator name}, forming the industrial evaluation indicator set T2, and transmitting it to the industrial carbon reduction object classification module. The calculation method is: Among them, R(x) is the similarity matching value, is the text embedding vector extracted after training for the i-th keyword in T1; B j The text embedding vectors are extracted after training for industrial base, economic proportion and environmental impact respectively.

3. The urban construction industry carbon reduction path optimization and determination system according to claim 2 is characterized in that: In the industrial carbon reduction target classification module, combined with the urban land types classified in the "Classification of Urban and Rural Land Use and Planning and Construction Land Standards" GB50137, the urban construction industrial carbon reduction targets are locked into the manufacturing category. According to the corresponding evaluation indicators in the industrial evaluation indicator set T2, the entropy weight TOPSIS model is established to obtain the ranking of urban construction industrial categories. Based on the ranking, each category is divided into leading industries and non-leading industries in urban construction industrial development, and corresponding industrial type labels are assigned. According to the carbon benefit scores of each industrial category, the industry is further divided into encouraged, restricted and eliminated categories, forming an urban construction industrial category set T3 with industrial classification labels, and transmitted to the industrial carbon reduction technology path generation module.

4. The urban construction industry carbon reduction path and determination optimization system according to claim 3 is characterized in that the specific steps of establishing the entropy weight TOPSIS model to obtain the ranking of urban construction industry categories and form the urban construction industry category set T3 with industrial classification labels include: 1) Build a decision matrix A decision matrix is ​​constructed for m objects in the manufacturing industry and n evaluation indicators in the industrial evaluation indicator set T2 of each city. The evaluation object set is A = (A1, A2, ..., A m ), the industrial evaluation index set is T2=(T1,T2,…,T n ), A i The value of each indicator in T2 is X ij (i=1, 2, ..., m; j=1, 2, ..., n), forming a decision matrix: 2) Standardization of evaluation indicators The evaluation index includes positive index and negative index. For negative index, take X mn The reciprocal of the negative indicator is converted into a positive indicator and substituted into the decision matrix to replace X mn , obtain the standardized matrix; 3) Entropy weight method to determine the weight of evaluation indicators 3.1) Use the power coefficient method to perform dimensionless processing on the standardized matrix: 3.2) Calculate the entropy value based on the proportion of m evaluation objects under n evaluation indicators in the dimensionless data: Where: E j is the entropy value of evaluation index j, d ij is the proportion of the i-th evaluation object under the j-th evaluation index; 3.3) Calculate the index weight W based on the dimensionless processing results and entropy value j : 4) Establish a TOPSIS model based on entropy weight 4.1) Construct a weighted matrix based on the dimensionless data and entropy value. The weighted matrix Z is calculated as follows: WITH ij =In j ×D ij (6) Z=[Z ij ] m×n (7) 4.2) Determine the optimal solution Z in the weighted matrix for each evaluation index + and the worst solution Z - : Where: Z + is the optimal solution, is the maximum value of each column, Z - is the optimal solution, is the minimum value of each column, j = 1, 2, ..., n; 4.3) Obtain the distance between each evaluation object and the optimal solution and the worst solution. The distance calculation formula between the evaluation object and the optimal solution and the worst solution is as follows: Where: is the distance between the evaluation object and the optimal solution, is the distance between the evaluation object and the worst solution; 4.4) The relative closeness is calculated based on the distance between each evaluation object and the optimal solution and the worst solution. The closer each evaluation object is to the optimal solution, the higher the development potential of the industrial category. The closer each evaluation object is to the worst solution, the worse the development potential of the industrial category. The relative closeness K i The calculation formula is as follows: 5) Obtain the city construction industry development industry type According to K i The evaluation objects are ranked by the size of the values, and the industrial categories in the first half of the ranking are composed of the leading industries of urban construction industry development, and the industrial categories in the second half are composed of non-leading industries; The carbon benefits of the leading and non-leading industries in urban construction industry development are calculated respectively. The carbon benefit score calculation formula is as follows: C i =And i ×EF j (14) Where: CE i is the carbon efficiency score of the i-th industrial sector, ICE i is the carbon benefit of the i-th industrial sector, W i is the operating income of the i-th industrial category, C i is the carbon emissions of the i-th industrial sector, E i is the comprehensive energy consumption value of the i-th industrial sector, EF j ICE is the carbon emission factor corresponding to the converted energy in the industry's comprehensive energy consumption value. i0 is the median carbon benefit of the i-th industrial sector in cities across China, ICE imax is the optimal carbon benefit of the i-th industrial category in cities across the country, when CE i When the calculation result is less than zero, it is directly assigned to 0; Industries with carbon efficiency scores below 40 are classified as eliminated, industries with carbon efficiency scores above 80 are classified as encouraged, and industries with carbon efficiency scores between 40 and 80 are classified as restricted. According to the structure of "industry dominance-industry category", each industry is given a label P(B ij )={B 11 -Leading Industry-Encouraged Category, B 12 - Dominant Industry - Restricted Category, B 13 -Leading Industry-Eliminated Category, B 21 -Non-dominant industry-encouraged category, B 22 - Non-dominant industry - restricted category, B 23 - non-dominant industries-elimination categories}, forming an urban construction industrial category set T3 with industrial classification labels, which is transmitted to the industrial carbon reduction technology path generation module.

5. The urban construction industry carbon reduction path optimization and determination system according to claim 3 is characterized in that: The industrial carbon reduction technology path generation module, by searching policy documents, sorts out six major industrial carbon reduction measures and their corresponding carbon reduction technology types, namely green product substitution production, industrial infrastructure construction carbon reduction, industrial production process carbon reduction path design, resource recycling, industry substitution and exit policy, to form an industrial carbon reduction measure set T4. Based on the industrial classification labels in set T3 and the carbon reduction measure descriptions in the industrial carbon reduction measure set T4, a decision tree model is constructed to match industrial carbon reduction measures, thereby forming a composition of various types of industrial carbon reduction measures for low-carbon city construction. Select carbon reduction technologies suitable for each carbon reduction measure from the carbon reduction technology library for traversal combination, and assign labels P(B) to each industrial type of carbon reduction technology path based on the economic cost and carbon reduction effect of each carbon reduction technology path. ij C ijk )={B 11 -C ijk -Serial number-Carbon reduction cost-Carbon reduction amount, B 12 -C ijk -Serial number-Carbon reduction cost-Carbon reduction amount, B 13 -C ijk -Serial number-Carbon reduction cost-Carbon reduction amount, B 21 -C ijk -Serial number-Carbon reduction cost-Carbon reduction amount, B 22 -C ijk -Serial number-Carbon reduction cost-Carbon reduction amount, B 23 -C ijk -serial number-carbon reduction cost-carbon reduction amount}, forming a carbon reduction technology path set T5 of various types of industries with labels, and transmitting it to the carbon reduction technology path optimization module.

6. The urban construction industry carbon reduction path optimization and determination system according to claim 5 is characterized in that: The carbon reduction technology path optimization module classifies the paths according to the labels of the paths in the carbon reduction technology path set T5 for each type of industry, and then optimizes the carbon reduction technology path for each type of industry. Based on the expected weight coefficients of carbon reduction cost and carbon reduction effect set by the decision maker, the comprehensive evaluation score M of the carbon reduction path for urban construction industry is obtained. ij The difference between economic efficiency and carbon reduction effect D ij , the comprehensive evaluation score calculation formula is: M ij =X EF IS' ij +(1-X EF )C′ ij (18) D ij =C′ ij -IS' ij (19) Where: M ij is the comprehensive evaluation score of j carbon reduction technology pathways for industry type i; D ij is the difference between the economic efficiency and carbon reduction effect of j carbon reduction technology paths for type i industry; ER′ ij C′ is the amount of carbon dioxide reduced by the j carbon reduction technology pathways of type i industry within the applicable range after normalization, with a value range of 0-1; ij ER is the normalized carbon reduction cost of the j carbon reduction technology paths for the i-type industry within the applicable range, with a value range of 0-1; ij C is the amount of carbon dioxide reduced by j carbon reduction technology paths of type i industry within the applicable range; ij is the carbon reduction cost of j carbon reduction technology paths within the applicable range for type i industry; X EF ER is the expected weight coefficient of carbon reduction determined by decision makers based on their own needs, with a value range of 0-1; i,max and ER i,min C is the maximum and minimum amount of carbon dioxide reduced by the carbon reduction technology path of type i industry within the applicable range; i,max and C i,min The maximum and minimum carbon reduction costs of the carbon reduction technology path for type i industry within the applicable range.

7. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the system for optimizing and determining the carbon reduction path for urban construction industry according to any one of claims 1 to 6 is implemented.

8. A computer device, characterized in that: The system comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the system for optimizing and determining the carbon reduction path of the urban construction industry as claimed in any one of claims 1 to 6 is realized.

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