Coastal city land utilization pattern optimization method based on land-sea carbon sink potential excavation
By establishing the correlation between land and sea carbon sink potential, using two-dimensional hydrodynamic and particle tracer models to determine the scope of land-sea integration, calculating carbon emissions and carbon sink capacity, and constructing a multi-objective optimization model, the problem of carbon sink optimization in land use in coastal cities was solved, achieving refined planning and improved economic benefits.
Patent Information
- Application Number
- CN202511713983.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-20
- Publication Date
- 2026-02-27
AI Technical Summary
The current lack of methods to optimize land-sea carbon sinks through urban land planning has resulted in deficiencies in carbon emission calculation and utilization in coastal cities.
Establish the correlation between land use and land-sea carbon sink potential, determine the scope of land-sea integration through a two-dimensional hydrodynamic model and a Lagrange particle tracer model, calculate land-based carbon emissions and marine carbon sink capacity, and construct a multi-objective spatial optimization model to achieve land use pattern optimization.
It enables refined planning of land use in coastal cities, maximizes the potential of land and sea carbon sinks and economic benefits, ensures that ecological land is not reduced, and provides an optimized framework that comprehensively considers socio-economic goals.
Smart Images
Figure CN121581670A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application relates to a city land use pattern optimization method, in particular to a coastal city land use pattern optimization method based on land-sea carbon sink potential mining. BACKGROUND
[0002] With the increase of carbon dioxide content due to economic development, the temperature of the earth is provided year by year, and the environmental protection consciousness of people is gradually raised, and the calculation of carbon emission has become an important research topic.
[0003] The coastal city can fully utilize the geographical advantages of the coast, and the land area and the nearshore sea area of the city are taken as a whole to mine the carbon sink potential, but at present, there is a lack of a method for optimizing the land-sea carbon sink by planning the land area of the city. SUMMARY
[0004] The application discloses a coastal city land use pattern optimization method based on land-sea carbon sink potential mining, establishes the correlation between land use and land-sea carbon sink potential, and accounts for the land-sea carbon sink potential of the coastal city.
[0005] The application discloses a coastal city land use pattern optimization method based on land-sea carbon sink potential mining, which comprises the following steps: S1. determining a target sea area and its associated land, and delimiting the land-sea integration range of the coastal city; S2. accounting for the land carbon emission based on the land use data of the associated land; S3. establishing a calculation method of 'land use-pollution emission-sea carbon sink capacity' based on the measured data and the numerical model; S4. constructing a multi-objective spatial optimization model to realize land use pattern optimization.
[0006] Further, the S1. determining a target sea area and its associated land, and delimiting the land-sea integration range of the coastal city comprises the following steps: S11. establishing a two-dimensional hydrodynamic model; S12. establishing a Lagrangian particle tracking model for reflecting the spatial distribution and migration path of pollutants; S13. determining a target sea area and its associated land, and delimiting the land-sea integration range to determine the influence range of the land source on the nearshore sea area.
[0007] Further, the S12. establishing a Lagrangian particle tracking model comprises the following steps: S121. a calculation formula for solving results is established by coupling the Lagrangian particle tracking model with the hydrodynamic water quality model MIKE21 as follows:
[0008] In the formula, a is a drift term, b is a diffusion term, is a random number; S122. Given the first position under the Euler method and the physical quantity of the region where the first position is located, the position moved to the next moment can be obtained by using the recursive method, and the specific discrete format is as follows:
[0009] In the formula, Y is the trajectory position; is the increment of the Wiener process W obeying the standard Gaussian distribution, and the Wiener process is also called Brown motion process, which is a continuous-time random process with geographical increment on the sub-interval.
[0010] Further, the S2. calculates the land carbon emission based on the land use data, comprising: S21. Statistics of land use types are used to distinguish carbon sink type land and carbon source type land; S22. Calculate the amount of land carbon emission.
[0011] Further, the S22. calculates the amount of land carbon emission, comprising: The carbon sink coefficient method is used to calculate and determine the carbon sink capacity of the target sea area and its associated land, and the calculation formula is as follows:
[0012] In the formula, CA is the carbon emission of land use, A is the area of each land type, and a is the carbon emission coefficient of each land type.
[0013] Further, the S3 establishes a calculation method of "land use-pollution production and emission-sea carbon sink capacity" based on measured data and numerical model, comprising: S31. Total amount of land source pollution production and emission estimation; S32. Establish the relationship between land source pollution production and emission and sea water quality index; S33. Relationship between seawater quality index and CO2 partial pressure in seawater; S34. Calculate the carbon sink capacity of seawater.
[0014] Further, the S4 constructs a multi-objective spatial optimization model to realize the optimization of land use pattern, comprising: S41. The research area is divided to form a plurality of small grid units, and the definition decision variable corresponding to each unit is determined; S42. Set one or more objectives to establish an objective function; S43. Determine the constraint conditions of the model; S44. Optimization model algorithm.
[0015] Further, the S42. sets one or more objectives to establish an objective function, comprising: The first objective is to maximize the total land and sea carbon sink capacity, expressed as Maximize(ZC) = CA + CS, where the land and sea areas are determined by S1, land carbon sinks are calculated using S2, and sea carbon sinks are calculated using S3. The second objective is to maximize economic benefits. Based on the data of agricultural, forestry, animal husbandry and fishery output value and secondary and tertiary industry output value in the national economic statistics of the study area over the years, the economic benefits of each land type are obtained. Dividing the economic benefits by the corresponding land type area yields the economic output per unit area of that land type, which is then used as the economic benefit coefficient of each land type.
[0016] The third objective is to minimize land use type changes by accumulating the number of land use type changes.
[0017] Furthermore, S43. Determining the constraints of the model includes: The first condition is that the sum of the areas of all types of land use equals the total area.
[0018] The second condition is that the total area of construction land is ≥ the planned (existing) population × the per capita construction land standard.
[0019] The third condition is that the total area of cultivated land is greater than or equal to the red line for the protection of basic farmland.
[0020] The fourth condition is that ecological land use will only increase and not decrease.
[0021] Furthermore, the S44. optimization model algorithm includes: S441. Initialization: Randomly generate multiple "chromosomes", each chromosome representing a random land use layout scheme; S442. Evaluation: Evaluate each solution using the objective function and constraints; S443. Selection, Crossover, and Mutation: Set a score, cross-mix and randomly fine-tune the high-scoring schemes to generate a new generation of schemes; S444. Iteration: Repeat steps S442 and S443 until the predetermined number of iterations is reached or the solution set tends to stabilize, then stop; S445. Output: Finally, a set of Pareto fronts is obtained, resulting in the optimized model algorithm.
[0022] Compared with existing technologies, this invention incorporates the socio-economic goals of land and sea carbon sink potential into a spatially explicit multi-objective optimization framework, and develops corresponding calculation models and algorithms to achieve refined spatial planning of "land-sea integration", proposing an innovative approach to land use planning in coastal cities. Attached Figure Description
[0023] Figure 1 This is a flowchart of the optimization method according to an embodiment of the present invention. Detailed Implementation
[0024] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.
[0025] This invention discloses a method for optimizing land use patterns in coastal cities based on the exploration of land-sea carbon sink potential, such as... Figure 1 As shown, it includes: S1. Determine the target sea area and its associated land, and delineate the scope of integrated land and sea development for coastal cities; S2. Calculate land-based carbon emissions based on land use data of associated land areas; S3. Based on measured data and numerical models, establish a calculation method for "land use - pollution generation and discharge - marine carbon sequestration capacity"; S4. Construct a multi-objective spatial optimization model to achieve land use pattern optimization.
[0026] This invention integrates the socio-economic goals of land-sea carbon sink potential into a spatially explicit multi-objective optimization framework, and develops corresponding computational models and algorithms to achieve refined spatial planning of "land-sea integration", proposing an innovative approach to land use planning in coastal cities.
[0027] Optionally, S1. Determining the target sea area and its associated land, and delineating the integrated land-sea area of coastal cities, includes: S11. Establish a two-dimensional hydrodynamic model; Among them, the DHI MIKE software was used to establish a two-dimensional hydrodynamic numerical model of the sea area under the jurisdiction of the coastal city, and the measured tidal level and tidal current data were used to verify the establishment of the two-dimensional hydrodynamic model. S12. Establish a Lagrange particle tracing model to reflect the spatial distribution and migration paths of pollutants; Among them, the main principle of the particle tracer model is based on the flow field results calculated in real time by the hydrodynamic model. It relies on the particle motion trajectory to clearly show the spatial water flow motion, thereby reflecting the spatial distribution and migration path of pollutants. S13. Determine the target sea area and its associated land, and delineate the integrated land-sea area to determine the extent of the land-based influence on the nearshore sea area; The study sets up scenarios for the release of land-based particles, with key release areas including rivers flowing into the sea and areas with dense human activity. It simulates particle drift trajectories under different scenarios, delineates the range of land-based influence on nearshore waters within the jurisdictional sea area, and thus determines the scope of land-sea integration.
[0028] Specifically, S12. Establishing the Lagrange particle tracer model includes: S121. The calculation formula for the solution result is established by coupling the hydrodynamic water quality model MIKE21 with the Lagrange particle tracer model as follows:
[0029] In the formula, a is the drift term and b is the diffusion term. It is a random number; S122. Given the first position and the physical quantities of the region where the first position is located under the Euler method, the position at the next moment can be obtained by recursion. The specific discretization scheme used is as follows:
[0030] In the formula, Y represents the trajectory position; The increment of W in a Wiener process that follows a standard Gaussian distribution is called the Wiener process, also known as the Brownian process, which is a type of process in... A continuous-time stochastic process with geographical increments over subintervals.
[0031] Optionally, S2. Calculating land-based carbon emissions based on land use data includes: S21. Statistical analysis of land use types to distinguish between carbon sink land and carbon source land; The statistics include the area of six land use types: forest land, grassland, cultivated land, water area, unused land, and construction land. Forest land, grassland, water area, and unused land have significant carbon absorption capacity and are considered carbon sinks; cultivated land and construction land have carbon emission capacity and are considered carbon sources. S22. Calculate land-based carbon emissions.
[0032] Specifically, S22. calculating land-based carbon emissions includes: The carbon sequestration capacity of forest land, grassland, cultivated land, water area, unused land, and built-up land is calculated using the carbon sequestration coefficient method. The calculation formulas are as follows:
[0033] In the formula, CA represents the carbon emissions from land use, A represents the area of each land type, and a represents the carbon emission coefficient for each land type.
[0034] The carbon emission coefficient for construction land can be obtained through an indirect method. Carbon emission coefficients for other land use types can be found in relevant literature. For example, the carbon emission coefficients for forest land, grassland, cultivated land, water bodies, and unused land in the Jiangsu and Zhejiang regions are -0.581, -0.021, 0.497, -0.253, -0.005, and tons / hectare, respectively. The calculation of carbon emissions from construction land can initially employ the IPCC carbon emission coefficient method, indirectly based on the consumption of fossil fuels used in production and daily life and their carbon emission coefficients. The calculation formula is as follows:
[0035] In the formula, CB represents indirect carbon emissions from land use (tons), B represents energy consumption (tons), and θ represents the energy conversion factor to standard coal equivalent. This is the energy carbon emission factor. Based on the city's consumption of various energy types recorded in local statistical yearbooks, the standard coal equivalent coefficient is given according to the *China Energy Statistical Yearbook*, and the energy carbon emission factor is given according to the *IPCC National Greenhouse Gas Inventory Guidelines*. Then, by dividing by the area of the construction land, the carbon emission factor of the construction land is calculated.
[0036] Optionally, S3 establishes a calculation method for "land use-pollution generation and discharge-marine carbon sequestration capacity" based on measured data and numerical models, including: S31. Estimation of total wastewater discharge from land-based sources; The total land-based pollution generation and discharge for different land use types is calculated using the pollution generation and discharge coefficient method. The formula is as follows:
[0037] In the formula, ZA represents the amount of pollutants generated and discharged by land use (tons), A represents the area of each land type (hectares), ECi represents the pollutant generation coefficient corresponding to each land use type, and SCi represents the sea discharge coefficient corresponding to each land use type.
[0038] The pollutant generation coefficient (ECi) refers to the amount of pollutants generated per unit area of land use per unit time. It is determined through the fusion of multi-source data, including historical literature reviews, field monitoring, municipal statistical reports, and model inversion, and uncertainty analysis is performed. The main pollutant indicators include: chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP).
[0039] The sea discharge coefficient (SCi) refers to the proportion (0-1) of pollutants that migrate from their place of origin to the land-based sea discharge outlet. This coefficient comprehensively considers multiple processes, including distance from the coast, surface runoff, soil interception, degradation in ditches, and removal by wastewater treatment plants.
[0040] S32. Establish the relationship between land-based pollution generation and marine water quality indicators; The hydrodynamic model established in S11 was used to couple with a water quality model. The pollutant load fluxes calculated in S31 were generalized to the coastal area and used as point source inputs for the model. Degradation coefficients were set to calculate the concentrations of chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) in nearshore waters. S33. Relationship between seawater quality indicators and CO2 partial pressure in seawater; This involves collecting a large amount of synchronous field observation data covering different seasons, including pCO2 (dependent variable) and synchronously collected chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) (independent variables). Field monitoring can also be conducted to enrich the data, using surface seawater pCO2, COD, TN, and TP as parameters. Algorithms such as Random Forest, XGBoost, or ANN are used to train a nonlinear mapping model from multiple environmental parameters (COD, TN, TP) to pCO2. The water quality / environmental parameter field output from S32 is then input into this trained machine learning model to obtain the pCO2 (water) distribution field for the entire sea area. S34. Calculate the carbon sequestration capacity of seawater.
[0041] The calculation of the net CO2 flux distribution field at the air-sea interface represents the annual carbon sequestration capacity of the sea area. The formula for calculating the net CO2 flux at the air-sea interface (F, mmol / m2 / d) is as follows:
[0042] In the formula, k is the air-sea exchange coefficient, α is the solubility of CO2 in seawater, pCO2(water) is the partial pressure of CO2 in seawater (calculated in step 3.3), and pCO2(air) is the partial pressure of CO2 in the atmosphere (obtainable from meteorological data). The formula for calculating solubility α is as follows:
[0043] In the formula, T is the seawater temperature at the inlet (T is the absolute temperature, T = t + 273.15), and S is the seawater salinity. The coefficients A1, A2, B1, B2, and B3 have values of -60.2409, 93.4517, 23.3585, 0.023517, -0.023656, and 0.0047036 mol / kg / atm, respectively. The exchange coefficient k is related to wind speed and sea surface temperature, and its calculation formula is as follows:
[0044] In the formula, u10 is the wind speed at a height of 10m above the sea surface, and Sc is the Schmidt number at the surface sea temperature.
[0045] The formula for calculating marine carbon sequestration capacity (CS) is as follows:
[0046] In the formula, CS represents the carbon emissions / absorption of the sea area (tons), Fi represents the calculated net CO2 flux at the air-sea interface (mmol / m2 / d), Si represents the sea area corresponding to Fi (m2), and its total area is consistent with the sea area range obtained in step 1. The corresponding area Si of Fi can be obtained through the distribution field (contour lines) of F. mm is the conversion factor, calculated based on 365 days a year and a molar mass of carbon of 12 grams. .
[0047] Optionally, S4 constructs a multi-objective spatial optimization model to achieve land use pattern optimization, including: S41. Divide the study area into multiple small grid units and determine the decision variables corresponding to each unit; The study area is divided into many small grid units (e.g., 30x30 meters). For each unit i, a decision variable Xi is defined, whose value represents the land use type assigned to that unit (e.g., 1=woodland, 2=grassland, 3=arable land, 4=water area, 5=unused land, 6=construction land). The algorithm works by trying to change the value of Xi to find an optimal spatial combination of land use types. S42. Set one or more objectives and establish an objective function; S43. Determine the constraints of the model; S44. Optimize the model algorithm.
[0048] Specifically, S42. sets one or more objectives and establishes an objective function, including: The first objective is to maximize the total land and sea carbon sink capacity, expressed as Maximize(ZC) = CA + CS, where the land and sea areas are determined by S1, land carbon sinks are calculated using S2, and sea carbon sinks are calculated using S3. The second objective is to maximize economic benefits. Based on the data of agricultural, forestry, animal husbandry and fishery output value and secondary and tertiary industry output value in the national economic statistics of the study area over the years, the economic benefits of each land type are obtained. Dividing the economic benefits by the corresponding land type area yields the economic output per unit area of that land type, which is then used as the economic benefit coefficient of each land type.
[0049] The third objective is to minimize land use type changes by accumulating the number of land use type changes.
[0050] Specifically, S43. Determining the constraints of the model includes: The first condition is that the sum of the areas of all types of land use equals the total area.
[0051] The second condition is that the total area of construction land is ≥ the planned (existing) population × the per capita construction land standard.
[0052] The third condition is that the total area of cultivated land is greater than or equal to the red line for the protection of basic farmland.
[0053] The fourth condition is that ecological land use will only increase and not decrease; In principle, the area of forest land, grassland, and water area shall not be reduced.
[0054] Specifically, the S44. optimization model algorithm includes: S441. Initialization: Randomly generate multiple "chromosomes", each chromosome representing a random land use layout scheme; S442. Evaluation: Evaluate each solution using the objective function and constraints; S443. Selection, Crossover, and Mutation: Set a score, cross-mix and randomly fine-tune the high-scoring schemes to generate a new generation of schemes; S444. Iteration: Repeat steps S442 and S443 until the predetermined number of iterations is reached or the solution set tends to stabilize, then stop; The number of iterations can reach thousands; S445. Output: Finally, a set of Pareto fronts is obtained, resulting in the optimization model algorithm. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this application specification, they can still modify or make equivalent substitutions to the specific implementation of the present invention, but these modifications or changes do not depart from the protection scope of the pending claims of the present invention.
Claims
1. A method for optimizing land use patterns in coastal cities based on the exploration of land-sea carbon sink potential, characterized in that, include: S1. Determine the target sea area and its associated land, and delineate the scope of integrated land and sea development for coastal cities; S2. Calculate land-based carbon emissions based on land use data of associated land areas; S3. Based on measured data and numerical models, establish a calculation method for "land use - pollution generation and discharge - marine carbon sequestration capacity"; S4. Construct a multi-objective spatial optimization model to achieve land use pattern optimization.
2. The method for optimizing land use patterns in coastal cities based on the exploration of land-sea carbon sink potential, as described in claim 1, is characterized in that... S1. Determining the target sea area and its associated land, and delineating the integrated land-sea area of coastal cities, includes: S11. Establish a two-dimensional hydrodynamic model; S12. Establish a Lagrange particle tracing model to reflect the spatial distribution and migration paths of pollutants; S13. Determine the target sea area and its associated land, and delineate the integrated land-sea area to determine the extent of the land-based influence on the nearshore sea area.
3. The method for optimizing land use patterns in coastal cities based on the exploration of land-sea carbon sink potential, as described in claim 2, is characterized in that... S12. Establishing a Lagrange particle tracer model includes: S121. The calculation formula for the solution result is established by coupling the hydrodynamic water quality model MIKE21 with the Lagrange particle tracer model as follows: In the formula, a is the drift term and b is the diffusion term. It is a random number; S122. Given the first position and the physical quantities of the region where the first position is located under the Euler method, the position at the next moment can be obtained by recursion. The specific discretization scheme used is as follows: In the formula, Y represents the trajectory position; The increment of W in a Wiener process that follows a standard Gaussian distribution is called the Wiener process, also known as the Brownian process, which is a type of process in... A continuous-time stochastic process with geographical increments over subintervals.
4. The method for optimizing land use patterns in coastal cities based on the exploration of land-sea carbon sink potential, as described in claim 1, is characterized in that... S2. Based on land use data, calculate land-based carbon emissions, including: S21. Statistical analysis of land use types to distinguish between carbon sink land and carbon source land; S22. Calculate land-based carbon emissions.
5. The method for optimizing land use patterns in coastal cities based on the exploration of land-sea carbon sink potential, as described in claim 4, is characterized in that... S22. Calculating land-based carbon emissions includes: The carbon sink coefficient method is used to calculate and determine the carbon sink capacity of the target sea area and its associated land, respectively. The calculation formula is as follows: In the formula, CA represents the carbon emissions from land use, A represents the area of each land type, and a represents the carbon emission coefficient for each land type.
6. The method for optimizing land use patterns in coastal cities based on the exploration of land-sea carbon sink potential, as described in claim 1, is characterized in that... S3 establishes a calculation method for "land use - pollution generation and discharge - marine carbon sequestration capacity" based on measured data and numerical models, including: S31. Estimation of total wastewater discharge from land-based sources; S32. Establish the relationship between land-based pollution generation and marine water quality indicators; S33. Relationship between seawater quality indicators and CO2 partial pressure in seawater; S34. Calculate the carbon sequestration capacity of seawater.
7. The method for optimizing land use patterns in coastal cities based on the exploration of land-sea carbon sink potential, as described in claim 1, is characterized in that... The S4 constructs a multi-objective spatial optimization model to achieve land use pattern optimization, including: S41. Divide the study area into multiple small grid cells and determine the decision variables corresponding to each cell; S42. Set one or more objectives and establish the objective function; S43. Determine the constraints of the model; S44. Optimize the model algorithm.
8. The method for optimizing land use patterns in coastal cities based on the exploration of land-sea carbon sink potential, as described in claim 7, is characterized in that... S42. Set one or more objectives and establish an objective function, including: The first objective is to maximize the total land and sea carbon sink capacity, expressed as Maximize(ZC) = CA + CS, where the land and sea areas are determined by S1, land carbon sinks are calculated using S2, and sea carbon sinks are calculated using S3. The second objective is to maximize economic benefits. Based on the data of agricultural, forestry, animal husbandry and fishery output value and secondary and tertiary industry output value in the national economic statistics of the study area over the years, the economic benefits of each land type are obtained. Dividing the economic benefits by the corresponding land type area yields the economic output per unit area of that land type, which is then used as the economic benefit coefficient of each land type. The third objective is to minimize land use type changes by accumulating the number of land use type changes.
9. The method for optimizing land use patterns in coastal cities based on the exploration of land-sea carbon sink potential, as described in claim 7, is characterized in that... S43. Determining the constraints of the model includes: The first condition is that the sum of the areas of all types of land use equals the total area. The second condition is that the total area of construction land is ≥ the planned (existing) population × the per capita construction land standard. The third condition is that the total area of cultivated land is greater than or equal to the red line for the protection of basic farmland. The fourth condition is that ecological land use will only increase and not decrease.
10. The method for optimizing land use patterns in coastal cities based on the exploration of land-sea carbon sink potential, as described in claim 7, is characterized in that... The S44. optimization model algorithm includes: S441. Initialization: Randomly generate multiple "chromosomes", each chromosome representing a random land use layout scheme; S442. Evaluation: Evaluate each solution using the objective function and constraints; S443. Selection, Crossover, and Mutation: Set a score, cross-mix and randomly fine-tune the high-scoring schemes to generate a new generation of schemes; S444. Iteration: Repeat steps S442 and S443 until the predetermined number of iterations is reached or the solution set tends to stabilize, then stop; S445. Output: Finally, a set of Pareto fronts is obtained, resulting in the optimized model algorithm.