Gis-based industrial green land carbon assessment decision system and method

Abstract

The application belongs to the technical field of industrial green land planning, and discloses a GIS-based industrial green land carbon assessment decision system and method. Through acquisition of multi-source dynamic data, spatial unit division, prediction of dynamic carbon emission, identification of hot spot areas and time, and integrated scheme decision considering factors such as vegetation characteristics and budget constraints, the system can comprehensively consider the dynamic changes of industrial development, the budget of green land construction in different stages, and the time sequence characteristics of vegetation growth, and generate a green land planning scheme that optimally matches carbon sink benefits and cost input within a planning period.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial green land planning, in particular to a GIS-based industrial green land carbon assessment decision system and method. BACKGROUND

[0002] The core challenge faced by urban planning departments in planning industrial green land systems is how to maximize the carbon sink capacity of green land under the constraints of limited greening land and budget, so as to effectively offset the carbon emissions generated by industrial production activities. The existing technology usually uses geographic information system (GIS) to obtain multi-source spatial data of industrial areas, and combines a carbon assessment model based on plant physiology and ecology to quantitatively assess the carbon sink potential of different plots planted with specific vegetation. On this basis, a spatial optimization algorithm is used to generate a green land spatial configuration scheme that meets the statutory planning indicators, and the location and type of green land are determined.

[0003] However, the operation of an industrial area is a dynamic process, with its industrial structure, enterprise layout, and production activities evolving over time, resulting in dynamic changes in carbon emission sources in space and time. At the same time, the construction of green land systems and the growth of vegetation also have significant temporal properties, with the carbon sink capacity of vegetation being a function that grows over time, and the budget and resource investment for green land construction usually being carried out in annual and phased stages. The existing technology often fails to effectively integrate these dynamic and time-related factors when planning green land. They usually separate the dynamic prediction of industrial development, phased financial budget constraints, and the evolution of carbon sink capacity brought about by the growth cycle of vegetation. For example, spatial layout optimization based on static data is performed first, and then phased construction planning is attempted. This fragmented approach results in the existing methods lacking a mechanism that integrates "spatial configuration", "time series" (including industrial development, vegetation growth, and budget allocation), and "cost-effectiveness" into a single optimization framework for integrated optimization.

[0004] Therefore, the existing technology is difficult to generate a green land planning scheme that can simultaneously consider green land spatial location, vegetation type selection, and planting timing, and achieve the maximization of carbon sink benefits and the optimization of input-output ratio throughout the planning period under the constraints of dynamic changes in carbon emissions, phased budget, and vegetation growth.

[0005] In view of the above problems, the existing technology needs to be improved. SUMMARY

[0006] The purpose of the present application is to provide a GIS-based industrial green land carbon assessment decision system and method that can comprehensively consider the dynamic changes in industrial development, the phased green land construction budget, and the timing characteristics of vegetation growth, and generate a green land planning scheme that optimizes the matching of carbon sink benefits and cost input throughout the planning period.

[0007] In a first aspect, the application provides a GIS-based industrial green land carbon assessment decision-making method for industrial zone green land planning, comprising the following steps:

[0008] A1. Obtain GIS data of an industrial zone, industrial development planning information within a planning period, annual green land construction budget information within the planning period, and vegetation characteristic information of various types of vegetation;

[0009] A2. Divide the industrial zone into units according to the GIS data;

[0010] A3. According to the industrial development planning information, predict the carbon emission intensity of each unit at different time points within the planning period;

[0011] A4. According to the carbon emission intensity of each unit at different time points within the planning period, determine the carbon emission hotspot units and the corresponding emission peak time;

[0012] A5. According to the location and emission peak time of each carbon emission hotspot unit, the vegetation characteristic information of various types of vegetation, the GIS data, and the annual green land construction budget information, determine a green land planning scheme; the green land planning scheme includes the location of green land space, vegetation type, and planting sequence.

[0013] In a second aspect, the application provides a GIS-based industrial green land carbon assessment decision-making system for industrial zone green land planning, comprising:

[0014] A data acquisition module for acquiring GIS data of an industrial zone, industrial development planning information within a planning period, annual green land construction budget information within the planning period, and vegetation characteristic information of various types of vegetation;

[0015] A unit division module for dividing the industrial zone into units according to the GIS data;

[0016] A carbon emission intensity prediction module for predicting the carbon emission intensity of each unit at different time points within the planning period according to the industrial development planning information;

[0017] A carbon emission hotspot determination module for determining carbon emission hotspot units and corresponding emission peak times according to the carbon emission intensity of each unit at different time points within the planning period;

[0018] A scheme decision-making module for determining a green land planning scheme according to the location and emission peak time of each carbon emission hotspot unit, the vegetation characteristic information of various types of vegetation, the GIS data, and the annual green land construction budget information; the green land planning scheme includes the location of green land space, vegetation type, and planting sequence.

[0019] Beneficial effects: The GIS-based industrial green land carbon assessment decision system and method provided by the application can comprehensively consider the dynamic changes of industrial development, the budget of green land construction in different stages, and the time sequence characteristics of vegetation growth, generate a green land planning scheme that optimizes the matching of carbon sink benefits and cost investment within the planning period. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 The flowchart of the GIS-based industrial green land carbon assessment decision method provided by the embodiment of the application.

[0021] Figure 2 The structural schematic diagram of the GIS-based industrial green land carbon assessment decision system provided by the embodiment of the application.

[0022] Label explanation: 1, data acquisition module; 2, unit division module; 3, carbon emission intensity prediction module; 4, carbon emission hotspot determination module; 5, scheme decision module. DETAILED DESCRIPTION

[0023] The technical model in the application will be described clearly and completely in combination with the drawings in the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. The components of the application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the application provided in the drawings is not intended to limit the scope of the claimed application, but only represents selected embodiments of the application. Based on the embodiments of the application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the application.

[0024] It should be noted that: similar labels and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the application, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0025] Reference Figure 1 The application provides a GIS-based industrial green land carbon assessment decision method for industrial green land planning, and the steps of the method include:

[0026] A1. Obtain GIS data of the industrial zone, industrial development planning information within the planning period, annual green land construction budget information within the planning period, and vegetation characteristic information of various types of vegetation;

[0027] A2. Divide the industrial zone into units according to the GIS data;

[0028] A3. According to the industrial development planning information, predict the carbon emission intensity of each unit at different time points within the planning period;

[0029] A4. According to the carbon emission intensity of each unit at different time points within the planning period, determine the carbon emission hotspots and corresponding emission peak times;

[0030] A5. According to the location and emission peak time of each carbon emission hotspot unit, the vegetation characteristic information of various types of vegetation, the GIS data, and the annual green land construction budget information, determine the green land planning scheme; the green land planning scheme includes the location of green land space, vegetation type and planting time sequence.

[0031] Wherein, GIS data refers to a collection of digital information used to describe the geographical space characteristics of the industrial zone, which can be realized in the form of vector data, raster data or attribute data, such as plot boundary layer, land use status layer, digital elevation model, which is mainly used to provide the basic spatial framework and environmental background information of the industrial zone.

[0032] Wherein, the industrial development planning information refers to the data reflecting the expected changes of the industrial structure, enterprise layout and production activities of the industrial zone in the future, which can be realized in the form of enterprise entry plan table, capacity prediction report, carbon emission list, etc., such as the type, location, capacity and emission coefficient of the expected entry enterprises, which is mainly used to predict the carbon emission load of the industrial zone at different time points in the future.

[0033] Wherein, the annual green land construction budget information refers to the amount of funds available for green land construction and maintenance each year within the planning period, which can be realized in the form of annual financial appropriation plan, project fund allocation table, etc., such as the total investment of green engineering in each year, which is mainly used to constrain the implementation cost of green land planning scheme.

[0034] Wherein, the vegetation characteristic information refers to the ecological and economic attribute data of different vegetation types in the growth process, which can be realized in the form of plant growth model parameters, carbon sink capacity curve, construction cost list, maintenance cost list, etc., such as the change curve of carbon fixation rate per unit area of trees, shrubs and lawns with time and the corresponding planting and maintenance costs, which is mainly used to evaluate the ecological benefits and economic costs of different green land configuration schemes.

[0035] The unit division refers to dividing the overall space of the industrial zone into multiple analysis regions with relatively consistent characteristics, which can be achieved by spatial segmentation based on plot boundaries, region aggregation based on land use types, or grid division based on environmental similarity, for example, dividing the industrial zone into several functional areas or plot units, which is mainly to discretize complex spatial problems for fine data analysis and model calculation.

[0036] The carbon emission intensity prediction refers to estimating the unit area carbon emission of each spatial unit at different time points within the planning period, which can be achieved by emission factor method based on industry type and capacity, emission allocation method based on enterprise spatial location, or trend extrapolation method based on historical data, for example, predicting the total emission of the type and capacity of the enterprise and allocating it to the corresponding unit according to its location, which is mainly to quantify the carbon emission pressure of each region and time point.

[0037] The carbon emission hotspot unit determination refers to identifying spatial units with high carbon emission load or long duration within the planning period, which can be achieved by setting a carbon emission intensity threshold for screening, calculating cumulative emissions for sorting, or analyzing the peak value and duration of the emission intensity time curve, for example, identifying regions with long-term average carbon emission intensity, which is mainly to determine the areas where green carbon sink needs to be offset.

[0038] The emission peak time determination refers to identifying the specific time point or time period when the carbon emission intensity of the carbon emission hotspot unit reaches the maximum value or is at a high level within the planning period, which can be achieved by analyzing the peak position of the carbon emission intensity time curve, calculating the duration of high intensity, or identifying multiple peaks, for example, determining the highest carbon emission of a certain region in a specific year or quarter, which is mainly to clarify the time node that the green carbon sink capacity needs to be matched.

[0039] The green land planning scheme determination refers to generating a specific implementation plan containing green space location, vegetation type, and planting time sequence by considering multiple factors, for example, determining which plot to plant which tree and when to plant, which is mainly to provide an operational and guiding green land construction scheme.

[0040] The core innovation of the present application is to effectively couple GIS-based spatial analysis with time series data reflecting industrial development dynamics, vegetation growth characteristics, and annual budget constraints, thereby simultaneously optimizing green space configuration, vegetation type selection, and planting time sequence within a single decision framework, achieving the effect of generating a green land planning scheme that can dynamically adapt to industrial zone development, maximize carbon sink benefits throughout the life cycle, and optimize cost-effectiveness.

[0041] Specifically, the method of the present application first acquires multi-source basic data of the industrial zone, including spatial geographic information, future industrial development prediction, annual construction budget, and growth and cost characteristics of different vegetation. Based on the acquired geographic information data, the industrial zone is finely divided into spatial units, and the complex region is decomposed into basic units for analysis. Subsequently, using the industrial development planning information, the carbon emission intensity of these spatial units at different time points in the future planning period is predicted, thereby converting the macro industrial development trend into micro spatial carbon emission load. Further, by analyzing the dynamic carbon emission intensity of each unit, the carbon emission hotspot unit that is most concentrated and most needs carbon sink offset by green land is identified, and the key time point with the highest carbon emission intensity of these hotspot regions is determined. Finally, considering the location and emission peak time of these carbon emission hotspot units, the carbon sink capacity and cost of different vegetation types over time, the spatial constraints of the industrial zone, and the annual construction budget constraints, a green land planning scheme is determined. The scheme not only specifies the spatial location of green land construction and the selected vegetation type, but more importantly, it clearly specifies the planting time arrangement of different regions and different vegetation, so that the growth of green land carbon sink capacity can match the carbon emission peak time of the industrial zone, while meeting the annual budget constraints, forming an overall solution that is optimized in both spatial and temporal dimensions.

[0042] Through the above scheme, the present application can effectively couple factors with different time attributes such as dynamic industrial development of the industrial zone, phased construction budget, and growth cycle of vegetation, and simultaneously optimize green land spatial layout, vegetation type selection, and planting time sequence in a unified framework, thereby solving the technical problems of being difficult to handle multiple dynamic constraints and achieve globally optimal planning in the prior art, improving the scientificity and adaptability of the green land planning scheme, and helping to more effectively utilize limited resources to offset industrial carbon emissions and improve the ecological environmental benefits of the industrial zone.

[0043] Specifically, the GIS data includes plot boundary information, land use status information, topographic information, soil type information, existing water system information, planned building layout information, and planned road layout information of the industrial zone.

[0044] The industrial development planning information includes enterprise type information, estimated time of entry information, enterprise spatial location information, estimated production capacity information, and unit carbon emission information of the expected enterprises;

[0045] The vegetation characteristic information includes unit area carbon sink capacity growth curve information, unit area initial construction cost information, and unit area annual maintenance cost information.

[0046] Among them, the GIS data can be represented in vector data (such as plot boundaries, building layout, road layout, water system) or raster data (such as terrain elevation, soil type, land use classification) format. These detailed spatial information is the basis for the spatial unit division of the industrial park, and is the key to identifying and evaluating potential green space construction space.

[0047] Among them, the industrial development planning information reflects the predictive data of the future industrial structure, enterprise activities and their environmental impact of the industrial park, which can be stored in the form of structured database, spreadsheet or text report. These information is the direct basis for predicting the future carbon emission intensity and its spatio-temporal distribution of each region in the industrial park.

[0048] Among them, the vegetation characteristics information describes the biological and economic attributes of different vegetation types in growth, carbon sequestration and maintenance, which can be organized in the form of database table, parameter list or mathematical function. These information is the basis for evaluating the carbon sink capacity of different vegetation types at different time points and the cost of construction and maintenance.

[0049] Specifically, detailed GIS data, including plot boundaries, land use status, topography, soil type, existing water system, planned building layout and planned road layout information, provides spatial geometry and attribute basis for unit division of the industrial park according to GIS data, so that the unit division can reflect the actual spatial structure and environmental characteristics of the industrial park. At the same time, these spatial information also supports the identification of suitable green space construction in the vicinity of carbon emission hotspots. Detailed industrial development planning information, including the type of expected enterprises, the expected time of entering, the spatial location, the expected capacity and the carbon emission per unit capacity information, provides enterprise-level activity data and emission factors for predicting the carbon emission intensity of each unit at different time points in the planning period according to the industrial development planning information, so that the carbon emission prediction can reflect the dynamic changes of industrial development in the industrial park. Based on these dynamic predicted carbon emission intensity, carbon emission hotspots and corresponding emission peak time can be more accurately determined. Detailed vegetation characteristics information, including unit area carbon sink capacity growth curve, unit area initial construction cost and unit area annual maintenance cost information, provides quantitative parameters for evaluating the benefits and inputs of different vegetation types at different times and costs, so that the timeliness of vegetation growth and the economy of construction and maintenance can be fully considered when determining the green space planning scheme. By providing these detailed, multi-dimensional data necessary for fine spatial analysis, dynamic carbon emission prediction, and time-dependent vegetation benefit / cost evaluation, the method provides a solid data foundation for subsequent determination of green space planning scheme based on carbon emission hotspots, vegetation characteristics information, GIS data and annual green space construction budget information, so that the method can effectively solve the complex optimization problem under dynamic and multi-constraints involved in the green space planning of the industrial park.

[0050] In some embodiments, step A2 comprises:

[0051] A201. determining basic spatial partitions of the industrial park according to the plot boundary information, the planned building layout information, and the planned road layout information;

[0052] A202. based on the basic spatial partitions, combining the land use status information, the topographic information, the soil type information, and the existing water system information, to perform internal subdivision or merging for each basic spatial partition, to form units with relatively consistent land use types or environmental characteristics;

[0053] A203. uniquely identifying each of the units and recording the location and area of each of the units.

[0054] In step A201, the basic spatial partitions of the industrial park are determined using the plot boundary information, the planned building layout information, and the planned road layout information. These information represent the physical structure and main partitions of the industrial park, and are the basic framework for constructing more detailed spatial units. Through these basic spatial data, regions with certain independence can be initially defined, such as regions enclosed by roads or plot boundaries. In step A202, based on these basic spatial partitions, land use status information, topographic information, soil type information, and existing water system information are introduced. These data provide more detailed attribute information about the function of the region, natural conditions, etc., for identifying differences or similarities within the basic partitions or between adjacent partitions, and thus for performing subdivision or merging operations. In step A203, the units after division are uniquely identified, and their locations and areas are recorded, which is a standardized processing of the division results, facilitating subsequent data association, calculation, and management.

[0055] Through the synergy of the above steps, the unit division method of the present application can systematically utilize multi-source GIS data and finely divide the industrial park in stages. First, step A201 utilizes structural data such as plot boundaries, buildings, and roads to construct the basic spatial skeleton of the industrial park, forming initial partitions. These initial partitions reflect the main physical separation of the industrial park. Then, step A202 superimposes and analyzes attribute data such as land use, terrain, soil, and water system on the basis of these initial partitions. Through spatial analysis techniques, differences in function or environmental attributes within the initial partitions or between adjacent partitions are identified. For partitions with large internal attribute differences, subdivision processing is performed; for adjacent partitions with similar attributes, merging processing is performed. The units formed in this way have relatively consistent land use types or environmental characteristics within them, and can more accurately reflect the actual situation of the region in terms of carbon emissions, green space construction suitability, etc. Finally, step A203 uniformly identifies and records the attributes of these finely divided units, providing standardized spatial carriers and basic data for subsequent carbon emission prediction, hotspot identification, and green space planning scheme formulation.

[0056] This step-by-step, multi-source data fusion division process enables the final units to effectively capture the spatial heterogeneity within the industrial park, providing a more reliable spatial foundation for subsequent analysis and decision-making based on units.

[0057] As a specific implementation, the above unit division process can be implemented as follows: First, in the GIS software environment, import the vector data layers of plot boundaries, planned building layouts, and planned road layouts. By performing overlay analysis or topological processing on these layers, such as performing Union or Dissolve operations, a polygon layer representing the basic spatial partitions of the industrial park can be generated. Next, import the vector or raster data layers of land use status, terrain (such as contour lines or digital elevation models), soil type distribution, and existing water systems. Based on the basic spatial partition layer, combined with these attribute layers, more complex spatial analysis can be performed. For example, Spatial Join can be used to associate attribute information to the basic spatial partitions, and then split the basic spatial partitions according to changes in attribute values (such as different land use types). Alternatively, adjacent basic spatial partitions can be merged based on attribute similarity (such as adjacent regions having the same soil type and land use type). Finally, a unit polygon layer reflecting internal heterogeneity is obtained. Finally, in the attribute table of the unit polygon layer, add a field to store a unique identifier, and automatically calculate the area and centroid coordinates of each polygon.

[0058] In some embodiments, step A3 comprises:

[0059] A301. According to the expected time to settle information, the expected capacity information, and the unit capacity carbon emission amount information in the industrial development planning information, the carbon emission amount of each prospective settled enterprise at different time points in the planning period is determined.

[0060] A302. According to the enterprise space location information in the industrial development planning information, the carbon emission amount of each prospective settled enterprise at different time points in the planning period is allocated to each unit, and the carbon emission intensity of each unit at different time points in the planning period is calculated in combination with the area of each unit.

[0061] The scheme specifically defines the specific method of predicting the carbon emission intensity of each unit at different time points in the planning period, aiming to solve the problem of how to convert dynamic industrial development information at the enterprise level into spatial and temporal carbon emission intensity data at the unit level.

[0062] This scheme achieves this goal through two steps. First, according to the expected time to settle information, the expected capacity information, and the unit capacity carbon emission amount information in the industrial development planning information, the carbon emission amount of each prospective settled enterprise at different time points in the planning period is determined. This step utilizes detailed enterprise-level dynamic data and can calculate the carbon emission amount of each enterprise at each specific time point in the planning period, thereby obtaining carbon emission data at the enterprise level that changes over time. Second, according to the enterprise space location information in the industrial development planning information, the carbon emission amount of each prospective settled enterprise at different time points in the planning period is allocated to each unit, and the carbon emission intensity of each unit at different time points in the planning period is calculated in combination with the area of each unit. This step utilizes the specific spatial location information of enterprises to accurately map and allocate the carbon emission amount calculated in the previous step at the enterprise level that changes over time to each spatial unit previously divided in the industrial park. Subsequently, by dividing the carbon emission amount allocated to each unit by the area of the unit, the carbon emission intensity per unit area, i.e., the carbon emission intensity, is calculated.

[0063] The reason for adopting the above steps is that the carbon emissions of the industrial park are derived from specific enterprise production activities, and these activities have clear time (entry, capacity change) and spatial (enterprise location) attributes. Based only on the total amount or static average of the industrial park, the dynamic nature and spatial heterogeneity cannot be captured. It is precisely because the time-varying carbon emissions are first accurately calculated at the enterprise level, and then accurately allocated to the pre-divided spatial unit using spatial location information, and combined with unit area for division operation processing, that the dynamic emission data at the enterprise level can be converted into carbon emission intensity data at the unit level with fine spatio-temporal resolution. This process combines the time attribute and spatial attribute of enterprise activities with the spatial division structure of the industrial park, realizing the conversion from discrete enterprise emission sources to continuous regional carbon emission intensity field. In this way, the spatio-temporal dynamic distribution and change trend of carbon emissions in the industrial park can be accurately reflected, providing accurate and dynamic data support for identifying carbon emission hotspots and emission peak time in subsequent planning. Compared with the method based only on total allocation or static factors, the present scheme can more realistically simulate the evolution process of carbon emissions in the industrial park, thereby providing more reliable basic data for green space planning decisions based on dynamic changes in carbon emissions.

[0064] For example, a data table containing the identification of the expected entering enterprise, the expected entering year, the expected capacity of each year, and the corresponding unit capacity carbon emission factor can be obtained from the industrial development planning information. In step A301, for each enterprise, the expected carbon emission of the enterprise in the year can be obtained by multiplying the expected capacity and the unit capacity carbon emission factor of the enterprise in the year according to the expected entering year of the enterprise. By repeating this process, the carbon emission of the enterprise in each year of the planning period can be calculated. Performing this operation on all expected entering enterprises can obtain the carbon emission data set of each enterprise at different time points in the planning period. In step A302, the spatial position information (such as the centroid coordinates or boundary of the enterprise plot) of the enterprise can be used to perform spatial overlay analysis with the industrial park unit division result using GIS tools. If the spatial position of an enterprise falls completely within a unit, the carbon emission of the enterprise at each time point is allocated to the unit. If an enterprise crosses multiple units, the total carbon emission of the enterprise at each time point can be proportionally allocated to the corresponding unit according to the area proportion of the enterprise plot in each unit. After the allocation is completed, for each time point in the planning period, the carbon emissions of all enterprises allocated to each unit are accumulated to obtain the total carbon emission of the unit at the time point. Finally, the total carbon emission of the unit at the time point is divided by the area of the unit to calculate the carbon emission intensity of the unit at the time point. By repeating this process, the carbon emission intensity data of all units at different time points in the planning period can be obtained.

[0065] In some embodiments, step A4 comprises:

[0066] A401. generating a carbon emission intensity time series curve of each unit in the planning period according to the carbon emission intensity of each unit at different time points in the planning period;

[0067] A402. extracting the cumulative time when the carbon emission intensity of each unit exceeds the preset intensity threshold according to the carbon emission intensity time series curve of each unit;

[0068] A403. determining the carbon emission hotspot unit according to the cumulative time;

[0069] A404. extracting the time corresponding to the maximum carbon emission intensity in the carbon emission intensity time series curve of each carbon emission hotspot unit as the emission peak time of the carbon emission hotspot unit.

[0070] Wherein, the carbon emission intensity time series curve refers to a graph reflecting the change of carbon emission intensity of a unit at different time points in the planning period, which can be represented by a two-dimensional curve graph taking time as the horizontal axis and carbon emission intensity as the vertical axis.

[0071] Wherein, the preset intensity threshold refers to a reference value for defining high carbon emission intensity, which can be set according to the overall emission level of the industrial park, the environmental carrying capacity or relevant regulatory standards.

[0072] Wherein, the cumulative time refers to the total duration of the carbon emission intensity of a unit continuously or intermittently exceeding the preset intensity threshold in the planning period.

[0073] In this embodiment, the emission peak time is the time point when the carbon emission hotspot unit reaches the highest intensity value in its carbon emission intensity time series curve.

[0074] The above steps work together to determine the carbon emission hotspot unit and the corresponding emission peak time. First, by generating the carbon emission intensity time series curve of each unit, the discrete time point data is converted into continuous or quasi-continuous curve representation, which lays the foundation for subsequent time dimension analysis. Then, by extracting the cumulative time of carbon emission intensity exceeding the preset intensity threshold, the concept of continuous high emission is introduced, which can better reflect the actual emission pressure and problem severity of the region than simply focusing on the instantaneous peak value. Based on the cumulative time to determine the carbon emission hotspot unit, it can more accurately identify those regions that are in high emission state for a long time and have a lasting impact on the environment, thereby ensuring that the key area selection of green land planning has stronger scientificity and pertinence. Finally, for these units determined as hotspots, further analyze their carbon emission intensity time series curve to extract the time corresponding to the maximum carbon emission intensity, which provides key time information. The combination of these steps makes the identification of the carbon emission problem of the industrial area from static and instantaneous to dynamic and continuous, not only positioning the spatial hotspots, but also clearly identifying the key nodes in time, providing more comprehensive and accurate input data for subsequent green land planning. This method can more effectively guide the allocation of green land construction resources and the selection of vegetation types by considering emission intensity, duration and peak time, in order to maximize the carbon sink benefit in the most serious emission area and time period.

[0075] To further illustrate the technical solutions of the present application, a specific implementation is provided below. Assuming that the planning period is ten years, and the time unit is annual. For each unit in the industrial area, according to the predicted data, the carbon emission intensity value of each year (i.e. each time point) can be obtained. Connecting these annual intensity values in chronological order can generate the carbon emission intensity time series curve of the unit. Set a preset intensity threshold, for example, more than 5 tons of carbon dioxide per hectare per year. Then, count the total number of years in the ten-year planning period that the carbon emission intensity of the unit exceeds 5 tons / ha / year, which is the cumulative time. For example, if the emission intensity of a unit exceeds the threshold for seven years in ten years, its cumulative time is seven years. According to the set rules, for example, the cumulative time exceeding five years is determined as a carbon emission hotspot unit, then the unit is identified as a carbon emission hotspot unit. For this carbon emission hotspot unit, find the year with the highest intensity value in its ten-year carbon emission intensity time series curve, and determine that year as the emission peak time of the hotspot unit. For example, if the unit reaches the highest emission intensity in the third year, then the third year is the emission peak time.

[0076] By adopting the method, the following technical effects can be achieved: the method can more accurately identify the areas in the industrial zone that need to be prioritized for green land construction, avoiding the deviation caused by instantaneous high emission determination. At the same time, the method can determine the emission peak time of these hot spot areas within the planning period, providing key time basis for vegetation selection and planting timing in the green land planning scheme, thereby improving the pertinence and effectiveness of the green land planning.

[0077] In some embodiments, step A5 comprises:

[0078] A501. Obtain the carbon emission demand curve of each carbon emission hot spot unit;

[0079] A502. For each carbon emission hot spot unit, identify the alternative space available for green land construction according to the GIS data;

[0080] A503. For each carbon emission hot spot unit, according to the vegetation characteristic information, evaluate the total carbon sink capacity development curve and the required total construction and maintenance cost timing data provided by different combination schemes; the combination scheme is the combination of the selected alternative space and its vegetation type and planting timing;

[0081] A504. For each carbon emission hot spot unit, according to the total carbon sink capacity development curve and the carbon emission demand curve, select the standby combination scheme from the corresponding combination scheme, which can meet the carbon emission demand of the carbon emission hot spot unit at the emission peak time of the carbon emission hot spot unit;

[0082] A505. According to the annual green land construction budget information and the total construction and maintenance cost timing data, combine the standby combination scheme of each carbon emission hot spot unit to obtain a global scheme that meets the budget constraint; the global scheme includes one standby combination scheme corresponding to each carbon emission hot spot unit;

[0083] A506. Select the optimal scheme from the global scheme as the green land planning scheme.

[0084] The carbon emission demand curve refers to a curve describing the change of carbon emission amount of a specific carbon emission hot spot unit at different time points within the planning period, which can be calculated (multiplied) according to the carbon emission intensity timing curve and the unit area.

[0085] The alternative space refers to a plot or area suitable for green space construction in the range of the carbon emission hotspot unit and / or adjacent units (i.e., other units within a preset distance threshold from the center of the carbon emission hotspot unit), which is identified according to GIS data, and can be determined by filtering according to land use status, topography, soil type, existing water system, planned building layout, and planned road layout, etc. The specific filtering rules can be set according to actual needs.

[0086] The combination scheme refers to a specific green space construction scheme for a carbon emission hotspot unit, which selects different numbers of alternative spaces, considers different vegetation types of different alternative spaces, and different planting time sequences, such as planting trees in alternative space A and implementing in the first year, or planting shrubs in alternative space A and alternative space B and implementing in the third year, which are different combination schemes.

[0087] The total carbon sink capacity development curve refers to the curve of the total carbon sink capacity provided by a specific combination scheme at different time points within the planning period, which can be calculated according to the selected alternative space area, the unit area carbon sink capacity growth curve corresponding to the vegetation type, and the planting time sequence.

[0088] The total construction and maintenance cost time sequence data refers to the construction and maintenance cost data required by a specific combination scheme at different time points within the planning period, which can be calculated according to the selected alternative space area, the unit area initial construction cost and the unit area annual maintenance cost corresponding to the vegetation type, and the planting time sequence.

[0089] The selected combination scheme refers to a combination scheme set that can meet the carbon emission demand of a hotspot unit at its emission peak time, which is selected from all possible combination schemes of the hotspot unit.

[0090] The global scheme (also referred to as the overall scheme) refers to a global green space planning scheme formed by combining the selected combination schemes of all carbon emission hotspot units, and the total construction and maintenance cost of the global scheme meets the annual green space construction budget constraint.

[0091] The optimal scheme refers to the best global scheme selected from the global scheme set that meets the budget constraint, according to the set optimization target (such as maximizing the global carbon sink capacity, minimizing the total cost, or considering multiple factors).

[0092] The scheme effectively integrates dynamic carbon emission demand, vegetation carbon sink capacity with growth cycle, annual construction budget, and multiple dynamic and multi-constraint factors such as spatial location, vegetation type, and planting timing through a series of steps for integrated optimization. First, the carbon emission demand curve of each carbon emission hotspot unit is obtained, and the specific dynamic carbon emission target that the green space planning needs to address is determined, which is the basis for subsequent scheme design and evaluation. Next, for each carbon emission hotspot unit, the available space for green space construction is identified based on GIS data, which provides a feasible spatial carrier for green space planning and limits the spatial range of the scheme. Then, for each carbon emission hotspot unit, the total carbon sink capacity development curve and the required total construction and maintenance cost timing data of different combination schemes are evaluated based on vegetation characteristics information. This step links the three decision variables of space, vegetation type, and planting timing with the carbon sink capacity of vegetation over time and the phased construction and maintenance costs, forming a quantifiable evaluation scheme unit and overcoming the difficulty of dealing with vegetation growth cycle and phased budget. Based on this, for each carbon emission hotspot unit, the total carbon sink capacity development curve and the carbon emission demand curve are used to select the selected combination scheme from the corresponding combination scheme, which can meet the carbon emission demand of the carbon emission hotspot unit at the emission peak time of the carbon emission hotspot unit. This selection process ensures that the selected scheme can effectively play a carbon sink role at the time point of the most concentrated carbon emission, and realizes the matching of dynamic carbon emission demand and dynamic carbon sink capacity. Further, according to the annual green space construction budget information and the total construction and maintenance cost timing data, the selected combination schemes of each carbon emission hotspot unit are combined to obtain a global scheme that meets the budget constraints. By considering the annual budget constraints, the phased financial investment is taken into account in the scheme optimization, ensuring the implementability of the final scheme and avoiding the problem of the scheme being unable to be implemented due to exceeding the budget. Finally, the optimal scheme is selected from the global scheme that meets the budget constraints as the final green space planning scheme. This step selects the overall optimal scheme from the scheme set that meets all the constraints (including space, carbon emission demand, budget, etc.) through a certain evaluation mechanism, thereby realizing global optimization under complex dynamic constraints. The entire scheme integrates dynamic carbon emission, dynamic carbon sink capacity, annual budget, spatial location, vegetation type, planting timing, and other factors in a unified framework for evaluation and selection, overcoming the shortcomings of fragmented handling of these factors, and can generate a green space configuration and implementation strategy that maximizes carbon sink benefits and optimizes cost-effectiveness throughout the life cycle under long-term, multi-stage, and dynamic changing complex constraints.

[0093] For example, in a specific implementation, one can first generate a carbon emission demand curve at a discrete time point for each carbon emission hotspot unit by analyzing historical carbon emission data and industry development forecasts, such as a data sequence in years. Then, using GIS software, overlay the land use layer, planning layer, etc. of the industrial park to identify all non- construction land, non-road, non-water system and non-occupied by existing green space plots as candidate spaces, and record their location and area. Then, for each hotspot unit, select different numbers of candidate spaces (if there is only one candidate space, only consider that candidate space), consider multiple possible vegetation types (such as different combinations of trees, shrubs, lawns) and multiple possible planting schedules (such as planting all at once in the first year, planting over three years, etc.), forming a large number of combination schemes. For each combination scheme, according to the carbon sink growth model and cost model corresponding to the vegetation type (i.e. unit area carbon sink capacity growth curve information, unit area initial construction cost information, and unit area annual maintenance cost information), combined with the area of the candidate space and the planting schedule, calculate and generate the total carbon sink capacity and total construction and maintenance cost data of the combination scheme in each year of the planning period. Subsequently, compare the carbon emission demand curve of each hotspot unit with the total carbon sink capacity development curve of each combination scheme within the hotspot unit, and select those combination schemes whose total carbon sink capacity at the peak time point of carbon emission in the hotspot unit is greater than or equal to the carbon emission demand, forming a set of candidate combination schemes for the hotspot unit. Next, select one scheme from each set of candidate combination schemes for each hotspot unit to form a preliminary global scheme combination, calculate the total construction and maintenance cost of the global scheme combination in each year of the planning period, and compare it with the annual green space construction budget year by year, retaining all global scheme combinations that meet the budget constraints. Finally, for these global schemes that meet the budget constraints, calculate the comprehensive score of each global scheme according to the preset evaluation indicators (such as cumulative carbon sink amount, total cost, pollutant adsorption capacity, etc.), and select the highest scoring scheme as the final green space planning scheme.

[0094] Through the above method, the dynamically changing carbon emission demand, the vegetation carbon sink capacity growing over time, the annual construction budget, and the spatial location, vegetation type and planting schedule of the green space, etc. multiple interrelated factors are considered in a unified optimization framework. This makes the determined green space planning scheme not only spatially reasonable, but also dynamically adapts to the carbon emission changes of the industrial park in the time dimension, while meeting the phased budget constraints. The final scheme is a green space spatial configuration and implementation strategy that optimizes the whole life cycle, considering carbon sink benefits and cost inputs, effectively solving the problem that existing methods cannot handle green space planning optimization under multiple dynamic constraints.

[0095] Preferably, step A503 can include:

[0096] According to the selected alternative space, vegetation type and planting time sequence in the combination scheme, the unit area carbon sink capacity growth curve information, the unit area initial construction cost information and the unit area annual maintenance cost information are extracted from the vegetation characteristic information of the corresponding vegetation;

[0097] According to the area of the selected alternative space in the combination scheme, the extracted unit area carbon sink capacity growth curve information and the planting time sequence in the combination scheme, the total carbon sink capacity of the combination scheme at each time point in the planning period is calculated, and the total carbon sink capacity development curve is formed.

[0098] According to the area of the selected alternative space in the combination scheme, the extracted unit area carbon sink capacity growth curve information and the planting time sequence in the combination scheme, the total carbon sink capacity of the combination scheme at each time point in the planning period is calculated, and the total carbon sink capacity development curve is formed.

[0099] Wherein, the unit area carbon sink capacity growth curve information refers to the function or discrete data sequence of the carbon sink capacity of a specific vegetation type per unit area with time, which can be realized by mathematical model based on measured data fitting, simulation data based on plant physiological and ecological model or table data based on empirical statistics.

[0100] Wherein, the unit area initial construction cost information refers to the initial investment required for planting a specific vegetation type per unit area, including land consolidation, nursery purchase, planting labor and other costs, which can be realized by average cost based on historical project data, current cost based on market inquiry or estimated cost based on detailed engineering quantity list.

[0101] Wherein, the unit area annual maintenance cost information refers to the annual maintenance cost required after planting a specific vegetation type per unit area, including irrigation, fertilization, pest control, pruning and other costs, which can be realized by statistical cost based on historical maintenance records, estimated cost based on maintenance standards or service cost based on contract.

[0102] The scheme of the present application elaborates how to accurately and dynamically evaluate the total carbon sink capacity and the total construction and maintenance cost over time of a specific green space combination scheme (including selected alternative spaces, vegetation types and planting time sequence) and preset vegetation characteristic information. First, for a specific green space combination scheme, the scheme specifies which alternative spaces (with a certain area), what vegetation types will be planted, and at which time point in the planning period the key planting will be carried out. Based on these scheme details, the system searches and extracts the unit area carbon sink capacity growth curve information, unit area initial construction cost information and unit area annual maintenance cost information corresponding to the vegetation types selected in the scheme from the pre-stored vegetation characteristic information database. These extracted unit area data are the basis for subsequent calculation. Then, using the extracted unit area carbon sink capacity growth curve information, combined with the actual area of the alternative spaces selected in the combination scheme and the planting time sequence specified in the scheme, the total carbon sink capacity of the combination scheme at each time point in the planning period is calculated. This calculation process takes into account the area effect (unit area capacity multiplied by area) and time effect (growth curve changes over time, and calculation starts from the planting time sequence) of vegetation carbon sink capacity, thus forming a total carbon sink capacity development curve reflecting the dynamic growth of total carbon sink capacity over time. At the same time, using the extracted unit area initial construction cost information and unit area annual maintenance cost information, combined with the area of the alternative spaces and the planting time sequence, the total construction and maintenance cost of the combination scheme at each time point in the planning period is calculated. This calculation process takes into account that the initial construction cost usually occurs in the planting year or before, while the annual maintenance cost starts to occur continuously after planting, and both are related to the area of green space, thus forming total construction and maintenance cost time sequence data reflecting the phased investment of cost. Through the above steps, the scheme can provide accurate and time-dimensioned total carbon sink capacity development curve and total construction and maintenance cost time sequence data for subsequent scheme screening and combination steps. These data enable the subsequent steps to fully consider the time characteristics of vegetation growth, phased construction and maintenance cost investment, and the response ability of green space scheme to dynamic carbon emission demand over time.

[0103] By inputting the dynamic evaluation data provided by the present scheme into the subsequent scheme screening and combination logic, the entire green space planning decision-making process can consider the dynamic factors of space, time and cost simultaneously in a unified framework, thus more effectively determining the green space configuration and implementation strategy that maximizes the whole life cycle carbon sink benefit and optimizes the cost benefit under the complex constraints of long period, multiple stages and dynamic changes.

[0104] Preferably, step A505 can include:

[0105] combinations of the preliminary global scheme combinations are obtained; the preliminary global scheme combinations include one scheme selected from the candidate combination schemes of each of the carbon emission hot spot units;

[0106] For each of the preliminary global scheme combinations, the total construction and maintenance costs of each of the candidate combination schemes included in the preliminary global scheme combination at each time point in the planning period are accumulated to obtain the global construction and maintenance costs of the preliminary global scheme combination at each time point in the planning period;

[0107] According to the annual green land construction budget information, the global construction and maintenance costs of the preliminary global scheme combinations at each time point in the planning period are compared year by year, and the preliminary global scheme combinations whose global construction and maintenance costs at each time point in the planning period are not greater than the corresponding annual green land construction budget information are selected as the final global scheme combinations.

[0108] Wherein, the permutation and combination refers to selecting one element from each of a plurality of sets to form an ordered or unordered tuple, which can be implemented by using a traversal algorithm, a backtracking algorithm or an iterator-based method. The preliminary global scheme combination refers to selecting one scheme from each of a group of candidate combination schemes of each carbon emission hot spot unit, and collecting the selected schemes to form a whole scheme set.

[0109] Wherein, the global construction and maintenance cost refers to the total of all construction and maintenance costs required by a preliminary global scheme combination at any specific time point (for example, a certain year) in the planning period. The comparison year by year refers to comparing the global construction and maintenance costs of the preliminary global scheme combinations at each time point (for example, each year) in the planning period with the annual green land construction budget information corresponding to the time point.

[0110] The method of the present application specifically defines how to systematically generate a global scheme combination meeting the annual budget constraint from multiple candidate schemes of multiple hotspot units. First, a plurality of preliminary global scheme combinations are obtained by arranging and combining the candidate schemes of each carbon emission hotspot unit. This step systematically generates a set of all possible global green space construction schemes, and each preliminary global scheme combination represents the overall strategy of selecting a specific candidate scheme for each carbon emission hotspot unit. Through the way of arrangement and combination, all possible scheme combinations are considered, providing a comprehensive basis for subsequent screening. Secondly, for each preliminary global scheme combination, the total construction and maintenance cost of each candidate scheme at each time point in the planning period is accumulated to obtain the global construction and maintenance cost of the preliminary global scheme combination at each time point in the planning period. This step quantitatively evaluates the cost input of each preliminary global scheme combination over time in the entire planning period. By accumulating the cost of each component scheme at the same time point, the total construction and maintenance cost required for the global scheme in each year can be accurately calculated to form a time sequence data of global cost, which is a necessary basis for comparison with the annual budget. Finally, according to the annual green space construction budget information, the global construction and maintenance cost of the preliminary global scheme combination at each time point in the planning period is compared year by year, and the preliminary global scheme combination whose global construction and maintenance cost at each time point in the planning period is not greater than the corresponding annual green space construction budget information is selected as the final global scheme combination. This step accurately selects the feasible global scheme according to the actual annual budget constraint. By comparing the cost of each preliminary global scheme combination at each time point in the planning period with the annual green space construction budget of each year, it can be ensured that the selected scheme will not exceed the budget limit in each year of the entire planning period. This year-by-year comparison method, rather than a simple total cost comparison, accurately reflects the actual constraints of the annual budget, thereby selecting a truly implementable global scheme combination. When determining the green space planning scheme, the method of the present application systematically generates and screens global schemes meeting the annual budget constraint, so that the final green space planning scheme not only meets the carbon sink demand, but also meets the actual financial investment capacity in the entire planning period, avoiding the problem that the scheme cannot be implemented due to exceeding the annual budget, and improving the feasibility and practicality of the scheme.

[0111] Through the above method, the present application can systematically generate all possible global scheme combinations and accurately screen them based on the annual budget information, thereby comprehensively and accurately identifying all global schemes meeting the annual budget constraint. This solves the problem that the existing method cannot comprehensively and accurately identify global schemes meeting the annual budget constraint, and improves the feasibility and implementation efficiency of the green space planning scheme.

[0112] Preferably, step A506 can include:

[0113] obtaining expected development stage information of the industrial zone at different time points in a planning period;

[0114] determining weights of each optimization target parameter corresponding to each time point in the planning period according to the expected development stage information; the optimization target parameters include global carbon sink capacity, global pollutant adsorption capacity, and global construction and maintenance cost;

[0115] for each global scheme, calculating a comprehensive benefit score of the global scheme according to its global carbon sink capacity, global pollutant adsorption capacity, and global construction and maintenance cost at each time point in the planning period, combined with the corresponding weights;

[0116] selecting the global scheme with the highest comprehensive benefit score as the optimal scheme.

[0117] The expected development stage information refers to data reflecting the development state or characteristics of the industrial zone at different time points in the planning period. It can be represented by preset development stage labels (for example, initial construction stage, industrial expansion stage, stable operation stage, and transformation and upgrading stage) or key indicators related to the development state (for example, expected number of registered enterprises, expected total output value, and expected total carbon emission trend).

[0118] The determination of the weights of each optimization target parameter corresponding to each time point in the planning period refers to assigning a numerical value to each of the three optimization targets, i.e., global carbon sink capacity, global pollutant adsorption capacity, and global construction and maintenance cost, according to the expected development stage of the industrial zone at a specific time point. This numerical value reflects the importance or priority of the optimization target at that time point. For example, expert scoring method can be used to determine the relative importance of each optimization target at different development stages, and then converted into specific weight values.

[0119] The calculation of the comprehensive benefit score of the global scheme refers to the combination of the numerical values of the global carbon sink capacity, global pollutant adsorption capacity, and global construction and maintenance cost of each global scheme at each time point in the planning period with the weights of each optimization target corresponding to that time point, to obtain a numerical value that can comprehensively reflect the benefit performance of the scheme in the entire planning period. Various multi-objective decision analysis methods such as weighted summation method, multi-attribute utility theory, or ideal point method can be used to achieve this.

[0120] The global carbon sink capacity of the global scheme at each time point in the planning period is obtained by accumulating the total carbon sink capacity of all combination schemes in the global scheme at the corresponding time point in the planning period.

[0121] The global annual adsorption capacity of the global scheme at each time point in the planning period is obtained by adding up the annual adsorption capacity of the key pollutant at the time point of all the alternative spaces in the global scheme. The key pollutant is determined according to the main pollutants generated by the main industries in the industrial park. For the calculation of the annual adsorption capacity of the key pollutant at each time point of the alternative space, the unit area adsorption capacity growth curve of the key pollutant according to the vegetation type on the alternative space (this information can be included in the vegetation characteristic information) and the planting time sequence can be used to determine the unit area adsorption capacity at the time point, and then the unit area adsorption capacity is multiplied by the area of the alternative space to obtain the annual adsorption capacity.

[0122] The scheme solves the problem of how to select a green land planning scheme that truly adapts to the needs of different development stages under the background of dynamic development of industrial zones. Specifically, first, the expected development stage information of the industrial zone at different time points within the planning period is obtained, which recognizes that the development of the industrial zone is a dynamic process and different time points may be in different development stages, laying the foundation for subsequent dynamic evaluation. Second, according to the expected development stage information, the weights of the optimization target parameters corresponding to each time point in the planning period are determined, including global carbon sink capacity, global pollutant adsorption capacity and global construction and maintenance cost. This step no longer uses fixed weights to evaluate the scheme, but dynamically assigns different importance to the three key optimization targets of global carbon sink capacity, global pollutant adsorption capacity and global construction and maintenance cost according to the expected development stage of different time points. For example, in the early stage of industrial zone construction, more emphasis is usually placed on rapid greening and pollutant adsorption, while in the stable operation period, more emphasis is usually placed on long-term carbon sink capacity. By dynamically adjusting the weights, the scheme evaluation is more in line with the actual needs of the industrial zone at different development stages. Then, for each global scheme, the comprehensive benefit score of the global scheme is calculated according to its global carbon sink capacity, global pollutant adsorption capacity, global construction and maintenance cost at each time point within the planning period, combined with the corresponding weights. This step combines the performance of the scheme in multiple optimization targets with the dynamically adjusted weights to calculate a comprehensive score that fully reflects the comprehensive benefits of the scheme at different stages within the entire planning period. Finally, the global scheme with the highest comprehensive benefit score is selected as the optimal scheme. By comparing the comprehensive benefit scores of all global schemes that meet the budget constraints, the scheme with the highest score is selected as the final green land planning scheme. This scheme not only meets the budget constraints, but also is considered to be the most efficient scheme after considering the dynamic development needs of the industrial zone, carbon sink, pollutant adsorption and cost, etc. It can better adapt to the dynamic development of the industrial zone and achieve the maximization of comprehensive benefits in the whole life cycle. The scheme further considers the dynamic nature of industrial zone development and the needs of multi-objective optimization on the basis of generating global schemes that meet the budget constraints. By dynamically adjusting the evaluation weights, the final selected scheme can better adapt to the actual situation of the industrial zone at different development stages, thereby achieving better comprehensive benefits in the entire planning period.

[0123] In one embodiment, the expected development stage information of the industrial area at different time points within the planning period can be obtained by consulting the overall planning documents or industrial development planning reports of the industrial area, which usually contain descriptions or predictions of future development stages. Determining the weights according to the expected development stage information can be achieved by establishing a stage-weight mapping table, for example, mapping the "initial construction stage" to a set of weights (such as carbon sink weight 0.3, pollutant adsorption weight 0.5, and cost weight 0.2), and mapping the "stable operation stage" to another set of weights (such as carbon sink weight 0.5, pollutant adsorption weight 0.3, and cost weight 0.2). The comprehensive benefit score of the global scheme can be calculated by weighted summation. For each global scheme at each time point t, the benefit score at that time point is calculated, for example, by the following formula: F(t) = w1(t) * P1(t) + w2(t) * P2(t) - w3(t) * P3(t), where F(t) is the benefit score at time point t, w1(t) is the carbon sink weight at time point t, P1(t) is the global carbon sink capacity at time point t, w2(t) is the pollutant adsorption weight at time point t, P2(t) is the global pollutant adsorption capacity at time point t, w3(t) is the cost weight at time point t, and P3(t) is the global construction and maintenance cost at time point t. Then, the benefit scores at all time points within the planning period are accumulated or averaged to obtain the comprehensive benefit score of the global scheme. Finally, by comparing the comprehensive benefit scores of all global schemes, the scheme with the highest score is selected as the optimal scheme.

[0124] Through the above method, the application can dynamically adjust the weights of the three optimization objectives of global carbon sink capacity, global pollutant adsorption capacity, and global construction and maintenance cost according to the expected development stage of the industrial area at different time points within the planning period, thereby calculating the comprehensive benefit score of the global scheme and selecting the global scheme with the highest score as the optimal scheme. This makes the selected green land planning scheme better adapt to the actual needs of the industrial area at different development stages, solves the problem that the existing method is difficult to select a truly demand-adaptive green land planning scheme under the background of dynamic development of the industrial area, and realizes the maximization of comprehensive benefit in the whole life cycle.

[0125] Reference Figure 2 The application provides a GIS-based industrial green land carbon assessment and decision-making system for industrial green land planning, which comprises:

[0126] A data acquisition module 1 is configured to acquire GIS data of the industrial area, industrial development planning information within the planning period, annual green land construction budget information within the planning period, and vegetation characteristic information of various types of vegetation (the specific process can refer to step A1 in the foregoing description).

[0127] a unit division module 2 configured to divide the industrial area into units according to the GIS data (for details, refer to step A2 in the foregoing description);

[0128] a carbon emission intensity prediction module 3 configured to predict carbon emission intensities of the units at different time points in the planning period according to the industrial development planning information (for details, refer to step A3 in the foregoing description);

[0129] a carbon emission hotspot determination module 4 configured to determine carbon emission hotspot units and corresponding emission peak times according to the carbon emission intensities of the units at different time points in the planning period (for details, refer to step A4 in the foregoing description);

[0130] a scheme decision module 5 configured to determine a green space planning scheme according to locations and emission peak times of the carbon emission hotspot units, vegetation characteristic information of various types of vegetation, the GIS data, and the annual green space construction budget information; the green space planning scheme includes locations of green space, types of vegetation, and planting time sequences (for details, refer to step A5 in the foregoing description).

[0131] The above merely describes embodiments of the present application and is not intended to limit the protection scope of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A GIS-based industrial green space carbon assessment decision-making method for industrial green space planning, characterized in that, The steps of this method include: A1. Obtain GIS data of the industrial zone, industrial development planning information within the planning period, annual green space construction budget information within the planning period, and vegetation characteristic information of various types of vegetation; A2. Divide the industrial zone into units based on the GIS data; A3. Based on the aforementioned industrial development planning information, predict the carbon emission intensity of each unit at different points in time within the planning period; A4. Based on the carbon emission intensity of each unit at different time points within the planning period, determine the carbon emission hotspot units and the corresponding emission peak times; A5. Based on the location and peak emission time of each carbon emission hotspot, vegetation characteristic information of various types of vegetation, the GIS data, and the annual green space construction budget information, determine the green space planning scheme; the green space planning scheme includes the location of green space, vegetation type, and planting sequence; Step A5 includes: A501. Obtain the carbon emission demand curves for each of the aforementioned carbon emission hotspot units; A502. For each of the aforementioned carbon emission hotspot units, identify alternative spaces that can be used for green space construction based on the aforementioned GIS data; A503. For each carbon emission hotspot unit, based on the vegetation characteristic information, evaluate the development curve of the total carbon sink capacity and the time series data of the total construction and maintenance costs required by different combination schemes; the combination scheme is the selection of alternative spaces and their vegetation types and planting times. A504. For each carbon emission hotspot unit, based on the total carbon sink capacity development curve and the carbon emission demand curve, select candidate combination schemes from the corresponding combination schemes whose total carbon sink capacity can meet the carbon emission demand of the carbon emission hotspot unit during the peak emission time of the carbon emission hotspot unit. A505. Based on the annual green space construction budget information and the time-series data of total construction and maintenance costs, the candidate combination schemes for each carbon emission hotspot unit are combined to obtain a global scheme that meets the budget constraints; the global scheme includes one candidate combination scheme corresponding to each carbon emission hotspot unit; A506. Select the optimal scheme from the global schemes as the green space planning scheme; Step A506 includes: Obtain information on the expected development stages of the industrial zone at different points in time within the planning period; Based on the expected development stage information, the weights of each optimization target parameter corresponding to each time point within the planning period are determined; the optimization target parameters include global carbon sequestration capacity, global pollutant adsorption capacity, and global construction and maintenance costs. For each global scheme, the comprehensive benefit score of the global scheme is calculated based on its global carbon sequestration capacity, global pollutant adsorption capacity, and global construction and maintenance cost at each time point within the planning period, combined with the corresponding weights. The global solution with the highest comprehensive benefit score is selected as the optimal solution.

2. The GIS-based industrial green space carbon assessment decision-making method according to claim 1, characterized in that, The GIS data includes information on the boundaries of industrial zones, current land use, topography, soil type, existing water system, planned building layout, and planned road layout. The industrial development planning information includes information on the types of enterprises expected to move in, the expected time of move-in, the spatial location of the enterprises, the expected production capacity, and the carbon emissions per unit of production capacity. The vegetation characteristic information includes carbon sequestration capacity growth curve information per unit area, initial construction cost information per unit area, and annual maintenance cost information per unit area.

3. The GIS-based industrial green space carbon assessment decision-making method according to claim 2, characterized in that, Step A2 includes: A201. Based on the land parcel boundary information, the planned building layout information, and the planned road layout information, determine the basic spatial zoning of the industrial zone; A202. Based on the basic spatial partitions, and in conjunction with the current land use information, the topographic information, the soil type information, and the existing water system information, each basic spatial partition is further subdivided or merged to form units with relatively consistent land use types or environmental characteristics. A203. Each of the aforementioned units is uniquely identified, and the location and area of ​​each unit are recorded.

4. The GIS-based industrial green space carbon assessment and decision-making method according to claim 2, characterized in that, Step A3 includes: A301. Based on the expected entry time information, expected production capacity information, and carbon emissions per unit production capacity information in the aforementioned industrial development planning information, determine the carbon emissions of each expected enterprise at different time points within the planning period; A302. Based on the spatial location information of enterprises in the industrial development planning information, the carbon emissions of each expected enterprise at different time points within the planning period are allocated to each of the units. Combined with the area of ​​each unit, the carbon emission intensity of each unit at different time points within the planning period is calculated.

5. The GIS-based industrial green space carbon assessment decision-making method according to claim 1, characterized in that, Step A4 includes: A401. Generate a time series curve of carbon emission intensity for each unit during the planning period based on the carbon emission intensity of each unit at different time points within the planning period; A402. Based on the carbon emission intensity time series curve of each unit, extract the cumulative time when the carbon emission intensity of each unit exceeds the preset intensity threshold; A403. Based on the accumulated time, determine the carbon emission hotspot units; A404. For each carbon emission hotspot unit, extract the time corresponding to the maximum carbon emission intensity in the corresponding carbon emission intensity time series curve, and use it as the emission peak time of the carbon emission hotspot unit.

6. The GIS-based industrial green space carbon assessment decision-making method according to claim 2, characterized in that, Step A503 includes: Based on the alternative spaces, vegetation types and planting sequences selected in the combined scheme, the carbon sequestration capacity growth curve information, initial construction cost information and annual maintenance cost information per unit area are extracted from the vegetation characteristic information of the corresponding vegetation. Based on the area of ​​the candidate space selected in the combined scheme, the extracted carbon sequestration capacity growth curve information per unit area, and the planting sequence in the combined scheme, the total carbon sequestration capacity of the combined scheme at each time point within the planning period is calculated, and the total carbon sequestration capacity development curve is formed. Based on the area of ​​the alternative spaces selected in the combined scheme, the extracted initial construction cost information per unit area, the extracted annual maintenance cost information per unit area, and the planting sequence in the combined scheme, the total construction and maintenance cost of the combined scheme at each time point within the planning period is calculated, and the time series data of the total construction and maintenance cost is formed.

7. The GIS-based industrial green space carbon assessment decision-making method according to claim 1, characterized in that, Step A505 includes: The candidate combination schemes of each carbon emission hotspot unit are arranged and combined to obtain multiple preliminary global scheme combinations; the preliminary global scheme combination includes one scheme selected from the candidate combination schemes of each carbon emission hotspot unit; For each of the preliminary global scheme combinations, the total construction and maintenance costs of each of the candidate combination schemes included therein at each time point within the planning period are summed to obtain the global construction and maintenance costs of the preliminary global scheme combination at each time point within the planning period. Based on the annual green space construction budget information, the global construction and maintenance costs of the preliminary global scheme combination at each time point within the planning period are compared year by year. The preliminary global scheme combination in which the global construction and maintenance costs at each time point within the planning period are not greater than the corresponding annual green space construction budget information is selected as the final global scheme combination.

8. A GIS-based industrial green space carbon assessment and decision-making system for industrial green space planning, characterized in that, The system includes: The data acquisition module is used to acquire GIS data of the industrial zone, industrial development planning information within the planning period, annual green space construction budget information within the planning period, and vegetation characteristic information of various types of vegetation. The unit division module is used to divide the industrial zone into units based on the GIS data. The carbon emission intensity prediction module is used to predict the carbon emission intensity of each unit at different time points within the planning period based on the industrial development planning information. The carbon emission hotspot identification module is used to determine the carbon emission hotspot units and their corresponding emission peak times based on the carbon emission intensity of each unit at different time points within the planning period. The scheme decision module is used to determine the green space planning scheme based on the location and peak emission time of each carbon emission hotspot, vegetation characteristic information of various types of vegetation, the GIS data, and the annual green space construction budget information; the green space planning scheme includes the location of green space, vegetation type, and planting sequence. When determining a green space planning scheme based on the location and peak emission times of each carbon emission hotspot, vegetation characteristics of various types of vegetation, the GIS data, and the annual green space construction budget information, the scheme decision module executes the following: A501. Obtain the carbon emission demand curves for each of the aforementioned carbon emission hotspot units; A502. For each of the aforementioned carbon emission hotspot units, identify alternative spaces that can be used for green space construction based on the aforementioned GIS data; A503. For each carbon emission hotspot unit, based on the vegetation characteristic information, evaluate the development curve of the total carbon sink capacity and the time series data of the total construction and maintenance costs required by different combination schemes; the combination scheme is the selection of alternative spaces and their vegetation types and planting times. A504. For each carbon emission hotspot unit, based on the total carbon sink capacity development curve and the carbon emission demand curve, select candidate combination schemes from the corresponding combination schemes whose total carbon sink capacity can meet the carbon emission demand of the carbon emission hotspot unit during the peak emission time of the carbon emission hotspot unit. A505. Based on the annual green space construction budget information and the time-series data of total construction and maintenance costs, the candidate combination schemes for each carbon emission hotspot unit are combined to obtain a global scheme that meets the budget constraints; the global scheme includes one candidate combination scheme corresponding to each carbon emission hotspot unit; A506. Select the optimal scheme from the global schemes as the green space planning scheme; Step A506 includes: Obtain information on the expected development stages of the industrial zone at different points in time within the planning period; Based on the expected development stage information, the weights of each optimization target parameter corresponding to each time point within the planning period are determined; the optimization target parameters include global carbon sequestration capacity, global pollutant adsorption capacity, and global construction and maintenance costs. For each global scheme, the comprehensive benefit score of the global scheme is calculated based on its global carbon sequestration capacity, global pollutant adsorption capacity, and global construction and maintenance cost at each time point within the planning period, combined with the corresponding weights. The global solution with the highest comprehensive benefit score is selected as the optimal solution.

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