Cold region city layout optimization method for reducing carbon emission

Abstract

The invention belongs to the field of environmental protection, and particularly relates to a cold region city layout optimization method for reducing carbon emission. The invention aims to solve the problem of high carbon emission in the existing cold region city layout optimization. The invention provides a cold region city layout optimization method for reducing carbon emission. The method comprises the following steps: S1, acquiring cold region city data; s2, constructing a multi-target land utilization distribution optimization model; inputting the acquired cold region city data into a multi-target land utilization distribution optimization model; an optimized cold region city layout scheme is obtained; urban space optimization is combined with specific data, the technicality and scientificity of the cold region city in a low-carbon city planning party are effectively improved, and the problem that carbon emission is high in existing cold region city layout optimization is solved.

Description

Technical Field

[0001] The present invention belongs to the field of environmental protection, and in particular relates to a cold-region city layout optimization method for reducing carbon emissions. Background Art

[0002] As global climate change intensifies, carbon emissions have become a global concern. Cold-region cities, due to their unique climatic and geographical environments, face even more severe carbon emission challenges. High winter heating demand, high building energy consumption, and carbon emissions from transportation, industry, and other sectors make carbon reduction in these cities particularly challenging. Existing carbon emission optimization methods are mostly targeted at temperate or tropical cities and fail to consider the specific characteristics of cold-region cities, resulting in poor results in practical applications.

[0003] Buildings in cold-region cities require a lot of heating in winter, resulting in significantly higher building energy consumption than in other areas. Existing building energy-saving technologies often fail to fully consider the extreme climatic conditions of cold-region cities, resulting in limited energy-saving effects. Land use planning in cold-region cities often fails to fully consider the impact of carbon emissions, resulting in low land use efficiency and high carbon emissions. For example, the unreasonable layout of industrial and residential areas increases transportation carbon emissions. Existing carbon emission optimization methods have shortcomings in data acquisition and model construction, especially the difficulty in obtaining carbon emission data for cold-region cities, and the lack of targeted model construction, resulting in unsatisfactory optimization results. In addition, existing carbon emission optimization methods often only focus on a single objective, such as economic benefits or carbon emissions, and lack the consideration of multi-objective comprehensive optimization, making it difficult to achieve a balance between economic and environmental benefits in practical applications.

[0004] Climate data for cold-region cities, such as winter heating demand and building insulation performance, can be incorporated into carbon emission optimization models to ensure that optimization solutions are tailored to the unique climate conditions of these cities. By constructing a multi-objective land use allocation optimization model that comprehensively considers multiple objectives, including economic benefits, carbon emissions, and land use efficiency, optimal land use allocation in cold-region cities can be achieved. Introducing big data and artificial intelligence technologies can enable dynamic monitoring and real-time optimization of carbon emissions, further enhancing carbon reduction effectiveness.

[0005] This paper first constructs a multi-objective land use allocation optimization model: by constructing a multi-objective land use allocation optimization model, it comprehensively considers multiple objectives such as economic benefits, carbon emissions, and land use efficiency to achieve optimal land use configuration in cold-region cities. The model includes a land structure optimization model and a land space optimization model, which optimize land use structure and spatial layout respectively. Secondly, a data-driven optimization method is proposed: using GDP data, building infrastructure data, land use data, building energy consumption data, carbon emission grid data, and population data of cold-region cities, the partial least squares (PLS) method and the land structure optimization model are used to calculate the land structure plan and land space plan of cold-region cities, achieving data-driven carbon emission optimization. In addition, advanced algorithms are introduced in the calculation aspect: the linear interactive general optimization algorithm (Lingo) and the NSGA-II algorithm in Python are used to perform multi-objective optimization solutions to ensure the scientificity and feasibility of the optimization results. The ideal point method and congestion calculation are used to ensure that the optimization results achieve a balance between multiple objectives.

[0006] During model construction and optimization, this paper fully considers the unique climatic conditions and geographical environment of cold-region cities, ensuring the feasibility and effectiveness of the optimization scheme in practical applications. For example, in land use planning, winter heating needs are prioritized, building layout and transportation networks are optimized, and carbon emissions are reduced. By constructing a multi-objective land use allocation optimization model, combined with data-driven optimization methods and advanced algorithms, these issues can be effectively addressed, providing a scientific basis and technical support for achieving low-carbon development in cold-region cities. Summary of the Invention

[0007] The present invention aims to solve the problem of high carbon emissions in existing cold region urban layout optimization. A cold region urban layout optimization method for reducing carbon emissions is provided, comprising:

[0008] S1: Obtain cold region city data;

[0009] S2: Construct a multi-objective land use allocation optimization model; input the acquired cold region city data into the multi-objective land use allocation optimization model; and obtain the optimized cold region city layout plan;

[0010] The cold-region city data in S1 include: cold-region city GDP data, cold-region city building basic data, cold-region city land use data, cold-region city existing building energy consumption data, cold-region city carbon emission grid data and cold-region city population data.

[0011] The GDP data of cold-region cities can be obtained from the official website of the National Bureau of Statistics;

[0012] The basic data of buildings in cold regions cities include data on land use properties in cold regions cities, etc.

[0013] The carbon emission grid data of the cold-region city is obtained from the Emissions Database for Global Atmospheric Research (EDGAR) database;

[0014] Furthermore, the multi-objective land use allocation optimization model in S2 includes: a land structure optimization model and a land space optimization model;

[0015] The multi-objective land use allocation optimization model is constructed; the acquired cold region city data is input into the multi-objective land use allocation optimization model; and an optimized cold region city layout plan is obtained. The specific process is as follows:

[0016] S2.1: Construct a land structure optimization model; input GDP data, land use data, existing building energy consumption data, and carbon emission grid data of cold-region cities into the land structure optimization model to calculate a land structure plan for the cold-region cities; the land structure plan for the cold-region cities is the amount of each type of land;

[0017] S2.2: Construct a land space optimization model; input the cold-region city population data, cold-region city building infrastructure data, and cold-region city land structure plan into the land space optimization model to calculate the cold-region city land space plan;

[0018] The land space plan of the cold-region city is an optimized map of land use properties;.

[0019] Compared with the previous cold-region city carbon emission optimization method, the invention has the following innovations:

[0020] The introduction of a multiscale optimization model has led to a dual-scale optimization model combining the structural and spatial scales. This approach not only considers the quantitative structure of land use but also the impact of spatial layout on carbon emissions in urban land use optimization. This multiscale optimization framework addresses the shortcomings of existing single-scale approaches and enables more comprehensive low-carbon urban planning solutions.

[0021] The integrated application of multi-objective algorithms, combining Ideal Point Multi-Objective Linear Programming (IMLP) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), is used to optimize low-carbon land use layout. IMLP optimizes the quantitative structure to balance economic benefits and carbon emissions, while NSGA-II optimizes the spatial layout to improve land use compactness and suitability. This integrated application significantly improves the efficiency and adaptability of the optimization model.

[0022] Combining urban space optimization with specific data has effectively improved the technical and scientific nature of low-carbon urban planning in cold-region cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a cold region city layout optimization method for reducing carbon emissions according to the present invention. DETAILED DESCRIPTION

[0024] Specific implementation method 1: Combination Figure 1 Describe the present invention,

[0025] S1: Obtain cold region city data;

[0026] S2: Construct a multi-objective land use allocation optimization model; input the acquired cold region city data into the multi-objective land use allocation optimization model; and obtain the optimized cold region city layout plan;

[0027] The cold-region city data in S1 include: cold-region city GDP data, cold-region city building basic data, cold-region city land use data, cold-region city existing building energy consumption data, cold-region city carbon emission grid data and cold-region city population data.

[0028] The GDP data of cold-region cities can be obtained from the official website of the National Bureau of Statistics;

[0029] The basic data of buildings in cold regions cities include data on land use properties in cold regions cities, etc.

[0030] The carbon emission grid data of the cold-region city is obtained from the Emissions Database for Global Atmospheric Research (EDGAR) database;

[0031] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that:

[0032] The multi-objective land use allocation optimization model in S2 includes: a land structure optimization model and a land space optimization model;

[0033] The multi-objective land use allocation optimization model is constructed; the acquired cold region city data is input into the multi-objective land use allocation optimization model; and an optimized cold region city layout plan is obtained. The specific process is as follows:

[0034] S2.1: Construct a land structure optimization model; input GDP data, land use data, existing building energy consumption data, and carbon emission grid data of cold-region cities into the land structure optimization model to calculate a land structure plan for the cold-region cities; the land structure plan for the cold-region cities is the amount of each type of land;

[0035] S2.2: Construct a land space optimization model; input the cold-region city population data, cold-region city building infrastructure data, and cold-region city land structure plan into the land space optimization model to calculate the cold-region city land space plan;

[0036] The land space plan of the cold-region city is an optimized map of land use properties;

[0037] Other steps and parameters are the same as those in the first embodiment.

[0038] Specific embodiment 3: This embodiment differs from specific embodiments 1 to 2 in that:

[0039] In S2.1, a land structure optimization model is constructed; GDP data of cold-region cities, land use data of cold-region cities, existing building energy consumption data of cold-region cities, and carbon emission grid data of cold-region cities are input into the land structure optimization model to calculate the land structure plan of the cold-region cities; the specific process is as follows:

[0040] S2.1.1: Based on GDP data, land use data, and existing building energy consumption data for cold-region cities, use the partial least squares (PLS) method to calculate the GDP coefficient and carbon emission coefficient for cold-region cities.

[0041] The land use data of cold-region cities include: the area of ​​commercial land, the area of ​​industrial land, the area of ​​residential land and the area of ​​public service land;

[0042] The GDP coefficient of cold-region cities includes: the economic coefficient of commercial land, the economic coefficient of industrial land, the economic coefficient of residential land and the economic coefficient of public service land;

[0043] The carbon emission coefficients of cold-region cities include: the carbon emission coefficients of commercial land, industrial land, residential land, and public service land;

[0044] S2.1.2: Construct a land structure optimization model based on the GDP coefficient, carbon emission coefficient, and carbon emission grid data of cold-region cities;

[0045] S2.1.3: Solve the land structure optimization model obtained in S2.1.2 to obtain the land structure plan for cold-region cities.

[0046] The other steps and parameters are the same as those in the first and second embodiments.

[0047] Specific embodiment 4: This embodiment differs from specific embodiments 1 to 3 in that:

[0048] In S2.1.1, the GDP coefficient and carbon emission coefficient of cold-region cities are calculated using the partial least squares (PLS) method based on the GDP data, land use data, and existing building energy consumption data of cold-region cities. The formula is:

[0049] f eb =g1·X1+g2·X2+g3·X3+g4·X4

[0050] f ce =e1·X1+e2·X2+g3·X3+e4·X4

[0051] X1 represents the area of ​​commercial land; X2 represents the area of ​​industrial land; X3 represents the area of ​​residential land; X4 represents the area of ​​public service land; it is obtained based on the land use data of cold-region cities and is well known to those skilled in the art.

[0052] f eb Indicates economic benefits; obtained based on GDP data of cold-region cities; well known to those in this field

[0053] f ce Represents carbon emissions; Carbon emissions are obtained by estimating fossil fuel consumption based on existing building energy consumption data in cold-region cities, which is well known to those in the field.

[0054] e1 represents the unit economic benefit coefficient of commercial land; e2 represents the unit economic benefit coefficient of industrial land; e3 represents the unit economic benefit coefficient of residential land; e4 represents the unit economic benefit coefficient of public service land;

[0055] g1 represents the unit carbon emission coefficient of commercial land; g2 represents the unit carbon emission coefficient of industrial land; g3 represents the unit carbon emission coefficient of residential land; g4 represents the carbon emission coefficient of public service land; other steps and parameters are the same as those in one of the specific implementation methods one to three.

[0056] Specific embodiment 5: This embodiment differs from specific embodiments 1 to 4 in that:

[0057] In S2.1.2, the land structure optimization model is constructed based on the GDP coefficient and carbon emission coefficient of cold-region cities. The specific process is as follows:

[0058] The objective function of the land structure optimization model is constructed based on the GDP coefficient and the carbon emission coefficient of the cold-region city; the land structure optimization model is obtained; the objective function of the land structure optimization model is expressed as follows:

[0059] Zy1=e1·XC1+e2·XI1+e3·XR1+e4·XT1

[0060] Zy2=e1·XC2+e2·XI2+e3·XR2+e4·XT2

[0061] …

[0062] Zy n =e1·XC n +e2·XI n +e3·XR n +e4 XT n

[0063] …

[0064] Zy N =e1·XC N +e2·XI N +e3·XR N +e4 XT N

[0065] Zz1=g1·XC1+g2·XI1+g3·XR1+g4·XT1

[0066] Zz2=g1·XC2+g2·XI2+g3·XR2+g4·XT2

[0067] …

[0068] Zz n =g1·XC n +g2·XI n +g3·XR n +g4·XT n

[0069] …

[0070] Zz N =g1·XC N +g2·XI N +g3·XR N +g4·XT N

[0071] N represents the total number of samples; Zy n Represents the emission data within the grid of the nth sample; Zz n represents the GDP data in the grid of the nth sample; XC n represents the number of commercial land areas in the grid of the nth sample; XI n represents the number of industrial land areas in the grid of the nth sample; XR n Indicates the number of residential land areas in the grid of the nth sample; XT n Indicates the number of public service land areas within the grid of the nth sample; the grid resolution is 1000m;

[0072] The other steps and parameters are the same as those in the first to fourth embodiments.

[0073] Specific embodiment 6: This embodiment differs from specific embodiments 1 to 5 in that:

[0074] In S2.1.3, the land structure optimization model obtained by solving S2.1.2 is used to obtain the land structure plan for cold-region cities. The specific process is as follows:

[0075] S2.1.3.1: Use the linear interactive general optimization algorithm (Lingo) to solve the various objective functions in the land structure optimization model obtained in S2.1.2, and obtain a result A = [XC n ,XI n ,XR n ,XT n ];

[0076] The Linear Interactive General Optimizer (Lingo) algorithm is an abbreviation of Linear Interactive and General Optimizer, which is an "interactive linear and general optimization solver" launched by Lindo Systems Inc. in the United States. It can be used to solve nonlinear programming and some linear and nonlinear equations. It is very powerful and is a well-known algorithm for solving optimization models.

[0077] The linear interactive general optimization algorithm (Lingo) method is used to calculate the shortest distance ideal point of each objective function in the land structure optimization model obtained in S2.1.2, which can be expressed as follows:

[0078] Max Zz n =g1·XC n +g2·XI n +g3·XR n +g4·XT n

[0079] Min Zy n =e1·XC n +e2·XI n +e3·XR n +e4 XT n

[0080]

[0081] Z——represents the i-th objective function, Z i * Represents the ideal solution of the objective function. The size of the ideal solution is artificially set. Use the ideal point method to solve x so that n objective functions Zi (x) as close to Z as possible i * , thus obtaining the solution of the multi-objective problem A=[XC n ,XI n ,XR n ,XT n ].

[0082] S2.1.3.2: Set a result deviation threshold Δ and calculate a deviation value ΔA based on result A. If the deviation value ΔA is less than the deviation threshold Δ, use result A as the land structure plan for the cold-region city. Otherwise, return to S2.1.3.1 to update result A.

[0083] The formula for calculating the deviation value ΔA based on the result A is:

[0084]

[0085] in:

[0086] f e (A) is the economic benefit value under outcome A;

[0087] f c (A) is the carbon emission value under result A;

[0088] It is the ideal maximum value of economic benefits;

[0089] It is the ideal minimum value for carbon emissions;

[0090] The calculation formulas for economic benefits and carbon emissions are introduced in 2.1.1.

[0091] f e (A) = g1·XC n +g2·XI n +g3·XR n +g4·XT n

[0092] f c (A) = e1·XC n +e2·XI n +g3·XR n +e4 XT n

[0093] The size of the ideal solution is artificially set. It is the ideal maximum value of economic benefits; is the ideal minimum value of carbon emissions; the normalization process (dividing by Ze*Ze* and Zc*Zc*) is to eliminate the dimensional differences between different targets. The other steps and parameters are the same as those of the first to fifth embodiments.

[0094] Specific embodiment 7: This embodiment differs from specific embodiments 1 to 6 in that:

[0095] In S2.2, a land space optimization model is constructed; the population data of cold-region cities, the basic building data of cold-region cities, and the land structure plan of cold-region cities are input into the land space optimization model to calculate the land space plan of cold-region cities; the specific process is as follows:

[0096] S2.2.1: Set the parameters of the land space optimization model and use Python to set the input parameters. The land space optimization model parameters are based on the multi-objective optimization model of NSGA-II;

[0097] The land space optimization model parameters include: the maximum number of iterations of the NSGA-II algorithm, the crossover rate, and the mutation rate;

[0098] The population data of the selected area and the actual perimeter data of the selected area are obtained based on the population data of the cold-region city, the basic building data of the cold-region city, and the land structure plan of the cold-region city;

[0099] S2.2.2: Use Python to construct the objective function of the land space optimization model based on the population data of cold-region cities, the basic building data of cold-region cities, and the land structure plan of cold-region cities.

[0100] The objective functions of the land space optimization model include: compactness maximization calculation function; suitability maximization calculation function; spatial carbon emission minimization calculation function;

[0101] S2.2.3: Construct a land space optimization model based on the land space optimization model parameters set in S2.2.1 and the objective function of the land space optimization model constructed in S2.2.2;

[0102] S2.2.4: Use the NSGA-II algorithm to solve the land space optimization model and obtain the land space plan for the cold region city;

[0103] The other steps and parameters are the same as those in the first to sixth embodiments.

[0104] Specific embodiment eight: This embodiment differs from specific embodiments one to seven in that:

[0105] The compactness maximization calculation function in S2.2.2 includes: the commercial land compactness maximization calculation function Maxf compactness 1. Maximization calculation function of industrial land compactness Maxf compactness2 ; Residential land compactness maximization calculation function Maxf compactness3; Calculation function Maxf for maximizing the compactness of public service land compactness4 ; expressed as:

[0106]

[0107] Where, L Sum 1 represents the actual perimeter of the commercial land plot; L MaxSum 1 represents the maximum possible perimeter of a commercial land parcel;

[0108] L MinSum 1 represents the minimum possible perimeter of a commercial land parcel;

[0109] L Sum2 Indicates the actual perimeter of the industrial land plot; L MaxSum2 Indicates the maximum possible perimeter of an industrial land parcel;

[0110] L MinSum2 Indicates the minimum possible perimeter of an industrial land parcel;

[0111] L Sum3 Indicates the actual perimeter of the residential land plot; L MaxSum3 It represents the maximum possible perimeter of a residential land plot;

[0112] L MinSum3 It represents the minimum possible perimeter of a residential land plot;

[0113] L Sum4 Indicates the actual perimeter of the public service land parcel; L MaxSum4 It represents the maximum possible perimeter of a public service land parcel;

[0114] L MinSum4 The minimum possible perimeter of a public service land parcel is expressed as:

[0115] L MaxSum 1=4XC n

[0116]

[0117] L Sum ——Actual perimeter; actual perimeter of the plot L Sumi is the solution obtained;

[0118] L MaxSumi ——maximum possible circumference; maximum possible circumference L MaxSumi It is the product of the total number of land parcels of type i and 4, assuming that all land use grids are not adjacent.

[0119] L MinSumi ——The minimum possible perimeter of the i-th type of land use plot; and the minimum possible perimeter L MinSumiIt is the circumference of a circle assuming that all land-use rasters are connected.

[0120] The other steps and parameters are the same as those in the first to seventh embodiments.

[0121] Specific embodiment 9: This embodiment differs from specific embodiments 1 to 8 in that the suitability maximization calculation function in S2.2.2 is expressed as follows:

[0122]

[0123] In the formula, suit ij represents the suitability value of the i-th commercial land parcel and the j-th residential land parcel, x ij It represents the combination variable of selecting the i-th commercial land parcel and the j-th residential land parcel. The calculation formula of the suitability value is well known to those skilled in the art.

[0124] x ij The value of is subject to land use restrictions, urban land area constraints, and neighborhood constraints for land use conversion.

[0125] 1. Land use restrictions

[0126]

[0127] When the land use nature is rivers, lakes, ecological protection areas and other lands that are not allowed to be developed in the city ij is 0, except that x ij is 1.

[0128] 2. All x ij The total area is less than or equal to the total urban land area;

[0129] 3. Neighborhood constraints for land use conversion: When a plot of land in a cold-region urban area has the same land use nature as the eight surrounding plots, the land cannot be converted into non-urban land during calculation.

[0130]

[0131] The spatial carbon emission minimization calculation function is expressed as follows:

[0132]

[0133] Where, ——Carbon emissions; CI——Physical compactness index of a city; Z i and represents the population of the i-th commercial land plot; Z j represents the population of the jth residential land plot; d 2(i,j) represents the Euclidean distance between the i-th commercial land parcel and the j-th residential land parcel; C represents the area variable, and the value of C is 100m2;

[0134] B represents the total number of plots; a represents the slope, and b represents the intercept term;

[0135] The slope is obtained by regression analysis of the data to fit the data to express the relationship between physical compactness index (CI) and carbon emissions (f carbon ) The negative sign indicates that as CI increases, carbon emissions will decrease. According to experimental calculations, the preferred value of a is -1.3588; b is the intercept term, which indicates the carbon emissions when CI is zero. According to experimental calculations, the preferred value of b is 25.506;

[0136] The other steps and parameters are the same as those in the first to eighth embodiments.

[0137] Specific embodiment 10: This embodiment differs from specific embodiments 1 to 9 in that:

[0138] In S2.2.4, the NSGA-II algorithm is used to solve the land space optimization model to obtain the land space plan for the cold region city. The specific process is as follows:

[0139] S2.2.4.1: Generate an initial population for initializing the NSGA-II algorithm. The initial population consists of K individuals, where K is a positive integer. Each individual represents a land space solution (i.e., what is the land use of each plot in the city)

[0140] S2.2.4.2: Calculate all objective function values ​​for each individual in the initial population based on the objective function of the land space optimization model;

[0141] According to the dominance relationship in the NSGA-II algorithm and all the objective function values ​​of each individual, all individuals are stratified.

[0142] According to the dominance relationship in the NSGA-II algorithm and all the objective function values ​​of each individual, all individuals are stratified. The process is well known to those skilled in the art. The level of each stratum is different. Individuals in the same stratum have the same level.

[0143] S2.2.4.3: Calculate the crowding degree of each individual in its layer;

[0144] The degree of density of each individual in its non-dominated layer is measured by crowding calculation. Crowding calculation involves scoring the density of each solution in the target space. Solutions with higher density are more favored.

[0145] S2.2.4.4: Based on the crowding degree of each individual in its layer, use the elite selection strategy to process the initial population and obtain A1 elite individuals;

[0146] Take the elite individuals as the parent individuals, perform crossover and mutation genetic processing in sequence, and obtain A2 offspring individuals; form a new population with A1 elite individuals and A2 offspring individuals;

[0147] The elite selection strategy belongs to the processing process of the NSGA-II algorithm well known to those skilled in the art, and the crossover genetic and mutation genetic processing also belong to the processing process of the NSGA-II algorithm well known to those skilled in the art;

[0148] S2.2.4.5: Determine whether the number of iterations has reached the maximum number of iterations.

[0149] When the number of iterations does not reach the maximum number of iterations, return to S2.2.4.2;

[0150] When the maximum number of iterations is reached, the individual with the highest objective function value is selected from the new population as the output individual;

[0151] S2.2.4.6: Obtain the land space plan of cold region cities based on the output individuals

[0152] The other steps and parameters are the same as those in the first to ninth embodiments.

[0153] Explanation with reference to specific implementations one to ten

[0154] This paper presents a multi-level design-based approach to optimizing urban carbon emissions. This framework combines structural and spatial optimization to create a dual-scale optimization framework. At the structural scale, the quantitative structure of land use is optimized to limit the expansion of high-carbon-emitting land while maintaining adequate economic returns. At the spatial scale, land compactness and suitability are optimized to reduce urban carbon emissions and enhance urban compactness. The approach involves data preparation and collection, structural-scale IMPL model calculation, spatial-scale NSGA-II algorithm solution, and optimized layout generation.

[0155] This paper proposes a multi-scale, low-carbon, multi-objective land use allocation optimization model that combines ideal point multi-objective linear programming (IMLP) and the non-dominated sorting genetic algorithm II (NSGA-II) to optimize low-carbon emission patterns in cities at both the structural and spatial scales. Structural optimization focuses on balancing economic and carbon reduction benefits, while spatial optimization focuses on the compactness and suitability of urban space to reduce carbon emissions.

[0156] Compared with previous methods for optimizing carbon emissions in cold-region cities, this invention has the following innovations:

[0157] 1. The introduction of a multi-scale optimization model proposes a dual-scale optimization model that combines the structural scale and the spatial layout scale. This approach not only focuses on the quantitative structure of land use in urban land use optimization, but also considers the impact of spatial layout on carbon emissions. This multi-scale optimization framework addresses the shortcomings of existing single-scale research and enables a more comprehensive low-carbon urban planning approach.

[0158] 2. The integrated application of multi-objective algorithms combines the Ideal Point Multi-Objective Linear Programming (IMLP) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to optimize low-carbon land use layout. IMLP optimizes the quantitative structure to balance economic benefits and carbon emissions, while NSGA-II optimizes the spatial layout to improve land use compactness and suitability. This integrated application significantly improves the efficiency and adaptability of the optimization model.

[0159] 3. Combining urban space optimization with specific data effectively improves the technical and scientific nature of low-carbon urban planning in cold-region cities.

[0160] The above only describes the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the above-mentioned specific implementation methods. Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. Any technician familiar with this profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical content disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent replacements and improvements made to the above embodiments without departing from the content of the technical solution of the present invention, based on the technical essence of the present invention, within the spirit and principles of the present invention, still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for optimizing the layout of cold-region cities to reduce carbon emissions, characterized in that: include: S1: Obtain cold region city data; S2: Construct a multi-objective land use allocation optimization model; The acquired cold region city data were input into the multi-objective land use allocation optimization model; Obtained the optimized cold region city layout plan; The cold-region city data in S1 include: cold-region city GDP data, cold-region city building basic data, cold-region city land use data, cold-region city existing building energy consumption data, cold-region city carbon emission grid data and cold-region city population data.

2. A cold region city layout optimization method for reducing carbon emissions according to claim 1, characterized in that: The multi-objective land use allocation optimization model in S2 includes: a land structure optimization model and a land space optimization model; The multi-objective land use allocation optimization model is constructed; the acquired cold region city data is input into the multi-objective land use allocation optimization model; and an optimized cold region city layout plan is obtained. The specific process is as follows: S2.1: Construct a land structure optimization model; input GDP data, land use data, existing building energy consumption data, and carbon emission grid data of cold-region cities into the land structure optimization model to calculate the land structure plan for cold-region cities; S2.2: Construct a land space optimization model; input the population data of cold-region cities, the basic building data of cold-region cities, and the land structure plan of cold-region cities into the land space optimization model to calculate the land space plan of cold-region cities.

3. A cold region city layout optimization method for reducing carbon emissions according to claim 2, characterized in that: In S2.1, a land structure optimization model is constructed; GDP data of cold-region cities, land use data of cold-region cities, existing building energy consumption data of cold-region cities, and carbon emission grid data of cold-region cities are input into the land structure optimization model to calculate the land structure plan of the cold-region cities; the specific process is as follows: S2.1.1: Based on GDP data, land use data, and existing building energy consumption data for cold-region cities, calculate the GDP coefficient and carbon emission coefficient for cold-region cities using the partial least squares (PLS) method; The land use data of cold-region cities include: the area of ​​commercial land, the area of ​​industrial land, the area of ​​residential land and the area of ​​public service land; The GDP coefficient of cold-region cities includes: the economic coefficient of commercial land, the economic coefficient of industrial land, the economic coefficient of residential land and the economic coefficient of public service land; The carbon emission coefficients of cold-region cities include: the carbon emission coefficients of commercial land, industrial land, residential land, and public service land; S2.1.2: Construct a land structure optimization model based on the GDP coefficient, carbon emission coefficient, and carbon emission grid data of cold-region cities; S2.1.3: Solve the land structure optimization model obtained in S2.1.2 to obtain the land structure plan for cold-region cities.

4. A cold region city layout optimization method for reducing carbon emissions according to claim 3, characterized in that: In S2.1.1, the GDP coefficient and carbon emission coefficient of cold-region cities are calculated using the partial least squares (PLS) method based on the GDP data, land use data, and existing building energy consumption data of cold-region cities. The formula is: f eb =g1·X1+g2·X2+g3·X3+g4·X4 f ce =e1·X1+e2·X2+g3·X3+e4·X4 X1 represents the area of ​​commercial land; X2 represents the area of ​​industrial land; X3 represents the area of ​​residential land; X4 represents the area of ​​public service land; f eb Indicates economic benefits; f ce represents carbon emissions; e1 represents the unit economic benefit coefficient of commercial land; e2 represents the unit economic benefit coefficient of industrial land; e3 represents the unit economic benefit coefficient of residential land; e4 represents the unit economic benefit coefficient of public service land; g1 represents the unit carbon emission coefficient of commercial land; g2 represents the unit carbon emission coefficient of industrial land; g3 represents the unit carbon emission coefficient of residential land; g4 represents the unit carbon emission coefficient of public service land.

5. A cold region city layout optimization method for reducing carbon emissions according to claim 4, characterized in that: In S2.1.2, the land structure optimization model is constructed based on the GDP coefficient and carbon emission coefficient of cold-region cities. The specific process is as follows: The objective function of the land structure optimization model is constructed based on the obtained GDP coefficient and carbon emission coefficient of the cold-region city; the land structure optimization model is obtained; the objective function of the land structure optimization model is expressed as follows: Zy1=e1·XC1+e2·XI1+e3·XR1+e4·XT1 Zy2=e1·XC2+e2·XI2+e3·XR2+e4·XT2 …… Zy n =e1·XC n +e2·XI n +e3·XR n +e4·XT n …… Zy N =e1·XC N +e2·XI N +e3·XR N +e4·XT N Zz1=g1·XC1+g2·XI1+g3·XR1+g4·XT1 Zz2=g1·XC2+g2·XI2+g3·XR2+g4·XT2 …… Zz n =g1·XC n +g2·XI n +g3·XR n +g4·XT n …… Zz N =g1·XC N +g2·XI N +g3·XR N +g4·XT N N represents the total number of samples; Zy n Represents the emission data within the grid of the nth sample; Zz n represents the GDP data in the grid of the nth sample; XC n represents the number of commercial land areas in the grid of the nth sample; XI n represents the number of industrial land areas in the grid of the nth sample; XR n Indicates the number of residential land areas in the grid of the nth sample; XT n represents the number of areas corresponding to public service land in the grid of the nth sample; the grid scale resolution of the grid is 1000m; · represents multiplication.

6. A cold region city layout optimization method for reducing carbon emissions according to claim 5, characterized in that: In S2.1.3, the land structure optimization model obtained by solving S2.1.2 is used to obtain the land structure plan for cold-region cities. The specific process is as follows: S2.1.3.1: Use the linear interactive general optimization algorithm to solve the various objective functions in the land structure optimization model obtained in S2.1.2 and obtain a result A = [XC n ,XI n ,XR n ,XT n ]; S2.1.3.2: Set a result deviation threshold Δ, calculate a deviation value ΔA based on result A, and if the deviation value ΔA is less than the deviation threshold Δ, use result A as the land structure plan for the cold region city. Otherwise, return to S2.1.

2. The formula for calculating the deviation value ΔA based on the result A is: in: f e (A) is the economic benefit value under outcome A; f c (A) is the carbon emission value under result A; It is the ideal maximum value of economic benefits; It is the ideal minimum value for carbon emissions.

7. A cold region city layout optimization method for reducing carbon emissions according to claim 6, characterized in that: In said S2.2, a land space optimization model is constructed; population data of cold-region cities, basic building data of cold-region cities, and land structure plans of cold-region cities are input into the land space optimization model to calculate the land space plans of cold-region cities; The specific process is: S2.2.1: Set parameters of the land space optimization model; The land space optimization model parameters include: the maximum number of iterations of the NSGA-II algorithm, the crossover rate, and the mutation rate; S2.2.2: Construct the objective function of the land space optimization model based on the population data of cold-region cities, the basic building data of cold-region cities, and the land structure plan of cold-region cities. The objective functions of the land space optimization model include: compactness maximization calculation function; suitability maximization calculation function; spatial carbon emission minimization calculation function; S2.2.3: Construct a land space optimization model based on the land space optimization model parameters set in S2.2.1 and the objective function of the land space optimization model constructed in S2.2.2; S2.2.4: Use the NSGA-II algorithm to solve the land space optimization model and obtain the land space plan for cold-region cities.

8. A cold region city layout optimization method for reducing carbon emissions according to claim 7, characterized in that: The compactness maximization calculation function in S2.2.2 includes: the commercial land compactness maximization calculation function Maxf compactness1 ; Industrial land compactness maximization calculation function Maxf compactness2 ; Residential land compactness maximization calculation function Maxf compactness3 ; Calculation function Maxf for maximizing the compactness of public service land compactness4 ; expressed as: Where, L Sum1 Indicates the actual perimeter of the commercial land plot; L MaxSum1 Indicates the maximum possible perimeter of a commercial land parcel; L MinSum1 Indicates the minimum possible perimeter of a commercial land parcel; L Sum2 Indicates the actual perimeter of the industrial land plot; L MaxSum2 Indicates the maximum possible perimeter of an industrial land parcel; L MinSum2 Indicates the minimum possible perimeter of an industrial land parcel; L Sum3 Indicates the actual perimeter of the residential land plot; L MaxSum3 It represents the maximum possible perimeter of a residential land plot; L MinSum3 It represents the minimum possible perimeter of a residential land plot; L Sum4 Indicates the actual perimeter of the public service land parcel; L MaxSum4 It represents the maximum possible perimeter of a public service land parcel; L MinSum4 The minimum possible perimeter of a public service land parcel is expressed as: L MaxSum1 =4XC n L MaxSum2 =4XI n IT MaxSum3 =4XR n THE MaxSum4 =4XT n 9. The method for optimizing the layout of cold-region cities to reduce carbon emissions according to claim 8, characterized in that: The suitability maximization calculation function in S2.2.2 is expressed as follows: In the formula, suit ij represents the suitability value of the i-th commercial land parcel and the j-th residential land parcel, x ij represents the combination variable of selecting the i-th commercial land parcel and the j-th residential land parcel; The spatial carbon emission minimization calculation function is expressed as follows: Where, ——Carbon emissions; CI——Physical compactness index of a city; Z i and represents the population of the i-th commercial land plot; Z j represents the population of the jth residential land plot; d 2 (i,j) represents the Euclidean distance between the i-th commercial land parcel and the j-th residential land parcel; C represents the area variable, B represents the total number of plots; a represents the slope, and b represents the intercept term.

10. A cold region city layout optimization method for reducing carbon emissions according to claim 9, characterized in that: In S2.2.4, the NSGA-II algorithm is used to solve the land space optimization model to obtain the land space plan for the cold region city. The specific process is as follows: S2.2.4.1: Generate an initial population for initializing the NSGA-II algorithm, wherein the initial population includes K individuals, where K is a positive integer; S2.2.4.2: Calculate all objective function values ​​for each individual in the initial population based on the objective function of the land space optimization model; According to the dominance relationship in the NSGA-II algorithm and all the objective function values ​​of each individual, all individuals are stratified; S2.2.4.3: Calculate the crowding degree of each individual in its layer; S2.2.4.4: Based on the crowding degree of each individual in its layer, use the elite selection strategy to process the initial population and obtain A1 elite individuals; Take the elite individuals as the parent individuals, perform crossover and mutation genetic processing in sequence, and obtain A2 offspring individuals; A1 elite individuals and A2 offspring individuals form a new population; S2.2.4.5: Determine whether the number of iterations has reached the maximum number of iterations. When the number of iterations does not reach the maximum number of iterations; Return to S2.2.4.2; When the maximum number of iterations is reached, the individual with the highest objective function value is selected from the new population as the output individual; S2.2.4.6: Obtain the land space plan for cold-region cities based on the output individuals.

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